);
}
```
## 注意事项
1. **路径安全性**
- 实际路径（`path`）包含线程 ID，确保隔离
- API 会验证路径，防止目录遍历攻击
- 前端不应直接使用 `path`，而应使用 `artifact_url`
2. **Agent 使用**
- Agent 只能看到和使用 `virtual_path`
- 沙箱系统自动映射到实际路径
- Agent 不需要知道实际的文件系统结构
3. **前端集成**
- 始终使用 `artifact_url` 访问文件
- 不要尝试直接访问文件系统路径
- 使用 `?download=true` 参数强制下载
4. **Markdown 转换**
- 转换成功时，会返回额外的 `markdown_*` 字段
- 建议优先使用 Markdown 版本（更易处理）
- 原始文件始终保留
================================================
FILE: backend/docs/README.md
================================================
# Documentation
This directory contains detailed documentation for the DeerFlow backend.
## Quick Links
| Document | Description |
|----------|-------------|
| [ARCHITECTURE.md](ARCHITECTURE.md) | System architecture overview |
| [API.md](API.md) | Complete API reference |
| [CONFIGURATION.md](CONFIGURATION.md) | Configuration options |
| [SETUP.md](SETUP.md) | Quick setup guide |
## Feature Documentation
| Document | Description |
|----------|-------------|
| [FILE_UPLOAD.md](FILE_UPLOAD.md) | File upload functionality |
| [PATH_EXAMPLES.md](PATH_EXAMPLES.md) | Path types and usage examples |
| [summarization.md](summarization.md) | Context summarization feature |
| [plan_mode_usage.md](plan_mode_usage.md) | Plan mode with TodoList |
| [AUTO_TITLE_GENERATION.md](AUTO_TITLE_GENERATION.md) | Automatic title generation |
## Development
| Document | Description |
|----------|-------------|
| [TODO.md](TODO.md) | Planned features and known issues |
## Getting Started
1. **New to DeerFlow?** Start with [SETUP.md](SETUP.md) for quick installation
2. **Configuring the system?** See [CONFIGURATION.md](CONFIGURATION.md)
3. **Understanding the architecture?** Read [ARCHITECTURE.md](ARCHITECTURE.md)
4. **Building integrations?** Check [API.md](API.md) for API reference
## Document Organization
```
docs/
├── README.md # This file
├── ARCHITECTURE.md # System architecture
├── API.md # API reference
├── CONFIGURATION.md # Configuration guide
├── SETUP.md # Setup instructions
├── FILE_UPLOAD.md # File upload feature
├── PATH_EXAMPLES.md # Path usage examples
├── summarization.md # Summarization feature
├── plan_mode_usage.md # Plan mode feature
├── AUTO_TITLE_GENERATION.md # Title generation
├── TITLE_GENERATION_IMPLEMENTATION.md # Title implementation details
└── TODO.md # Roadmap and issues
```
================================================
FILE: backend/docs/SETUP.md
================================================
# Setup Guide
Quick setup instructions for DeerFlow.
## Configuration Setup
DeerFlow uses a YAML configuration file that should be placed in the **project root directory**.
### Steps
1. **Navigate to project root**:
```bash
cd /path/to/deer-flow
```
2. **Copy example configuration**:
```bash
cp config.example.yaml config.yaml
```
3. **Edit configuration**:
```bash
# Option A: Set environment variables (recommended)
export OPENAI_API_KEY="your-key-here"
# Option B: Edit config.yaml directly
vim config.yaml # or your preferred editor
```
4. **Verify configuration**:
```bash
cd backend
python -c "from deerflow.config import get_app_config; print('✓ Config loaded:', get_app_config().models[0].name)"
```
## Important Notes
- **Location**: `config.yaml` should be in `deer-flow/` (project root), not `deer-flow/backend/`
- **Git**: `config.yaml` is automatically ignored by git (contains secrets)
- **Priority**: If both `backend/config.yaml` and `../config.yaml` exist, backend version takes precedence
## Configuration File Locations
The backend searches for `config.yaml` in this order:
1. `DEER_FLOW_CONFIG_PATH` environment variable (if set)
2. `backend/config.yaml` (current directory when running from backend/)
3. `deer-flow/config.yaml` (parent directory - **recommended location**)
**Recommended**: Place `config.yaml` in project root (`deer-flow/config.yaml`).
## Sandbox Setup (Optional but Recommended)
If you plan to use Docker/Container-based sandbox (configured in `config.yaml` under `sandbox.use: deerflow.community.aio_sandbox:AioSandboxProvider`), it's highly recommended to pre-pull the container image:
```bash
# From project root
make setup-sandbox
```
**Why pre-pull?**
- The sandbox image (~500MB+) is pulled on first use, causing a long wait
- Pre-pulling provides clear progress indication
- Avoids confusion when first using the agent
If you skip this step, the image will be automatically pulled on first agent execution, which may take several minutes depending on your network speed.
## Troubleshooting
### Config file not found
```bash
# Check where the backend is looking
cd deer-flow/backend
python -c "from deerflow.config.app_config import AppConfig; print(AppConfig.resolve_config_path())"
```
If it can't find the config:
1. Ensure you've copied `config.example.yaml` to `config.yaml`
2. Verify you're in the correct directory
3. Check the file exists: `ls -la ../config.yaml`
### Permission denied
```bash
chmod 600 ../config.yaml # Protect sensitive configuration
```
## See Also
- [Configuration Guide](docs/CONFIGURATION.md) - Detailed configuration options
- [Architecture Overview](CLAUDE.md) - System architecture
================================================
FILE: backend/docs/TITLE_GENERATION_IMPLEMENTATION.md
================================================
# 自动 Title 生成功能实现总结
## ✅ 已完成的工作
### 1. 核心实现文件
#### [`packages/harness/deerflow/agents/thread_state.py`](../packages/harness/deerflow/agents/thread_state.py)
- ✅ 添加 `title: str | None = None` 字段到 `ThreadState`
#### [`packages/harness/deerflow/config/title_config.py`](../packages/harness/deerflow/config/title_config.py) (新建)
- ✅ 创建 `TitleConfig` 配置类
- ✅ 支持配置：enabled, max_words, max_chars, model_name, prompt_template
- ✅ 提供 `get_title_config()` 和 `set_title_config()` 函数
- ✅ 提供 `load_title_config_from_dict()` 从配置文件加载
#### [`packages/harness/deerflow/agents/title_middleware.py`](../packages/harness/deerflow/agents/title_middleware.py) (新建)
- ✅ 创建 `TitleMiddleware` 类
- ✅ 实现 `_should_generate_title()` 检查是否需要生成
- ✅ 实现 `_generate_title()` 调用 LLM 生成标题
- ✅ 实现 `after_agent()` 钩子，在首次对话后自动触发
- ✅ 包含 fallback 策略（LLM 失败时使用用户消息前几个词）
#### [`packages/harness/deerflow/config/app_config.py`](../packages/harness/deerflow/config/app_config.py)
- ✅ 导入 `load_title_config_from_dict`
- ✅ 在 `from_file()` 中加载 title 配置
#### [`packages/harness/deerflow/agents/lead_agent/agent.py`](../packages/harness/deerflow/agents/lead_agent/agent.py)
- ✅ 导入 `TitleMiddleware`
- ✅ 注册到 `middleware` 列表：`[SandboxMiddleware(), TitleMiddleware()]`
### 2. 配置文件
#### [`config.yaml`](../config.yaml)
- ✅ 添加 title 配置段：
```yaml
title:
enabled: true
max_words: 6
max_chars: 60
model_name: null
```
### 3. 文档
#### [`docs/AUTO_TITLE_GENERATION.md`](../docs/AUTO_TITLE_GENERATION.md) (新建)
- ✅ 完整的功能说明文档
- ✅ 实现方式和架构设计
- ✅ 配置说明
- ✅ 客户端使用示例（TypeScript）
- ✅ 工作流程图（Mermaid）
- ✅ 故障排查指南
- ✅ State vs Metadata 对比
#### [`BACKEND_TODO.md`](../BACKEND_TODO.md)
- ✅ 添加功能完成记录
### 4. 测试
#### [`tests/test_title_generation.py`](../tests/test_title_generation.py) (新建)
- ✅ 配置类测试
- ✅ Middleware 初始化测试
- ✅ TODO: 集成测试（需要 mock Runtime）
---
## 🎯 核心设计决策
### 为什么使用 State 而非 Metadata？
| 方面 | State (✅ 采用) | Metadata (❌ 未采用) |
|------|----------------|---------------------|
| **持久化** | 自动（通过 checkpointer） | 取决于实现，不可靠 |
| **版本控制** | 支持时间旅行 | 不支持 |
| **类型安全** | TypedDict 定义 | 任意字典 |
| **标准化** | LangGraph 核心机制 | 扩展功能 |
### 工作流程
```
用户发送首条消息
↓
Agent 处理并返回回复
↓
TitleMiddleware.after_agent() 触发
↓
检查：是否首次对话？是否已有 title？
↓
调用 LLM 生成 title
↓
返回 {"title": "..."} 更新 state
↓
Checkpointer 自动持久化（如果配置了）
↓
客户端从 state.values.title 读取
```
---
## 📋 使用指南
### 后端配置
1. **启用/禁用功能**
```yaml
# config.yaml
title:
enabled: true # 设为 false 禁用
```
2. **自定义配置**
```yaml
title:
enabled: true
max_words: 8 # 标题最多 8 个词
max_chars: 80 # 标题最多 80 个字符
model_name: null # 使用默认模型
```
3. **配置持久化（可选）**
如果需要在本地开发时持久化 title：
```python
# checkpointer.py
from langgraph.checkpoint.sqlite import SqliteSaver
checkpointer = SqliteSaver.from_conn_string("checkpoints.db")
```
```json
// langgraph.json
{
"graphs": {
"lead_agent": "deerflow.agents:lead_agent"
},
"checkpointer": "checkpointer:checkpointer"
}
```
### 客户端使用
```typescript
// 获取 thread title
const state = await client.threads.getState(threadId);
const title = state.values.title || "New Conversation";
// 显示在对话列表
{title}
```
**⚠️ 注意**：Title 在 `state.values.title`，而非 `thread.metadata.title`
---
## 🧪 测试
```bash
# 运行测试
pytest tests/test_title_generation.py -v
# 运行所有测试
pytest
```
---
## 🔍 故障排查
### Title 没有生成？
1. 检查配置：`title.enabled = true`
2. 查看日志：搜索 "Generated thread title"
3. 确认是首次对话（1 个用户消息 + 1 个助手回复）
### Title 生成但看不到？
1. 确认读取位置：`state.values.title`（不是 `thread.metadata.title`）
2. 检查 API 响应是否包含 title
3. 重新获取 state
### Title 重启后丢失？
1. 本地开发需要配置 checkpointer
2. LangGraph Platform 会自动持久化
3. 检查数据库确认 checkpointer 工作正常
---
## 📊 性能影响
- **延迟增加**：约 0.5-1 秒（LLM 调用）
- **并发安全**：在 `after_agent` 中运行，不阻塞主流程
- **资源消耗**：每个 thread 只生成一次
### 优化建议
1. 使用更快的模型（如 `gpt-3.5-turbo`）
2. 减少 `max_words` 和 `max_chars`
3. 调整 prompt 使其更简洁
---
## 🚀 下一步
- [ ] 添加集成测试（需要 mock LangGraph Runtime）
- [ ] 支持自定义 prompt template
- [ ] 支持多语言 title 生成
- [ ] 添加 title 重新生成功能
- [ ] 监控 title 生成成功率和延迟
---
## 📚 相关资源
- [完整文档](../docs/AUTO_TITLE_GENERATION.md)
- [LangGraph Middleware](https://langchain-ai.github.io/langgraph/concepts/middleware/)
- [LangGraph State 管理](https://langchain-ai.github.io/langgraph/concepts/low_level/#state)
- [LangGraph Checkpointer](https://langchain-ai.github.io/langgraph/concepts/persistence/)
---
*实现完成时间: 2026-01-14*
================================================
FILE: backend/docs/TODO.md
================================================
# TODO List
## Completed Features
- [x] Launch the sandbox only after the first file system or bash tool is called
- [x] Add Clarification Process for the whole process
- [x] Implement Context Summarization Mechanism to avoid context explosion
- [x] Integrate MCP (Model Context Protocol) for extensible tools
- [x] Add file upload support with automatic document conversion
- [x] Implement automatic thread title generation
- [x] Add Plan Mode with TodoList middleware
- [x] Add vision model support with ViewImageMiddleware
- [x] Skills system with SKILL.md format
## Planned Features
- [ ] Pooling the sandbox resources to reduce the number of sandbox containers
- [ ] Add authentication/authorization layer
- [ ] Implement rate limiting
- [ ] Add metrics and monitoring
- [ ] Support for more document formats in upload
- [ ] Skill marketplace / remote skill installation
- [ ] Optimize async concurrency in agent hot path (IM channels multi-task scenario)
- Replace `time.sleep(5)` with `asyncio.sleep()` in `packages/harness/deerflow/tools/builtins/task_tool.py` (subagent polling)
- Replace `subprocess.run()` with `asyncio.create_subprocess_shell()` in `packages/harness/deerflow/sandbox/local/local_sandbox.py`
- Replace sync `requests` with `httpx.AsyncClient` in community tools (tavily, jina_ai, firecrawl, infoquest, image_search)
- Replace sync `model.invoke()` with async `model.ainvoke()` in title_middleware and memory updater
- Consider `asyncio.to_thread()` wrapper for remaining blocking file I/O
- For production: use `langgraph up` (multi-worker) instead of `langgraph dev` (single-worker)
## Resolved Issues
- [x] Make sure that no duplicated files in `state.artifacts`
- [x] Long thinking but with empty content (answer inside thinking process)
================================================
FILE: backend/docs/plan_mode_usage.md
================================================
# Plan Mode with TodoList Middleware
This document describes how to enable and use the Plan Mode feature with TodoList middleware in DeerFlow 2.0.
## Overview
Plan Mode adds a TodoList middleware to the agent, which provides a `write_todos` tool that helps the agent:
- Break down complex tasks into smaller, manageable steps
- Track progress as work progresses
- Provide visibility to users about what's being done
The TodoList middleware is built on LangChain's `TodoListMiddleware`.
## Configuration
### Enabling Plan Mode
Plan mode is controlled via **runtime configuration** through the `is_plan_mode` parameter in the `configurable` section of `RunnableConfig`. This allows you to dynamically enable or disable plan mode on a per-request basis.
```python
from langchain_core.runnables import RunnableConfig
from deerflow.agents.lead_agent.agent import make_lead_agent
# Enable plan mode via runtime configuration
config = RunnableConfig(
configurable={
"thread_id": "example-thread",
"thinking_enabled": True,
"is_plan_mode": True, # Enable plan mode
}
)
# Create agent with plan mode enabled
agent = make_lead_agent(config)
```
### Configuration Options
- **is_plan_mode** (bool): Whether to enable plan mode with TodoList middleware. Default: `False`
- Pass via `config.get("configurable", {}).get("is_plan_mode", False)`
- Can be set dynamically for each agent invocation
- No global configuration needed
## Default Behavior
When plan mode is enabled with default settings, the agent will have access to a `write_todos` tool with the following behavior:
### When to Use TodoList
The agent will use the todo list for:
1. Complex multi-step tasks (3+ distinct steps)
2. Non-trivial tasks requiring careful planning
3. When user explicitly requests a todo list
4. When user provides multiple tasks
### When NOT to Use TodoList
The agent will skip using the todo list for:
1. Single, straightforward tasks
2. Trivial tasks (< 3 steps)
3. Purely conversational or informational requests
### Task States
- **pending**: Task not yet started
- **in_progress**: Currently working on (can have multiple parallel tasks)
- **completed**: Task finished successfully
## Usage Examples
### Basic Usage
```python
from langchain_core.runnables import RunnableConfig
from deerflow.agents.lead_agent.agent import make_lead_agent
# Create agent with plan mode ENABLED
config_with_plan_mode = RunnableConfig(
configurable={
"thread_id": "example-thread",
"thinking_enabled": True,
"is_plan_mode": True, # TodoList middleware will be added
}
)
agent_with_todos = make_lead_agent(config_with_plan_mode)
# Create agent with plan mode DISABLED (default)
config_without_plan_mode = RunnableConfig(
configurable={
"thread_id": "another-thread",
"thinking_enabled": True,
"is_plan_mode": False, # No TodoList middleware
}
)
agent_without_todos = make_lead_agent(config_without_plan_mode)
```
### Dynamic Plan Mode per Request
You can enable/disable plan mode dynamically for different conversations or tasks:
```python
from langchain_core.runnables import RunnableConfig
from deerflow.agents.lead_agent.agent import make_lead_agent
def create_agent_for_task(task_complexity: str):
"""Create agent with plan mode based on task complexity."""
is_complex = task_complexity in ["high", "very_high"]
config = RunnableConfig(
configurable={
"thread_id": f"task-{task_complexity}",
"thinking_enabled": True,
"is_plan_mode": is_complex, # Enable only for complex tasks
}
)
return make_lead_agent(config)
# Simple task - no TodoList needed
simple_agent = create_agent_for_task("low")
# Complex task - TodoList enabled for better tracking
complex_agent = create_agent_for_task("high")
```
## How It Works
1. When `make_lead_agent(config)` is called, it extracts `is_plan_mode` from `config.configurable`
2. The config is passed to `_build_middlewares(config)`
3. `_build_middlewares()` reads `is_plan_mode` and calls `_create_todo_list_middleware(is_plan_mode)`
4. If `is_plan_mode=True`, a `TodoListMiddleware` instance is created and added to the middleware chain
5. The middleware automatically adds a `write_todos` tool to the agent's toolset
6. The agent can use this tool to manage tasks during execution
7. The middleware handles the todo list state and provides it to the agent
## Architecture
```
make_lead_agent(config)
│
├─> Extracts: is_plan_mode = config.configurable.get("is_plan_mode", False)
│
└─> _build_middlewares(config)
│
├─> ThreadDataMiddleware
├─> SandboxMiddleware
├─> SummarizationMiddleware (if enabled via global config)
├─> TodoListMiddleware (if is_plan_mode=True) ← NEW
├─> TitleMiddleware
└─> ClarificationMiddleware
```
## Implementation Details
### Agent Module
- **Location**: `packages/harness/deerflow/agents/lead_agent/agent.py`
- **Function**: `_create_todo_list_middleware(is_plan_mode: bool)` - Creates TodoListMiddleware if plan mode is enabled
- **Function**: `_build_middlewares(config: RunnableConfig)` - Builds middleware chain based on runtime config
- **Function**: `make_lead_agent(config: RunnableConfig)` - Creates agent with appropriate middlewares
### Runtime Configuration
Plan mode is controlled via the `is_plan_mode` parameter in `RunnableConfig.configurable`:
```python
config = RunnableConfig(
configurable={
"is_plan_mode": True, # Enable plan mode
# ... other configurable options
}
)
```
## Key Benefits
1. **Dynamic Control**: Enable/disable plan mode per request without global state
2. **Flexibility**: Different conversations can have different plan mode settings
3. **Simplicity**: No need for global configuration management
4. **Context-Aware**: Plan mode decision can be based on task complexity, user preferences, etc.
## Custom Prompts
DeerFlow uses custom `system_prompt` and `tool_description` for the TodoListMiddleware that match the overall DeerFlow prompt style:
### System Prompt Features
- Uses XML tags (``) for structure consistency with DeerFlow's main prompt
- Emphasizes CRITICAL rules and best practices
- Clear "When to Use" vs "When NOT to Use" guidelines
- Focuses on real-time updates and immediate task completion
### Tool Description Features
- Detailed usage scenarios with examples
- Strong emphasis on NOT using for simple tasks
- Clear task state definitions (pending, in_progress, completed)
- Comprehensive best practices section
- Task completion requirements to prevent premature marking
The custom prompts are defined in `_create_todo_list_middleware()` in `/Users/hetao/workspace/deer-flow/backend/packages/harness/deerflow/agents/lead_agent/agent.py:57`.
## Notes
- TodoList middleware uses LangChain's built-in `TodoListMiddleware` with **custom DeerFlow-style prompts**
- Plan mode is **disabled by default** (`is_plan_mode=False`) to maintain backward compatibility
- The middleware is positioned before `ClarificationMiddleware` to allow todo management during clarification flows
- Custom prompts emphasize the same principles as DeerFlow's main system prompt (clarity, action-oriented, critical rules)
================================================
FILE: backend/docs/summarization.md
================================================
# Conversation Summarization
DeerFlow includes automatic conversation summarization to handle long conversations that approach model token limits. When enabled, the system automatically condenses older messages while preserving recent context.
## Overview
The summarization feature uses LangChain's `SummarizationMiddleware` to monitor conversation history and trigger summarization based on configurable thresholds. When activated, it:
1. Monitors message token counts in real-time
2. Triggers summarization when thresholds are met
3. Keeps recent messages intact while summarizing older exchanges
4. Maintains AI/Tool message pairs together for context continuity
5. Injects the summary back into the conversation
## Configuration
Summarization is configured in `config.yaml` under the `summarization` key:
```yaml
summarization:
enabled: true
model_name: null # Use default model or specify a lightweight model
# Trigger conditions (OR logic - any condition triggers summarization)
trigger:
- type: tokens
value: 4000
# Additional triggers (optional)
# - type: messages
# value: 50
# - type: fraction
# value: 0.8 # 80% of model's max input tokens
# Context retention policy
keep:
type: messages
value: 20
# Token trimming for summarization call
trim_tokens_to_summarize: 4000
# Custom summary prompt (optional)
summary_prompt: null
```
### Configuration Options
#### `enabled`
- **Type**: Boolean
- **Default**: `false`
- **Description**: Enable or disable automatic summarization
#### `model_name`
- **Type**: String or null
- **Default**: `null` (uses default model)
- **Description**: Model to use for generating summaries. Recommended to use a lightweight, cost-effective model like `gpt-4o-mini` or equivalent.
#### `trigger`
- **Type**: Single `ContextSize` or list of `ContextSize` objects
- **Required**: At least one trigger must be specified when enabled
- **Description**: Thresholds that trigger summarization. Uses OR logic - summarization runs when ANY threshold is met.
**ContextSize Types:**
1. **Token-based trigger**: Activates when token count reaches the specified value
```yaml
trigger:
type: tokens
value: 4000
```
2. **Message-based trigger**: Activates when message count reaches the specified value
```yaml
trigger:
type: messages
value: 50
```
3. **Fraction-based trigger**: Activates when token usage reaches a percentage of the model's maximum input tokens
```yaml
trigger:
type: fraction
value: 0.8 # 80% of max input tokens
```
**Multiple Triggers:**
```yaml
trigger:
- type: tokens
value: 4000
- type: messages
value: 50
```
#### `keep`
- **Type**: `ContextSize` object
- **Default**: `{type: messages, value: 20}`
- **Description**: Specifies how much recent conversation history to preserve after summarization.
**Examples:**
```yaml
# Keep most recent 20 messages
keep:
type: messages
value: 20
# Keep most recent 3000 tokens
keep:
type: tokens
value: 3000
# Keep most recent 30% of model's max input tokens
keep:
type: fraction
value: 0.3
```
#### `trim_tokens_to_summarize`
- **Type**: Integer or null
- **Default**: `4000`
- **Description**: Maximum tokens to include when preparing messages for the summarization call itself. Set to `null` to skip trimming (not recommended for very long conversations).
#### `summary_prompt`
- **Type**: String or null
- **Default**: `null` (uses LangChain's default prompt)
- **Description**: Custom prompt template for generating summaries. The prompt should guide the model to extract the most important context.
**Default Prompt Behavior:**
The default LangChain prompt instructs the model to:
- Extract highest quality/most relevant context
- Focus on information critical to the overall goal
- Avoid repeating completed actions
- Return only the extracted context
## How It Works
### Summarization Flow
1. **Monitoring**: Before each model call, the middleware counts tokens in the message history
2. **Trigger Check**: If any configured threshold is met, summarization is triggered
3. **Message Partitioning**: Messages are split into:
- Messages to summarize (older messages beyond the `keep` threshold)
- Messages to preserve (recent messages within the `keep` threshold)
4. **Summary Generation**: The model generates a concise summary of the older messages
5. **Context Replacement**: The message history is updated:
- All old messages are removed
- A single summary message is added
- Recent messages are preserved
6. **AI/Tool Pair Protection**: The system ensures AI messages and their corresponding tool messages stay together
### Token Counting
- Uses approximate token counting based on character count
- For Anthropic models: ~3.3 characters per token
- For other models: Uses LangChain's default estimation
- Can be customized with a custom `token_counter` function
### Message Preservation
The middleware intelligently preserves message context:
- **Recent Messages**: Always kept intact based on `keep` configuration
- **AI/Tool Pairs**: Never split - if a cutoff point falls within tool messages, the system adjusts to keep the entire AI + Tool message sequence together
- **Summary Format**: Summary is injected as a HumanMessage with the format:
```
Here is a summary of the conversation to date:
[Generated summary text]
```
## Best Practices
### Choosing Trigger Thresholds
1. **Token-based triggers**: Recommended for most use cases
- Set to 60-80% of your model's context window
- Example: For 8K context, use 4000-6000 tokens
2. **Message-based triggers**: Useful for controlling conversation length
- Good for applications with many short messages
- Example: 50-100 messages depending on average message length
3. **Fraction-based triggers**: Ideal when using multiple models
- Automatically adapts to each model's capacity
- Example: 0.8 (80% of model's max input tokens)
### Choosing Retention Policy (`keep`)
1. **Message-based retention**: Best for most scenarios
- Preserves natural conversation flow
- Recommended: 15-25 messages
2. **Token-based retention**: Use when precise control is needed
- Good for managing exact token budgets
- Recommended: 2000-4000 tokens
3. **Fraction-based retention**: For multi-model setups
- Automatically scales with model capacity
- Recommended: 0.2-0.4 (20-40% of max input)
### Model Selection
- **Recommended**: Use a lightweight, cost-effective model for summaries
- Examples: `gpt-4o-mini`, `claude-haiku`, or equivalent
- Summaries don't require the most powerful models
- Significant cost savings on high-volume applications
- **Default**: If `model_name` is `null`, uses the default model
- May be more expensive but ensures consistency
- Good for simple setups
### Optimization Tips
1. **Balance triggers**: Combine token and message triggers for robust handling
```yaml
trigger:
- type: tokens
value: 4000
- type: messages
value: 50
```
2. **Conservative retention**: Keep more messages initially, adjust based on performance
```yaml
keep:
type: messages
value: 25 # Start higher, reduce if needed
```
3. **Trim strategically**: Limit tokens sent to summarization model
```yaml
trim_tokens_to_summarize: 4000 # Prevents expensive summarization calls
```
4. **Monitor and iterate**: Track summary quality and adjust configuration
## Troubleshooting
### Summary Quality Issues
**Problem**: Summaries losing important context
**Solutions**:
1. Increase `keep` value to preserve more messages
2. Decrease trigger thresholds to summarize earlier
3. Customize `summary_prompt` to emphasize key information
4. Use a more capable model for summarization
### Performance Issues
**Problem**: Summarization calls taking too long
**Solutions**:
1. Use a faster model for summaries (e.g., `gpt-4o-mini`)
2. Reduce `trim_tokens_to_summarize` to send less context
3. Increase trigger thresholds to summarize less frequently
### Token Limit Errors
**Problem**: Still hitting token limits despite summarization
**Solutions**:
1. Lower trigger thresholds to summarize earlier
2. Reduce `keep` value to preserve fewer messages
3. Check if individual messages are very large
4. Consider using fraction-based triggers
## Implementation Details
### Code Structure
- **Configuration**: `packages/harness/deerflow/config/summarization_config.py`
- **Integration**: `packages/harness/deerflow/agents/lead_agent/agent.py`
- **Middleware**: Uses `langchain.agents.middleware.SummarizationMiddleware`
### Middleware Order
Summarization runs after ThreadData and Sandbox initialization but before Title and Clarification:
1. ThreadDataMiddleware
2. SandboxMiddleware
3. **SummarizationMiddleware** ← Runs here
4. TitleMiddleware
5. ClarificationMiddleware
### State Management
- Summarization is stateless - configuration is loaded once at startup
- Summaries are added as regular messages in the conversation history
- The checkpointer persists the summarized history automatically
## Example Configurations
### Minimal Configuration
```yaml
summarization:
enabled: true
trigger:
type: tokens
value: 4000
keep:
type: messages
value: 20
```
### Production Configuration
```yaml
summarization:
enabled: true
model_name: gpt-4o-mini # Lightweight model for cost efficiency
trigger:
- type: tokens
value: 6000
- type: messages
value: 75
keep:
type: messages
value: 25
trim_tokens_to_summarize: 5000
```
### Multi-Model Configuration
```yaml
summarization:
enabled: true
model_name: gpt-4o-mini
trigger:
type: fraction
value: 0.7 # 70% of model's max input
keep:
type: fraction
value: 0.3 # Keep 30% of max input
trim_tokens_to_summarize: 4000
```
### Conservative Configuration (High Quality)
```yaml
summarization:
enabled: true
model_name: gpt-4 # Use full model for high-quality summaries
trigger:
type: tokens
value: 8000
keep:
type: messages
value: 40 # Keep more context
trim_tokens_to_summarize: null # No trimming
```
## References
- [LangChain Summarization Middleware Documentation](https://docs.langchain.com/oss/python/langchain/middleware/built-in#summarization)
- [LangChain Source Code](https://github.com/langchain-ai/langchain)
================================================
FILE: backend/docs/task_tool_improvements.md
================================================
# Task Tool Improvements
## Overview
The task tool has been improved to eliminate wasteful LLM polling. Previously, when using background tasks, the LLM had to repeatedly call `task_status` to poll for completion, causing unnecessary API requests.
## Changes Made
### 1. Removed `run_in_background` Parameter
The `run_in_background` parameter has been removed from the `task` tool. All subagent tasks now run asynchronously by default, but the tool handles completion automatically.
**Before:**
```python
# LLM had to manage polling
task_id = task(
subagent_type="bash",
prompt="Run tests",
description="Run tests",
run_in_background=True
)
# Then LLM had to poll repeatedly:
while True:
status = task_status(task_id)
if completed:
break
```
**After:**
```python
# Tool blocks until complete, polling happens in backend
result = task(
subagent_type="bash",
prompt="Run tests",
description="Run tests"
)
# Result is available immediately after the call returns
```
### 2. Backend Polling
The `task_tool` now:
- Starts the subagent task asynchronously
- Polls for completion in the backend (every 2 seconds)
- Blocks the tool call until completion
- Returns the final result directly
This means:
- ✅ LLM makes only ONE tool call
- ✅ No wasteful LLM polling requests
- ✅ Backend handles all status checking
- ✅ Timeout protection (5 minutes max)
### 3. Removed `task_status` from LLM Tools
The `task_status_tool` is no longer exposed to the LLM. It's kept in the codebase for potential internal/debugging use, but the LLM cannot call it.
### 4. Updated Documentation
- Updated `SUBAGENT_SECTION` in `prompt.py` to remove all references to background tasks and polling
- Simplified usage examples
- Made it clear that the tool automatically waits for completion
## Implementation Details
### Polling Logic
Located in `packages/harness/deerflow/tools/builtins/task_tool.py`:
```python
# Start background execution
task_id = executor.execute_async(prompt)
# Poll for task completion in backend
while True:
result = get_background_task_result(task_id)
# Check if task completed or failed
if result.status == SubagentStatus.COMPLETED:
return f"[Subagent: {subagent_type}]\n\n{result.result}"
elif result.status == SubagentStatus.FAILED:
return f"[Subagent: {subagent_type}] Task failed: {result.error}"
# Wait before next poll
time.sleep(2)
# Timeout protection (5 minutes)
if poll_count > 150:
return "Task timed out after 5 minutes"
```
### Execution Timeout
In addition to polling timeout, subagent execution now has a built-in timeout mechanism:
**Configuration** (`packages/harness/deerflow/subagents/config.py`):
```python
@dataclass
class SubagentConfig:
# ...
timeout_seconds: int = 300 # 5 minutes default
```
**Thread Pool Architecture**:
To avoid nested thread pools and resource waste, we use two dedicated thread pools:
1. **Scheduler Pool** (`_scheduler_pool`):
- Max workers: 4
- Purpose: Orchestrates background task execution
- Runs `run_task()` function that manages task lifecycle
2. **Execution Pool** (`_execution_pool`):
- Max workers: 8 (larger to avoid blocking)
- Purpose: Actual subagent execution with timeout support
- Runs `execute()` method that invokes the agent
**How it works**:
```python
# In execute_async():
_scheduler_pool.submit(run_task) # Submit orchestration task
# In run_task():
future = _execution_pool.submit(self.execute, task) # Submit execution
exec_result = future.result(timeout=timeout_seconds) # Wait with timeout
```
**Benefits**:
- ✅ Clean separation of concerns (scheduling vs execution)
- ✅ No nested thread pools
- ✅ Timeout enforcement at the right level
- ✅ Better resource utilization
**Two-Level Timeout Protection**:
1. **Execution Timeout**: Subagent execution itself has a 5-minute timeout (configurable in SubagentConfig)
2. **Polling Timeout**: Tool polling has a 5-minute timeout (30 polls × 10 seconds)
This ensures that even if subagent execution hangs, the system won't wait indefinitely.
### Benefits
1. **Reduced API Costs**: No more repeated LLM requests for polling
2. **Simpler UX**: LLM doesn't need to manage polling logic
3. **Better Reliability**: Backend handles all status checking consistently
4. **Timeout Protection**: Two-level timeout prevents infinite waiting (execution + polling)
## Testing
To verify the changes work correctly:
1. Start a subagent task that takes a few seconds
2. Verify the tool call blocks until completion
3. Verify the result is returned directly
4. Verify no `task_status` calls are made
Example test scenario:
```python
# This should block for ~10 seconds then return result
result = task(
subagent_type="bash",
prompt="sleep 10 && echo 'Done'",
description="Test task"
)
# result should contain "Done"
```
## Migration Notes
For users/code that previously used `run_in_background=True`:
- Simply remove the parameter
- Remove any polling logic
- The tool will automatically wait for completion
No other changes needed - the API is backward compatible (minus the removed parameter).
================================================
FILE: backend/langgraph.json
================================================
{
"$schema": "https://langgra.ph/schema.json",
"python_version": "3.12",
"dependencies": [
"."
],
"env": ".env",
"graphs": {
"lead_agent": "deerflow.agents:make_lead_agent"
},
"checkpointer": {
"path": "./packages/harness/deerflow/agents/checkpointer/async_provider.py:make_checkpointer"
}
}
================================================
FILE: backend/packages/harness/deerflow/__init__.py
================================================
================================================
FILE: backend/packages/harness/deerflow/agents/__init__.py
================================================
from .checkpointer import get_checkpointer, make_checkpointer, reset_checkpointer
from .lead_agent import make_lead_agent
from .thread_state import SandboxState, ThreadState
__all__ = ["make_lead_agent", "SandboxState", "ThreadState", "get_checkpointer", "reset_checkpointer", "make_checkpointer"]
================================================
FILE: backend/packages/harness/deerflow/agents/checkpointer/__init__.py
================================================
from .async_provider import make_checkpointer
from .provider import checkpointer_context, get_checkpointer, reset_checkpointer
__all__ = [
"get_checkpointer",
"reset_checkpointer",
"checkpointer_context",
"make_checkpointer",
]
================================================
FILE: backend/packages/harness/deerflow/agents/checkpointer/async_provider.py
================================================
"""Async checkpointer factory.
Provides an **async context manager** for long-running async servers that need
proper resource cleanup.
Supported backends: memory, sqlite, postgres.
Usage (e.g. FastAPI lifespan)::
from deerflow.agents.checkpointer.async_provider import make_checkpointer
async with make_checkpointer() as checkpointer:
app.state.checkpointer = checkpointer # InMemorySaver if not configured
For sync usage see :mod:`deerflow.agents.checkpointer.provider`.
"""
from __future__ import annotations
import contextlib
import logging
from collections.abc import AsyncIterator
from langgraph.types import Checkpointer
from deerflow.agents.checkpointer.provider import (
POSTGRES_CONN_REQUIRED,
POSTGRES_INSTALL,
SQLITE_INSTALL,
_resolve_sqlite_conn_str,
)
from deerflow.config.app_config import get_app_config
logger = logging.getLogger(__name__)
# ---------------------------------------------------------------------------
# Async factory
# ---------------------------------------------------------------------------
@contextlib.asynccontextmanager
async def _async_checkpointer(config) -> AsyncIterator[Checkpointer]:
"""Async context manager that constructs and tears down a checkpointer."""
if config.type == "memory":
from langgraph.checkpoint.memory import InMemorySaver
yield InMemorySaver()
return
if config.type == "sqlite":
try:
from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver
except ImportError as exc:
raise ImportError(SQLITE_INSTALL) from exc
import pathlib
conn_str = _resolve_sqlite_conn_str(config.connection_string or "store.db")
# Only create parent directories for real filesystem paths
if conn_str != ":memory:" and not conn_str.startswith("file:"):
pathlib.Path(conn_str).parent.mkdir(parents=True, exist_ok=True)
async with AsyncSqliteSaver.from_conn_string(conn_str) as saver:
await saver.setup()
yield saver
return
if config.type == "postgres":
try:
from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
except ImportError as exc:
raise ImportError(POSTGRES_INSTALL) from exc
if not config.connection_string:
raise ValueError(POSTGRES_CONN_REQUIRED)
async with AsyncPostgresSaver.from_conn_string(config.connection_string) as saver:
await saver.setup()
yield saver
return
raise ValueError(f"Unknown checkpointer type: {config.type!r}")
# ---------------------------------------------------------------------------
# Public async context manager
# ---------------------------------------------------------------------------
@contextlib.asynccontextmanager
async def make_checkpointer() -> AsyncIterator[Checkpointer]:
"""Async context manager that yields a checkpointer for the caller's lifetime.
Resources are opened on enter and closed on exit — no global state::
async with make_checkpointer() as checkpointer:
app.state.checkpointer = checkpointer
Yields an ``InMemorySaver`` when no checkpointer is configured in *config.yaml*.
"""
config = get_app_config()
if config.checkpointer is None:
from langgraph.checkpoint.memory import InMemorySaver
yield InMemorySaver()
return
async with _async_checkpointer(config.checkpointer) as saver:
yield saver
================================================
FILE: backend/packages/harness/deerflow/agents/checkpointer/provider.py
================================================
"""Sync checkpointer factory.
Provides a **sync singleton** and a **sync context manager** for LangGraph
graph compilation and CLI tools.
Supported backends: memory, sqlite, postgres.
Usage::
from deerflow.agents.checkpointer.provider import get_checkpointer, checkpointer_context
# Singleton — reused across calls, closed on process exit
cp = get_checkpointer()
# One-shot — fresh connection, closed on block exit
with checkpointer_context() as cp:
graph.invoke(input, config={"configurable": {"thread_id": "1"}})
"""
from __future__ import annotations
import contextlib
import logging
from collections.abc import Iterator
from langgraph.types import Checkpointer
from deerflow.config.app_config import get_app_config
from deerflow.config.checkpointer_config import CheckpointerConfig
from deerflow.config.paths import resolve_path
logger = logging.getLogger(__name__)
# ---------------------------------------------------------------------------
# Error message constants — imported by aio.provider too
# ---------------------------------------------------------------------------
SQLITE_INSTALL = "langgraph-checkpoint-sqlite is required for the SQLite checkpointer. Install it with: uv add langgraph-checkpoint-sqlite"
POSTGRES_INSTALL = "langgraph-checkpoint-postgres is required for the PostgreSQL checkpointer. Install it with: uv add langgraph-checkpoint-postgres psycopg[binary] psycopg-pool"
POSTGRES_CONN_REQUIRED = "checkpointer.connection_string is required for the postgres backend"
# ---------------------------------------------------------------------------
# Sync factory
# ---------------------------------------------------------------------------
def _resolve_sqlite_conn_str(raw: str) -> str:
"""Return a SQLite connection string ready for use with ``SqliteSaver``.
SQLite special strings (``":memory:"`` and ``file:`` URIs) are returned
unchanged. Plain filesystem paths — relative or absolute — are resolved
to an absolute string via :func:`resolve_path`.
"""
if raw == ":memory:" or raw.startswith("file:"):
return raw
return str(resolve_path(raw))
@contextlib.contextmanager
def _sync_checkpointer_cm(config: CheckpointerConfig) -> Iterator[Checkpointer]:
"""Context manager that creates and tears down a sync checkpointer.
Returns a configured ``Checkpointer`` instance. Resource cleanup for any
underlying connections or pools is handled by higher-level helpers in
this module (such as the singleton factory or context manager); this
function does not return a separate cleanup callback.
"""
if config.type == "memory":
from langgraph.checkpoint.memory import InMemorySaver
logger.info("Checkpointer: using InMemorySaver (in-process, not persistent)")
yield InMemorySaver()
return
if config.type == "sqlite":
try:
from langgraph.checkpoint.sqlite import SqliteSaver
except ImportError as exc:
raise ImportError(SQLITE_INSTALL) from exc
conn_str = _resolve_sqlite_conn_str(config.connection_string or "store.db")
with SqliteSaver.from_conn_string(conn_str) as saver:
saver.setup()
logger.info("Checkpointer: using SqliteSaver (%s)", conn_str)
yield saver
return
if config.type == "postgres":
try:
from langgraph.checkpoint.postgres import PostgresSaver
except ImportError as exc:
raise ImportError(POSTGRES_INSTALL) from exc
if not config.connection_string:
raise ValueError(POSTGRES_CONN_REQUIRED)
with PostgresSaver.from_conn_string(config.connection_string) as saver:
saver.setup()
logger.info("Checkpointer: using PostgresSaver")
yield saver
return
raise ValueError(f"Unknown checkpointer type: {config.type!r}")
# ---------------------------------------------------------------------------
# Sync singleton
# ---------------------------------------------------------------------------
_checkpointer: Checkpointer | None = None
_checkpointer_ctx = None # open context manager keeping the connection alive
def get_checkpointer() -> Checkpointer:
"""Return the global sync checkpointer singleton, creating it on first call.
Returns an ``InMemorySaver`` when no checkpointer is configured in *config.yaml*.
Raises:
ImportError: If the required package for the configured backend is not installed.
ValueError: If ``connection_string`` is missing for a backend that requires it.
"""
global _checkpointer, _checkpointer_ctx
if _checkpointer is not None:
return _checkpointer
# Ensure app config is loaded before checking checkpointer config
# This prevents returning InMemorySaver when config.yaml actually has a checkpointer section
# but hasn't been loaded yet
from deerflow.config.app_config import _app_config
from deerflow.config.checkpointer_config import get_checkpointer_config
config = get_checkpointer_config()
if config is None and _app_config is None:
# Only load app config lazily when neither the app config nor an explicit
# checkpointer config has been initialized yet. This keeps tests that
# intentionally set the global checkpointer config isolated from any
# ambient config.yaml on disk.
try:
get_app_config()
except FileNotFoundError:
# In test environments without config.yaml, this is expected.
pass
config = get_checkpointer_config()
if config is None:
from langgraph.checkpoint.memory import InMemorySaver
logger.info("Checkpointer: using InMemorySaver (in-process, not persistent)")
_checkpointer = InMemorySaver()
return _checkpointer
_checkpointer_ctx = _sync_checkpointer_cm(config)
_checkpointer = _checkpointer_ctx.__enter__()
return _checkpointer
def reset_checkpointer() -> None:
"""Reset the sync singleton, forcing recreation on the next call.
Closes any open backend connections and clears the cached instance.
Useful in tests or after a configuration change.
"""
global _checkpointer, _checkpointer_ctx
if _checkpointer_ctx is not None:
try:
_checkpointer_ctx.__exit__(None, None, None)
except Exception:
logger.warning("Error during checkpointer cleanup", exc_info=True)
_checkpointer_ctx = None
_checkpointer = None
# ---------------------------------------------------------------------------
# Sync context manager
# ---------------------------------------------------------------------------
@contextlib.contextmanager
def checkpointer_context() -> Iterator[Checkpointer]:
"""Sync context manager that yields a checkpointer and cleans up on exit.
Unlike :func:`get_checkpointer`, this does **not** cache the instance —
each ``with`` block creates and destroys its own connection. Use it in
CLI scripts or tests where you want deterministic cleanup::
with checkpointer_context() as cp:
graph.invoke(input, config={"configurable": {"thread_id": "1"}})
Yields an ``InMemorySaver`` when no checkpointer is configured in *config.yaml*.
"""
config = get_app_config()
if config.checkpointer is None:
from langgraph.checkpoint.memory import InMemorySaver
yield InMemorySaver()
return
with _sync_checkpointer_cm(config.checkpointer) as saver:
yield saver
================================================
FILE: backend/packages/harness/deerflow/agents/lead_agent/__init__.py
================================================
from .agent import make_lead_agent
__all__ = ["make_lead_agent"]
================================================
FILE: backend/packages/harness/deerflow/agents/lead_agent/agent.py
================================================
import logging
from langchain.agents import create_agent
from langchain.agents.middleware import SummarizationMiddleware
from langchain_core.runnables import RunnableConfig
from deerflow.agents.lead_agent.prompt import apply_prompt_template
from deerflow.agents.middlewares.clarification_middleware import ClarificationMiddleware
from deerflow.agents.middlewares.loop_detection_middleware import LoopDetectionMiddleware
from deerflow.agents.middlewares.memory_middleware import MemoryMiddleware
from deerflow.agents.middlewares.subagent_limit_middleware import SubagentLimitMiddleware
from deerflow.agents.middlewares.title_middleware import TitleMiddleware
from deerflow.agents.middlewares.todo_middleware import TodoMiddleware
from deerflow.agents.middlewares.tool_error_handling_middleware import build_lead_runtime_middlewares
from deerflow.agents.middlewares.view_image_middleware import ViewImageMiddleware
from deerflow.agents.thread_state import ThreadState
from deerflow.config.agents_config import load_agent_config
from deerflow.config.app_config import get_app_config
from deerflow.config.summarization_config import get_summarization_config
from deerflow.models import create_chat_model
logger = logging.getLogger(__name__)
def _resolve_model_name(requested_model_name: str | None = None) -> str:
"""Resolve a runtime model name safely, falling back to default if invalid. Returns None if no models are configured."""
app_config = get_app_config()
default_model_name = app_config.models[0].name if app_config.models else None
if default_model_name is None:
raise ValueError("No chat models are configured. Please configure at least one model in config.yaml.")
if requested_model_name and app_config.get_model_config(requested_model_name):
return requested_model_name
if requested_model_name and requested_model_name != default_model_name:
logger.warning(f"Model '{requested_model_name}' not found in config; fallback to default model '{default_model_name}'.")
return default_model_name
def _create_summarization_middleware() -> SummarizationMiddleware | None:
"""Create and configure the summarization middleware from config."""
config = get_summarization_config()
if not config.enabled:
return None
# Prepare trigger parameter
trigger = None
if config.trigger is not None:
if isinstance(config.trigger, list):
trigger = [t.to_tuple() for t in config.trigger]
else:
trigger = config.trigger.to_tuple()
# Prepare keep parameter
keep = config.keep.to_tuple()
# Prepare model parameter
if config.model_name:
model = config.model_name
else:
# Use a lightweight model for summarization to save costs
# Falls back to default model if not explicitly specified
model = create_chat_model(thinking_enabled=False)
# Prepare kwargs
kwargs = {
"model": model,
"trigger": trigger,
"keep": keep,
}
if config.trim_tokens_to_summarize is not None:
kwargs["trim_tokens_to_summarize"] = config.trim_tokens_to_summarize
if config.summary_prompt is not None:
kwargs["summary_prompt"] = config.summary_prompt
return SummarizationMiddleware(**kwargs)
def _create_todo_list_middleware(is_plan_mode: bool) -> TodoMiddleware | None:
"""Create and configure the TodoList middleware.
Args:
is_plan_mode: Whether to enable plan mode with TodoList middleware.
Returns:
TodoMiddleware instance if plan mode is enabled, None otherwise.
"""
if not is_plan_mode:
return None
# Custom prompts matching DeerFlow's style
system_prompt = """
You have access to the `write_todos` tool to help you manage and track complex multi-step objectives.
**CRITICAL RULES:**
- Mark todos as completed IMMEDIATELY after finishing each step - do NOT batch completions
- Keep EXACTLY ONE task as `in_progress` at any time (unless tasks can run in parallel)
- Update the todo list in REAL-TIME as you work - this gives users visibility into your progress
- DO NOT use this tool for simple tasks (< 3 steps) - just complete them directly
**When to Use:**
This tool is designed for complex objectives that require systematic tracking:
- Complex multi-step tasks requiring 3+ distinct steps
- Non-trivial tasks needing careful planning and execution
- User explicitly requests a todo list
- User provides multiple tasks (numbered or comma-separated list)
- The plan may need revisions based on intermediate results
**When NOT to Use:**
- Single, straightforward tasks
- Trivial tasks (< 3 steps)
- Purely conversational or informational requests
- Simple tool calls where the approach is obvious
**Best Practices:**
- Break down complex tasks into smaller, actionable steps
- Use clear, descriptive task names
- Remove tasks that become irrelevant
- Add new tasks discovered during implementation
- Don't be afraid to revise the todo list as you learn more
**Task Management:**
Writing todos takes time and tokens - use it when helpful for managing complex problems, not for simple requests.
"""
tool_description = """Use this tool to create and manage a structured task list for complex work sessions.
**IMPORTANT: Only use this tool for complex tasks (3+ steps). For simple requests, just do the work directly.**
## When to Use
Use this tool in these scenarios:
1. **Complex multi-step tasks**: When a task requires 3 or more distinct steps or actions
2. **Non-trivial tasks**: Tasks requiring careful planning or multiple operations
3. **User explicitly requests todo list**: When the user directly asks you to track tasks
4. **Multiple tasks**: When users provide a list of things to be done
5. **Dynamic planning**: When the plan may need updates based on intermediate results
## When NOT to Use
Skip this tool when:
1. The task is straightforward and takes less than 3 steps
2. The task is trivial and tracking provides no benefit
3. The task is purely conversational or informational
4. It's clear what needs to be done and you can just do it
## How to Use
1. **Starting a task**: Mark it as `in_progress` BEFORE beginning work
2. **Completing a task**: Mark it as `completed` IMMEDIATELY after finishing
3. **Updating the list**: Add new tasks, remove irrelevant ones, or update descriptions as needed
4. **Multiple updates**: You can make several updates at once (e.g., complete one task and start the next)
## Task States
- `pending`: Task not yet started
- `in_progress`: Currently working on (can have multiple if tasks run in parallel)
- `completed`: Task finished successfully
## Task Completion Requirements
**CRITICAL: Only mark a task as completed when you have FULLY accomplished it.**
Never mark a task as completed if:
- There are unresolved issues or errors
- Work is partial or incomplete
- You encountered blockers preventing completion
- You couldn't find necessary resources or dependencies
- Quality standards haven't been met
If blocked, keep the task as `in_progress` and create a new task describing what needs to be resolved.
## Best Practices
- Create specific, actionable items
- Break complex tasks into smaller, manageable steps
- Use clear, descriptive task names
- Update task status in real-time as you work
- Mark tasks complete IMMEDIATELY after finishing (don't batch completions)
- Remove tasks that are no longer relevant
- **IMPORTANT**: When you write the todo list, mark your first task(s) as `in_progress` immediately
- **IMPORTANT**: Unless all tasks are completed, always have at least one task `in_progress` to show progress
Being proactive with task management demonstrates thoroughness and ensures all requirements are completed successfully.
**Remember**: If you only need a few tool calls to complete a task and it's clear what to do, it's better to just do the task directly and NOT use this tool at all.
"""
return TodoMiddleware(system_prompt=system_prompt, tool_description=tool_description)
# ThreadDataMiddleware must be before SandboxMiddleware to ensure thread_id is available
# UploadsMiddleware should be after ThreadDataMiddleware to access thread_id
# DanglingToolCallMiddleware patches missing ToolMessages before model sees the history
# SummarizationMiddleware should be early to reduce context before other processing
# TodoListMiddleware should be before ClarificationMiddleware to allow todo management
# TitleMiddleware generates title after first exchange
# MemoryMiddleware queues conversation for memory update (after TitleMiddleware)
# ViewImageMiddleware should be before ClarificationMiddleware to inject image details before LLM
# ToolErrorHandlingMiddleware should be before ClarificationMiddleware to convert tool exceptions to ToolMessages
# ClarificationMiddleware should be last to intercept clarification requests after model calls
def _build_middlewares(config: RunnableConfig, model_name: str | None, agent_name: str | None = None):
"""Build middleware chain based on runtime configuration.
Args:
config: Runtime configuration containing configurable options like is_plan_mode.
agent_name: If provided, MemoryMiddleware will use per-agent memory storage.
Returns:
List of middleware instances.
"""
middlewares = build_lead_runtime_middlewares(lazy_init=True)
# Add summarization middleware if enabled
summarization_middleware = _create_summarization_middleware()
if summarization_middleware is not None:
middlewares.append(summarization_middleware)
# Add TodoList middleware if plan mode is enabled
is_plan_mode = config.get("configurable", {}).get("is_plan_mode", False)
todo_list_middleware = _create_todo_list_middleware(is_plan_mode)
if todo_list_middleware is not None:
middlewares.append(todo_list_middleware)
# Add TitleMiddleware
middlewares.append(TitleMiddleware())
# Add MemoryMiddleware (after TitleMiddleware)
middlewares.append(MemoryMiddleware(agent_name=agent_name))
# Add ViewImageMiddleware only if the current model supports vision.
# Use the resolved runtime model_name from make_lead_agent to avoid stale config values.
app_config = get_app_config()
model_config = app_config.get_model_config(model_name) if model_name else None
if model_config is not None and model_config.supports_vision:
middlewares.append(ViewImageMiddleware())
# Add DeferredToolFilterMiddleware to hide deferred tool schemas from model binding
if app_config.tool_search.enabled:
from deerflow.agents.middlewares.deferred_tool_filter_middleware import DeferredToolFilterMiddleware
middlewares.append(DeferredToolFilterMiddleware())
# Add SubagentLimitMiddleware to truncate excess parallel task calls
subagent_enabled = config.get("configurable", {}).get("subagent_enabled", False)
if subagent_enabled:
max_concurrent_subagents = config.get("configurable", {}).get("max_concurrent_subagents", 3)
middlewares.append(SubagentLimitMiddleware(max_concurrent=max_concurrent_subagents))
# LoopDetectionMiddleware — detect and break repetitive tool call loops
middlewares.append(LoopDetectionMiddleware())
# ClarificationMiddleware should always be last
middlewares.append(ClarificationMiddleware())
return middlewares
def make_lead_agent(config: RunnableConfig):
# Lazy import to avoid circular dependency
from deerflow.tools import get_available_tools
from deerflow.tools.builtins import setup_agent
cfg = config.get("configurable", {})
thinking_enabled = cfg.get("thinking_enabled", True)
reasoning_effort = cfg.get("reasoning_effort", None)
requested_model_name: str | None = cfg.get("model_name") or cfg.get("model")
is_plan_mode = cfg.get("is_plan_mode", False)
subagent_enabled = cfg.get("subagent_enabled", False)
max_concurrent_subagents = cfg.get("max_concurrent_subagents", 3)
is_bootstrap = cfg.get("is_bootstrap", False)
agent_name = cfg.get("agent_name")
agent_config = load_agent_config(agent_name) if not is_bootstrap else None
# Custom agent model or fallback to global/default model resolution
agent_model_name = agent_config.model if agent_config and agent_config.model else _resolve_model_name()
# Final model name resolution with request override, then agent config, then global default
model_name = requested_model_name or agent_model_name
app_config = get_app_config()
model_config = app_config.get_model_config(model_name) if model_name else None
if model_config is None:
raise ValueError("No chat model could be resolved. Please configure at least one model in config.yaml or provide a valid 'model_name'/'model' in the request.")
if thinking_enabled and not model_config.supports_thinking:
logger.warning(f"Thinking mode is enabled but model '{model_name}' does not support it; fallback to non-thinking mode.")
thinking_enabled = False
logger.info(
"Create Agent(%s) -> thinking_enabled: %s, reasoning_effort: %s, model_name: %s, is_plan_mode: %s, subagent_enabled: %s, max_concurrent_subagents: %s",
agent_name or "default",
thinking_enabled,
reasoning_effort,
model_name,
is_plan_mode,
subagent_enabled,
max_concurrent_subagents,
)
# Inject run metadata for LangSmith trace tagging
if "metadata" not in config:
config["metadata"] = {}
config["metadata"].update(
{
"agent_name": agent_name or "default",
"model_name": model_name or "default",
"thinking_enabled": thinking_enabled,
"reasoning_effort": reasoning_effort,
"is_plan_mode": is_plan_mode,
"subagent_enabled": subagent_enabled,
}
)
if is_bootstrap:
# Special bootstrap agent with minimal prompt for initial custom agent creation flow
return create_agent(
model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled),
tools=get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled) + [setup_agent],
middleware=_build_middlewares(config, model_name=model_name),
system_prompt=apply_prompt_template(subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, available_skills=set(["bootstrap"])),
state_schema=ThreadState,
)
# Default lead agent (unchanged behavior)
return create_agent(
model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort),
tools=get_available_tools(model_name=model_name, groups=agent_config.tool_groups if agent_config else None, subagent_enabled=subagent_enabled),
middleware=_build_middlewares(config, model_name=model_name, agent_name=agent_name),
system_prompt=apply_prompt_template(subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents, agent_name=agent_name),
state_schema=ThreadState,
)
================================================
FILE: backend/packages/harness/deerflow/agents/lead_agent/prompt.py
================================================
from datetime import datetime
from deerflow.config.agents_config import load_agent_soul
from deerflow.skills import load_skills
def _build_subagent_section(max_concurrent: int) -> str:
"""Build the subagent system prompt section with dynamic concurrency limit.
Args:
max_concurrent: Maximum number of concurrent subagent calls allowed per response.
Returns:
Formatted subagent section string.
"""
n = max_concurrent
return f"""
**🚀 SUBAGENT MODE ACTIVE - DECOMPOSE, DELEGATE, SYNTHESIZE**
You are running with subagent capabilities enabled. Your role is to be a **task orchestrator**:
1. **DECOMPOSE**: Break complex tasks into parallel sub-tasks
2. **DELEGATE**: Launch multiple subagents simultaneously using parallel `task` calls
3. **SYNTHESIZE**: Collect and integrate results into a coherent answer
**CORE PRINCIPLE: Complex tasks should be decomposed and distributed across multiple subagents for parallel execution.**
**⛔ HARD CONCURRENCY LIMIT: MAXIMUM {n} `task` CALLS PER RESPONSE. THIS IS NOT OPTIONAL.**
- Each response, you may include **at most {n}** `task` tool calls. Any excess calls are **silently discarded** by the system — you will lose that work.
- **Before launching subagents, you MUST count your sub-tasks in your thinking:**
- If count ≤ {n}: Launch all in this response.
- If count > {n}: **Pick the {n} most important/foundational sub-tasks for this turn.** Save the rest for the next turn.
- **Multi-batch execution** (for >{n} sub-tasks):
- Turn 1: Launch sub-tasks 1-{n} in parallel → wait for results
- Turn 2: Launch next batch in parallel → wait for results
- ... continue until all sub-tasks are complete
- Final turn: Synthesize ALL results into a coherent answer
- **Example thinking pattern**: "I identified 6 sub-tasks. Since the limit is {n} per turn, I will launch the first {n} now, and the rest in the next turn."
**Available Subagents:**
- **general-purpose**: For ANY non-trivial task - web research, code exploration, file operations, analysis, etc.
- **bash**: For command execution (git, build, test, deploy operations)
**Your Orchestration Strategy:**
✅ **DECOMPOSE + PARALLEL EXECUTION (Preferred Approach):**
For complex queries, break them down into focused sub-tasks and execute in parallel batches (max {n} per turn):
**Example 1: "Why is Tencent's stock price declining?" (3 sub-tasks → 1 batch)**
→ Turn 1: Launch 3 subagents in parallel:
- Subagent 1: Recent financial reports, earnings data, and revenue trends
- Subagent 2: Negative news, controversies, and regulatory issues
- Subagent 3: Industry trends, competitor performance, and market sentiment
→ Turn 2: Synthesize results
**Example 2: "Compare 5 cloud providers" (5 sub-tasks → multi-batch)**
→ Turn 1: Launch {n} subagents in parallel (first batch)
→ Turn 2: Launch remaining subagents in parallel
→ Final turn: Synthesize ALL results into comprehensive comparison
**Example 3: "Refactor the authentication system"**
→ Turn 1: Launch 3 subagents in parallel:
- Subagent 1: Analyze current auth implementation and technical debt
- Subagent 2: Research best practices and security patterns
- Subagent 3: Review related tests, documentation, and vulnerabilities
→ Turn 2: Synthesize results
✅ **USE Parallel Subagents (max {n} per turn) when:**
- **Complex research questions**: Requires multiple information sources or perspectives
- **Multi-aspect analysis**: Task has several independent dimensions to explore
- **Large codebases**: Need to analyze different parts simultaneously
- **Comprehensive investigations**: Questions requiring thorough coverage from multiple angles
❌ **DO NOT use subagents (execute directly) when:**
- **Task cannot be decomposed**: If you can't break it into 2+ meaningful parallel sub-tasks, execute directly
- **Ultra-simple actions**: Read one file, quick edits, single commands
- **Need immediate clarification**: Must ask user before proceeding
- **Meta conversation**: Questions about conversation history
- **Sequential dependencies**: Each step depends on previous results (do steps yourself sequentially)
**CRITICAL WORKFLOW** (STRICTLY follow this before EVERY action):
1. **COUNT**: In your thinking, list all sub-tasks and count them explicitly: "I have N sub-tasks"
2. **PLAN BATCHES**: If N > {n}, explicitly plan which sub-tasks go in which batch:
- "Batch 1 (this turn): first {n} sub-tasks"
- "Batch 2 (next turn): next batch of sub-tasks"
3. **EXECUTE**: Launch ONLY the current batch (max {n} `task` calls). Do NOT launch sub-tasks from future batches.
4. **REPEAT**: After results return, launch the next batch. Continue until all batches complete.
5. **SYNTHESIZE**: After ALL batches are done, synthesize all results.
6. **Cannot decompose** → Execute directly using available tools (bash, read_file, web_search, etc.)
**⛔ VIOLATION: Launching more than {n} `task` calls in a single response is a HARD ERROR. The system WILL discard excess calls and you WILL lose work. Always batch.**
**Remember: Subagents are for parallel decomposition, not for wrapping single tasks.**
**How It Works:**
- The task tool runs subagents asynchronously in the background
- The backend automatically polls for completion (you don't need to poll)
- The tool call will block until the subagent completes its work
- Once complete, the result is returned to you directly
**Usage Example 1 - Single Batch (≤{n} sub-tasks):**
```python
# User asks: "Why is Tencent's stock price declining?"
# Thinking: 3 sub-tasks → fits in 1 batch
# Turn 1: Launch 3 subagents in parallel
task(description="Tencent financial data", prompt="...", subagent_type="general-purpose")
task(description="Tencent news & regulation", prompt="...", subagent_type="general-purpose")
task(description="Industry & market trends", prompt="...", subagent_type="general-purpose")
# All 3 run in parallel → synthesize results
```
**Usage Example 2 - Multiple Batches (>{n} sub-tasks):**
```python
# User asks: "Compare AWS, Azure, GCP, Alibaba Cloud, and Oracle Cloud"
# Thinking: 5 sub-tasks → need multiple batches (max {n} per batch)
# Turn 1: Launch first batch of {n}
task(description="AWS analysis", prompt="...", subagent_type="general-purpose")
task(description="Azure analysis", prompt="...", subagent_type="general-purpose")
task(description="GCP analysis", prompt="...", subagent_type="general-purpose")
# Turn 2: Launch remaining batch (after first batch completes)
task(description="Alibaba Cloud analysis", prompt="...", subagent_type="general-purpose")
task(description="Oracle Cloud analysis", prompt="...", subagent_type="general-purpose")
# Turn 3: Synthesize ALL results from both batches
```
**Counter-Example - Direct Execution (NO subagents):**
```python
# User asks: "Run the tests"
# Thinking: Cannot decompose into parallel sub-tasks
# → Execute directly
bash("npm test") # Direct execution, not task()
```
**CRITICAL**:
- **Max {n} `task` calls per turn** - the system enforces this, excess calls are discarded
- Only use `task` when you can launch 2+ subagents in parallel
- Single task = No value from subagents = Execute directly
- For >{n} sub-tasks, use sequential batches of {n} across multiple turns
"""
SYSTEM_PROMPT_TEMPLATE = """
You are {agent_name}, an open-source super agent.
{soul}
{memory_context}
- Think concisely and strategically about the user's request BEFORE taking action
- Break down the task: What is clear? What is ambiguous? What is missing?
- **PRIORITY CHECK: If anything is unclear, missing, or has multiple interpretations, you MUST ask for clarification FIRST - do NOT proceed with work**
{subagent_thinking}- Never write down your full final answer or report in thinking process, but only outline
- CRITICAL: After thinking, you MUST provide your actual response to the user. Thinking is for planning, the response is for delivery.
- Your response must contain the actual answer, not just a reference to what you thought about
**WORKFLOW PRIORITY: CLARIFY → PLAN → ACT**
1. **FIRST**: Analyze the request in your thinking - identify what's unclear, missing, or ambiguous
2. **SECOND**: If clarification is needed, call `ask_clarification` tool IMMEDIATELY - do NOT start working
3. **THIRD**: Only after all clarifications are resolved, proceed with planning and execution
**CRITICAL RULE: Clarification ALWAYS comes BEFORE action. Never start working and clarify mid-execution.**
**MANDATORY Clarification Scenarios - You MUST call ask_clarification BEFORE starting work when:**
1. **Missing Information** (`missing_info`): Required details not provided
- Example: User says "create a web scraper" but doesn't specify the target website
- Example: "Deploy the app" without specifying environment
- **REQUIRED ACTION**: Call ask_clarification to get the missing information
2. **Ambiguous Requirements** (`ambiguous_requirement`): Multiple valid interpretations exist
- Example: "Optimize the code" could mean performance, readability, or memory usage
- Example: "Make it better" is unclear what aspect to improve
- **REQUIRED ACTION**: Call ask_clarification to clarify the exact requirement
3. **Approach Choices** (`approach_choice`): Several valid approaches exist
- Example: "Add authentication" could use JWT, OAuth, session-based, or API keys
- Example: "Store data" could use database, files, cache, etc.
- **REQUIRED ACTION**: Call ask_clarification to let user choose the approach
4. **Risky Operations** (`risk_confirmation`): Destructive actions need confirmation
- Example: Deleting files, modifying production configs, database operations
- Example: Overwriting existing code or data
- **REQUIRED ACTION**: Call ask_clarification to get explicit confirmation
5. **Suggestions** (`suggestion`): You have a recommendation but want approval
- Example: "I recommend refactoring this code. Should I proceed?"
- **REQUIRED ACTION**: Call ask_clarification to get approval
**STRICT ENFORCEMENT:**
- ❌ DO NOT start working and then ask for clarification mid-execution - clarify FIRST
- ❌ DO NOT skip clarification for "efficiency" - accuracy matters more than speed
- ❌ DO NOT make assumptions when information is missing - ALWAYS ask
- ❌ DO NOT proceed with guesses - STOP and call ask_clarification first
- ✅ Analyze the request in thinking → Identify unclear aspects → Ask BEFORE any action
- ✅ If you identify the need for clarification in your thinking, you MUST call the tool IMMEDIATELY
- ✅ After calling ask_clarification, execution will be interrupted automatically
- ✅ Wait for user response - do NOT continue with assumptions
**How to Use:**
```python
ask_clarification(
question="Your specific question here?",
clarification_type="missing_info", # or other type
context="Why you need this information", # optional but recommended
options=["option1", "option2"] # optional, for choices
)
```
**Example:**
User: "Deploy the application"
You (thinking): Missing environment info - I MUST ask for clarification
You (action): ask_clarification(
question="Which environment should I deploy to?",
clarification_type="approach_choice",
context="I need to know the target environment for proper configuration",
options=["development", "staging", "production"]
)
[Execution stops - wait for user response]
User: "staging"
You: "Deploying to staging..." [proceed]
{skills_section}
{deferred_tools_section}
{subagent_section}
- User uploads: `/mnt/user-data/uploads` - Files uploaded by the user (automatically listed in context)
- User workspace: `/mnt/user-data/workspace` - Working directory for temporary files
- Output files: `/mnt/user-data/outputs` - Final deliverables must be saved here
**File Management:**
- Uploaded files are automatically listed in the section before each request
- Use `read_file` tool to read uploaded files using their paths from the list
- For PDF, PPT, Excel, and Word files, converted Markdown versions (*.md) are available alongside originals
- All temporary work happens in `/mnt/user-data/workspace`
- Final deliverables must be copied to `/mnt/user-data/outputs` and presented using `present_file` tool
- Clear and Concise: Avoid over-formatting unless requested
- Natural Tone: Use paragraphs and prose, not bullet points by default
- Action-Oriented: Focus on delivering results, not explaining processes
**CRITICAL: Always include citations when using web search results**
- **When to Use**: MANDATORY after web_search, web_fetch, or any external information source
- **Format**: Use Markdown link format `[citation:TITLE](URL)` immediately after the claim
- **Placement**: Inline citations should appear right after the sentence or claim they support
- **Sources Section**: Also collect all citations in a "Sources" section at the end of reports
**Example - Inline Citations:**
```markdown
The key AI trends for 2026 include enhanced reasoning capabilities and multimodal integration
[citation:AI Trends 2026](https://techcrunch.com/ai-trends).
Recent breakthroughs in language models have also accelerated progress
[citation:OpenAI Research](https://openai.com/research).
```
**Example - Deep Research Report with Citations:**
```markdown
## Executive Summary
DeerFlow is an open-source AI agent framework that gained significant traction in early 2026
[citation:GitHub Repository](https://github.com/bytedance/deer-flow). The project focuses on
providing a production-ready agent system with sandbox execution and memory management
[citation:DeerFlow Documentation](https://deer-flow.dev/docs).
## Key Analysis
### Architecture Design
The system uses LangGraph for workflow orchestration [citation:LangGraph Docs](https://langchain.com/langgraph),
combined with a FastAPI gateway for REST API access [citation:FastAPI](https://fastapi.tiangolo.com).
## Sources
### Primary Sources
- [GitHub Repository](https://github.com/bytedance/deer-flow) - Official source code and documentation
- [DeerFlow Documentation](https://deer-flow.dev/docs) - Technical specifications
### Media Coverage
- [AI Trends 2026](https://techcrunch.com/ai-trends) - Industry analysis
```
**CRITICAL: Sources section format:**
- Every item in the Sources section MUST be a clickable markdown link with URL
- Use standard markdown link `[Title](URL) - Description` format (NOT `[citation:...]` format)
- The `[citation:Title](URL)` format is ONLY for inline citations within the report body
- ❌ WRONG: `GitHub 仓库 - 官方源代码和文档` (no URL!)
- ❌ WRONG in Sources: `[citation:GitHub Repository](url)` (citation prefix is for inline only!)
- ✅ RIGHT in Sources: `[GitHub Repository](https://github.com/bytedance/deer-flow) - 官方源代码和文档`
**WORKFLOW for Research Tasks:**
1. Use web_search to find sources → Extract {{title, url, snippet}} from results
2. Write content with inline citations: `claim [citation:Title](url)`
3. Collect all citations in a "Sources" section at the end
4. NEVER write claims without citations when sources are available
**CRITICAL RULES:**
- ❌ DO NOT write research content without citations
- ❌ DO NOT forget to extract URLs from search results
- ✅ ALWAYS add `[citation:Title](URL)` after claims from external sources
- ✅ ALWAYS include a "Sources" section listing all references
- **Clarification First**: ALWAYS clarify unclear/missing/ambiguous requirements BEFORE starting work - never assume or guess
{subagent_reminder}- Skill First: Always load the relevant skill before starting **complex** tasks.
- Progressive Loading: Load resources incrementally as referenced in skills
- Output Files: Final deliverables must be in `/mnt/user-data/outputs`
- Clarity: Be direct and helpful, avoid unnecessary meta-commentary
- Including Images and Mermaid: Images and Mermaid diagrams are always welcomed in the Markdown format, and you're encouraged to use `\n\n` or "```mermaid" to display images in response or Markdown files
- Multi-task: Better utilize parallel tool calling to call multiple tools at one time for better performance
- Language Consistency: Keep using the same language as user's
- Always Respond: Your thinking is internal. You MUST always provide a visible response to the user after thinking.
"""
def _get_memory_context(agent_name: str | None = None) -> str:
"""Get memory context for injection into system prompt.
Args:
agent_name: If provided, loads per-agent memory. If None, loads global memory.
Returns:
Formatted memory context string wrapped in XML tags, or empty string if disabled.
"""
try:
from deerflow.agents.memory import format_memory_for_injection, get_memory_data
from deerflow.config.memory_config import get_memory_config
config = get_memory_config()
if not config.enabled or not config.injection_enabled:
return ""
memory_data = get_memory_data(agent_name)
memory_content = format_memory_for_injection(memory_data, max_tokens=config.max_injection_tokens)
if not memory_content.strip():
return ""
return f"""
{memory_content}
"""
except Exception as e:
print(f"Failed to load memory context: {e}")
return ""
def get_skills_prompt_section(available_skills: set[str] | None = None) -> str:
"""Generate the skills prompt section with available skills list.
Returns the ... block listing all enabled skills,
suitable for injection into any agent's system prompt.
"""
skills = load_skills(enabled_only=True)
try:
from deerflow.config import get_app_config
config = get_app_config()
container_base_path = config.skills.container_path
except Exception:
container_base_path = "/mnt/skills"
if not skills:
return ""
if available_skills is not None:
skills = [skill for skill in skills if skill.name in available_skills]
skill_items = "\n".join(
f" \n {skill.name}\n {skill.description}\n {skill.get_container_file_path(container_base_path)}\n " for skill in skills
)
skills_list = f"\n{skill_items}\n"
return f"""
You have access to skills that provide optimized workflows for specific tasks. Each skill contains best practices, frameworks, and references to additional resources.
**Progressive Loading Pattern:**
1. When a user query matches a skill's use case, immediately call `read_file` on the skill's main file using the path attribute provided in the skill tag below
2. Read and understand the skill's workflow and instructions
3. The skill file contains references to external resources under the same folder
4. Load referenced resources only when needed during execution
5. Follow the skill's instructions precisely
**Skills are located at:** {container_base_path}
{skills_list}
"""
def get_agent_soul(agent_name: str | None) -> str:
# Append SOUL.md (agent personality) if present
soul = load_agent_soul(agent_name)
if soul:
return f"\n{soul}\n\n" if soul else ""
return ""
def get_deferred_tools_prompt_section() -> str:
"""Generate block for the system prompt.
Lists only deferred tool names so the agent knows what exists
and can use tool_search to load them.
Returns empty string when tool_search is disabled or no tools are deferred.
"""
from deerflow.tools.builtins.tool_search import get_deferred_registry
try:
from deerflow.config import get_app_config
if not get_app_config().tool_search.enabled:
return ""
except FileNotFoundError:
return ""
registry = get_deferred_registry()
if not registry:
return ""
names = "\n".join(e.name for e in registry.entries)
return f"\n{names}\n"
def apply_prompt_template(subagent_enabled: bool = False, max_concurrent_subagents: int = 3, *, agent_name: str | None = None, available_skills: set[str] | None = None) -> str:
# Get memory context
memory_context = _get_memory_context(agent_name)
# Include subagent section only if enabled (from runtime parameter)
n = max_concurrent_subagents
subagent_section = _build_subagent_section(n) if subagent_enabled else ""
# Add subagent reminder to critical_reminders if enabled
subagent_reminder = (
"- **Orchestrator Mode**: You are a task orchestrator - decompose complex tasks into parallel sub-tasks. "
f"**HARD LIMIT: max {n} `task` calls per response.** "
f"If >{n} sub-tasks, split into sequential batches of ≤{n}. Synthesize after ALL batches complete.\n"
if subagent_enabled
else ""
)
# Add subagent thinking guidance if enabled
subagent_thinking = (
"- **DECOMPOSITION CHECK: Can this task be broken into 2+ parallel sub-tasks? If YES, COUNT them. "
f"If count > {n}, you MUST plan batches of ≤{n} and only launch the FIRST batch now. "
f"NEVER launch more than {n} `task` calls in one response.**\n"
if subagent_enabled
else ""
)
# Get skills section
skills_section = get_skills_prompt_section(available_skills)
# Get deferred tools section (tool_search)
deferred_tools_section = get_deferred_tools_prompt_section()
# Format the prompt with dynamic skills and memory
prompt = SYSTEM_PROMPT_TEMPLATE.format(
agent_name=agent_name or "DeerFlow 2.0",
soul=get_agent_soul(agent_name),
skills_section=skills_section,
deferred_tools_section=deferred_tools_section,
memory_context=memory_context,
subagent_section=subagent_section,
subagent_reminder=subagent_reminder,
subagent_thinking=subagent_thinking,
)
return prompt + f"\n{datetime.now().strftime('%Y-%m-%d, %A')}"
================================================
FILE: backend/packages/harness/deerflow/agents/memory/__init__.py
================================================
"""Memory module for DeerFlow.
This module provides a global memory mechanism that:
- Stores user context and conversation history in memory.json
- Uses LLM to summarize and extract facts from conversations
- Injects relevant memory into system prompts for personalized responses
"""
from deerflow.agents.memory.prompt import (
FACT_EXTRACTION_PROMPT,
MEMORY_UPDATE_PROMPT,
format_conversation_for_update,
format_memory_for_injection,
)
from deerflow.agents.memory.queue import (
ConversationContext,
MemoryUpdateQueue,
get_memory_queue,
reset_memory_queue,
)
from deerflow.agents.memory.updater import (
MemoryUpdater,
get_memory_data,
reload_memory_data,
update_memory_from_conversation,
)
__all__ = [
# Prompt utilities
"MEMORY_UPDATE_PROMPT",
"FACT_EXTRACTION_PROMPT",
"format_memory_for_injection",
"format_conversation_for_update",
# Queue
"ConversationContext",
"MemoryUpdateQueue",
"get_memory_queue",
"reset_memory_queue",
# Updater
"MemoryUpdater",
"get_memory_data",
"reload_memory_data",
"update_memory_from_conversation",
]
================================================
FILE: backend/packages/harness/deerflow/agents/memory/prompt.py
================================================
"""Prompt templates for memory update and injection."""
import math
import re
from typing import Any
try:
import tiktoken
TIKTOKEN_AVAILABLE = True
except ImportError:
TIKTOKEN_AVAILABLE = False
# Prompt template for updating memory based on conversation
MEMORY_UPDATE_PROMPT = """You are a memory management system. Your task is to analyze a conversation and update the user's memory profile.
Current Memory State:
{current_memory}
New Conversation to Process:
{conversation}
Instructions:
1. Analyze the conversation for important information about the user
2. Extract relevant facts, preferences, and context with specific details (numbers, names, technologies)
3. Update the memory sections as needed following the detailed length guidelines below
Memory Section Guidelines:
**User Context** (Current state - concise summaries):
- workContext: Professional role, company, key projects, main technologies (2-3 sentences)
Example: Core contributor, project names with metrics (16k+ stars), technical stack
- personalContext: Languages, communication preferences, key interests (1-2 sentences)
Example: Bilingual capabilities, specific interest areas, expertise domains
- topOfMind: Multiple ongoing focus areas and priorities (3-5 sentences, detailed paragraph)
Example: Primary project work, parallel technical investigations, ongoing learning/tracking
Include: Active implementation work, troubleshooting issues, market/research interests
Note: This captures SEVERAL concurrent focus areas, not just one task
**History** (Temporal context - rich paragraphs):
- recentMonths: Detailed summary of recent activities (4-6 sentences or 1-2 paragraphs)
Timeline: Last 1-3 months of interactions
Include: Technologies explored, projects worked on, problems solved, interests demonstrated
- earlierContext: Important historical patterns (3-5 sentences or 1 paragraph)
Timeline: 3-12 months ago
Include: Past projects, learning journeys, established patterns
- longTermBackground: Persistent background and foundational context (2-4 sentences)
Timeline: Overall/foundational information
Include: Core expertise, longstanding interests, fundamental working style
**Facts Extraction**:
- Extract specific, quantifiable details (e.g., "16k+ GitHub stars", "200+ datasets")
- Include proper nouns (company names, project names, technology names)
- Preserve technical terminology and version numbers
- Categories:
* preference: Tools, styles, approaches user prefers/dislikes
* knowledge: Specific expertise, technologies mastered, domain knowledge
* context: Background facts (job title, projects, locations, languages)
* behavior: Working patterns, communication habits, problem-solving approaches
* goal: Stated objectives, learning targets, project ambitions
- Confidence levels:
* 0.9-1.0: Explicitly stated facts ("I work on X", "My role is Y")
* 0.7-0.8: Strongly implied from actions/discussions
* 0.5-0.6: Inferred patterns (use sparingly, only for clear patterns)
**What Goes Where**:
- workContext: Current job, active projects, primary tech stack
- personalContext: Languages, personality, interests outside direct work tasks
- topOfMind: Multiple ongoing priorities and focus areas user cares about recently (gets updated most frequently)
Should capture 3-5 concurrent themes: main work, side explorations, learning/tracking interests
- recentMonths: Detailed account of recent technical explorations and work
- earlierContext: Patterns from slightly older interactions still relevant
- longTermBackground: Unchanging foundational facts about the user
**Multilingual Content**:
- Preserve original language for proper nouns and company names
- Keep technical terms in their original form (DeepSeek, LangGraph, etc.)
- Note language capabilities in personalContext
Output Format (JSON):
{{
"user": {{
"workContext": {{ "summary": "...", "shouldUpdate": true/false }},
"personalContext": {{ "summary": "...", "shouldUpdate": true/false }},
"topOfMind": {{ "summary": "...", "shouldUpdate": true/false }}
}},
"history": {{
"recentMonths": {{ "summary": "...", "shouldUpdate": true/false }},
"earlierContext": {{ "summary": "...", "shouldUpdate": true/false }},
"longTermBackground": {{ "summary": "...", "shouldUpdate": true/false }}
}},
"newFacts": [
{{ "content": "...", "category": "preference|knowledge|context|behavior|goal", "confidence": 0.0-1.0 }}
],
"factsToRemove": ["fact_id_1", "fact_id_2"]
}}
Important Rules:
- Only set shouldUpdate=true if there's meaningful new information
- Follow length guidelines: workContext/personalContext are concise (1-3 sentences), topOfMind and history sections are detailed (paragraphs)
- Include specific metrics, version numbers, and proper nouns in facts
- Only add facts that are clearly stated (0.9+) or strongly implied (0.7+)
- Remove facts that are contradicted by new information
- When updating topOfMind, integrate new focus areas while removing completed/abandoned ones
Keep 3-5 concurrent focus themes that are still active and relevant
- For history sections, integrate new information chronologically into appropriate time period
- Preserve technical accuracy - keep exact names of technologies, companies, projects
- Focus on information useful for future interactions and personalization
- IMPORTANT: Do NOT record file upload events in memory. Uploaded files are
session-specific and ephemeral — they will not be accessible in future sessions.
Recording upload events causes confusion in subsequent conversations.
Return ONLY valid JSON, no explanation or markdown."""
# Prompt template for extracting facts from a single message
FACT_EXTRACTION_PROMPT = """Extract factual information about the user from this message.
Message:
{message}
Extract facts in this JSON format:
{{
"facts": [
{{ "content": "...", "category": "preference|knowledge|context|behavior|goal", "confidence": 0.0-1.0 }}
]
}}
Categories:
- preference: User preferences (likes/dislikes, styles, tools)
- knowledge: User's expertise or knowledge areas
- context: Background context (location, job, projects)
- behavior: Behavioral patterns
- goal: User's goals or objectives
Rules:
- Only extract clear, specific facts
- Confidence should reflect certainty (explicit statement = 0.9+, implied = 0.6-0.8)
- Skip vague or temporary information
Return ONLY valid JSON."""
def _count_tokens(text: str, encoding_name: str = "cl100k_base") -> int:
"""Count tokens in text using tiktoken.
Args:
text: The text to count tokens for.
encoding_name: The encoding to use (default: cl100k_base for GPT-4/3.5).
Returns:
The number of tokens in the text.
"""
if not TIKTOKEN_AVAILABLE:
# Fallback to character-based estimation if tiktoken is not available
return len(text) // 4
try:
encoding = tiktoken.get_encoding(encoding_name)
return len(encoding.encode(text))
except Exception:
# Fallback to character-based estimation on error
return len(text) // 4
def _coerce_confidence(value: Any, default: float = 0.0) -> float:
"""Coerce a confidence-like value to a bounded float in [0, 1].
Non-finite values (NaN, inf, -inf) are treated as invalid and fall back
to the default before clamping, preventing them from dominating ranking.
The ``default`` parameter is assumed to be a finite value.
"""
try:
confidence = float(value)
except (TypeError, ValueError):
return max(0.0, min(1.0, default))
if not math.isfinite(confidence):
return max(0.0, min(1.0, default))
return max(0.0, min(1.0, confidence))
def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2000) -> str:
"""Format memory data for injection into system prompt.
Args:
memory_data: The memory data dictionary.
max_tokens: Maximum tokens to use (counted via tiktoken for accuracy).
Returns:
Formatted memory string for system prompt injection.
"""
if not memory_data:
return ""
sections = []
# Format user context
user_data = memory_data.get("user", {})
if user_data:
user_sections = []
work_ctx = user_data.get("workContext", {})
if work_ctx.get("summary"):
user_sections.append(f"Work: {work_ctx['summary']}")
personal_ctx = user_data.get("personalContext", {})
if personal_ctx.get("summary"):
user_sections.append(f"Personal: {personal_ctx['summary']}")
top_of_mind = user_data.get("topOfMind", {})
if top_of_mind.get("summary"):
user_sections.append(f"Current Focus: {top_of_mind['summary']}")
if user_sections:
sections.append("User Context:\n" + "\n".join(f"- {s}" for s in user_sections))
# Format history
history_data = memory_data.get("history", {})
if history_data:
history_sections = []
recent = history_data.get("recentMonths", {})
if recent.get("summary"):
history_sections.append(f"Recent: {recent['summary']}")
earlier = history_data.get("earlierContext", {})
if earlier.get("summary"):
history_sections.append(f"Earlier: {earlier['summary']}")
if history_sections:
sections.append("History:\n" + "\n".join(f"- {s}" for s in history_sections))
# Format facts (sorted by confidence; include as many as token budget allows)
facts_data = memory_data.get("facts", [])
if isinstance(facts_data, list) and facts_data:
ranked_facts = sorted(
(
f
for f in facts_data
if isinstance(f, dict)
and isinstance(f.get("content"), str)
and f.get("content").strip()
),
key=lambda fact: _coerce_confidence(fact.get("confidence"), default=0.0),
reverse=True,
)
# Compute token count for existing sections once, then account
# incrementally for each fact line to avoid full-string re-tokenization.
base_text = "\n\n".join(sections)
base_tokens = _count_tokens(base_text) if base_text else 0
# Account for the separator between existing sections and the facts section.
facts_header = "Facts:\n"
separator_tokens = _count_tokens("\n\n" + facts_header) if base_text else _count_tokens(facts_header)
running_tokens = base_tokens + separator_tokens
fact_lines: list[str] = []
for fact in ranked_facts:
content_value = fact.get("content")
if not isinstance(content_value, str):
continue
content = content_value.strip()
if not content:
continue
category = str(fact.get("category", "context")).strip() or "context"
confidence = _coerce_confidence(fact.get("confidence"), default=0.0)
line = f"- [{category} | {confidence:.2f}] {content}"
# Each additional line is preceded by a newline (except the first).
line_text = ("\n" + line) if fact_lines else line
line_tokens = _count_tokens(line_text)
if running_tokens + line_tokens <= max_tokens:
fact_lines.append(line)
running_tokens += line_tokens
else:
break
if fact_lines:
sections.append("Facts:\n" + "\n".join(fact_lines))
if not sections:
return ""
result = "\n\n".join(sections)
# Use accurate token counting with tiktoken
token_count = _count_tokens(result)
if token_count > max_tokens:
# Truncate to fit within token limit
# Estimate characters to remove based on token ratio
char_per_token = len(result) / token_count
target_chars = int(max_tokens * char_per_token * 0.95) # 95% to leave margin
result = result[:target_chars] + "\n..."
return result
def format_conversation_for_update(messages: list[Any]) -> str:
"""Format conversation messages for memory update prompt.
Args:
messages: List of conversation messages.
Returns:
Formatted conversation string.
"""
lines = []
for msg in messages:
role = getattr(msg, "type", "unknown")
content = getattr(msg, "content", str(msg))
# Handle content that might be a list (multimodal)
if isinstance(content, list):
text_parts = []
for p in content:
if isinstance(p, str):
text_parts.append(p)
elif isinstance(p, dict):
text_val = p.get("text")
if isinstance(text_val, str):
text_parts.append(text_val)
content = " ".join(text_parts) if text_parts else str(content)
# Strip uploaded_files tags from human messages to avoid persisting
# ephemeral file path info into long-term memory. Skip the turn entirely
# when nothing remains after stripping (upload-only message).
if role == "human":
content = re.sub(r"[\s\S]*?\n*", "", str(content)).strip()
if not content:
continue
# Truncate very long messages
if len(str(content)) > 1000:
content = str(content)[:1000] + "..."
if role == "human":
lines.append(f"User: {content}")
elif role == "ai":
lines.append(f"Assistant: {content}")
return "\n\n".join(lines)
================================================
FILE: backend/packages/harness/deerflow/agents/memory/queue.py
================================================
"""Memory update queue with debounce mechanism."""
import threading
import time
from dataclasses import dataclass, field
from datetime import datetime
from typing import Any
from deerflow.config.memory_config import get_memory_config
@dataclass
class ConversationContext:
"""Context for a conversation to be processed for memory update."""
thread_id: str
messages: list[Any]
timestamp: datetime = field(default_factory=datetime.utcnow)
agent_name: str | None = None
class MemoryUpdateQueue:
"""Queue for memory updates with debounce mechanism.
This queue collects conversation contexts and processes them after
a configurable debounce period. Multiple conversations received within
the debounce window are batched together.
"""
def __init__(self):
"""Initialize the memory update queue."""
self._queue: list[ConversationContext] = []
self._lock = threading.Lock()
self._timer: threading.Timer | None = None
self._processing = False
def add(self, thread_id: str, messages: list[Any], agent_name: str | None = None) -> None:
"""Add a conversation to the update queue.
Args:
thread_id: The thread ID.
messages: The conversation messages.
agent_name: If provided, memory is stored per-agent. If None, uses global memory.
"""
config = get_memory_config()
if not config.enabled:
return
context = ConversationContext(
thread_id=thread_id,
messages=messages,
agent_name=agent_name,
)
with self._lock:
# Check if this thread already has a pending update
# If so, replace it with the newer one
self._queue = [c for c in self._queue if c.thread_id != thread_id]
self._queue.append(context)
# Reset or start the debounce timer
self._reset_timer()
print(f"Memory update queued for thread {thread_id}, queue size: {len(self._queue)}")
def _reset_timer(self) -> None:
"""Reset the debounce timer."""
config = get_memory_config()
# Cancel existing timer if any
if self._timer is not None:
self._timer.cancel()
# Start new timer
self._timer = threading.Timer(
config.debounce_seconds,
self._process_queue,
)
self._timer.daemon = True
self._timer.start()
print(f"Memory update timer set for {config.debounce_seconds}s")
def _process_queue(self) -> None:
"""Process all queued conversation contexts."""
# Import here to avoid circular dependency
from deerflow.agents.memory.updater import MemoryUpdater
with self._lock:
if self._processing:
# Already processing, reschedule
self._reset_timer()
return
if not self._queue:
return
self._processing = True
contexts_to_process = self._queue.copy()
self._queue.clear()
self._timer = None
print(f"Processing {len(contexts_to_process)} queued memory updates")
try:
updater = MemoryUpdater()
for context in contexts_to_process:
try:
print(f"Updating memory for thread {context.thread_id}")
success = updater.update_memory(
messages=context.messages,
thread_id=context.thread_id,
agent_name=context.agent_name,
)
if success:
print(f"Memory updated successfully for thread {context.thread_id}")
else:
print(f"Memory update skipped/failed for thread {context.thread_id}")
except Exception as e:
print(f"Error updating memory for thread {context.thread_id}: {e}")
# Small delay between updates to avoid rate limiting
if len(contexts_to_process) > 1:
time.sleep(0.5)
finally:
with self._lock:
self._processing = False
def flush(self) -> None:
"""Force immediate processing of the queue.
This is useful for testing or graceful shutdown.
"""
with self._lock:
if self._timer is not None:
self._timer.cancel()
self._timer = None
self._process_queue()
def clear(self) -> None:
"""Clear the queue without processing.
This is useful for testing.
"""
with self._lock:
if self._timer is not None:
self._timer.cancel()
self._timer = None
self._queue.clear()
self._processing = False
@property
def pending_count(self) -> int:
"""Get the number of pending updates."""
with self._lock:
return len(self._queue)
@property
def is_processing(self) -> bool:
"""Check if the queue is currently being processed."""
with self._lock:
return self._processing
# Global singleton instance
_memory_queue: MemoryUpdateQueue | None = None
_queue_lock = threading.Lock()
def get_memory_queue() -> MemoryUpdateQueue:
"""Get the global memory update queue singleton.
Returns:
The memory update queue instance.
"""
global _memory_queue
with _queue_lock:
if _memory_queue is None:
_memory_queue = MemoryUpdateQueue()
return _memory_queue
def reset_memory_queue() -> None:
"""Reset the global memory queue.
This is useful for testing.
"""
global _memory_queue
with _queue_lock:
if _memory_queue is not None:
_memory_queue.clear()
_memory_queue = None
================================================
FILE: backend/packages/harness/deerflow/agents/memory/updater.py
================================================
"""Memory updater for reading, writing, and updating memory data."""
import json
import logging
import re
import uuid
from datetime import datetime
from pathlib import Path
from typing import Any
from deerflow.agents.memory.prompt import (
MEMORY_UPDATE_PROMPT,
format_conversation_for_update,
)
from deerflow.config.memory_config import get_memory_config
from deerflow.config.paths import get_paths
from deerflow.models import create_chat_model
logger = logging.getLogger(__name__)
def _get_memory_file_path(agent_name: str | None = None) -> Path:
"""Get the path to the memory file.
Args:
agent_name: If provided, returns the per-agent memory file path.
If None, returns the global memory file path.
Returns:
Path to the memory file.
"""
if agent_name is not None:
return get_paths().agent_memory_file(agent_name)
config = get_memory_config()
if config.storage_path:
p = Path(config.storage_path)
# Absolute path: use as-is; relative path: resolve against base_dir
return p if p.is_absolute() else get_paths().base_dir / p
return get_paths().memory_file
def _create_empty_memory() -> dict[str, Any]:
"""Create an empty memory structure."""
return {
"version": "1.0",
"lastUpdated": datetime.utcnow().isoformat() + "Z",
"user": {
"workContext": {"summary": "", "updatedAt": ""},
"personalContext": {"summary": "", "updatedAt": ""},
"topOfMind": {"summary": "", "updatedAt": ""},
},
"history": {
"recentMonths": {"summary": "", "updatedAt": ""},
"earlierContext": {"summary": "", "updatedAt": ""},
"longTermBackground": {"summary": "", "updatedAt": ""},
},
"facts": [],
}
# Per-agent memory cache: keyed by agent_name (None = global)
# Value: (memory_data, file_mtime)
_memory_cache: dict[str | None, tuple[dict[str, Any], float | None]] = {}
def get_memory_data(agent_name: str | None = None) -> dict[str, Any]:
"""Get the current memory data (cached with file modification time check).
The cache is automatically invalidated if the memory file has been modified
since the last load, ensuring fresh data is always returned.
Args:
agent_name: If provided, loads per-agent memory. If None, loads global memory.
Returns:
The memory data dictionary.
"""
file_path = _get_memory_file_path(agent_name)
# Get current file modification time
try:
current_mtime = file_path.stat().st_mtime if file_path.exists() else None
except OSError:
current_mtime = None
cached = _memory_cache.get(agent_name)
# Invalidate cache if file has been modified or doesn't exist
if cached is None or cached[1] != current_mtime:
memory_data = _load_memory_from_file(agent_name)
_memory_cache[agent_name] = (memory_data, current_mtime)
return memory_data
return cached[0]
def reload_memory_data(agent_name: str | None = None) -> dict[str, Any]:
"""Reload memory data from file, forcing cache invalidation.
Args:
agent_name: If provided, reloads per-agent memory. If None, reloads global memory.
Returns:
The reloaded memory data dictionary.
"""
file_path = _get_memory_file_path(agent_name)
memory_data = _load_memory_from_file(agent_name)
try:
mtime = file_path.stat().st_mtime if file_path.exists() else None
except OSError:
mtime = None
_memory_cache[agent_name] = (memory_data, mtime)
return memory_data
def _extract_text(content: Any) -> str:
"""Extract plain text from LLM response content (str or list of content blocks).
Modern LLMs may return structured content as a list of blocks instead of a
plain string, e.g. [{"type": "text", "text": "..."}]. Using str() on such
content produces Python repr instead of the actual text, breaking JSON
parsing downstream.
String chunks are concatenated without separators to avoid corrupting
chunked JSON/text payloads. Dict-based text blocks are treated as full text
blocks and joined with newlines for readability.
"""
if isinstance(content, str):
return content
if isinstance(content, list):
pieces: list[str] = []
pending_str_parts: list[str] = []
def flush_pending_str_parts() -> None:
if pending_str_parts:
pieces.append("".join(pending_str_parts))
pending_str_parts.clear()
for block in content:
if isinstance(block, str):
pending_str_parts.append(block)
elif isinstance(block, dict):
flush_pending_str_parts()
text_val = block.get("text")
if isinstance(text_val, str):
pieces.append(text_val)
flush_pending_str_parts()
return "\n".join(pieces)
return str(content)
def _load_memory_from_file(agent_name: str | None = None) -> dict[str, Any]:
"""Load memory data from file.
Args:
agent_name: If provided, loads per-agent memory file. If None, loads global.
Returns:
The memory data dictionary.
"""
file_path = _get_memory_file_path(agent_name)
if not file_path.exists():
return _create_empty_memory()
try:
with open(file_path, encoding="utf-8") as f:
data = json.load(f)
return data
except (json.JSONDecodeError, OSError) as e:
logger.warning("Failed to load memory file: %s", e)
return _create_empty_memory()
# Matches sentences that describe a file-upload *event* rather than general
# file-related work. Deliberately narrow to avoid removing legitimate facts
# such as "User works with CSV files" or "prefers PDF export".
_UPLOAD_SENTENCE_RE = re.compile(
r"[^.!?]*\b(?:"
r"upload(?:ed|ing)?(?:\s+\w+){0,3}\s+(?:file|files?|document|documents?|attachment|attachments?)"
r"|file\s+upload"
r"|/mnt/user-data/uploads/"
r"|"
r")[^.!?]*[.!?]?\s*",
re.IGNORECASE,
)
def _strip_upload_mentions_from_memory(memory_data: dict[str, Any]) -> dict[str, Any]:
"""Remove sentences about file uploads from all memory summaries and facts.
Uploaded files are session-scoped; persisting upload events in long-term
memory causes the agent to search for non-existent files in future sessions.
"""
# Scrub summaries in user/history sections
for section in ("user", "history"):
section_data = memory_data.get(section, {})
for _key, val in section_data.items():
if isinstance(val, dict) and "summary" in val:
cleaned = _UPLOAD_SENTENCE_RE.sub("", val["summary"]).strip()
cleaned = re.sub(r" +", " ", cleaned)
val["summary"] = cleaned
# Also remove any facts that describe upload events
facts = memory_data.get("facts", [])
if facts:
memory_data["facts"] = [f for f in facts if not _UPLOAD_SENTENCE_RE.search(f.get("content", ""))]
return memory_data
def _fact_content_key(content: Any) -> str | None:
if not isinstance(content, str):
return None
stripped = content.strip()
if not stripped:
return None
return stripped
def _save_memory_to_file(memory_data: dict[str, Any], agent_name: str | None = None) -> bool:
"""Save memory data to file and update cache.
Args:
memory_data: The memory data to save.
agent_name: If provided, saves to per-agent memory file. If None, saves to global.
Returns:
True if successful, False otherwise.
"""
file_path = _get_memory_file_path(agent_name)
try:
# Ensure directory exists
file_path.parent.mkdir(parents=True, exist_ok=True)
# Update lastUpdated timestamp
memory_data["lastUpdated"] = datetime.utcnow().isoformat() + "Z"
# Write atomically using temp file
temp_path = file_path.with_suffix(".tmp")
with open(temp_path, "w", encoding="utf-8") as f:
json.dump(memory_data, f, indent=2, ensure_ascii=False)
# Rename temp file to actual file (atomic on most systems)
temp_path.replace(file_path)
# Update cache and file modification time
try:
mtime = file_path.stat().st_mtime
except OSError:
mtime = None
_memory_cache[agent_name] = (memory_data, mtime)
logger.info("Memory saved to %s", file_path)
return True
except OSError as e:
logger.error("Failed to save memory file: %s", e)
return False
class MemoryUpdater:
"""Updates memory using LLM based on conversation context."""
def __init__(self, model_name: str | None = None):
"""Initialize the memory updater.
Args:
model_name: Optional model name to use. If None, uses config or default.
"""
self._model_name = model_name
def _get_model(self):
"""Get the model for memory updates."""
config = get_memory_config()
model_name = self._model_name or config.model_name
return create_chat_model(name=model_name, thinking_enabled=False)
def update_memory(self, messages: list[Any], thread_id: str | None = None, agent_name: str | None = None) -> bool:
"""Update memory based on conversation messages.
Args:
messages: List of conversation messages.
thread_id: Optional thread ID for tracking source.
agent_name: If provided, updates per-agent memory. If None, updates global memory.
Returns:
True if update was successful, False otherwise.
"""
config = get_memory_config()
if not config.enabled:
return False
if not messages:
return False
try:
# Get current memory
current_memory = get_memory_data(agent_name)
# Format conversation for prompt
conversation_text = format_conversation_for_update(messages)
if not conversation_text.strip():
return False
# Build prompt
prompt = MEMORY_UPDATE_PROMPT.format(
current_memory=json.dumps(current_memory, indent=2),
conversation=conversation_text,
)
# Call LLM
model = self._get_model()
response = model.invoke(prompt)
response_text = _extract_text(response.content).strip()
# Parse response
# Remove markdown code blocks if present
if response_text.startswith("```"):
lines = response_text.split("\n")
response_text = "\n".join(lines[1:-1] if lines[-1] == "```" else lines[1:])
update_data = json.loads(response_text)
# Apply updates
updated_memory = self._apply_updates(current_memory, update_data, thread_id)
# Strip file-upload mentions from all summaries before saving.
# Uploaded files are session-scoped and won't exist in future sessions,
# so recording upload events in long-term memory causes the agent to
# try (and fail) to locate those files in subsequent conversations.
updated_memory = _strip_upload_mentions_from_memory(updated_memory)
# Save
return _save_memory_to_file(updated_memory, agent_name)
except json.JSONDecodeError as e:
logger.warning("Failed to parse LLM response for memory update: %s", e)
return False
except Exception as e:
logger.exception("Memory update failed: %s", e)
return False
def _apply_updates(
self,
current_memory: dict[str, Any],
update_data: dict[str, Any],
thread_id: str | None = None,
) -> dict[str, Any]:
"""Apply LLM-generated updates to memory.
Args:
current_memory: Current memory data.
update_data: Updates from LLM.
thread_id: Optional thread ID for tracking.
Returns:
Updated memory data.
"""
config = get_memory_config()
now = datetime.utcnow().isoformat() + "Z"
# Update user sections
user_updates = update_data.get("user", {})
for section in ["workContext", "personalContext", "topOfMind"]:
section_data = user_updates.get(section, {})
if section_data.get("shouldUpdate") and section_data.get("summary"):
current_memory["user"][section] = {
"summary": section_data["summary"],
"updatedAt": now,
}
# Update history sections
history_updates = update_data.get("history", {})
for section in ["recentMonths", "earlierContext", "longTermBackground"]:
section_data = history_updates.get(section, {})
if section_data.get("shouldUpdate") and section_data.get("summary"):
current_memory["history"][section] = {
"summary": section_data["summary"],
"updatedAt": now,
}
# Remove facts
facts_to_remove = set(update_data.get("factsToRemove", []))
if facts_to_remove:
current_memory["facts"] = [f for f in current_memory.get("facts", []) if f.get("id") not in facts_to_remove]
# Add new facts
existing_fact_keys = {
fact_key
for fact_key in (
_fact_content_key(fact.get("content"))
for fact in current_memory.get("facts", [])
)
if fact_key is not None
}
new_facts = update_data.get("newFacts", [])
for fact in new_facts:
confidence = fact.get("confidence", 0.5)
if confidence >= config.fact_confidence_threshold:
raw_content = fact.get("content", "")
normalized_content = raw_content.strip()
fact_key = _fact_content_key(normalized_content)
if fact_key is not None and fact_key in existing_fact_keys:
continue
fact_entry = {
"id": f"fact_{uuid.uuid4().hex[:8]}",
"content": normalized_content,
"category": fact.get("category", "context"),
"confidence": confidence,
"createdAt": now,
"source": thread_id or "unknown",
}
current_memory["facts"].append(fact_entry)
if fact_key is not None:
existing_fact_keys.add(fact_key)
# Enforce max facts limit
if len(current_memory["facts"]) > config.max_facts:
# Sort by confidence and keep top ones
current_memory["facts"] = sorted(
current_memory["facts"],
key=lambda f: f.get("confidence", 0),
reverse=True,
)[: config.max_facts]
return current_memory
def update_memory_from_conversation(messages: list[Any], thread_id: str | None = None, agent_name: str | None = None) -> bool:
"""Convenience function to update memory from a conversation.
Args:
messages: List of conversation messages.
thread_id: Optional thread ID.
agent_name: If provided, updates per-agent memory. If None, updates global memory.
Returns:
True if successful, False otherwise.
"""
updater = MemoryUpdater()
return updater.update_memory(messages, thread_id, agent_name)
================================================
FILE: backend/packages/harness/deerflow/agents/middlewares/clarification_middleware.py
================================================
"""Middleware for intercepting clarification requests and presenting them to the user."""
from collections.abc import Callable
from typing import override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langchain_core.messages import ToolMessage
from langgraph.graph import END
from langgraph.prebuilt.tool_node import ToolCallRequest
from langgraph.types import Command
class ClarificationMiddlewareState(AgentState):
"""Compatible with the `ThreadState` schema."""
pass
class ClarificationMiddleware(AgentMiddleware[ClarificationMiddlewareState]):
"""Intercepts clarification tool calls and interrupts execution to present questions to the user.
When the model calls the `ask_clarification` tool, this middleware:
1. Intercepts the tool call before execution
2. Extracts the clarification question and metadata
3. Formats a user-friendly message
4. Returns a Command that interrupts execution and presents the question
5. Waits for user response before continuing
This replaces the tool-based approach where clarification continued the conversation flow.
"""
state_schema = ClarificationMiddlewareState
def _is_chinese(self, text: str) -> bool:
"""Check if text contains Chinese characters.
Args:
text: Text to check
Returns:
True if text contains Chinese characters
"""
return any("\u4e00" <= char <= "\u9fff" for char in text)
def _format_clarification_message(self, args: dict) -> str:
"""Format the clarification arguments into a user-friendly message.
Args:
args: The tool call arguments containing clarification details
Returns:
Formatted message string
"""
question = args.get("question", "")
clarification_type = args.get("clarification_type", "missing_info")
context = args.get("context")
options = args.get("options", [])
# Type-specific icons
type_icons = {
"missing_info": "❓",
"ambiguous_requirement": "🤔",
"approach_choice": "🔀",
"risk_confirmation": "⚠️",
"suggestion": "💡",
}
icon = type_icons.get(clarification_type, "❓")
# Build the message naturally
message_parts = []
# Add icon and question together for a more natural flow
if context:
# If there's context, present it first as background
message_parts.append(f"{icon} {context}")
message_parts.append(f"\n{question}")
else:
# Just the question with icon
message_parts.append(f"{icon} {question}")
# Add options in a cleaner format
if options and len(options) > 0:
message_parts.append("") # blank line for spacing
for i, option in enumerate(options, 1):
message_parts.append(f" {i}. {option}")
return "\n".join(message_parts)
def _handle_clarification(self, request: ToolCallRequest) -> Command:
"""Handle clarification request and return command to interrupt execution.
Args:
request: Tool call request
Returns:
Command that interrupts execution with the formatted clarification message
"""
# Extract clarification arguments
args = request.tool_call.get("args", {})
question = args.get("question", "")
print("[ClarificationMiddleware] Intercepted clarification request")
print(f"[ClarificationMiddleware] Question: {question}")
# Format the clarification message
formatted_message = self._format_clarification_message(args)
# Get the tool call ID
tool_call_id = request.tool_call.get("id", "")
# Create a ToolMessage with the formatted question
# This will be added to the message history
tool_message = ToolMessage(
content=formatted_message,
tool_call_id=tool_call_id,
name="ask_clarification",
)
# Return a Command that:
# 1. Adds the formatted tool message
# 2. Interrupts execution by going to __end__
# Note: We don't add an extra AIMessage here - the frontend will detect
# and display ask_clarification tool messages directly
return Command(
update={"messages": [tool_message]},
goto=END,
)
@override
def wrap_tool_call(
self,
request: ToolCallRequest,
handler: Callable[[ToolCallRequest], ToolMessage | Command],
) -> ToolMessage | Command:
"""Intercept ask_clarification tool calls and interrupt execution (sync version).
Args:
request: Tool call request
handler: Original tool execution handler
Returns:
Command that interrupts execution with the formatted clarification message
"""
# Check if this is an ask_clarification tool call
if request.tool_call.get("name") != "ask_clarification":
# Not a clarification call, execute normally
return handler(request)
return self._handle_clarification(request)
@override
async def awrap_tool_call(
self,
request: ToolCallRequest,
handler: Callable[[ToolCallRequest], ToolMessage | Command],
) -> ToolMessage | Command:
"""Intercept ask_clarification tool calls and interrupt execution (async version).
Args:
request: Tool call request
handler: Original tool execution handler (async)
Returns:
Command that interrupts execution with the formatted clarification message
"""
# Check if this is an ask_clarification tool call
if request.tool_call.get("name") != "ask_clarification":
# Not a clarification call, execute normally
return await handler(request)
return self._handle_clarification(request)
================================================
FILE: backend/packages/harness/deerflow/agents/middlewares/dangling_tool_call_middleware.py
================================================
"""Middleware to fix dangling tool calls in message history.
A dangling tool call occurs when an AIMessage contains tool_calls but there are
no corresponding ToolMessages in the history (e.g., due to user interruption or
request cancellation). This causes LLM errors due to incomplete message format.
This middleware intercepts the model call to detect and patch such gaps by
inserting synthetic ToolMessages with an error indicator immediately after the
AIMessage that made the tool calls, ensuring correct message ordering.
Note: Uses wrap_model_call instead of before_model to ensure patches are inserted
at the correct positions (immediately after each dangling AIMessage), not appended
to the end of the message list as before_model + add_messages reducer would do.
"""
import logging
from collections.abc import Awaitable, Callable
from typing import override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langchain.agents.middleware.types import ModelCallResult, ModelRequest, ModelResponse
from langchain_core.messages import ToolMessage
logger = logging.getLogger(__name__)
class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
"""Inserts placeholder ToolMessages for dangling tool calls before model invocation.
Scans the message history for AIMessages whose tool_calls lack corresponding
ToolMessages, and injects synthetic error responses immediately after the
offending AIMessage so the LLM receives a well-formed conversation.
"""
def _build_patched_messages(self, messages: list) -> list | None:
"""Return a new message list with patches inserted at the correct positions.
For each AIMessage with dangling tool_calls (no corresponding ToolMessage),
a synthetic ToolMessage is inserted immediately after that AIMessage.
Returns None if no patches are needed.
"""
# Collect IDs of all existing ToolMessages
existing_tool_msg_ids: set[str] = set()
for msg in messages:
if isinstance(msg, ToolMessage):
existing_tool_msg_ids.add(msg.tool_call_id)
# Check if any patching is needed
needs_patch = False
for msg in messages:
if getattr(msg, "type", None) != "ai":
continue
for tc in getattr(msg, "tool_calls", None) or []:
tc_id = tc.get("id")
if tc_id and tc_id not in existing_tool_msg_ids:
needs_patch = True
break
if needs_patch:
break
if not needs_patch:
return None
# Build new list with patches inserted right after each dangling AIMessage
patched: list = []
patched_ids: set[str] = set()
patch_count = 0
for msg in messages:
patched.append(msg)
if getattr(msg, "type", None) != "ai":
continue
for tc in getattr(msg, "tool_calls", None) or []:
tc_id = tc.get("id")
if tc_id and tc_id not in existing_tool_msg_ids and tc_id not in patched_ids:
patched.append(
ToolMessage(
content="[Tool call was interrupted and did not return a result.]",
tool_call_id=tc_id,
name=tc.get("name", "unknown"),
status="error",
)
)
patched_ids.add(tc_id)
patch_count += 1
logger.warning(f"Injecting {patch_count} placeholder ToolMessage(s) for dangling tool calls")
return patched
@override
def wrap_model_call(
self,
request: ModelRequest,
handler: Callable[[ModelRequest], ModelResponse],
) -> ModelCallResult:
patched = self._build_patched_messages(request.messages)
if patched is not None:
request = request.override(messages=patched)
return handler(request)
@override
async def awrap_model_call(
self,
request: ModelRequest,
handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
) -> ModelCallResult:
patched = self._build_patched_messages(request.messages)
if patched is not None:
request = request.override(messages=patched)
return await handler(request)
================================================
FILE: backend/packages/harness/deerflow/agents/middlewares/deferred_tool_filter_middleware.py
================================================
"""Middleware to filter deferred tool schemas from model binding.
When tool_search is enabled, MCP tools are registered in the DeferredToolRegistry
and passed to ToolNode for execution, but their schemas should NOT be sent to the
LLM via bind_tools (that's the whole point of deferral — saving context tokens).
This middleware intercepts wrap_model_call and removes deferred tools from
request.tools so that model.bind_tools only receives active tool schemas.
The agent discovers deferred tools at runtime via the tool_search tool.
"""
import logging
from collections.abc import Awaitable, Callable
from typing import override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langchain.agents.middleware.types import ModelCallResult, ModelRequest, ModelResponse
logger = logging.getLogger(__name__)
class DeferredToolFilterMiddleware(AgentMiddleware[AgentState]):
"""Remove deferred tools from request.tools before model binding.
ToolNode still holds all tools (including deferred) for execution routing,
but the LLM only sees active tool schemas — deferred tools are discoverable
via tool_search at runtime.
"""
def _filter_tools(self, request: ModelRequest) -> ModelRequest:
from deerflow.tools.builtins.tool_search import get_deferred_registry
registry = get_deferred_registry()
if not registry:
return request
deferred_names = {e.name for e in registry.entries}
active_tools = [t for t in request.tools if getattr(t, "name", None) not in deferred_names]
if len(active_tools) < len(request.tools):
logger.debug(f"Filtered {len(request.tools) - len(active_tools)} deferred tool schema(s) from model binding")
return request.override(tools=active_tools)
@override
def wrap_model_call(
self,
request: ModelRequest,
handler: Callable[[ModelRequest], ModelResponse],
) -> ModelCallResult:
return handler(self._filter_tools(request))
@override
async def awrap_model_call(
self,
request: ModelRequest,
handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
) -> ModelCallResult:
return await handler(self._filter_tools(request))
================================================
FILE: backend/packages/harness/deerflow/agents/middlewares/loop_detection_middleware.py
================================================
"""Middleware to detect and break repetitive tool call loops.
P0 safety: prevents the agent from calling the same tool with the same
arguments indefinitely until the recursion limit kills the run.
Detection strategy:
1. After each model response, hash the tool calls (name + args).
2. Track recent hashes in a sliding window.
3. If the same hash appears >= warn_threshold times, inject a
"you are repeating yourself — wrap up" system message (once per hash).
4. If it appears >= hard_limit times, strip all tool_calls from the
response so the agent is forced to produce a final text answer.
"""
import hashlib
import json
import logging
import threading
from collections import OrderedDict, defaultdict
from typing import override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langchain_core.messages import SystemMessage
from langgraph.runtime import Runtime
logger = logging.getLogger(__name__)
# Defaults — can be overridden via constructor
_DEFAULT_WARN_THRESHOLD = 3 # inject warning after 3 identical calls
_DEFAULT_HARD_LIMIT = 5 # force-stop after 5 identical calls
_DEFAULT_WINDOW_SIZE = 20 # track last N tool calls
_DEFAULT_MAX_TRACKED_THREADS = 100 # LRU eviction limit
def _hash_tool_calls(tool_calls: list[dict]) -> str:
"""Deterministic hash of a set of tool calls (name + args).
This is intended to be order-independent: the same multiset of tool calls
should always produce the same hash, regardless of their input order.
"""
# First normalize each tool call to a minimal (name, args) structure.
normalized: list[dict] = []
for tc in tool_calls:
normalized.append(
{
"name": tc.get("name", ""),
"args": tc.get("args", {}),
}
)
# Sort by both name and a deterministic serialization of args so that
# permutations of the same multiset of calls yield the same ordering.
normalized.sort(
key=lambda tc: (
tc["name"],
json.dumps(tc["args"], sort_keys=True, default=str),
)
)
blob = json.dumps(normalized, sort_keys=True, default=str)
return hashlib.md5(blob.encode()).hexdigest()[:12]
_WARNING_MSG = (
"[LOOP DETECTED] You are repeating the same tool calls. "
"Stop calling tools and produce your final answer now. "
"If you cannot complete the task, summarize what you accomplished so far."
)
_HARD_STOP_MSG = (
"[FORCED STOP] Repeated tool calls exceeded the safety limit. "
"Producing final answer with results collected so far."
)
class LoopDetectionMiddleware(AgentMiddleware[AgentState]):
"""Detects and breaks repetitive tool call loops.
Args:
warn_threshold: Number of identical tool call sets before injecting
a warning message. Default: 3.
hard_limit: Number of identical tool call sets before stripping
tool_calls entirely. Default: 5.
window_size: Size of the sliding window for tracking calls.
Default: 20.
max_tracked_threads: Maximum number of threads to track before
evicting the least recently used. Default: 100.
"""
def __init__(
self,
warn_threshold: int = _DEFAULT_WARN_THRESHOLD,
hard_limit: int = _DEFAULT_HARD_LIMIT,
window_size: int = _DEFAULT_WINDOW_SIZE,
max_tracked_threads: int = _DEFAULT_MAX_TRACKED_THREADS,
):
super().__init__()
self.warn_threshold = warn_threshold
self.hard_limit = hard_limit
self.window_size = window_size
self.max_tracked_threads = max_tracked_threads
self._lock = threading.Lock()
# Per-thread tracking using OrderedDict for LRU eviction
self._history: OrderedDict[str, list[str]] = OrderedDict()
self._warned: dict[str, set[str]] = defaultdict(set)
def _get_thread_id(self, runtime: Runtime) -> str:
"""Extract thread_id from runtime context for per-thread tracking."""
thread_id = runtime.context.get("thread_id")
if thread_id:
return thread_id
return "default"
def _evict_if_needed(self) -> None:
"""Evict least recently used threads if over the limit.
Must be called while holding self._lock.
"""
while len(self._history) > self.max_tracked_threads:
evicted_id, _ = self._history.popitem(last=False)
self._warned.pop(evicted_id, None)
logger.debug("Evicted loop tracking for thread %s (LRU)", evicted_id)
def _track_and_check(self, state: AgentState, runtime: Runtime) -> tuple[str | None, bool]:
"""Track tool calls and check for loops.
Returns:
(warning_message_or_none, should_hard_stop)
"""
messages = state.get("messages", [])
if not messages:
return None, False
last_msg = messages[-1]
if getattr(last_msg, "type", None) != "ai":
return None, False
tool_calls = getattr(last_msg, "tool_calls", None)
if not tool_calls:
return None, False
thread_id = self._get_thread_id(runtime)
call_hash = _hash_tool_calls(tool_calls)
with self._lock:
# Touch / create entry (move to end for LRU)
if thread_id in self._history:
self._history.move_to_end(thread_id)
else:
self._history[thread_id] = []
self._evict_if_needed()
history = self._history[thread_id]
history.append(call_hash)
if len(history) > self.window_size:
history[:] = history[-self.window_size:]
count = history.count(call_hash)
tool_names = [tc.get("name", "?") for tc in tool_calls]
if count >= self.hard_limit:
logger.error(
"Loop hard limit reached — forcing stop",
extra={
"thread_id": thread_id,
"call_hash": call_hash,
"count": count,
"tools": tool_names,
},
)
return _HARD_STOP_MSG, True
if count >= self.warn_threshold:
warned = self._warned[thread_id]
if call_hash not in warned:
warned.add(call_hash)
logger.warning(
"Repetitive tool calls detected — injecting warning",
extra={
"thread_id": thread_id,
"call_hash": call_hash,
"count": count,
"tools": tool_names,
},
)
return _WARNING_MSG, False
# Warning already injected for this hash — suppress
return None, False
return None, False
def _apply(self, state: AgentState, runtime: Runtime) -> dict | None:
warning, hard_stop = self._track_and_check(state, runtime)
if hard_stop:
# Strip tool_calls from the last AIMessage to force text output
messages = state.get("messages", [])
last_msg = messages[-1]
stripped_msg = last_msg.model_copy(update={
"tool_calls": [],
"content": (last_msg.content or "") + f"\n\n{_HARD_STOP_MSG}",
})
return {"messages": [stripped_msg]}
if warning:
# Inject a system message warning the model
return {"messages": [SystemMessage(content=warning)]}
return None
@override
def after_model(self, state: AgentState, runtime: Runtime) -> dict | None:
return self._apply(state, runtime)
@override
async def aafter_model(self, state: AgentState, runtime: Runtime) -> dict | None:
return self._apply(state, runtime)
def reset(self, thread_id: str | None = None) -> None:
"""Clear tracking state. If thread_id given, clear only that thread."""
with self._lock:
if thread_id:
self._history.pop(thread_id, None)
self._warned.pop(thread_id, None)
else:
self._history.clear()
self._warned.clear()
================================================
FILE: backend/packages/harness/deerflow/agents/middlewares/memory_middleware.py
================================================
"""Middleware for memory mechanism."""
import re
from typing import Any, override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langgraph.runtime import Runtime
from deerflow.agents.memory.queue import get_memory_queue
from deerflow.config.memory_config import get_memory_config
class MemoryMiddlewareState(AgentState):
"""Compatible with the `ThreadState` schema."""
pass
def _filter_messages_for_memory(messages: list[Any]) -> list[Any]:
"""Filter messages to keep only user inputs and final assistant responses.
This filters out:
- Tool messages (intermediate tool call results)
- AI messages with tool_calls (intermediate steps, not final responses)
- The block injected by UploadsMiddleware into human messages
(file paths are session-scoped and must not persist in long-term memory).
The user's actual question is preserved; only turns whose content is entirely
the upload block (nothing remains after stripping) are dropped along with
their paired assistant response.
Only keeps:
- Human messages (with the ephemeral upload block removed)
- AI messages without tool_calls (final assistant responses), unless the
paired human turn was upload-only and had no real user text.
Args:
messages: List of all conversation messages.
Returns:
Filtered list containing only user inputs and final assistant responses.
"""
_UPLOAD_BLOCK_RE = re.compile(r"[\s\S]*?\n*", re.IGNORECASE)
filtered = []
skip_next_ai = False
for msg in messages:
msg_type = getattr(msg, "type", None)
if msg_type == "human":
content = getattr(msg, "content", "")
if isinstance(content, list):
content = " ".join(p.get("text", "") for p in content if isinstance(p, dict))
content_str = str(content)
if "" in content_str:
# Strip the ephemeral upload block; keep the user's real question.
stripped = _UPLOAD_BLOCK_RE.sub("", content_str).strip()
if not stripped:
# Nothing left — the entire turn was upload bookkeeping;
# skip it and the paired assistant response.
skip_next_ai = True
continue
# Rebuild the message with cleaned content so the user's question
# is still available for memory summarisation.
from copy import copy
clean_msg = copy(msg)
clean_msg.content = stripped
filtered.append(clean_msg)
skip_next_ai = False
else:
filtered.append(msg)
skip_next_ai = False
elif msg_type == "ai":
tool_calls = getattr(msg, "tool_calls", None)
if not tool_calls:
if skip_next_ai:
skip_next_ai = False
continue
filtered.append(msg)
# Skip tool messages and AI messages with tool_calls
return filtered
class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
"""Middleware that queues conversation for memory update after agent execution.
This middleware:
1. After each agent execution, queues the conversation for memory update
2. Only includes user inputs and final assistant responses (ignores tool calls)
3. The queue uses debouncing to batch multiple updates together
4. Memory is updated asynchronously via LLM summarization
"""
state_schema = MemoryMiddlewareState
def __init__(self, agent_name: str | None = None):
"""Initialize the MemoryMiddleware.
Args:
agent_name: If provided, memory is stored per-agent. If None, uses global memory.
"""
super().__init__()
self._agent_name = agent_name
@override
def after_agent(self, state: MemoryMiddlewareState, runtime: Runtime) -> dict | None:
"""Queue conversation for memory update after agent completes.
Args:
state: The current agent state.
runtime: The runtime context.
Returns:
None (no state changes needed from this middleware).
"""
config = get_memory_config()
if not config.enabled:
return None
# Get thread ID from runtime context
thread_id = runtime.context.get("thread_id")
if not thread_id:
print("MemoryMiddleware: No thread_id in context, skipping memory update")
return None
# Get messages from state
messages = state.get("messages", [])
if not messages:
print("MemoryMiddleware: No messages in state, skipping memory update")
return None
# Filter to only keep user inputs and final assistant responses
filtered_messages = _filter_messages_for_memory(messages)
# Only queue if there's meaningful conversation
# At minimum need one user message and one assistant response
user_messages = [m for m in filtered_messages if getattr(m, "type", None) == "human"]
assistant_messages = [m for m in filtered_messages if getattr(m, "type", None) == "ai"]
if not user_messages or not assistant_messages:
return None
# Queue the filtered conversation for memory update
queue = get_memory_queue()
queue.add(thread_id=thread_id, messages=filtered_messages, agent_name=self._agent_name)
return None
================================================
FILE: backend/packages/harness/deerflow/agents/middlewares/subagent_limit_middleware.py
================================================
"""Middleware to enforce maximum concurrent subagent tool calls per model response."""
import logging
from typing import override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langgraph.runtime import Runtime
from deerflow.subagents.executor import MAX_CONCURRENT_SUBAGENTS
logger = logging.getLogger(__name__)
# Valid range for max_concurrent_subagents
MIN_SUBAGENT_LIMIT = 2
MAX_SUBAGENT_LIMIT = 4
def _clamp_subagent_limit(value: int) -> int:
"""Clamp subagent limit to valid range [2, 4]."""
return max(MIN_SUBAGENT_LIMIT, min(MAX_SUBAGENT_LIMIT, value))
class SubagentLimitMiddleware(AgentMiddleware[AgentState]):
"""Truncates excess 'task' tool calls from a single model response.
When an LLM generates more than max_concurrent parallel task tool calls
in one response, this middleware keeps only the first max_concurrent and
discards the rest. This is more reliable than prompt-based limits.
Args:
max_concurrent: Maximum number of concurrent subagent calls allowed.
Defaults to MAX_CONCURRENT_SUBAGENTS (3). Clamped to [2, 4].
"""
def __init__(self, max_concurrent: int = MAX_CONCURRENT_SUBAGENTS):
super().__init__()
self.max_concurrent = _clamp_subagent_limit(max_concurrent)
def _truncate_task_calls(self, state: AgentState) -> dict | None:
messages = state.get("messages", [])
if not messages:
return None
last_msg = messages[-1]
if getattr(last_msg, "type", None) != "ai":
return None
tool_calls = getattr(last_msg, "tool_calls", None)
if not tool_calls:
return None
# Count task tool calls
task_indices = [i for i, tc in enumerate(tool_calls) if tc.get("name") == "task"]
if len(task_indices) <= self.max_concurrent:
return None
# Build set of indices to drop (excess task calls beyond the limit)
indices_to_drop = set(task_indices[self.max_concurrent :])
truncated_tool_calls = [tc for i, tc in enumerate(tool_calls) if i not in indices_to_drop]
dropped_count = len(indices_to_drop)
logger.warning(f"Truncated {dropped_count} excess task tool call(s) from model response (limit: {self.max_concurrent})")
# Replace the AIMessage with truncated tool_calls (same id triggers replacement)
updated_msg = last_msg.model_copy(update={"tool_calls": truncated_tool_calls})
return {"messages": [updated_msg]}
@override
def after_model(self, state: AgentState, runtime: Runtime) -> dict | None:
return self._truncate_task_calls(state)
@override
async def aafter_model(self, state: AgentState, runtime: Runtime) -> dict | None:
return self._truncate_task_calls(state)
================================================
FILE: backend/packages/harness/deerflow/agents/middlewares/thread_data_middleware.py
================================================
from typing import NotRequired, override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langgraph.config import get_config
from langgraph.runtime import Runtime
from deerflow.agents.thread_state import ThreadDataState
from deerflow.config.paths import Paths, get_paths
class ThreadDataMiddlewareState(AgentState):
"""Compatible with the `ThreadState` schema."""
thread_data: NotRequired[ThreadDataState | None]
class ThreadDataMiddleware(AgentMiddleware[ThreadDataMiddlewareState]):
"""Create thread data directories for each thread execution.
Creates the following directory structure:
- {base_dir}/threads/{thread_id}/user-data/workspace
- {base_dir}/threads/{thread_id}/user-data/uploads
- {base_dir}/threads/{thread_id}/user-data/outputs
Lifecycle Management:
- With lazy_init=True (default): Only compute paths, directories created on-demand
- With lazy_init=False: Eagerly create directories in before_agent()
"""
state_schema = ThreadDataMiddlewareState
def __init__(self, base_dir: str | None = None, lazy_init: bool = True):
"""Initialize the middleware.
Args:
base_dir: Base directory for thread data. Defaults to Paths resolution.
lazy_init: If True, defer directory creation until needed.
If False, create directories eagerly in before_agent().
Default is True for optimal performance.
"""
super().__init__()
self._paths = Paths(base_dir) if base_dir else get_paths()
self._lazy_init = lazy_init
def _get_thread_paths(self, thread_id: str) -> dict[str, str]:
"""Get the paths for a thread's data directories.
Args:
thread_id: The thread ID.
Returns:
Dictionary with workspace_path, uploads_path, and outputs_path.
"""
return {
"workspace_path": str(self._paths.sandbox_work_dir(thread_id)),
"uploads_path": str(self._paths.sandbox_uploads_dir(thread_id)),
"outputs_path": str(self._paths.sandbox_outputs_dir(thread_id)),
}
def _create_thread_directories(self, thread_id: str) -> dict[str, str]:
"""Create the thread data directories.
Args:
thread_id: The thread ID.
Returns:
Dictionary with the created directory paths.
"""
self._paths.ensure_thread_dirs(thread_id)
return self._get_thread_paths(thread_id)
@override
def before_agent(self, state: ThreadDataMiddlewareState, runtime: Runtime) -> dict | None:
context = runtime.context or {}
thread_id = context.get("thread_id")
if thread_id is None:
config = get_config()
thread_id = config.get("configurable", {}).get("thread_id")
if thread_id is None:
raise ValueError("Thread ID is required in runtime context or config.configurable")
if self._lazy_init:
# Lazy initialization: only compute paths, don't create directories
paths = self._get_thread_paths(thread_id)
else:
# Eager initialization: create directories immediately
paths = self._create_thread_directories(thread_id)
print(f"Created thread data directories for thread {thread_id}")
return {
"thread_data": {
**paths,
}
}
================================================
FILE: backend/packages/harness/deerflow/agents/middlewares/title_middleware.py
================================================
"""Middleware for automatic thread title generation."""
import logging
from typing import NotRequired, override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langgraph.runtime import Runtime
from deerflow.config.title_config import get_title_config
from deerflow.models import create_chat_model
logger = logging.getLogger(__name__)
class TitleMiddlewareState(AgentState):
"""Compatible with the `ThreadState` schema."""
title: NotRequired[str | None]
class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
"""Automatically generate a title for the thread after the first user message."""
state_schema = TitleMiddlewareState
def _normalize_content(self, content: object) -> str:
if isinstance(content, str):
return content
if isinstance(content, list):
parts = [self._normalize_content(item) for item in content]
return "\n".join(part for part in parts if part)
if isinstance(content, dict):
text_value = content.get("text")
if isinstance(text_value, str):
return text_value
nested_content = content.get("content")
if nested_content is not None:
return self._normalize_content(nested_content)
return ""
def _should_generate_title(self, state: TitleMiddlewareState) -> bool:
"""Check if we should generate a title for this thread."""
config = get_title_config()
if not config.enabled:
return False
# Check if thread already has a title in state
if state.get("title"):
return False
# Check if this is the first turn (has at least one user message and one assistant response)
messages = state.get("messages", [])
if len(messages) < 2:
return False
# Count user and assistant messages
user_messages = [m for m in messages if m.type == "human"]
assistant_messages = [m for m in messages if m.type == "ai"]
# Generate title after first complete exchange
return len(user_messages) == 1 and len(assistant_messages) >= 1
def _build_title_prompt(self, state: TitleMiddlewareState) -> tuple[str, str]:
"""Extract user/assistant messages and build the title prompt.
Returns (prompt_string, user_msg) so callers can use user_msg as fallback.
"""
config = get_title_config()
messages = state.get("messages", [])
user_msg_content = next((m.content for m in messages if m.type == "human"), "")
assistant_msg_content = next((m.content for m in messages if m.type == "ai"), "")
user_msg = self._normalize_content(user_msg_content)
assistant_msg = self._normalize_content(assistant_msg_content)
prompt = config.prompt_template.format(
max_words=config.max_words,
user_msg=user_msg[:500],
assistant_msg=assistant_msg[:500],
)
return prompt, user_msg
def _parse_title(self, content: object) -> str:
"""Normalize model output into a clean title string."""
config = get_title_config()
title_content = self._normalize_content(content)
title = title_content.strip().strip('"').strip("'")
return title[: config.max_chars] if len(title) > config.max_chars else title
def _fallback_title(self, user_msg: str) -> str:
config = get_title_config()
fallback_chars = min(config.max_chars, 50)
if len(user_msg) > fallback_chars:
return user_msg[:fallback_chars].rstrip() + "..."
return user_msg if user_msg else "New Conversation"
def _generate_title_result(self, state: TitleMiddlewareState) -> dict | None:
"""Synchronously generate a title. Returns state update or None."""
if not self._should_generate_title(state):
return None
prompt, user_msg = self._build_title_prompt(state)
config = get_title_config()
model = create_chat_model(name=config.model_name, thinking_enabled=False)
try:
response = model.invoke(prompt)
title = self._parse_title(response.content)
if not title:
title = self._fallback_title(user_msg)
except Exception:
logger.exception("Failed to generate title (sync)")
title = self._fallback_title(user_msg)
return {"title": title}
async def _agenerate_title_result(self, state: TitleMiddlewareState) -> dict | None:
"""Asynchronously generate a title. Returns state update or None."""
if not self._should_generate_title(state):
return None
prompt, user_msg = self._build_title_prompt(state)
config = get_title_config()
model = create_chat_model(name=config.model_name, thinking_enabled=False)
try:
response = await model.ainvoke(prompt)
title = self._parse_title(response.content)
if not title:
title = self._fallback_title(user_msg)
except Exception:
logger.exception("Failed to generate title (async)")
title = self._fallback_title(user_msg)
return {"title": title}
@override
def after_model(self, state: TitleMiddlewareState, runtime: Runtime) -> dict | None:
return self._generate_title_result(state)
@override
async def aafter_model(self, state: TitleMiddlewareState, runtime: Runtime) -> dict | None:
return await self._agenerate_title_result(state)
================================================
FILE: backend/packages/harness/deerflow/agents/middlewares/todo_middleware.py
================================================
"""Middleware that extends TodoListMiddleware with context-loss detection.
When the message history is truncated (e.g., by SummarizationMiddleware), the
original `write_todos` tool call and its ToolMessage can be scrolled out of the
active context window. This middleware detects that situation and injects a
reminder message so the model still knows about the outstanding todo list.
"""
from __future__ import annotations
from typing import Any, override
from langchain.agents.middleware import TodoListMiddleware
from langchain.agents.middleware.todo import PlanningState, Todo
from langchain_core.messages import AIMessage, HumanMessage
from langgraph.runtime import Runtime
def _todos_in_messages(messages: list[Any]) -> bool:
"""Return True if any AIMessage in *messages* contains a write_todos tool call."""
for msg in messages:
if isinstance(msg, AIMessage) and msg.tool_calls:
for tc in msg.tool_calls:
if tc.get("name") == "write_todos":
return True
return False
def _reminder_in_messages(messages: list[Any]) -> bool:
"""Return True if a todo_reminder HumanMessage is already present in *messages*."""
for msg in messages:
if isinstance(msg, HumanMessage) and getattr(msg, "name", None) == "todo_reminder":
return True
return False
def _format_todos(todos: list[Todo]) -> str:
"""Format a list of Todo items into a human-readable string."""
lines: list[str] = []
for todo in todos:
status = todo.get("status", "pending")
content = todo.get("content", "")
lines.append(f"- [{status}] {content}")
return "\n".join(lines)
class TodoMiddleware(TodoListMiddleware):
"""Extends TodoListMiddleware with `write_todos` context-loss detection.
When the original `write_todos` tool call has been truncated from the message
history (e.g., after summarization), the model loses awareness of the current
todo list. This middleware detects that gap in `before_model` / `abefore_model`
and injects a reminder message so the model can continue tracking progress.
"""
@override
def before_model(
self,
state: PlanningState,
runtime: Runtime, # noqa: ARG002
) -> dict[str, Any] | None:
"""Inject a todo-list reminder when write_todos has left the context window."""
todos: list[Todo] = state.get("todos") or [] # type: ignore[assignment]
if not todos:
return None
messages = state.get("messages") or []
if _todos_in_messages(messages):
# write_todos is still visible in context — nothing to do.
return None
if _reminder_in_messages(messages):
# A reminder was already injected and hasn't been truncated yet.
return None
# The todo list exists in state but the original write_todos call is gone.
# Inject a reminder as a HumanMessage so the model stays aware.
formatted = _format_todos(todos)
reminder = HumanMessage(
name="todo_reminder",
content=(
"\n"
"Your todo list from earlier is no longer visible in the current context window, "
"but it is still active. Here is the current state:\n\n"
f"{formatted}\n\n"
"Continue tracking and updating this todo list as you work. "
"Call `write_todos` whenever the status of any item changes.\n"
""
),
)
return {"messages": [reminder]}
@override
async def abefore_model(
self,
state: PlanningState,
runtime: Runtime,
) -> dict[str, Any] | None:
"""Async version of before_model."""
return self.before_model(state, runtime)
================================================
FILE: backend/packages/harness/deerflow/agents/middlewares/tool_error_handling_middleware.py
================================================
"""Tool error handling middleware and shared runtime middleware builders."""
import logging
from collections.abc import Awaitable, Callable
from typing import override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langchain_core.messages import ToolMessage
from langgraph.errors import GraphBubbleUp
from langgraph.prebuilt.tool_node import ToolCallRequest
from langgraph.types import Command
logger = logging.getLogger(__name__)
_MISSING_TOOL_CALL_ID = "missing_tool_call_id"
class ToolErrorHandlingMiddleware(AgentMiddleware[AgentState]):
"""Convert tool exceptions into error ToolMessages so the run can continue."""
def _build_error_message(self, request: ToolCallRequest, exc: Exception) -> ToolMessage:
tool_name = str(request.tool_call.get("name") or "unknown_tool")
tool_call_id = str(request.tool_call.get("id") or _MISSING_TOOL_CALL_ID)
detail = str(exc).strip() or exc.__class__.__name__
if len(detail) > 500:
detail = detail[:497] + "..."
content = f"Error: Tool '{tool_name}' failed with {exc.__class__.__name__}: {detail}. Continue with available context, or choose an alternative tool."
return ToolMessage(
content=content,
tool_call_id=tool_call_id,
name=tool_name,
status="error",
)
@override
def wrap_tool_call(
self,
request: ToolCallRequest,
handler: Callable[[ToolCallRequest], ToolMessage | Command],
) -> ToolMessage | Command:
try:
return handler(request)
except GraphBubbleUp:
# Preserve LangGraph control-flow signals (interrupt/pause/resume).
raise
except Exception as exc:
logger.exception("Tool execution failed (sync): name=%s id=%s", request.tool_call.get("name"), request.tool_call.get("id"))
return self._build_error_message(request, exc)
@override
async def awrap_tool_call(
self,
request: ToolCallRequest,
handler: Callable[[ToolCallRequest], Awaitable[ToolMessage | Command]],
) -> ToolMessage | Command:
try:
return await handler(request)
except GraphBubbleUp:
# Preserve LangGraph control-flow signals (interrupt/pause/resume).
raise
except Exception as exc:
logger.exception("Tool execution failed (async): name=%s id=%s", request.tool_call.get("name"), request.tool_call.get("id"))
return self._build_error_message(request, exc)
def _build_runtime_middlewares(
*,
include_uploads: bool,
include_dangling_tool_call_patch: bool,
lazy_init: bool = True,
) -> list[AgentMiddleware]:
"""Build shared base middlewares for agent execution."""
from deerflow.agents.middlewares.thread_data_middleware import ThreadDataMiddleware
from deerflow.sandbox.middleware import SandboxMiddleware
middlewares: list[AgentMiddleware] = [
ThreadDataMiddleware(lazy_init=lazy_init),
SandboxMiddleware(lazy_init=lazy_init),
]
if include_uploads:
from deerflow.agents.middlewares.uploads_middleware import UploadsMiddleware
middlewares.insert(1, UploadsMiddleware())
if include_dangling_tool_call_patch:
from deerflow.agents.middlewares.dangling_tool_call_middleware import DanglingToolCallMiddleware
middlewares.append(DanglingToolCallMiddleware())
middlewares.append(ToolErrorHandlingMiddleware())
return middlewares
def build_lead_runtime_middlewares(*, lazy_init: bool = True) -> list[AgentMiddleware]:
"""Middlewares shared by lead agent runtime before lead-only middlewares."""
return _build_runtime_middlewares(
include_uploads=True,
include_dangling_tool_call_patch=True,
lazy_init=lazy_init,
)
def build_subagent_runtime_middlewares(*, lazy_init: bool = True) -> list[AgentMiddleware]:
"""Middlewares shared by subagent runtime before subagent-only middlewares."""
return _build_runtime_middlewares(
include_uploads=False,
include_dangling_tool_call_patch=False,
lazy_init=lazy_init,
)
================================================
FILE: backend/packages/harness/deerflow/agents/middlewares/uploads_middleware.py
================================================
"""Middleware to inject uploaded files information into agent context."""
import logging
from pathlib import Path
from typing import NotRequired, override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langchain_core.messages import HumanMessage
from langgraph.runtime import Runtime
from deerflow.config.paths import Paths, get_paths
logger = logging.getLogger(__name__)
class UploadsMiddlewareState(AgentState):
"""State schema for uploads middleware."""
uploaded_files: NotRequired[list[dict] | None]
class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
"""Middleware to inject uploaded files information into the agent context.
Reads file metadata from the current message's additional_kwargs.files
(set by the frontend after upload) and prepends an block
to the last human message so the model knows which files are available.
"""
state_schema = UploadsMiddlewareState
def __init__(self, base_dir: str | None = None):
"""Initialize the middleware.
Args:
base_dir: Base directory for thread data. Defaults to Paths resolution.
"""
super().__init__()
self._paths = Paths(base_dir) if base_dir else get_paths()
def _create_files_message(self, new_files: list[dict], historical_files: list[dict]) -> str:
"""Create a formatted message listing uploaded files.
Args:
new_files: Files uploaded in the current message.
historical_files: Files uploaded in previous messages.
Returns:
Formatted string inside tags.
"""
lines = [""]
lines.append("The following files were uploaded in this message:")
lines.append("")
if new_files:
for file in new_files:
size_kb = file["size"] / 1024
size_str = f"{size_kb:.1f} KB" if size_kb < 1024 else f"{size_kb / 1024:.1f} MB"
lines.append(f"- {file['filename']} ({size_str})")
lines.append(f" Path: {file['path']}")
lines.append("")
else:
lines.append("(empty)")
if historical_files:
lines.append("The following files were uploaded in previous messages and are still available:")
lines.append("")
for file in historical_files:
size_kb = file["size"] / 1024
size_str = f"{size_kb:.1f} KB" if size_kb < 1024 else f"{size_kb / 1024:.1f} MB"
lines.append(f"- {file['filename']} ({size_str})")
lines.append(f" Path: {file['path']}")
lines.append("")
lines.append("You can read these files using the `read_file` tool with the paths shown above.")
lines.append("")
return "\n".join(lines)
def _files_from_kwargs(self, message: HumanMessage, uploads_dir: Path | None = None) -> list[dict] | None:
"""Extract file info from message additional_kwargs.files.
The frontend sends uploaded file metadata in additional_kwargs.files
after a successful upload. Each entry has: filename, size (bytes),
path (virtual path), status.
Args:
message: The human message to inspect.
uploads_dir: Physical uploads directory used to verify file existence.
When provided, entries whose files no longer exist are skipped.
Returns:
List of file dicts with virtual paths, or None if the field is absent or empty.
"""
kwargs_files = (message.additional_kwargs or {}).get("files")
if not isinstance(kwargs_files, list) or not kwargs_files:
return None
files = []
for f in kwargs_files:
if not isinstance(f, dict):
continue
filename = f.get("filename") or ""
if not filename or Path(filename).name != filename:
continue
if uploads_dir is not None and not (uploads_dir / filename).is_file():
continue
files.append(
{
"filename": filename,
"size": int(f.get("size") or 0),
"path": f"/mnt/user-data/uploads/{filename}",
"extension": Path(filename).suffix,
}
)
return files if files else None
@override
def before_agent(self, state: UploadsMiddlewareState, runtime: Runtime) -> dict | None:
"""Inject uploaded files information before agent execution.
New files come from the current message's additional_kwargs.files.
Historical files are scanned from the thread's uploads directory,
excluding the new ones.
Prepends context to the last human message content.
The original additional_kwargs (including files metadata) is preserved
on the updated message so the frontend can read it from the stream.
Args:
state: Current agent state.
runtime: Runtime context containing thread_id.
Returns:
State updates including uploaded files list.
"""
messages = list(state.get("messages", []))
if not messages:
return None
last_message_index = len(messages) - 1
last_message = messages[last_message_index]
if not isinstance(last_message, HumanMessage):
return None
# Resolve uploads directory for existence checks
thread_id = runtime.context.get("thread_id")
uploads_dir = self._paths.sandbox_uploads_dir(thread_id) if thread_id else None
# Get newly uploaded files from the current message's additional_kwargs.files
new_files = self._files_from_kwargs(last_message, uploads_dir) or []
# Collect historical files from the uploads directory (all except the new ones)
new_filenames = {f["filename"] for f in new_files}
historical_files: list[dict] = []
if uploads_dir and uploads_dir.exists():
for file_path in sorted(uploads_dir.iterdir()):
if file_path.is_file() and file_path.name not in new_filenames:
stat = file_path.stat()
historical_files.append(
{
"filename": file_path.name,
"size": stat.st_size,
"path": f"/mnt/user-data/uploads/{file_path.name}",
"extension": file_path.suffix,
}
)
if not new_files and not historical_files:
return None
logger.debug(f"New files: {[f['filename'] for f in new_files]}, historical: {[f['filename'] for f in historical_files]}")
# Create files message and prepend to the last human message content
files_message = self._create_files_message(new_files, historical_files)
# Extract original content - handle both string and list formats
original_content = ""
if isinstance(last_message.content, str):
original_content = last_message.content
elif isinstance(last_message.content, list):
text_parts = []
for block in last_message.content:
if isinstance(block, dict) and block.get("type") == "text":
text_parts.append(block.get("text", ""))
original_content = "\n".join(text_parts)
# Create new message with combined content.
# Preserve additional_kwargs (including files metadata) so the frontend
# can read structured file info from the streamed message.
updated_message = HumanMessage(
content=f"{files_message}\n\n{original_content}",
id=last_message.id,
additional_kwargs=last_message.additional_kwargs,
)
messages[last_message_index] = updated_message
return {
"uploaded_files": new_files,
"messages": messages,
}
================================================
FILE: backend/packages/harness/deerflow/agents/middlewares/view_image_middleware.py
================================================
"""Middleware for injecting image details into conversation before LLM call."""
from typing import NotRequired, override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
from langgraph.runtime import Runtime
from deerflow.agents.thread_state import ViewedImageData
class ViewImageMiddlewareState(AgentState):
"""Compatible with the `ThreadState` schema."""
viewed_images: NotRequired[dict[str, ViewedImageData] | None]
class ViewImageMiddleware(AgentMiddleware[ViewImageMiddlewareState]):
"""Injects image details as a human message before LLM calls when view_image tools have completed.
This middleware:
1. Runs before each LLM call
2. Checks if the last assistant message contains view_image tool calls
3. Verifies all tool calls in that message have been completed (have corresponding ToolMessages)
4. If conditions are met, creates a human message with all viewed image details (including base64 data)
5. Adds the message to state so the LLM can see and analyze the images
This enables the LLM to automatically receive and analyze images that were loaded via view_image tool,
without requiring explicit user prompts to describe the images.
"""
state_schema = ViewImageMiddlewareState
def _get_last_assistant_message(self, messages: list) -> AIMessage | None:
"""Get the last assistant message from the message list.
Args:
messages: List of messages
Returns:
Last AIMessage or None if not found
"""
for msg in reversed(messages):
if isinstance(msg, AIMessage):
return msg
return None
def _has_view_image_tool(self, message: AIMessage) -> bool:
"""Check if the assistant message contains view_image tool calls.
Args:
message: Assistant message to check
Returns:
True if message contains view_image tool calls
"""
if not hasattr(message, "tool_calls") or not message.tool_calls:
return False
return any(tool_call.get("name") == "view_image" for tool_call in message.tool_calls)
def _all_tools_completed(self, messages: list, assistant_msg: AIMessage) -> bool:
"""Check if all tool calls in the assistant message have been completed.
Args:
messages: List of all messages
assistant_msg: The assistant message containing tool calls
Returns:
True if all tool calls have corresponding ToolMessages
"""
if not hasattr(assistant_msg, "tool_calls") or not assistant_msg.tool_calls:
return False
# Get all tool call IDs from the assistant message
tool_call_ids = {tool_call.get("id") for tool_call in assistant_msg.tool_calls if tool_call.get("id")}
# Find the index of the assistant message
try:
assistant_idx = messages.index(assistant_msg)
except ValueError:
return False
# Get all ToolMessages after the assistant message
completed_tool_ids = set()
for msg in messages[assistant_idx + 1 :]:
if isinstance(msg, ToolMessage) and msg.tool_call_id:
completed_tool_ids.add(msg.tool_call_id)
# Check if all tool calls have been completed
return tool_call_ids.issubset(completed_tool_ids)
def _create_image_details_message(self, state: ViewImageMiddlewareState) -> list[str | dict]:
"""Create a formatted message with all viewed image details.
Args:
state: Current state containing viewed_images
Returns:
List of content blocks (text and images) for the HumanMessage
"""
viewed_images = state.get("viewed_images", {})
if not viewed_images:
return ["No images have been viewed."]
# Build the message with image information
content_blocks: list[str | dict] = [{"type": "text", "text": "Here are the images you've viewed:"}]
for image_path, image_data in viewed_images.items():
mime_type = image_data.get("mime_type", "unknown")
base64_data = image_data.get("base64", "")
# Add text description
content_blocks.append({"type": "text", "text": f"\n- **{image_path}** ({mime_type})"})
# Add the actual image data so LLM can "see" it
if base64_data:
content_blocks.append(
{
"type": "image_url",
"image_url": {"url": f"data:{mime_type};base64,{base64_data}"},
}
)
return content_blocks
def _should_inject_image_message(self, state: ViewImageMiddlewareState) -> bool:
"""Determine if we should inject an image details message.
Args:
state: Current state
Returns:
True if we should inject the message
"""
messages = state.get("messages", [])
if not messages:
return False
# Get the last assistant message
last_assistant_msg = self._get_last_assistant_message(messages)
if not last_assistant_msg:
return False
# Check if it has view_image tool calls
if not self._has_view_image_tool(last_assistant_msg):
return False
# Check if all tools have been completed
if not self._all_tools_completed(messages, last_assistant_msg):
return False
# Check if we've already added an image details message
# Look for a human message after the last assistant message that contains image details
assistant_idx = messages.index(last_assistant_msg)
for msg in messages[assistant_idx + 1 :]:
if isinstance(msg, HumanMessage):
content_str = str(msg.content)
if "Here are the images you've viewed" in content_str or "Here are the details of the images you've viewed" in content_str:
# Already added, don't add again
return False
return True
def _inject_image_message(self, state: ViewImageMiddlewareState) -> dict | None:
"""Internal helper to inject image details message.
Args:
state: Current state
Returns:
State update with additional human message, or None if no update needed
"""
if not self._should_inject_image_message(state):
return None
# Create the image details message with text and image content
image_content = self._create_image_details_message(state)
# Create a new human message with mixed content (text + images)
human_msg = HumanMessage(content=image_content)
print("[ViewImageMiddleware] Injecting image details message with images before LLM call")
# Return state update with the new message
return {"messages": [human_msg]}
@override
def before_model(self, state: ViewImageMiddlewareState, runtime: Runtime) -> dict | None:
"""Inject image details message before LLM call if view_image tools have completed (sync version).
This runs before each LLM call, checking if the previous turn included view_image
tool calls that have all completed. If so, it injects a human message with the image
details so the LLM can see and analyze the images.
Args:
state: Current state
runtime: Runtime context (unused but required by interface)
Returns:
State update with additional human message, or None if no update needed
"""
return self._inject_image_message(state)
@override
async def abefore_model(self, state: ViewImageMiddlewareState, runtime: Runtime) -> dict | None:
"""Inject image details message before LLM call if view_image tools have completed (async version).
This runs before each LLM call, checking if the previous turn included view_image
tool calls that have all completed. If so, it injects a human message with the image
details so the LLM can see and analyze the images.
Args:
state: Current state
runtime: Runtime context (unused but required by interface)
Returns:
State update with additional human message, or None if no update needed
"""
return self._inject_image_message(state)
================================================
FILE: backend/packages/harness/deerflow/agents/thread_state.py
================================================
from typing import Annotated, NotRequired, TypedDict
from langchain.agents import AgentState
class SandboxState(TypedDict):
sandbox_id: NotRequired[str | None]
class ThreadDataState(TypedDict):
workspace_path: NotRequired[str | None]
uploads_path: NotRequired[str | None]
outputs_path: NotRequired[str | None]
class ViewedImageData(TypedDict):
base64: str
mime_type: str
def merge_artifacts(existing: list[str] | None, new: list[str] | None) -> list[str]:
"""Reducer for artifacts list - merges and deduplicates artifacts."""
if existing is None:
return new or []
if new is None:
return existing
# Use dict.fromkeys to deduplicate while preserving order
return list(dict.fromkeys(existing + new))
def merge_viewed_images(existing: dict[str, ViewedImageData] | None, new: dict[str, ViewedImageData] | None) -> dict[str, ViewedImageData]:
"""Reducer for viewed_images dict - merges image dictionaries.
Special case: If new is an empty dict {}, it clears the existing images.
This allows middlewares to clear the viewed_images state after processing.
"""
if existing is None:
return new or {}
if new is None:
return existing
# Special case: empty dict means clear all viewed images
if len(new) == 0:
return {}
# Merge dictionaries, new values override existing ones for same keys
return {**existing, **new}
class ThreadState(AgentState):
sandbox: NotRequired[SandboxState | None]
thread_data: NotRequired[ThreadDataState | None]
title: NotRequired[str | None]
artifacts: Annotated[list[str], merge_artifacts]
todos: NotRequired[list | None]
uploaded_files: NotRequired[list[dict] | None]
viewed_images: Annotated[dict[str, ViewedImageData], merge_viewed_images] # image_path -> {base64, mime_type}
================================================
FILE: backend/packages/harness/deerflow/client.py
================================================
"""DeerFlowClient — Embedded Python client for DeerFlow agent system.
Provides direct programmatic access to DeerFlow's agent capabilities
without requiring LangGraph Server or Gateway API processes.
Usage:
from deerflow.client import DeerFlowClient
client = DeerFlowClient()
response = client.chat("Analyze this paper for me", thread_id="my-thread")
print(response)
# Streaming
for event in client.stream("hello"):
print(event)
"""
import asyncio
import json
import logging
import mimetypes
import os
import re
import shutil
import tempfile
import uuid
import zipfile
from collections.abc import Generator
from dataclasses import dataclass, field
from pathlib import Path
from typing import Any
from langchain.agents import create_agent
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
from langchain_core.runnables import RunnableConfig
from deerflow.agents.lead_agent.agent import _build_middlewares
from deerflow.agents.lead_agent.prompt import apply_prompt_template
from deerflow.agents.thread_state import ThreadState
from deerflow.config.agents_config import AGENT_NAME_PATTERN
from deerflow.config.app_config import get_app_config, reload_app_config
from deerflow.config.extensions_config import ExtensionsConfig, SkillStateConfig, get_extensions_config, reload_extensions_config
from deerflow.config.paths import get_paths
from deerflow.models import create_chat_model
logger = logging.getLogger(__name__)
@dataclass
class StreamEvent:
"""A single event from the streaming agent response.
Event types align with the LangGraph SSE protocol:
- ``"values"``: Full state snapshot (title, messages, artifacts).
- ``"messages-tuple"``: Per-message update (AI text, tool calls, tool results).
- ``"end"``: Stream finished.
Attributes:
type: Event type.
data: Event payload. Contents vary by type.
"""
type: str
data: dict[str, Any] = field(default_factory=dict)
class DeerFlowClient:
"""Embedded Python client for DeerFlow agent system.
Provides direct programmatic access to DeerFlow's agent capabilities
without requiring LangGraph Server or Gateway API processes.
Note:
Multi-turn conversations require a ``checkpointer``. Without one,
each ``stream()`` / ``chat()`` call is stateless — ``thread_id``
is only used for file isolation (uploads / artifacts).
The system prompt (including date, memory, and skills context) is
generated when the internal agent is first created and cached until
the configuration key changes. Call :meth:`reset_agent` to force
a refresh in long-running processes.
Example::
from deerflow.client import DeerFlowClient
client = DeerFlowClient()
# Simple one-shot
print(client.chat("hello"))
# Streaming
for event in client.stream("hello"):
print(event.type, event.data)
# Configuration queries
print(client.list_models())
print(client.list_skills())
"""
def __init__(
self,
config_path: str | None = None,
checkpointer=None,
*,
model_name: str | None = None,
thinking_enabled: bool = True,
subagent_enabled: bool = False,
plan_mode: bool = False,
agent_name: str | None = None,
):
"""Initialize the client.
Loads configuration but defers agent creation to first use.
Args:
config_path: Path to config.yaml. Uses default resolution if None.
checkpointer: LangGraph checkpointer instance for state persistence.
Required for multi-turn conversations on the same thread_id.
Without a checkpointer, each call is stateless.
model_name: Override the default model name from config.
thinking_enabled: Enable model's extended thinking.
subagent_enabled: Enable subagent delegation.
plan_mode: Enable TodoList middleware for plan mode.
agent_name: Name of the agent to use.
"""
if config_path is not None:
reload_app_config(config_path)
self._app_config = get_app_config()
if agent_name is not None and not AGENT_NAME_PATTERN.match(agent_name):
raise ValueError(f"Invalid agent name '{agent_name}'. Must match pattern: {AGENT_NAME_PATTERN.pattern}")
self._checkpointer = checkpointer
self._model_name = model_name
self._thinking_enabled = thinking_enabled
self._subagent_enabled = subagent_enabled
self._plan_mode = plan_mode
self._agent_name = agent_name
# Lazy agent — created on first call, recreated when config changes.
self._agent = None
self._agent_config_key: tuple | None = None
def reset_agent(self) -> None:
"""Force the internal agent to be recreated on the next call.
Use this after external changes (e.g. memory updates, skill
installations) that should be reflected in the system prompt
or tool set.
"""
self._agent = None
self._agent_config_key = None
# ------------------------------------------------------------------
# Internal helpers
# ------------------------------------------------------------------
@staticmethod
def _atomic_write_json(path: Path, data: dict) -> None:
"""Write JSON to *path* atomically (temp file + replace)."""
fd = tempfile.NamedTemporaryFile(
mode="w",
dir=path.parent,
suffix=".tmp",
delete=False,
)
try:
json.dump(data, fd, indent=2)
fd.close()
Path(fd.name).replace(path)
except BaseException:
fd.close()
Path(fd.name).unlink(missing_ok=True)
raise
def _get_runnable_config(self, thread_id: str, **overrides) -> RunnableConfig:
"""Build a RunnableConfig for agent invocation."""
configurable = {
"thread_id": thread_id,
"model_name": overrides.get("model_name", self._model_name),
"thinking_enabled": overrides.get("thinking_enabled", self._thinking_enabled),
"is_plan_mode": overrides.get("plan_mode", self._plan_mode),
"subagent_enabled": overrides.get("subagent_enabled", self._subagent_enabled),
}
return RunnableConfig(
configurable=configurable,
recursion_limit=overrides.get("recursion_limit", 100),
)
def _ensure_agent(self, config: RunnableConfig):
"""Create (or recreate) the agent when config-dependent params change."""
cfg = config.get("configurable", {})
key = (
cfg.get("model_name"),
cfg.get("thinking_enabled"),
cfg.get("is_plan_mode"),
cfg.get("subagent_enabled"),
)
if self._agent is not None and self._agent_config_key == key:
return
thinking_enabled = cfg.get("thinking_enabled", True)
model_name = cfg.get("model_name")
subagent_enabled = cfg.get("subagent_enabled", False)
max_concurrent_subagents = cfg.get("max_concurrent_subagents", 3)
kwargs: dict[str, Any] = {
"model": create_chat_model(name=model_name, thinking_enabled=thinking_enabled),
"tools": self._get_tools(model_name=model_name, subagent_enabled=subagent_enabled),
"middleware": _build_middlewares(config, model_name=model_name, agent_name=self._agent_name),
"system_prompt": apply_prompt_template(
subagent_enabled=subagent_enabled,
max_concurrent_subagents=max_concurrent_subagents,
agent_name=self._agent_name,
),
"state_schema": ThreadState,
}
checkpointer = self._checkpointer
if checkpointer is None:
from deerflow.agents.checkpointer import get_checkpointer
checkpointer = get_checkpointer()
if checkpointer is not None:
kwargs["checkpointer"] = checkpointer
self._agent = create_agent(**kwargs)
self._agent_config_key = key
logger.info("Agent created: agent_name=%s, model=%s, thinking=%s", self._agent_name, model_name, thinking_enabled)
@staticmethod
def _get_tools(*, model_name: str | None, subagent_enabled: bool):
"""Lazy import to avoid circular dependency at module level."""
from deerflow.tools import get_available_tools
return get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled)
@staticmethod
def _serialize_message(msg) -> dict:
"""Serialize a LangChain message to a plain dict for values events."""
if isinstance(msg, AIMessage):
d: dict[str, Any] = {"type": "ai", "content": msg.content, "id": getattr(msg, "id", None)}
if msg.tool_calls:
d["tool_calls"] = [{"name": tc["name"], "args": tc["args"], "id": tc.get("id")} for tc in msg.tool_calls]
if getattr(msg, "usage_metadata", None):
d["usage_metadata"] = msg.usage_metadata
return d
if isinstance(msg, ToolMessage):
return {
"type": "tool",
"content": DeerFlowClient._extract_text(msg.content),
"name": getattr(msg, "name", None),
"tool_call_id": getattr(msg, "tool_call_id", None),
"id": getattr(msg, "id", None),
}
if isinstance(msg, HumanMessage):
return {"type": "human", "content": msg.content, "id": getattr(msg, "id", None)}
if isinstance(msg, SystemMessage):
return {"type": "system", "content": msg.content, "id": getattr(msg, "id", None)}
return {"type": "unknown", "content": str(msg), "id": getattr(msg, "id", None)}
@staticmethod
def _extract_text(content) -> str:
"""Extract plain text from AIMessage content (str or list of blocks).
String chunks are concatenated without separators to avoid corrupting
token/character deltas or chunked JSON payloads. Dict-based text blocks
are treated as full text blocks and joined with newlines to preserve
readability.
"""
if isinstance(content, str):
return content
if isinstance(content, list):
if content and all(isinstance(block, str) for block in content):
chunk_like = len(content) > 1 and all(
isinstance(block, str)
and len(block) <= 20
and any(ch in block for ch in '{}[]":,')
for block in content
)
return "".join(content) if chunk_like else "\n".join(content)
pieces: list[str] = []
pending_str_parts: list[str] = []
def flush_pending_str_parts() -> None:
if pending_str_parts:
pieces.append("".join(pending_str_parts))
pending_str_parts.clear()
for block in content:
if isinstance(block, str):
pending_str_parts.append(block)
elif isinstance(block, dict):
flush_pending_str_parts()
text_val = block.get("text")
if isinstance(text_val, str):
pieces.append(text_val)
flush_pending_str_parts()
return "\n".join(pieces) if pieces else ""
return str(content)
# ------------------------------------------------------------------
# Public API — conversation
# ------------------------------------------------------------------
def stream(
self,
message: str,
*,
thread_id: str | None = None,
**kwargs,
) -> Generator[StreamEvent, None, None]:
"""Stream a conversation turn, yielding events incrementally.
Each call sends one user message and yields events until the agent
finishes its turn. A ``checkpointer`` must be provided at init time
for multi-turn context to be preserved across calls.
Event types align with the LangGraph SSE protocol so that
consumers can switch between HTTP streaming and embedded mode
without changing their event-handling logic.
Args:
message: User message text.
thread_id: Thread ID for conversation context. Auto-generated if None.
**kwargs: Override client defaults (model_name, thinking_enabled,
plan_mode, subagent_enabled, recursion_limit).
Yields:
StreamEvent with one of:
- type="values" data={"title": str|None, "messages": [...], "artifacts": [...]}
- type="messages-tuple" data={"type": "ai", "content": str, "id": str}
- type="messages-tuple" data={"type": "ai", "content": str, "id": str, "usage_metadata": {...}}
- type="messages-tuple" data={"type": "ai", "content": "", "id": str, "tool_calls": [...]}
- type="messages-tuple" data={"type": "tool", "content": str, "name": str, "tool_call_id": str, "id": str}
- type="end" data={"usage": {"input_tokens": int, "output_tokens": int, "total_tokens": int}}
"""
if thread_id is None:
thread_id = str(uuid.uuid4())
config = self._get_runnable_config(thread_id, **kwargs)
self._ensure_agent(config)
state: dict[str, Any] = {"messages": [HumanMessage(content=message)]}
context = {"thread_id": thread_id}
if self._agent_name:
context["agent_name"] = self._agent_name
seen_ids: set[str] = set()
cumulative_usage: dict[str, int] = {"input_tokens": 0, "output_tokens": 0, "total_tokens": 0}
for chunk in self._agent.stream(state, config=config, context=context, stream_mode="values"):
messages = chunk.get("messages", [])
for msg in messages:
msg_id = getattr(msg, "id", None)
if msg_id and msg_id in seen_ids:
continue
if msg_id:
seen_ids.add(msg_id)
if isinstance(msg, AIMessage):
# Track token usage from AI messages
usage = getattr(msg, "usage_metadata", None)
if usage:
cumulative_usage["input_tokens"] += usage.get("input_tokens", 0) or 0
cumulative_usage["output_tokens"] += usage.get("output_tokens", 0) or 0
cumulative_usage["total_tokens"] += usage.get("total_tokens", 0) or 0
if msg.tool_calls:
yield StreamEvent(
type="messages-tuple",
data={
"type": "ai",
"content": "",
"id": msg_id,
"tool_calls": [{"name": tc["name"], "args": tc["args"], "id": tc.get("id")} for tc in msg.tool_calls],
},
)
text = self._extract_text(msg.content)
if text:
event_data: dict[str, Any] = {"type": "ai", "content": text, "id": msg_id}
if usage:
event_data["usage_metadata"] = {
"input_tokens": usage.get("input_tokens", 0) or 0,
"output_tokens": usage.get("output_tokens", 0) or 0,
"total_tokens": usage.get("total_tokens", 0) or 0,
}
yield StreamEvent(type="messages-tuple", data=event_data)
elif isinstance(msg, ToolMessage):
yield StreamEvent(
type="messages-tuple",
data={
"type": "tool",
"content": self._extract_text(msg.content),
"name": getattr(msg, "name", None),
"tool_call_id": getattr(msg, "tool_call_id", None),
"id": msg_id,
},
)
# Emit a values event for each state snapshot
yield StreamEvent(
type="values",
data={
"title": chunk.get("title"),
"messages": [self._serialize_message(m) for m in messages],
"artifacts": chunk.get("artifacts", []),
},
)
yield StreamEvent(type="end", data={"usage": cumulative_usage})
def chat(self, message: str, *, thread_id: str | None = None, **kwargs) -> str:
"""Send a message and return the final text response.
Convenience wrapper around :meth:`stream` that returns only the
**last** AI text from ``messages-tuple`` events. If the agent emits
multiple text segments in one turn, intermediate segments are
discarded. Use :meth:`stream` directly to capture all events.
Args:
message: User message text.
thread_id: Thread ID for conversation context. Auto-generated if None.
**kwargs: Override client defaults (same as stream()).
Returns:
The last AI message text, or empty string if no response.
"""
last_text = ""
for event in self.stream(message, thread_id=thread_id, **kwargs):
if event.type == "messages-tuple" and event.data.get("type") == "ai":
content = event.data.get("content", "")
if content:
last_text = content
return last_text
# ------------------------------------------------------------------
# Public API — configuration queries
# ------------------------------------------------------------------
def list_models(self) -> dict:
"""List available models from configuration.
Returns:
Dict with "models" key containing list of model info dicts,
matching the Gateway API ``ModelsListResponse`` schema.
"""
return {
"models": [
{
"name": model.name,
"model": getattr(model, "model", None),
"display_name": getattr(model, "display_name", None),
"description": getattr(model, "description", None),
"supports_thinking": getattr(model, "supports_thinking", False),
"supports_reasoning_effort": getattr(model, "supports_reasoning_effort", False),
}
for model in self._app_config.models
]
}
def list_skills(self, enabled_only: bool = False) -> dict:
"""List available skills.
Args:
enabled_only: If True, only return enabled skills.
Returns:
Dict with "skills" key containing list of skill info dicts,
matching the Gateway API ``SkillsListResponse`` schema.
"""
from deerflow.skills.loader import load_skills
return {
"skills": [
{
"name": s.name,
"description": s.description,
"license": s.license,
"category": s.category,
"enabled": s.enabled,
}
for s in load_skills(enabled_only=enabled_only)
]
}
def get_memory(self) -> dict:
"""Get current memory data.
Returns:
Memory data dict (see src/agents/memory/updater.py for structure).
"""
from deerflow.agents.memory.updater import get_memory_data
return get_memory_data()
def get_model(self, name: str) -> dict | None:
"""Get a specific model's configuration by name.
Args:
name: Model name.
Returns:
Model info dict matching the Gateway API ``ModelResponse``
schema, or None if not found.
"""
model = self._app_config.get_model_config(name)
if model is None:
return None
return {
"name": model.name,
"model": getattr(model, "model", None),
"display_name": getattr(model, "display_name", None),
"description": getattr(model, "description", None),
"supports_thinking": getattr(model, "supports_thinking", False),
"supports_reasoning_effort": getattr(model, "supports_reasoning_effort", False),
}
# ------------------------------------------------------------------
# Public API — MCP configuration
# ------------------------------------------------------------------
def get_mcp_config(self) -> dict:
"""Get MCP server configurations.
Returns:
Dict with "mcp_servers" key mapping server name to config,
matching the Gateway API ``McpConfigResponse`` schema.
"""
config = get_extensions_config()
return {"mcp_servers": {name: server.model_dump() for name, server in config.mcp_servers.items()}}
def update_mcp_config(self, mcp_servers: dict[str, dict]) -> dict:
"""Update MCP server configurations.
Writes to extensions_config.json and reloads the cache.
Args:
mcp_servers: Dict mapping server name to config dict.
Each value should contain keys like enabled, type, command, args, env, url, etc.
Returns:
Dict with "mcp_servers" key, matching the Gateway API
``McpConfigResponse`` schema.
Raises:
OSError: If the config file cannot be written.
"""
config_path = ExtensionsConfig.resolve_config_path()
if config_path is None:
raise FileNotFoundError("Cannot locate extensions_config.json. Set DEER_FLOW_EXTENSIONS_CONFIG_PATH or ensure it exists in the project root.")
current_config = get_extensions_config()
config_data = {
"mcpServers": mcp_servers,
"skills": {name: {"enabled": skill.enabled} for name, skill in current_config.skills.items()},
}
self._atomic_write_json(config_path, config_data)
self._agent = None
reloaded = reload_extensions_config()
return {"mcp_servers": {name: server.model_dump() for name, server in reloaded.mcp_servers.items()}}
# ------------------------------------------------------------------
# Public API — skills management
# ------------------------------------------------------------------
def get_skill(self, name: str) -> dict | None:
"""Get a specific skill by name.
Args:
name: Skill name.
Returns:
Skill info dict, or None if not found.
"""
from deerflow.skills.loader import load_skills
skill = next((s for s in load_skills(enabled_only=False) if s.name == name), None)
if skill is None:
return None
return {
"name": skill.name,
"description": skill.description,
"license": skill.license,
"category": skill.category,
"enabled": skill.enabled,
}
def update_skill(self, name: str, *, enabled: bool) -> dict:
"""Update a skill's enabled status.
Args:
name: Skill name.
enabled: New enabled status.
Returns:
Updated skill info dict.
Raises:
ValueError: If the skill is not found.
OSError: If the config file cannot be written.
"""
from deerflow.skills.loader import load_skills
skills = load_skills(enabled_only=False)
skill = next((s for s in skills if s.name == name), None)
if skill is None:
raise ValueError(f"Skill '{name}' not found")
config_path = ExtensionsConfig.resolve_config_path()
if config_path is None:
raise FileNotFoundError("Cannot locate extensions_config.json. Set DEER_FLOW_EXTENSIONS_CONFIG_PATH or ensure it exists in the project root.")
extensions_config = get_extensions_config()
extensions_config.skills[name] = SkillStateConfig(enabled=enabled)
config_data = {
"mcpServers": {n: s.model_dump() for n, s in extensions_config.mcp_servers.items()},
"skills": {n: {"enabled": sc.enabled} for n, sc in extensions_config.skills.items()},
}
self._atomic_write_json(config_path, config_data)
self._agent = None
reload_extensions_config()
updated = next((s for s in load_skills(enabled_only=False) if s.name == name), None)
if updated is None:
raise RuntimeError(f"Skill '{name}' disappeared after update")
return {
"name": updated.name,
"description": updated.description,
"license": updated.license,
"category": updated.category,
"enabled": updated.enabled,
}
def install_skill(self, skill_path: str | Path) -> dict:
"""Install a skill from a .skill archive (ZIP).
Args:
skill_path: Path to the .skill file.
Returns:
Dict with success, skill_name, message.
Raises:
FileNotFoundError: If the file does not exist.
ValueError: If the file is invalid.
"""
from deerflow.skills.loader import get_skills_root_path
from deerflow.skills.validation import _validate_skill_frontmatter
path = Path(skill_path)
if not path.exists():
raise FileNotFoundError(f"Skill file not found: {skill_path}")
if not path.is_file():
raise ValueError(f"Path is not a file: {skill_path}")
if path.suffix != ".skill":
raise ValueError("File must have .skill extension")
if not zipfile.is_zipfile(path):
raise ValueError("File is not a valid ZIP archive")
skills_root = get_skills_root_path()
custom_dir = skills_root / "custom"
custom_dir.mkdir(parents=True, exist_ok=True)
with tempfile.TemporaryDirectory() as tmp:
tmp_path = Path(tmp)
with zipfile.ZipFile(path, "r") as zf:
total_size = sum(info.file_size for info in zf.infolist())
if total_size > 100 * 1024 * 1024:
raise ValueError("Skill archive too large when extracted (>100MB)")
for info in zf.infolist():
if Path(info.filename).is_absolute() or ".." in Path(info.filename).parts:
raise ValueError(f"Unsafe path in archive: {info.filename}")
zf.extractall(tmp_path)
for p in tmp_path.rglob("*"):
if p.is_symlink():
p.unlink()
items = list(tmp_path.iterdir())
if not items:
raise ValueError("Skill archive is empty")
skill_dir = items[0] if len(items) == 1 and items[0].is_dir() else tmp_path
is_valid, message, skill_name = _validate_skill_frontmatter(skill_dir)
if not is_valid:
raise ValueError(f"Invalid skill: {message}")
if not re.fullmatch(r"[a-zA-Z0-9_-]+", skill_name):
raise ValueError(f"Invalid skill name: {skill_name}")
target = custom_dir / skill_name
if target.exists():
raise ValueError(f"Skill '{skill_name}' already exists")
shutil.copytree(skill_dir, target)
return {"success": True, "skill_name": skill_name, "message": f"Skill '{skill_name}' installed successfully"}
# ------------------------------------------------------------------
# Public API — memory management
# ------------------------------------------------------------------
def reload_memory(self) -> dict:
"""Reload memory data from file, forcing cache invalidation.
Returns:
The reloaded memory data dict.
"""
from deerflow.agents.memory.updater import reload_memory_data
return reload_memory_data()
def get_memory_config(self) -> dict:
"""Get memory system configuration.
Returns:
Memory config dict.
"""
from deerflow.config.memory_config import get_memory_config
config = get_memory_config()
return {
"enabled": config.enabled,
"storage_path": config.storage_path,
"debounce_seconds": config.debounce_seconds,
"max_facts": config.max_facts,
"fact_confidence_threshold": config.fact_confidence_threshold,
"injection_enabled": config.injection_enabled,
"max_injection_tokens": config.max_injection_tokens,
}
def get_memory_status(self) -> dict:
"""Get memory status: config + current data.
Returns:
Dict with "config" and "data" keys.
"""
return {
"config": self.get_memory_config(),
"data": self.get_memory(),
}
# ------------------------------------------------------------------
# Public API — file uploads
# ------------------------------------------------------------------
@staticmethod
def _get_uploads_dir(thread_id: str) -> Path:
"""Get (and create) the uploads directory for a thread."""
base = get_paths().sandbox_uploads_dir(thread_id)
base.mkdir(parents=True, exist_ok=True)
return base
def upload_files(self, thread_id: str, files: list[str | Path]) -> dict:
"""Upload local files into a thread's uploads directory.
For PDF, PPT, Excel, and Word files, they are also converted to Markdown.
Args:
thread_id: Target thread ID.
files: List of local file paths to upload.
Returns:
Dict with success, files, message — matching the Gateway API
``UploadResponse`` schema.
Raises:
FileNotFoundError: If any file does not exist.
ValueError: If any supplied path exists but is not a regular file.
"""
from deerflow.utils.file_conversion import CONVERTIBLE_EXTENSIONS, convert_file_to_markdown
# Validate all files upfront to avoid partial uploads.
resolved_files = []
convertible_extensions = {ext.lower() for ext in CONVERTIBLE_EXTENSIONS}
has_convertible_file = False
for f in files:
p = Path(f)
if not p.exists():
raise FileNotFoundError(f"File not found: {f}")
if not p.is_file():
raise ValueError(f"Path is not a file: {f}")
resolved_files.append(p)
if not has_convertible_file and p.suffix.lower() in convertible_extensions:
has_convertible_file = True
uploads_dir = self._get_uploads_dir(thread_id)
uploaded_files: list[dict] = []
conversion_pool = None
if has_convertible_file:
try:
asyncio.get_running_loop()
except RuntimeError:
conversion_pool = None
else:
import concurrent.futures
# Reuse one worker when already inside an event loop to avoid
# creating a new ThreadPoolExecutor per converted file.
conversion_pool = concurrent.futures.ThreadPoolExecutor(max_workers=1)
def _convert_in_thread(path: Path):
return asyncio.run(convert_file_to_markdown(path))
try:
for src_path in resolved_files:
dest = uploads_dir / src_path.name
shutil.copy2(src_path, dest)
info: dict[str, Any] = {
"filename": src_path.name,
"size": str(dest.stat().st_size),
"path": str(dest),
"virtual_path": f"/mnt/user-data/uploads/{src_path.name}",
"artifact_url": f"/api/threads/{thread_id}/artifacts/mnt/user-data/uploads/{src_path.name}",
}
if src_path.suffix.lower() in convertible_extensions:
try:
if conversion_pool is not None:
md_path = conversion_pool.submit(_convert_in_thread, dest).result()
else:
md_path = asyncio.run(convert_file_to_markdown(dest))
except Exception:
logger.warning(
"Failed to convert %s to markdown",
src_path.name,
exc_info=True,
)
md_path = None
if md_path is not None:
info["markdown_file"] = md_path.name
info["markdown_virtual_path"] = f"/mnt/user-data/uploads/{md_path.name}"
info["markdown_artifact_url"] = f"/api/threads/{thread_id}/artifacts/mnt/user-data/uploads/{md_path.name}"
uploaded_files.append(info)
finally:
if conversion_pool is not None:
conversion_pool.shutdown(wait=True)
return {
"success": True,
"files": uploaded_files,
"message": f"Successfully uploaded {len(uploaded_files)} file(s)",
}
def list_uploads(self, thread_id: str) -> dict:
"""List files in a thread's uploads directory.
Args:
thread_id: Thread ID.
Returns:
Dict with "files" and "count" keys, matching the Gateway API
``list_uploaded_files`` response.
"""
uploads_dir = self._get_uploads_dir(thread_id)
if not uploads_dir.exists():
return {"files": [], "count": 0}
files = []
with os.scandir(uploads_dir) as entries:
file_entries = [entry for entry in entries if entry.is_file()]
for entry in sorted(file_entries, key=lambda item: item.name):
stat = entry.stat()
filename = entry.name
files.append(
{
"filename": filename,
"size": str(stat.st_size),
"path": str(Path(entry.path)),
"virtual_path": f"/mnt/user-data/uploads/{filename}",
"artifact_url": f"/api/threads/{thread_id}/artifacts/mnt/user-data/uploads/{filename}",
"extension": Path(filename).suffix,
"modified": stat.st_mtime,
}
)
return {"files": files, "count": len(files)}
def delete_upload(self, thread_id: str, filename: str) -> dict:
"""Delete a file from a thread's uploads directory.
Args:
thread_id: Thread ID.
filename: Filename to delete.
Returns:
Dict with success and message, matching the Gateway API
``delete_uploaded_file`` response.
Raises:
FileNotFoundError: If the file does not exist.
PermissionError: If path traversal is detected.
"""
uploads_dir = self._get_uploads_dir(thread_id)
file_path = (uploads_dir / filename).resolve()
try:
file_path.relative_to(uploads_dir.resolve())
except ValueError as exc:
raise PermissionError("Access denied: path traversal detected") from exc
if not file_path.is_file():
raise FileNotFoundError(f"File not found: {filename}")
file_path.unlink()
return {"success": True, "message": f"Deleted {filename}"}
# ------------------------------------------------------------------
# Public API — artifacts
# ------------------------------------------------------------------
def get_artifact(self, thread_id: str, path: str) -> tuple[bytes, str]:
"""Read an artifact file produced by the agent.
Args:
thread_id: Thread ID.
path: Virtual path (e.g. "mnt/user-data/outputs/file.txt").
Returns:
Tuple of (file_bytes, mime_type).
Raises:
FileNotFoundError: If the artifact does not exist.
ValueError: If the path is invalid.
"""
virtual_prefix = "mnt/user-data"
clean_path = path.lstrip("/")
if not clean_path.startswith(virtual_prefix):
raise ValueError(f"Path must start with /{virtual_prefix}")
relative = clean_path[len(virtual_prefix) :].lstrip("/")
base_dir = get_paths().sandbox_user_data_dir(thread_id)
actual = (base_dir / relative).resolve()
try:
actual.relative_to(base_dir.resolve())
except ValueError as exc:
raise PermissionError("Access denied: path traversal detected") from exc
if not actual.exists():
raise FileNotFoundError(f"Artifact not found: {path}")
if not actual.is_file():
raise ValueError(f"Path is not a file: {path}")
mime_type, _ = mimetypes.guess_type(actual)
return actual.read_bytes(), mime_type or "application/octet-stream"
================================================
FILE: backend/packages/harness/deerflow/community/aio_sandbox/__init__.py
================================================
from .aio_sandbox import AioSandbox
from .aio_sandbox_provider import AioSandboxProvider
from .backend import SandboxBackend
from .local_backend import LocalContainerBackend
from .remote_backend import RemoteSandboxBackend
from .sandbox_info import SandboxInfo
__all__ = [
"AioSandbox",
"AioSandboxProvider",
"LocalContainerBackend",
"RemoteSandboxBackend",
"SandboxBackend",
"SandboxInfo",
]
================================================
FILE: backend/packages/harness/deerflow/community/aio_sandbox/aio_sandbox.py
================================================
import base64
import logging
from agent_sandbox import Sandbox as AioSandboxClient
from deerflow.sandbox.sandbox import Sandbox
logger = logging.getLogger(__name__)
class AioSandbox(Sandbox):
"""Sandbox implementation using the agent-infra/sandbox Docker container.
This sandbox connects to a running AIO sandbox container via HTTP API.
"""
def __init__(self, id: str, base_url: str, home_dir: str | None = None):
"""Initialize the AIO sandbox.
Args:
id: Unique identifier for this sandbox instance.
base_url: URL of the sandbox API (e.g., http://localhost:8080).
home_dir: Home directory inside the sandbox. If None, will be fetched from the sandbox.
"""
super().__init__(id)
self._base_url = base_url
self._client = AioSandboxClient(base_url=base_url, timeout=600)
self._home_dir = home_dir
@property
def base_url(self) -> str:
return self._base_url
@property
def home_dir(self) -> str:
"""Get the home directory inside the sandbox."""
if self._home_dir is None:
context = self._client.sandbox.get_context()
self._home_dir = context.home_dir
return self._home_dir
def execute_command(self, command: str) -> str:
"""Execute a shell command in the sandbox.
Args:
command: The command to execute.
Returns:
The output of the command.
"""
try:
result = self._client.shell.exec_command(command=command)
output = result.data.output if result.data else ""
return output if output else "(no output)"
except Exception as e:
logger.error(f"Failed to execute command in sandbox: {e}")
return f"Error: {e}"
def read_file(self, path: str) -> str:
"""Read the content of a file in the sandbox.
Args:
path: The absolute path of the file to read.
Returns:
The content of the file.
"""
try:
result = self._client.file.read_file(file=path)
return result.data.content if result.data else ""
except Exception as e:
logger.error(f"Failed to read file in sandbox: {e}")
return f"Error: {e}"
def list_dir(self, path: str, max_depth: int = 2) -> list[str]:
"""List the contents of a directory in the sandbox.
Args:
path: The absolute path of the directory to list.
max_depth: The maximum depth to traverse. Default is 2.
Returns:
The contents of the directory.
"""
try:
# Use shell command to list directory with depth limit
# The -L flag limits the depth for the tree command
result = self._client.shell.exec_command(command=f"find {path} -maxdepth {max_depth} -type f -o -type d 2>/dev/null | head -500")
output = result.data.output if result.data else ""
if output:
return [line.strip() for line in output.strip().split("\n") if line.strip()]
return []
except Exception as e:
logger.error(f"Failed to list directory in sandbox: {e}")
return []
def write_file(self, path: str, content: str, append: bool = False) -> None:
"""Write content to a file in the sandbox.
Args:
path: The absolute path of the file to write to.
content: The text content to write to the file.
append: Whether to append the content to the file.
"""
try:
if append:
# Read existing content first and append
existing = self.read_file(path)
if not existing.startswith("Error:"):
content = existing + content
self._client.file.write_file(file=path, content=content)
except Exception as e:
logger.error(f"Failed to write file in sandbox: {e}")
raise
def update_file(self, path: str, content: bytes) -> None:
"""Update a file with binary content in the sandbox.
Args:
path: The absolute path of the file to update.
content: The binary content to write to the file.
"""
try:
base64_content = base64.b64encode(content).decode("utf-8")
self._client.file.write_file(file=path, content=base64_content, encoding="base64")
except Exception as e:
logger.error(f"Failed to update file in sandbox: {e}")
raise
================================================
FILE: backend/packages/harness/deerflow/community/aio_sandbox/aio_sandbox_provider.py
================================================
"""AIO Sandbox Provider — orchestrates sandbox lifecycle with pluggable backends.
This provider composes:
- SandboxBackend: how sandboxes are provisioned (local container vs remote/K8s)
The provider itself handles:
- In-process caching for fast repeated access
- Idle timeout management
- Graceful shutdown with signal handling
- Mount computation (thread-specific, skills)
"""
import atexit
import fcntl
import hashlib
import logging
import os
import signal
import threading
import time
import uuid
from deerflow.config import get_app_config
from deerflow.config.paths import VIRTUAL_PATH_PREFIX, Paths, get_paths
from deerflow.sandbox.sandbox import Sandbox
from deerflow.sandbox.sandbox_provider import SandboxProvider
from .aio_sandbox import AioSandbox
from .backend import SandboxBackend, wait_for_sandbox_ready
from .local_backend import LocalContainerBackend
from .remote_backend import RemoteSandboxBackend
from .sandbox_info import SandboxInfo
logger = logging.getLogger(__name__)
# Default configuration
DEFAULT_IMAGE = "enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest"
DEFAULT_PORT = 8080
DEFAULT_CONTAINER_PREFIX = "deer-flow-sandbox"
DEFAULT_IDLE_TIMEOUT = 600 # 10 minutes in seconds
DEFAULT_REPLICAS = 3 # Maximum concurrent sandbox containers
IDLE_CHECK_INTERVAL = 60 # Check every 60 seconds
class AioSandboxProvider(SandboxProvider):
"""Sandbox provider that manages containers running the AIO sandbox.
Architecture:
This provider composes a SandboxBackend (how to provision), enabling:
- Local Docker/Apple Container mode (auto-start containers)
- Remote/K8s mode (connect to pre-existing sandbox URL)
Configuration options in config.yaml under sandbox:
use: deerflow.community.aio_sandbox:AioSandboxProvider
image:
port: 8080 # Base port for local containers
container_prefix: deer-flow-sandbox
idle_timeout: 600 # Idle timeout in seconds (0 to disable)
replicas: 3 # Max concurrent sandbox containers (LRU eviction when exceeded)
mounts: # Volume mounts for local containers
- host_path: /path/on/host
container_path: /path/in/container
read_only: false
environment: # Environment variables for containers
NODE_ENV: production
API_KEY: $MY_API_KEY
"""
def __init__(self):
self._lock = threading.Lock()
self._sandboxes: dict[str, AioSandbox] = {} # sandbox_id -> AioSandbox instance
self._sandbox_infos: dict[str, SandboxInfo] = {} # sandbox_id -> SandboxInfo (for destroy)
self._thread_sandboxes: dict[str, str] = {} # thread_id -> sandbox_id
self._thread_locks: dict[str, threading.Lock] = {} # thread_id -> in-process lock
self._last_activity: dict[str, float] = {} # sandbox_id -> last activity timestamp
# Warm pool: released sandboxes whose containers are still running.
# Maps sandbox_id -> (SandboxInfo, release_timestamp).
# Containers here can be reclaimed quickly (no cold-start) or destroyed
# when replicas capacity is exhausted.
self._warm_pool: dict[str, tuple[SandboxInfo, float]] = {}
self._shutdown_called = False
self._idle_checker_stop = threading.Event()
self._idle_checker_thread: threading.Thread | None = None
self._config = self._load_config()
self._backend: SandboxBackend = self._create_backend()
# Register shutdown handler
atexit.register(self.shutdown)
self._register_signal_handlers()
# Start idle checker if enabled
if self._config.get("idle_timeout", DEFAULT_IDLE_TIMEOUT) > 0:
self._start_idle_checker()
# ── Factory methods ──────────────────────────────────────────────────
def _create_backend(self) -> SandboxBackend:
"""Create the appropriate backend based on configuration.
Selection logic (checked in order):
1. ``provisioner_url`` set → RemoteSandboxBackend (provisioner mode)
Provisioner dynamically creates Pods + Services in k3s.
2. Default → LocalContainerBackend (local mode)
Local provider manages container lifecycle directly (start/stop).
"""
provisioner_url = self._config.get("provisioner_url")
if provisioner_url:
logger.info(f"Using remote sandbox backend with provisioner at {provisioner_url}")
return RemoteSandboxBackend(provisioner_url=provisioner_url)
logger.info("Using local container sandbox backend")
return LocalContainerBackend(
image=self._config["image"],
base_port=self._config["port"],
container_prefix=self._config["container_prefix"],
config_mounts=self._config["mounts"],
environment=self._config["environment"],
)
# ── Configuration ────────────────────────────────────────────────────
def _load_config(self) -> dict:
"""Load sandbox configuration from app config."""
config = get_app_config()
sandbox_config = config.sandbox
idle_timeout = getattr(sandbox_config, "idle_timeout", None)
replicas = getattr(sandbox_config, "replicas", None)
return {
"image": sandbox_config.image or DEFAULT_IMAGE,
"port": sandbox_config.port or DEFAULT_PORT,
"container_prefix": sandbox_config.container_prefix or DEFAULT_CONTAINER_PREFIX,
"idle_timeout": idle_timeout if idle_timeout is not None else DEFAULT_IDLE_TIMEOUT,
"replicas": replicas if replicas is not None else DEFAULT_REPLICAS,
"mounts": sandbox_config.mounts or [],
"environment": self._resolve_env_vars(sandbox_config.environment or {}),
# provisioner URL for dynamic pod management (e.g. http://provisioner:8002)
"provisioner_url": getattr(sandbox_config, "provisioner_url", None) or "",
}
@staticmethod
def _resolve_env_vars(env_config: dict[str, str]) -> dict[str, str]:
"""Resolve environment variable references (values starting with $)."""
resolved = {}
for key, value in env_config.items():
if isinstance(value, str) and value.startswith("$"):
env_name = value[1:]
resolved[key] = os.environ.get(env_name, "")
else:
resolved[key] = str(value)
return resolved
# ── Deterministic ID ─────────────────────────────────────────────────
@staticmethod
def _deterministic_sandbox_id(thread_id: str) -> str:
"""Generate a deterministic sandbox ID from a thread ID.
Ensures all processes derive the same sandbox_id for a given thread,
enabling cross-process sandbox discovery without shared memory.
"""
return hashlib.sha256(thread_id.encode()).hexdigest()[:8]
# ── Mount helpers ────────────────────────────────────────────────────
def _get_extra_mounts(self, thread_id: str | None) -> list[tuple[str, str, bool]]:
"""Collect all extra mounts for a sandbox (thread-specific + skills)."""
mounts: list[tuple[str, str, bool]] = []
if thread_id:
mounts.extend(self._get_thread_mounts(thread_id))
logger.info(f"Adding thread mounts for thread {thread_id}: {mounts}")
skills_mount = self._get_skills_mount()
if skills_mount:
mounts.append(skills_mount)
logger.info(f"Adding skills mount: {skills_mount}")
return mounts
@staticmethod
def _get_thread_mounts(thread_id: str) -> list[tuple[str, str, bool]]:
"""Get volume mounts for a thread's data directories.
Creates directories if they don't exist (lazy initialization).
Mount sources use host_base_dir so that when running inside Docker with a
mounted Docker socket (DooD), the host Docker daemon can resolve the paths.
"""
paths = get_paths()
paths.ensure_thread_dirs(thread_id)
# host_paths resolves to the host-side base dir when DEER_FLOW_HOST_BASE_DIR
# is set, otherwise falls back to the container's own base dir (native mode).
host_paths = Paths(base_dir=paths.host_base_dir)
return [
(str(host_paths.sandbox_work_dir(thread_id)), f"{VIRTUAL_PATH_PREFIX}/workspace", False),
(str(host_paths.sandbox_uploads_dir(thread_id)), f"{VIRTUAL_PATH_PREFIX}/uploads", False),
(str(host_paths.sandbox_outputs_dir(thread_id)), f"{VIRTUAL_PATH_PREFIX}/outputs", False),
]
@staticmethod
def _get_skills_mount() -> tuple[str, str, bool] | None:
"""Get the skills directory mount configuration.
Mount source uses DEER_FLOW_HOST_SKILLS_PATH when running inside Docker (DooD)
so the host Docker daemon can resolve the path.
"""
try:
config = get_app_config()
skills_path = config.skills.get_skills_path()
container_path = config.skills.container_path
if skills_path.exists():
# When running inside Docker with DooD, use host-side skills path.
host_skills = os.environ.get("DEER_FLOW_HOST_SKILLS_PATH") or str(skills_path)
return (host_skills, container_path, True) # Read-only for security
except Exception as e:
logger.warning(f"Could not setup skills mount: {e}")
return None
# ── Idle timeout management ──────────────────────────────────────────
def _start_idle_checker(self) -> None:
"""Start the background thread that checks for idle sandboxes."""
self._idle_checker_thread = threading.Thread(
target=self._idle_checker_loop,
name="sandbox-idle-checker",
daemon=True,
)
self._idle_checker_thread.start()
logger.info(f"Started idle checker thread (timeout: {self._config.get('idle_timeout', DEFAULT_IDLE_TIMEOUT)}s)")
def _idle_checker_loop(self) -> None:
idle_timeout = self._config.get("idle_timeout", DEFAULT_IDLE_TIMEOUT)
while not self._idle_checker_stop.wait(timeout=IDLE_CHECK_INTERVAL):
try:
self._cleanup_idle_sandboxes(idle_timeout)
except Exception as e:
logger.error(f"Error in idle checker loop: {e}")
def _cleanup_idle_sandboxes(self, idle_timeout: float) -> None:
current_time = time.time()
active_to_destroy = []
warm_to_destroy: list[tuple[str, SandboxInfo]] = []
with self._lock:
# Active sandboxes: tracked via _last_activity
for sandbox_id, last_activity in self._last_activity.items():
idle_duration = current_time - last_activity
if idle_duration > idle_timeout:
active_to_destroy.append(sandbox_id)
logger.info(f"Sandbox {sandbox_id} idle for {idle_duration:.1f}s, marking for destroy")
# Warm pool: tracked via release_timestamp stored in _warm_pool
for sandbox_id, (info, release_ts) in list(self._warm_pool.items()):
warm_duration = current_time - release_ts
if warm_duration > idle_timeout:
warm_to_destroy.append((sandbox_id, info))
del self._warm_pool[sandbox_id]
logger.info(f"Warm-pool sandbox {sandbox_id} idle for {warm_duration:.1f}s, marking for destroy")
# Destroy active sandboxes (re-verify still idle before acting)
for sandbox_id in active_to_destroy:
try:
# Re-verify the sandbox is still idle under the lock before destroying.
# Between the snapshot above and here, the sandbox may have been
# re-acquired (last_activity updated) or already released/destroyed.
with self._lock:
last_activity = self._last_activity.get(sandbox_id)
if last_activity is None:
# Already released or destroyed by another path — skip.
logger.info(f"Sandbox {sandbox_id} already gone before idle destroy, skipping")
continue
if (time.time() - last_activity) < idle_timeout:
# Re-acquired (activity updated) since the snapshot — skip.
logger.info(f"Sandbox {sandbox_id} was re-acquired before idle destroy, skipping")
continue
logger.info(f"Destroying idle sandbox {sandbox_id}")
self.destroy(sandbox_id)
except Exception as e:
logger.error(f"Failed to destroy idle sandbox {sandbox_id}: {e}")
# Destroy warm-pool sandboxes (already removed from _warm_pool under lock above)
for sandbox_id, info in warm_to_destroy:
try:
self._backend.destroy(info)
logger.info(f"Destroyed idle warm-pool sandbox {sandbox_id}")
except Exception as e:
logger.error(f"Failed to destroy idle warm-pool sandbox {sandbox_id}: {e}")
# ── Signal handling ──────────────────────────────────────────────────
def _register_signal_handlers(self) -> None:
"""Register signal handlers for graceful shutdown."""
self._original_sigterm = signal.getsignal(signal.SIGTERM)
self._original_sigint = signal.getsignal(signal.SIGINT)
def signal_handler(signum, frame):
self.shutdown()
original = self._original_sigterm if signum == signal.SIGTERM else self._original_sigint
if callable(original):
original(signum, frame)
elif original == signal.SIG_DFL:
signal.signal(signum, signal.SIG_DFL)
signal.raise_signal(signum)
try:
signal.signal(signal.SIGTERM, signal_handler)
signal.signal(signal.SIGINT, signal_handler)
except ValueError:
logger.debug("Could not register signal handlers (not main thread)")
# ── Thread locking (in-process) ──────────────────────────────────────
def _get_thread_lock(self, thread_id: str) -> threading.Lock:
"""Get or create an in-process lock for a specific thread_id."""
with self._lock:
if thread_id not in self._thread_locks:
self._thread_locks[thread_id] = threading.Lock()
return self._thread_locks[thread_id]
# ── Core: acquire / get / release / shutdown ─────────────────────────
def acquire(self, thread_id: str | None = None) -> str:
"""Acquire a sandbox environment and return its ID.
For the same thread_id, this method will return the same sandbox_id
across multiple turns, multiple processes, and (with shared storage)
multiple pods.
Thread-safe with both in-process and cross-process locking.
Args:
thread_id: Optional thread ID for thread-specific configurations.
Returns:
The ID of the acquired sandbox environment.
"""
if thread_id:
thread_lock = self._get_thread_lock(thread_id)
with thread_lock:
return self._acquire_internal(thread_id)
else:
return self._acquire_internal(thread_id)
def _acquire_internal(self, thread_id: str | None) -> str:
"""Internal sandbox acquisition with two-layer consistency.
Layer 1: In-process cache (fastest, covers same-process repeated access)
Layer 2: Backend discovery (covers containers started by other processes;
sandbox_id is deterministic from thread_id so no shared state file
is needed — any process can derive the same container name)
"""
# ── Layer 1: In-process cache (fast path) ──
if thread_id:
with self._lock:
if thread_id in self._thread_sandboxes:
existing_id = self._thread_sandboxes[thread_id]
if existing_id in self._sandboxes:
logger.info(f"Reusing in-process sandbox {existing_id} for thread {thread_id}")
self._last_activity[existing_id] = time.time()
return existing_id
else:
del self._thread_sandboxes[thread_id]
# Deterministic ID for thread-specific, random for anonymous
sandbox_id = self._deterministic_sandbox_id(thread_id) if thread_id else str(uuid.uuid4())[:8]
# ── Layer 1.5: Warm pool (container still running, no cold-start) ──
if thread_id:
with self._lock:
if sandbox_id in self._warm_pool:
info, _ = self._warm_pool.pop(sandbox_id)
sandbox = AioSandbox(id=sandbox_id, base_url=info.sandbox_url)
self._sandboxes[sandbox_id] = sandbox
self._sandbox_infos[sandbox_id] = info
self._last_activity[sandbox_id] = time.time()
self._thread_sandboxes[thread_id] = sandbox_id
logger.info(f"Reclaimed warm-pool sandbox {sandbox_id} for thread {thread_id} at {info.sandbox_url}")
return sandbox_id
# ── Layer 2: Backend discovery + create (protected by cross-process lock) ──
# Use a file lock so that two processes racing to create the same sandbox
# for the same thread_id serialize here: the second process will discover
# the container started by the first instead of hitting a name-conflict.
if thread_id:
return self._discover_or_create_with_lock(thread_id, sandbox_id)
return self._create_sandbox(thread_id, sandbox_id)
def _discover_or_create_with_lock(self, thread_id: str, sandbox_id: str) -> str:
"""Discover an existing sandbox or create a new one under a cross-process file lock.
The file lock serializes concurrent sandbox creation for the same thread_id
across multiple processes, preventing container-name conflicts.
"""
paths = get_paths()
paths.ensure_thread_dirs(thread_id)
lock_path = paths.thread_dir(thread_id) / f"{sandbox_id}.lock"
with open(lock_path, "a", encoding="utf-8") as lock_file:
try:
fcntl.flock(lock_file, fcntl.LOCK_EX)
# Re-check in-process caches under the file lock in case another
# thread in this process won the race while we were waiting.
with self._lock:
if thread_id in self._thread_sandboxes:
existing_id = self._thread_sandboxes[thread_id]
if existing_id in self._sandboxes:
logger.info(f"Reusing in-process sandbox {existing_id} for thread {thread_id} (post-lock check)")
self._last_activity[existing_id] = time.time()
return existing_id
if sandbox_id in self._warm_pool:
info, _ = self._warm_pool.pop(sandbox_id)
sandbox = AioSandbox(id=sandbox_id, base_url=info.sandbox_url)
self._sandboxes[sandbox_id] = sandbox
self._sandbox_infos[sandbox_id] = info
self._last_activity[sandbox_id] = time.time()
self._thread_sandboxes[thread_id] = sandbox_id
logger.info(f"Reclaimed warm-pool sandbox {sandbox_id} for thread {thread_id} (post-lock check)")
return sandbox_id
# Backend discovery: another process may have created the container.
discovered = self._backend.discover(sandbox_id)
if discovered is not None:
sandbox = AioSandbox(id=discovered.sandbox_id, base_url=discovered.sandbox_url)
with self._lock:
self._sandboxes[discovered.sandbox_id] = sandbox
self._sandbox_infos[discovered.sandbox_id] = discovered
self._last_activity[discovered.sandbox_id] = time.time()
self._thread_sandboxes[thread_id] = discovered.sandbox_id
logger.info(f"Discovered existing sandbox {discovered.sandbox_id} for thread {thread_id} at {discovered.sandbox_url}")
return discovered.sandbox_id
return self._create_sandbox(thread_id, sandbox_id)
finally:
fcntl.flock(lock_file, fcntl.LOCK_UN)
def _evict_oldest_warm(self) -> str | None:
"""Destroy the oldest container in the warm pool to free capacity.
Returns:
The evicted sandbox_id, or None if warm pool is empty.
"""
with self._lock:
if not self._warm_pool:
return None
oldest_id = min(self._warm_pool, key=lambda sid: self._warm_pool[sid][1])
info, _ = self._warm_pool.pop(oldest_id)
try:
self._backend.destroy(info)
logger.info(f"Destroyed warm-pool sandbox {oldest_id}")
except Exception as e:
logger.error(f"Failed to destroy warm-pool sandbox {oldest_id}: {e}")
return None
return oldest_id
def _create_sandbox(self, thread_id: str | None, sandbox_id: str) -> str:
"""Create a new sandbox via the backend.
Args:
thread_id: Optional thread ID.
sandbox_id: The sandbox ID to use.
Returns:
The sandbox_id.
Raises:
RuntimeError: If sandbox creation or readiness check fails.
"""
extra_mounts = self._get_extra_mounts(thread_id)
# Enforce replicas: only warm-pool containers count toward eviction budget.
# Active sandboxes are in use by live threads and must not be forcibly stopped.
replicas = self._config.get("replicas", DEFAULT_REPLICAS)
with self._lock:
total = len(self._sandboxes) + len(self._warm_pool)
if total >= replicas:
evicted = self._evict_oldest_warm()
if evicted:
logger.info(f"Evicted warm-pool sandbox {evicted} to stay within replicas={replicas}")
else:
# All slots are occupied by active sandboxes — proceed anyway and log.
# The replicas limit is a soft cap; we never forcibly stop a container
# that is actively serving a thread.
logger.warning(f"All {replicas} replica slots are in active use; creating sandbox {sandbox_id} beyond the soft limit")
info = self._backend.create(thread_id, sandbox_id, extra_mounts=extra_mounts or None)
# Wait for sandbox to be ready
if not wait_for_sandbox_ready(info.sandbox_url, timeout=60):
self._backend.destroy(info)
raise RuntimeError(f"Sandbox {sandbox_id} failed to become ready within timeout at {info.sandbox_url}")
sandbox = AioSandbox(id=sandbox_id, base_url=info.sandbox_url)
with self._lock:
self._sandboxes[sandbox_id] = sandbox
self._sandbox_infos[sandbox_id] = info
self._last_activity[sandbox_id] = time.time()
if thread_id:
self._thread_sandboxes[thread_id] = sandbox_id
logger.info(f"Created sandbox {sandbox_id} for thread {thread_id} at {info.sandbox_url}")
return sandbox_id
def get(self, sandbox_id: str) -> Sandbox | None:
"""Get a sandbox by ID. Updates last activity timestamp.
Args:
sandbox_id: The ID of the sandbox.
Returns:
The sandbox instance if found, None otherwise.
"""
with self._lock:
sandbox = self._sandboxes.get(sandbox_id)
if sandbox is not None:
self._last_activity[sandbox_id] = time.time()
return sandbox
def release(self, sandbox_id: str) -> None:
"""Release a sandbox from active use into the warm pool.
The container is kept running so it can be reclaimed quickly by the same
thread on its next turn without a cold-start. The container will only be
stopped when the replicas limit forces eviction or during shutdown.
Args:
sandbox_id: The ID of the sandbox to release.
"""
info = None
thread_ids_to_remove: list[str] = []
with self._lock:
self._sandboxes.pop(sandbox_id, None)
info = self._sandbox_infos.pop(sandbox_id, None)
thread_ids_to_remove = [tid for tid, sid in self._thread_sandboxes.items() if sid == sandbox_id]
for tid in thread_ids_to_remove:
del self._thread_sandboxes[tid]
self._last_activity.pop(sandbox_id, None)
# Park in warm pool — container keeps running
if info and sandbox_id not in self._warm_pool:
self._warm_pool[sandbox_id] = (info, time.time())
logger.info(f"Released sandbox {sandbox_id} to warm pool (container still running)")
def destroy(self, sandbox_id: str) -> None:
"""Destroy a sandbox: stop the container and free all resources.
Unlike release(), this actually stops the container. Use this for
explicit cleanup, capacity-driven eviction, or shutdown.
Args:
sandbox_id: The ID of the sandbox to destroy.
"""
info = None
thread_ids_to_remove: list[str] = []
with self._lock:
self._sandboxes.pop(sandbox_id, None)
info = self._sandbox_infos.pop(sandbox_id, None)
thread_ids_to_remove = [tid for tid, sid in self._thread_sandboxes.items() if sid == sandbox_id]
for tid in thread_ids_to_remove:
del self._thread_sandboxes[tid]
self._last_activity.pop(sandbox_id, None)
# Also pull from warm pool if it was parked there
if info is None and sandbox_id in self._warm_pool:
info, _ = self._warm_pool.pop(sandbox_id)
else:
self._warm_pool.pop(sandbox_id, None)
if info:
self._backend.destroy(info)
logger.info(f"Destroyed sandbox {sandbox_id}")
def shutdown(self) -> None:
"""Shutdown all sandboxes. Thread-safe and idempotent."""
with self._lock:
if self._shutdown_called:
return
self._shutdown_called = True
sandbox_ids = list(self._sandboxes.keys())
warm_items = list(self._warm_pool.items())
self._warm_pool.clear()
# Stop idle checker
self._idle_checker_stop.set()
if self._idle_checker_thread is not None and self._idle_checker_thread.is_alive():
self._idle_checker_thread.join(timeout=5)
logger.info("Stopped idle checker thread")
logger.info(f"Shutting down {len(sandbox_ids)} active + {len(warm_items)} warm-pool sandbox(es)")
for sandbox_id in sandbox_ids:
try:
self.destroy(sandbox_id)
except Exception as e:
logger.error(f"Failed to destroy sandbox {sandbox_id} during shutdown: {e}")
for sandbox_id, (info, _) in warm_items:
try:
self._backend.destroy(info)
logger.info(f"Destroyed warm-pool sandbox {sandbox_id} during shutdown")
except Exception as e:
logger.error(f"Failed to destroy warm-pool sandbox {sandbox_id} during shutdown: {e}")
================================================
FILE: backend/packages/harness/deerflow/community/aio_sandbox/backend.py
================================================
"""Abstract base class for sandbox provisioning backends."""
from __future__ import annotations
import logging
import time
from abc import ABC, abstractmethod
import requests
from .sandbox_info import SandboxInfo
logger = logging.getLogger(__name__)
def wait_for_sandbox_ready(sandbox_url: str, timeout: int = 30) -> bool:
"""Poll sandbox health endpoint until ready or timeout.
Args:
sandbox_url: URL of the sandbox (e.g. http://k3s:30001).
timeout: Maximum time to wait in seconds.
Returns:
True if sandbox is ready, False otherwise.
"""
start_time = time.time()
while time.time() - start_time < timeout:
try:
response = requests.get(f"{sandbox_url}/v1/sandbox", timeout=5)
if response.status_code == 200:
return True
except requests.exceptions.RequestException:
pass
time.sleep(1)
return False
class SandboxBackend(ABC):
"""Abstract base for sandbox provisioning backends.
Two implementations:
- LocalContainerBackend: starts Docker/Apple Container locally, manages ports
- RemoteSandboxBackend: connects to a pre-existing URL (K8s service, external)
"""
@abstractmethod
def create(self, thread_id: str, sandbox_id: str, extra_mounts: list[tuple[str, str, bool]] | None = None) -> SandboxInfo:
"""Create/provision a new sandbox.
Args:
thread_id: Thread ID for which the sandbox is being created. Useful for backends that want to organize sandboxes by thread.
sandbox_id: Deterministic sandbox identifier.
extra_mounts: Additional volume mounts as (host_path, container_path, read_only) tuples.
Ignored by backends that don't manage containers (e.g., remote).
Returns:
SandboxInfo with connection details.
"""
...
@abstractmethod
def destroy(self, info: SandboxInfo) -> None:
"""Destroy/cleanup a sandbox and release its resources.
Args:
info: The sandbox metadata to destroy.
"""
...
@abstractmethod
def is_alive(self, info: SandboxInfo) -> bool:
"""Quick check whether a sandbox is still alive.
This should be a lightweight check (e.g., container inspect)
rather than a full health check.
Args:
info: The sandbox metadata to check.
Returns:
True if the sandbox appears to be alive.
"""
...
@abstractmethod
def discover(self, sandbox_id: str) -> SandboxInfo | None:
"""Try to discover an existing sandbox by its deterministic ID.
Used for cross-process recovery: when another process started a sandbox,
this process can discover it by the deterministic container name or URL.
Args:
sandbox_id: The deterministic sandbox ID to look for.
Returns:
SandboxInfo if found and healthy, None otherwise.
"""
...
================================================
FILE: backend/packages/harness/deerflow/community/aio_sandbox/local_backend.py
================================================
"""Local container backend for sandbox provisioning.
Manages sandbox containers using Docker or Apple Container on the local machine.
Handles container lifecycle, port allocation, and cross-process container discovery.
"""
from __future__ import annotations
import logging
import os
import subprocess
from deerflow.utils.network import get_free_port, release_port
from .backend import SandboxBackend, wait_for_sandbox_ready
from .sandbox_info import SandboxInfo
logger = logging.getLogger(__name__)
class LocalContainerBackend(SandboxBackend):
"""Backend that manages sandbox containers locally using Docker or Apple Container.
On macOS, automatically prefers Apple Container if available, otherwise falls back to Docker.
On other platforms, uses Docker.
Features:
- Deterministic container naming for cross-process discovery
- Port allocation with thread-safe utilities
- Container lifecycle management (start/stop with --rm)
- Support for volume mounts and environment variables
"""
def __init__(
self,
*,
image: str,
base_port: int,
container_prefix: str,
config_mounts: list,
environment: dict[str, str],
):
"""Initialize the local container backend.
Args:
image: Container image to use.
base_port: Base port number to start searching for free ports.
container_prefix: Prefix for container names (e.g., "deer-flow-sandbox").
config_mounts: Volume mount configurations from config (list of VolumeMountConfig).
environment: Environment variables to inject into containers.
"""
self._image = image
self._base_port = base_port
self._container_prefix = container_prefix
self._config_mounts = config_mounts
self._environment = environment
self._runtime = self._detect_runtime()
@property
def runtime(self) -> str:
"""The detected container runtime ("docker" or "container")."""
return self._runtime
def _detect_runtime(self) -> str:
"""Detect which container runtime to use.
On macOS, prefer Apple Container if available, otherwise fall back to Docker.
On other platforms, use Docker.
Returns:
"container" for Apple Container, "docker" for Docker.
"""
import platform
if platform.system() == "Darwin":
try:
result = subprocess.run(
["container", "--version"],
capture_output=True,
text=True,
check=True,
timeout=5,
)
logger.info(f"Detected Apple Container: {result.stdout.strip()}")
return "container"
except (FileNotFoundError, subprocess.CalledProcessError, subprocess.TimeoutExpired):
logger.info("Apple Container not available, falling back to Docker")
return "docker"
# ── SandboxBackend interface ──────────────────────────────────────────
def create(self, thread_id: str, sandbox_id: str, extra_mounts: list[tuple[str, str, bool]] | None = None) -> SandboxInfo:
"""Start a new container and return its connection info.
Args:
thread_id: Thread ID for which the sandbox is being created. Useful for backends that want to organize sandboxes by thread.
sandbox_id: Deterministic sandbox identifier (used in container name).
extra_mounts: Additional volume mounts as (host_path, container_path, read_only) tuples.
Returns:
SandboxInfo with container details.
Raises:
RuntimeError: If the container fails to start.
"""
container_name = f"{self._container_prefix}-{sandbox_id}"
# Retry loop: if Docker rejects the port (e.g. a stale container still
# holds the binding after a process restart), skip that port and try the
# next one. The socket-bind check in get_free_port mirrors Docker's
# 0.0.0.0 bind, but Docker's port-release can be slightly asynchronous,
# so a reactive fallback here ensures we always make progress.
_next_start = self._base_port
container_id: str | None = None
port: int = 0
for _attempt in range(10):
port = get_free_port(start_port=_next_start)
try:
container_id = self._start_container(container_name, port, extra_mounts)
break
except RuntimeError as exc:
release_port(port)
err = str(exc)
err_lower = err.lower()
# Port already bound: skip this port and retry with the next one.
if "port is already allocated" in err or "address already in use" in err_lower:
logger.warning(f"Port {port} rejected by Docker (already allocated), retrying with next port")
_next_start = port + 1
continue
# Container-name conflict: another process may have already started
# the deterministic sandbox container for this sandbox_id. Try to
# discover and adopt the existing container instead of failing.
if "is already in use by container" in err_lower or "conflict. the container name" in err_lower:
logger.warning(f"Container name {container_name} already in use, attempting to discover existing sandbox instance")
existing = self.discover(sandbox_id)
if existing is not None:
return existing
raise
else:
raise RuntimeError("Could not start sandbox container: all candidate ports are already allocated by Docker")
# When running inside Docker (DooD), sandbox containers are reachable via
# host.docker.internal rather than localhost (they run on the host daemon).
sandbox_host = os.environ.get("DEER_FLOW_SANDBOX_HOST", "localhost")
return SandboxInfo(
sandbox_id=sandbox_id,
sandbox_url=f"http://{sandbox_host}:{port}",
container_name=container_name,
container_id=container_id,
)
def destroy(self, info: SandboxInfo) -> None:
"""Stop the container and release its port."""
if info.container_id:
self._stop_container(info.container_id)
# Extract port from sandbox_url for release
try:
from urllib.parse import urlparse
port = urlparse(info.sandbox_url).port
if port:
release_port(port)
except Exception:
pass
def is_alive(self, info: SandboxInfo) -> bool:
"""Check if the container is still running (lightweight, no HTTP)."""
if info.container_name:
return self._is_container_running(info.container_name)
return False
def discover(self, sandbox_id: str) -> SandboxInfo | None:
"""Discover an existing container by its deterministic name.
Checks if a container with the expected name is running, retrieves its
port, and verifies it responds to health checks.
Args:
sandbox_id: The deterministic sandbox ID (determines container name).
Returns:
SandboxInfo if container found and healthy, None otherwise.
"""
container_name = f"{self._container_prefix}-{sandbox_id}"
if not self._is_container_running(container_name):
return None
port = self._get_container_port(container_name)
if port is None:
return None
sandbox_host = os.environ.get("DEER_FLOW_SANDBOX_HOST", "localhost")
sandbox_url = f"http://{sandbox_host}:{port}"
if not wait_for_sandbox_ready(sandbox_url, timeout=5):
return None
return SandboxInfo(
sandbox_id=sandbox_id,
sandbox_url=sandbox_url,
container_name=container_name,
)
# ── Container operations ─────────────────────────────────────────────
def _start_container(
self,
container_name: str,
port: int,
extra_mounts: list[tuple[str, str, bool]] | None = None,
) -> str:
"""Start a new container.
Args:
container_name: Name for the container.
port: Host port to map to container port 8080.
extra_mounts: Additional volume mounts.
Returns:
The container ID.
Raises:
RuntimeError: If container fails to start.
"""
cmd = [self._runtime, "run"]
# Docker-specific security options
if self._runtime == "docker":
cmd.extend(["--security-opt", "seccomp=unconfined"])
cmd.extend(
[
"--rm",
"-d",
"-p",
f"{port}:8080",
"--name",
container_name,
]
)
# Environment variables
for key, value in self._environment.items():
cmd.extend(["-e", f"{key}={value}"])
# Config-level volume mounts
for mount in self._config_mounts:
mount_spec = f"{mount.host_path}:{mount.container_path}"
if mount.read_only:
mount_spec += ":ro"
cmd.extend(["-v", mount_spec])
# Extra mounts (thread-specific, skills, etc.)
if extra_mounts:
for host_path, container_path, read_only in extra_mounts:
mount_spec = f"{host_path}:{container_path}"
if read_only:
mount_spec += ":ro"
cmd.extend(["-v", mount_spec])
cmd.append(self._image)
logger.info(f"Starting container using {self._runtime}: {' '.join(cmd)}")
try:
result = subprocess.run(cmd, capture_output=True, text=True, check=True)
container_id = result.stdout.strip()
logger.info(f"Started container {container_name} (ID: {container_id}) using {self._runtime}")
return container_id
except subprocess.CalledProcessError as e:
logger.error(f"Failed to start container using {self._runtime}: {e.stderr}")
raise RuntimeError(f"Failed to start sandbox container: {e.stderr}")
def _stop_container(self, container_id: str) -> None:
"""Stop a container (--rm ensures automatic removal)."""
try:
subprocess.run(
[self._runtime, "stop", container_id],
capture_output=True,
text=True,
check=True,
)
logger.info(f"Stopped container {container_id} using {self._runtime}")
except subprocess.CalledProcessError as e:
logger.warning(f"Failed to stop container {container_id}: {e.stderr}")
def _is_container_running(self, container_name: str) -> bool:
"""Check if a named container is currently running.
This enables cross-process container discovery — any process can detect
containers started by another process via the deterministic container name.
"""
try:
result = subprocess.run(
[self._runtime, "inspect", "-f", "{{.State.Running}}", container_name],
capture_output=True,
text=True,
timeout=5,
)
return result.returncode == 0 and result.stdout.strip().lower() == "true"
except (subprocess.CalledProcessError, subprocess.TimeoutExpired):
return False
def _get_container_port(self, container_name: str) -> int | None:
"""Get the host port of a running container.
Args:
container_name: The container name to inspect.
Returns:
The host port mapped to container port 8080, or None if not found.
"""
try:
result = subprocess.run(
[self._runtime, "port", container_name, "8080"],
capture_output=True,
text=True,
timeout=5,
)
if result.returncode == 0 and result.stdout.strip():
# Output format: "0.0.0.0:PORT" or ":::PORT"
port_str = result.stdout.strip().split(":")[-1]
return int(port_str)
except (subprocess.CalledProcessError, subprocess.TimeoutExpired, ValueError):
pass
return None
================================================
FILE: backend/packages/harness/deerflow/community/aio_sandbox/remote_backend.py
================================================
"""Remote sandbox backend — delegates Pod lifecycle to the provisioner service.
The provisioner dynamically creates per-sandbox-id Pods + NodePort Services
in k3s. The backend accesses sandbox pods directly via ``k3s:{NodePort}``.
Architecture:
┌────────────┐ HTTP ┌─────────────┐ K8s API ┌──────────┐
│ this file │ ──────▸ │ provisioner │ ────────▸ │ k3s │
│ (backend) │ │ :8002 │ │ :6443 │
└────────────┘ └─────────────┘ └─────┬────┘
│ creates
┌─────────────┐ ┌─────▼──────┐
│ backend │ ────────▸ │ sandbox │
│ │ direct │ Pod(s) │
└─────────────┘ k3s:NPort └────────────┘
"""
from __future__ import annotations
import logging
import requests
from .backend import SandboxBackend
from .sandbox_info import SandboxInfo
logger = logging.getLogger(__name__)
class RemoteSandboxBackend(SandboxBackend):
"""Backend that delegates sandbox lifecycle to the provisioner service.
All Pod creation, destruction, and discovery are handled by the
provisioner. This backend is a thin HTTP client.
Typical config.yaml::
sandbox:
use: deerflow.community.aio_sandbox:AioSandboxProvider
provisioner_url: http://provisioner:8002
"""
def __init__(self, provisioner_url: str):
"""Initialize with the provisioner service URL.
Args:
provisioner_url: URL of the provisioner service
(e.g., ``http://provisioner:8002``).
"""
self._provisioner_url = provisioner_url.rstrip("/")
@property
def provisioner_url(self) -> str:
return self._provisioner_url
# ── SandboxBackend interface ──────────────────────────────────────────
def create(
self,
thread_id: str,
sandbox_id: str,
extra_mounts: list[tuple[str, str, bool]] | None = None,
) -> SandboxInfo:
"""Create a sandbox Pod + Service via the provisioner.
Calls ``POST /api/sandboxes`` which creates a dedicated Pod +
NodePort Service in k3s.
"""
return self._provisioner_create(thread_id, sandbox_id, extra_mounts)
def destroy(self, info: SandboxInfo) -> None:
"""Destroy a sandbox Pod + Service via the provisioner."""
self._provisioner_destroy(info.sandbox_id)
def is_alive(self, info: SandboxInfo) -> bool:
"""Check whether the sandbox Pod is running."""
return self._provisioner_is_alive(info.sandbox_id)
def discover(self, sandbox_id: str) -> SandboxInfo | None:
"""Discover an existing sandbox via the provisioner.
Calls ``GET /api/sandboxes/{sandbox_id}`` and returns info if
the Pod exists.
"""
return self._provisioner_discover(sandbox_id)
# ── Provisioner API calls ─────────────────────────────────────────────
def _provisioner_create(self, thread_id: str, sandbox_id: str, extra_mounts: list[tuple[str, str, bool]] | None = None) -> SandboxInfo:
"""POST /api/sandboxes → create Pod + Service."""
try:
resp = requests.post(
f"{self._provisioner_url}/api/sandboxes",
json={
"sandbox_id": sandbox_id,
"thread_id": thread_id,
},
timeout=30,
)
resp.raise_for_status()
data = resp.json()
logger.info(f"Provisioner created sandbox {sandbox_id}: sandbox_url={data['sandbox_url']}")
return SandboxInfo(
sandbox_id=sandbox_id,
sandbox_url=data["sandbox_url"],
)
except requests.RequestException as exc:
logger.error(f"Provisioner create failed for {sandbox_id}: {exc}")
raise RuntimeError(f"Provisioner create failed: {exc}") from exc
def _provisioner_destroy(self, sandbox_id: str) -> None:
"""DELETE /api/sandboxes/{sandbox_id} → destroy Pod + Service."""
try:
resp = requests.delete(
f"{self._provisioner_url}/api/sandboxes/{sandbox_id}",
timeout=15,
)
if resp.ok:
logger.info(f"Provisioner destroyed sandbox {sandbox_id}")
else:
logger.warning(f"Provisioner destroy returned {resp.status_code}: {resp.text}")
except requests.RequestException as exc:
logger.warning(f"Provisioner destroy failed for {sandbox_id}: {exc}")
def _provisioner_is_alive(self, sandbox_id: str) -> bool:
"""GET /api/sandboxes/{sandbox_id} → check Pod phase."""
try:
resp = requests.get(
f"{self._provisioner_url}/api/sandboxes/{sandbox_id}",
timeout=10,
)
if resp.ok:
data = resp.json()
return data.get("status") == "Running"
return False
except requests.RequestException:
return False
def _provisioner_discover(self, sandbox_id: str) -> SandboxInfo | None:
"""GET /api/sandboxes/{sandbox_id} → discover existing sandbox."""
try:
resp = requests.get(
f"{self._provisioner_url}/api/sandboxes/{sandbox_id}",
timeout=10,
)
if resp.status_code == 404:
return None
resp.raise_for_status()
data = resp.json()
return SandboxInfo(
sandbox_id=sandbox_id,
sandbox_url=data["sandbox_url"],
)
except requests.RequestException as exc:
logger.debug(f"Provisioner discover failed for {sandbox_id}: {exc}")
return None
================================================
FILE: backend/packages/harness/deerflow/community/aio_sandbox/sandbox_info.py
================================================
"""Sandbox metadata for cross-process discovery and state persistence."""
from __future__ import annotations
import time
from dataclasses import dataclass, field
@dataclass
class SandboxInfo:
"""Persisted sandbox metadata that enables cross-process discovery.
This dataclass holds all the information needed to reconnect to an
existing sandbox from a different process (e.g., gateway vs langgraph,
multiple workers, or across K8s pods with shared storage).
"""
sandbox_id: str
sandbox_url: str # e.g. http://localhost:8080 or http://k3s:30001
container_name: str | None = None # Only for local container backend
container_id: str | None = None # Only for local container backend
created_at: float = field(default_factory=time.time)
def to_dict(self) -> dict:
return {
"sandbox_id": self.sandbox_id,
"sandbox_url": self.sandbox_url,
"container_name": self.container_name,
"container_id": self.container_id,
"created_at": self.created_at,
}
@classmethod
def from_dict(cls, data: dict) -> SandboxInfo:
return cls(
sandbox_id=data["sandbox_id"],
sandbox_url=data.get("sandbox_url", data.get("base_url", "")),
container_name=data.get("container_name"),
container_id=data.get("container_id"),
created_at=data.get("created_at", time.time()),
)
================================================
FILE: backend/packages/harness/deerflow/community/firecrawl/tools.py
================================================
import json
from firecrawl import FirecrawlApp
from langchain.tools import tool
from deerflow.config import get_app_config
def _get_firecrawl_client() -> FirecrawlApp:
config = get_app_config().get_tool_config("web_search")
api_key = None
if config is not None:
api_key = config.model_extra.get("api_key")
return FirecrawlApp(api_key=api_key) # type: ignore[arg-type]
@tool("web_search", parse_docstring=True)
def web_search_tool(query: str) -> str:
"""Search the web.
Args:
query: The query to search for.
"""
try:
config = get_app_config().get_tool_config("web_search")
max_results = 5
if config is not None:
max_results = config.model_extra.get("max_results", max_results)
client = _get_firecrawl_client()
result = client.search(query, limit=max_results)
# result.web contains list of SearchResultWeb objects
web_results = result.web or []
normalized_results = [
{
"title": getattr(item, "title", "") or "",
"url": getattr(item, "url", "") or "",
"snippet": getattr(item, "description", "") or "",
}
for item in web_results
]
json_results = json.dumps(normalized_results, indent=2, ensure_ascii=False)
return json_results
except Exception as e:
return f"Error: {str(e)}"
@tool("web_fetch", parse_docstring=True)
def web_fetch_tool(url: str) -> str:
"""Fetch the contents of a web page at a given URL.
Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
Do NOT add www. to URLs that do NOT have them.
URLs must include the schema: https://example.com is a valid URL while example.com is an invalid URL.
Args:
url: The URL to fetch the contents of.
"""
try:
client = _get_firecrawl_client()
result = client.scrape(url, formats=["markdown"])
markdown_content = result.markdown or ""
metadata = result.metadata
title = metadata.title if metadata and metadata.title else "Untitled"
if not markdown_content:
return "Error: No content found"
except Exception as e:
return f"Error: {str(e)}"
return f"# {title}\n\n{markdown_content[:4096]}"
================================================
FILE: backend/packages/harness/deerflow/community/image_search/__init__.py
================================================
from .tools import image_search_tool
__all__ = ["image_search_tool"]
================================================
FILE: backend/packages/harness/deerflow/community/image_search/tools.py
================================================
"""
Image Search Tool - Search images using DuckDuckGo for reference in image generation.
"""
import json
import logging
from langchain.tools import tool
from deerflow.config import get_app_config
logger = logging.getLogger(__name__)
def _search_images(
query: str,
max_results: int = 5,
region: str = "wt-wt",
safesearch: str = "moderate",
size: str | None = None,
color: str | None = None,
type_image: str | None = None,
layout: str | None = None,
license_image: str | None = None,
) -> list[dict]:
"""
Execute image search using DuckDuckGo.
Args:
query: Search keywords
max_results: Maximum number of results
region: Search region
safesearch: Safe search level
size: Image size (Small/Medium/Large/Wallpaper)
color: Color filter
type_image: Image type (photo/clipart/gif/transparent/line)
layout: Layout (Square/Tall/Wide)
license_image: License filter
Returns:
List of search results
"""
try:
from ddgs import DDGS
except ImportError:
logger.error("ddgs library not installed. Run: pip install ddgs")
return []
ddgs = DDGS(timeout=30)
try:
kwargs = {
"region": region,
"safesearch": safesearch,
"max_results": max_results,
}
if size:
kwargs["size"] = size
if color:
kwargs["color"] = color
if type_image:
kwargs["type_image"] = type_image
if layout:
kwargs["layout"] = layout
if license_image:
kwargs["license_image"] = license_image
results = ddgs.images(query, **kwargs)
return list(results) if results else []
except Exception as e:
logger.error(f"Failed to search images: {e}")
return []
@tool("image_search", parse_docstring=True)
def image_search_tool(
query: str,
max_results: int = 5,
size: str | None = None,
type_image: str | None = None,
layout: str | None = None,
) -> str:
"""Search for images online. Use this tool BEFORE image generation to find reference images for characters, portraits, objects, scenes, or any content requiring visual accuracy.
**When to use:**
- Before generating character/portrait images: search for similar poses, expressions, styles
- Before generating specific objects/products: search for accurate visual references
- Before generating scenes/locations: search for architectural or environmental references
- Before generating fashion/clothing: search for style and detail references
The returned image URLs can be used as reference images in image generation to significantly improve quality.
Args:
query: Search keywords describing the images you want to find. Be specific for better results (e.g., "Japanese woman street photography 1990s" instead of just "woman").
max_results: Maximum number of images to return. Default is 5.
size: Image size filter. Options: "Small", "Medium", "Large", "Wallpaper". Use "Large" for reference images.
type_image: Image type filter. Options: "photo", "clipart", "gif", "transparent", "line". Use "photo" for realistic references.
layout: Layout filter. Options: "Square", "Tall", "Wide". Choose based on your generation needs.
"""
config = get_app_config().get_tool_config("image_search")
# Override max_results from config if set
if config is not None and "max_results" in config.model_extra:
max_results = config.model_extra.get("max_results", max_results)
results = _search_images(
query=query,
max_results=max_results,
size=size,
type_image=type_image,
layout=layout,
)
if not results:
return json.dumps({"error": "No images found", "query": query}, ensure_ascii=False)
normalized_results = [
{
"title": r.get("title", ""),
"image_url": r.get("thumbnail", ""),
"thumbnail_url": r.get("thumbnail", ""),
}
for r in results
]
output = {
"query": query,
"total_results": len(normalized_results),
"results": normalized_results,
"usage_hint": "Use the 'image_url' values as reference images in image generation. Download them first if needed.",
}
return json.dumps(output, indent=2, ensure_ascii=False)
================================================
FILE: backend/packages/harness/deerflow/community/infoquest/infoquest_client.py
================================================
"""Util that calls InfoQuest Search And Fetch API.
In order to set this up, follow instructions at:
https://docs.byteplus.com/en/docs/InfoQuest/What_is_Info_Quest
"""
import json
import logging
import os
from typing import Any
import requests
logger = logging.getLogger(__name__)
class InfoQuestClient:
"""Client for interacting with the InfoQuest web search and fetch API."""
def __init__(self, fetch_time: int = -1, fetch_timeout: int = -1, fetch_navigation_timeout: int = -1, search_time_range: int = -1, image_search_time_range: int = -1, image_size: str = "i"):
logger.info("\n============================================\n🚀 BytePlus InfoQuest Client Initialization 🚀\n============================================")
self.fetch_time = fetch_time
self.fetch_timeout = fetch_timeout
self.fetch_navigation_timeout = fetch_navigation_timeout
self.search_time_range = search_time_range
self.image_search_time_range = image_search_time_range
self.image_size = image_size
self.api_key_set = bool(os.getenv("INFOQUEST_API_KEY"))
if logger.isEnabledFor(logging.DEBUG):
config_details = (
f"\n📋 Configuration Details:\n"
f"├── Fetch time: {fetch_time} {'(Default: No fetch time)' if fetch_time == -1 else '(Custom)'}\n"
f"├── Fetch Timeout: {fetch_timeout} {'(Default: No fetch timeout)' if fetch_timeout == -1 else '(Custom)'}\n"
f"├── Navigation Timeout: {fetch_navigation_timeout} {'(Default: No Navigation Timeout)' if fetch_navigation_timeout == -1 else '(Custom)'}\n"
f"├── Search Time Range: {search_time_range} {'(Default: No Search Time Range)' if search_time_range == -1 else '(Custom)'}\n"
f"├── Image Search Time Range: {image_search_time_range} {'(Default: No Image Search Time Range)' if image_search_time_range == -1 else '(Custom)'}\n"
f"├── Image Size: {image_size} {'(Default: Medium)' if image_size == 'm' else '(Custom)'}\n"
f"└── API Key: {'✅ Configured' if self.api_key_set else '❌ Not set'}"
)
logger.debug(config_details)
logger.debug("\n" + "*" * 70 + "\n")
def fetch(self, url: str, return_format: str = "html") -> str:
if logger.isEnabledFor(logging.DEBUG):
url_truncated = url[:50] + "..." if len(url) > 50 else url
logger.debug(
f"InfoQuest - Fetch API request initiated | "
f"operation=crawl url | "
f"url_truncated={url_truncated} | "
f"has_timeout_filter={self.fetch_timeout > 0} | timeout_filter={self.fetch_timeout} | "
f"has_fetch_time_filter={self.fetch_time > 0} | fetch_time_filter={self.fetch_time} | "
f"has_navigation_timeout_filter={self.fetch_navigation_timeout > 0} | navi_timeout_filter={self.fetch_navigation_timeout} | "
f"request_type=sync"
)
# Prepare headers
headers = self._prepare_headers()
# Prepare request data
data = self._prepare_crawl_request_data(url, return_format)
logger.debug("Sending crawl request to InfoQuest API")
try:
response = requests.post("https://reader.infoquest.bytepluses.com", headers=headers, json=data)
# Check if status code is not 200
if response.status_code != 200:
error_message = f"fetch API returned status {response.status_code}: {response.text}"
logger.debug("InfoQuest Crawler fetch API return status %d: %s for URL: %s", response.status_code, response.text, url)
return f"Error: {error_message}"
# Check for empty response
if not response.text or not response.text.strip():
error_message = "no result found"
logger.debug("InfoQuest Crawler returned empty response for URL: %s", url)
return f"Error: {error_message}"
# Try to parse response as JSON and extract reader_result
try:
response_data = json.loads(response.text)
# Extract reader_result if it exists
if "reader_result" in response_data:
logger.debug("Successfully extracted reader_result from JSON response")
return response_data["reader_result"]
elif "content" in response_data:
# Fallback to content field if reader_result is not available
logger.debug("reader_result missing in JSON response, falling back to content field: %s", response_data["content"])
return response_data["content"]
else:
# If neither field exists, return the original response
logger.warning("Neither reader_result nor content field found in JSON response")
except json.JSONDecodeError:
# If response is not JSON, return the original text
logger.debug("Response is not in JSON format, returning as-is")
return response.text
# Print partial response for debugging
if logger.isEnabledFor(logging.DEBUG):
response_sample = response.text[:200] + ("..." if len(response.text) > 200 else "")
logger.debug("Successfully received response, content length: %d bytes, first 200 chars: %s", len(response.text), response_sample)
return response.text
except Exception as e:
error_message = f"fetch API failed: {str(e)}"
logger.error(error_message)
return f"Error: {error_message}"
@staticmethod
def _prepare_headers() -> dict[str, str]:
"""Prepare request headers."""
headers = {
"Content-Type": "application/json",
}
# Add API key if available
if os.getenv("INFOQUEST_API_KEY"):
headers["Authorization"] = f"Bearer {os.getenv('INFOQUEST_API_KEY')}"
logger.debug("API key added to request headers")
else:
logger.warning("InfoQuest API key is not set. Provide your own key for authentication.")
return headers
def _prepare_crawl_request_data(self, url: str, return_format: str) -> dict[str, Any]:
"""Prepare request data with formatted parameters."""
# Normalize return_format
if return_format and return_format.lower() == "html":
normalized_format = "HTML"
else:
normalized_format = return_format
data = {"url": url, "format": normalized_format}
# Add timeout parameters if set to positive values
timeout_params = {}
if self.fetch_time > 0:
timeout_params["fetch_time"] = self.fetch_time
if self.fetch_timeout > 0:
timeout_params["timeout"] = self.fetch_timeout
if self.fetch_navigation_timeout > 0:
timeout_params["navi_timeout"] = self.fetch_navigation_timeout
# Log applied timeout parameters
if timeout_params:
logger.debug("Applying timeout parameters: %s", timeout_params)
data.update(timeout_params)
return data
def web_search_raw_results(
self,
query: str,
site: str,
output_format: str = "JSON",
) -> dict:
"""Get results from the InfoQuest Web-Search API synchronously."""
headers = self._prepare_headers()
params = {"format": output_format, "query": query}
if self.search_time_range > 0:
params["time_range"] = self.search_time_range
if site != "":
params["site"] = site
response = requests.post("https://search.infoquest.bytepluses.com", headers=headers, json=params)
response.raise_for_status()
# Print partial response for debugging
response_json = response.json()
if logger.isEnabledFor(logging.DEBUG):
response_sample = json.dumps(response_json)[:200] + ("..." if len(json.dumps(response_json)) > 200 else "")
logger.debug(f"Search API request completed successfully | service=InfoQuest | status=success | response_sample={response_sample}")
return response_json
@staticmethod
def clean_results(raw_results: list[dict[str, dict[str, dict[str, Any]]]]) -> list[dict]:
"""Clean results from InfoQuest Web-Search API."""
logger.debug("Processing web-search results")
seen_urls = set()
clean_results = []
counts = {"pages": 0, "news": 0}
for content_list in raw_results:
content = content_list["content"]
results = content["results"]
if results.get("organic"):
organic_results = results["organic"]
for result in organic_results:
clean_result = {
"type": "page",
}
if "title" in result:
clean_result["title"] = result["title"]
if "desc" in result:
clean_result["desc"] = result["desc"]
clean_result["snippet"] = result["desc"]
if "url" in result:
clean_result["url"] = result["url"]
url = clean_result["url"]
if isinstance(url, str) and url and url not in seen_urls:
seen_urls.add(url)
clean_results.append(clean_result)
counts["pages"] += 1
if results.get("top_stories"):
news = results["top_stories"]
for obj in news["items"]:
clean_result = {
"type": "news",
}
if "time_frame" in obj:
clean_result["time_frame"] = obj["time_frame"]
if "source" in obj:
clean_result["source"] = obj["source"]
title = obj.get("title")
url = obj.get("url")
if title:
clean_result["title"] = title
if url:
clean_result["url"] = url
if title and isinstance(url, str) and url and url not in seen_urls:
seen_urls.add(url)
clean_results.append(clean_result)
counts["news"] += 1
logger.debug(f"Results processing completed | total_results={len(clean_results)} | pages={counts['pages']} | news_items={counts['news']} | unique_urls={len(seen_urls)}")
return clean_results
def web_search(
self,
query: str,
site: str = "",
output_format: str = "JSON",
) -> str:
if logger.isEnabledFor(logging.DEBUG):
query_truncated = query[:50] + "..." if len(query) > 50 else query
logger.debug(
f"InfoQuest - Search API request initiated | "
f"operation=search webs | "
f"query_truncated={query_truncated} | "
f"has_time_filter={self.search_time_range > 0} | time_filter={self.search_time_range} | "
f"has_site_filter={bool(site)} | site={site} | "
f"request_type=sync"
)
try:
logger.debug("InfoQuest Web-Search - Executing search with parameters")
raw_results = self.web_search_raw_results(
query,
site,
output_format,
)
if "search_result" in raw_results:
logger.debug("InfoQuest Web-Search - Successfully extracted search_result from JSON response")
results = raw_results["search_result"]
logger.debug("InfoQuest Web-Search - Processing raw search results")
cleaned_results = self.clean_results(results["results"])
result_json = json.dumps(cleaned_results, indent=2, ensure_ascii=False)
logger.debug(f"InfoQuest Web-Search - Search tool execution completed | mode=synchronous | results_count={len(cleaned_results)}")
return result_json
elif "content" in raw_results:
# Fallback to content field if search_result is not available
error_message = "web search API return wrong format"
logger.error("web search API return wrong format, no search_result nor content field found in JSON response, content: %s", raw_results["content"])
return f"Error: {error_message}"
else:
# If neither field exists, return the original response
logger.warning("InfoQuest Web-Search - Neither search_result nor content field found in JSON response")
return json.dumps(raw_results, indent=2, ensure_ascii=False)
except Exception as e:
error_message = f"InfoQuest Web-Search - Search tool execution failed | mode=synchronous | error={str(e)}"
logger.error(error_message)
return f"Error: {error_message}"
@staticmethod
def clean_results_with_image_search(raw_results: list[dict[str, dict[str, dict[str, Any]]]]) -> list[dict]:
"""Clean results from InfoQuest Web-Search API."""
logger.debug("Processing web-search results")
seen_urls = set()
clean_results = []
counts = {"images": 0}
for content_list in raw_results:
content = content_list["content"]
results = content["results"]
if results.get("images_results"):
images_results = results["images_results"]
for result in images_results:
clean_result = {}
if "original" in result:
clean_result["image_url"] = result["original"]
url = clean_result["image_url"]
if isinstance(url, str) and url and url not in seen_urls:
seen_urls.add(url)
clean_results.append(clean_result)
counts["images"] += 1
if "title" in result:
clean_result["title"] = result["title"]
logger.debug(f"Results processing completed | total_results={len(clean_results)} | images={counts['images']} | unique_urls={len(seen_urls)}")
return clean_results
def image_search_raw_results(
self,
query: str,
site: str = "",
output_format: str = "JSON",
) -> dict:
"""Get image search results from the InfoQuest Web-Search API synchronously."""
headers = self._prepare_headers()
params = {"format": output_format, "query": query, "search_type": "Images"}
# Add time_range filter if specified (1-365)
if 1 <= self.image_search_time_range <= 365:
params["time_range"] = self.image_search_time_range
elif self.image_search_time_range > 0:
logger.warning(f"time_range {self.image_search_time_range} is out of valid range (1-365), ignoring")
# Add site filter if specified
if site:
params["site"] = site
# Add image_size filter if specified
if self.image_size and self.image_size in ["l", "m", "i"]:
params["image_size"] = self.image_size
elif self.image_size:
logger.warning(f"image_size {self.image_size} is not valid, must be 'l', 'm', or 'i'")
response = requests.post("https://search.infoquest.bytepluses.com", headers=headers, json=params)
response.raise_for_status()
# Print partial response for debugging
response_json = response.json()
if logger.isEnabledFor(logging.DEBUG):
response_sample = json.dumps(response_json)[:200] + ("..." if len(json.dumps(response_json)) > 200 else "")
logger.debug(f"Image Search API request completed successfully | service=InfoQuest | status=success | response_sample={response_sample}")
return response_json
def image_search(
self,
query: str,
site: str = "",
output_format: str = "JSON",
) -> str:
if logger.isEnabledFor(logging.DEBUG):
query_truncated = query[:50] + "..." if len(query) > 50 else query
logger.debug(
f"InfoQuest - Image Search API request initiated | "
f"operation=search images | "
f"query_truncated={query_truncated} | "
f"has_site_filter={bool(site)} | site={site} | "
f"image_search_time_range={self.image_search_time_range if self.image_search_time_range >= 1 and self.image_search_time_range <= 365 else 'default'} | "
f"image_size={self.image_size} |"
f"request_type=sync"
)
try:
logger.info("InfoQuest Image Search - Executing search with parameters")
raw_results = self.image_search_raw_results(
query,
site,
output_format,
)
if "search_result" in raw_results:
logger.debug("InfoQuest Image Search - Successfully extracted search_result from JSON response")
results = raw_results["search_result"]
logger.debug(f"InfoQuest Image Search - Processing raw image search results: {results}")
cleaned_results = self.clean_results_with_image_search(results["results"])
result_json = json.dumps(cleaned_results, indent=2, ensure_ascii=False)
logger.debug(f"InfoQuest Image Search - Image search tool execution completed | mode=synchronous | results_count={len(cleaned_results)}")
return result_json
elif "content" in raw_results:
# Fallback to content field if search_result is not available
error_message = "image search API return wrong format"
logger.error("image search API return wrong format, no search_result nor content field found in JSON response, content: %s", raw_results["content"])
return f"Error: {error_message}"
else:
# If neither field exists, return the original response
logger.warning("InfoQuest Image Search - Neither search_result nor content field found in JSON response")
return json.dumps(raw_results, indent=2, ensure_ascii=False)
except Exception as e:
error_message = f"InfoQuest Image Search - Image search tool execution failed | mode=synchronous | error={str(e)}"
logger.error(error_message)
return f"Error: {error_message}"
================================================
FILE: backend/packages/harness/deerflow/community/infoquest/tools.py
================================================
from langchain.tools import tool
from deerflow.config import get_app_config
from deerflow.utils.readability import ReadabilityExtractor
from .infoquest_client import InfoQuestClient
readability_extractor = ReadabilityExtractor()
def _get_infoquest_client() -> InfoQuestClient:
search_config = get_app_config().get_tool_config("web_search")
search_time_range = -1
if search_config is not None and "search_time_range" in search_config.model_extra:
search_time_range = search_config.model_extra.get("search_time_range")
fetch_config = get_app_config().get_tool_config("web_fetch")
fetch_time = -1
if fetch_config is not None and "fetch_time" in fetch_config.model_extra:
fetch_time = fetch_config.model_extra.get("fetch_time")
fetch_timeout = -1
if fetch_config is not None and "timeout" in fetch_config.model_extra:
fetch_timeout = fetch_config.model_extra.get("timeout")
navigation_timeout = -1
if fetch_config is not None and "navigation_timeout" in fetch_config.model_extra:
navigation_timeout = fetch_config.model_extra.get("navigation_timeout")
image_search_config = get_app_config().get_tool_config("image_search")
image_search_time_range = -1
if image_search_config is not None and "image_search_time_range" in image_search_config.model_extra:
image_search_time_range = image_search_config.model_extra.get("image_search_time_range")
image_size = "i"
if image_search_config is not None and "image_size" in image_search_config.model_extra:
image_size = image_search_config.model_extra.get("image_size")
return InfoQuestClient(
search_time_range=search_time_range,
fetch_timeout=fetch_timeout,
fetch_navigation_timeout=navigation_timeout,
fetch_time=fetch_time,
image_search_time_range=image_search_time_range,
image_size=image_size,
)
@tool("web_search", parse_docstring=True)
def web_search_tool(query: str) -> str:
"""Search the web.
Args:
query: The query to search for.
"""
client = _get_infoquest_client()
return client.web_search(query)
@tool("web_fetch", parse_docstring=True)
def web_fetch_tool(url: str) -> str:
"""Fetch the contents of a web page at a given URL.
Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
Do NOT add www. to URLs that do NOT have them.
URLs must include the schema: https://example.com is a valid URL while example.com is an invalid URL.
Args:
url: The URL to fetch the contents of.
"""
client = _get_infoquest_client()
result = client.fetch(url)
if result.startswith("Error: "):
return result
article = readability_extractor.extract_article(result)
return article.to_markdown()[:4096]
@tool("image_search", parse_docstring=True)
def image_search_tool(query: str) -> str:
"""Search for images online. Use this tool BEFORE image generation to find reference images for characters, portraits, objects, scenes, or any content requiring visual accuracy.
**When to use:**
- Before generating character/portrait images: search for similar poses, expressions, styles
- Before generating specific objects/products: search for accurate visual references
- Before generating scenes/locations: search for architectural or environmental references
- Before generating fashion/clothing: search for style and detail references
The returned image URLs can be used as reference images in image generation to significantly improve quality.
Args:
query: The query to search for images.
"""
client = _get_infoquest_client()
return client.image_search(query)
================================================
FILE: backend/packages/harness/deerflow/community/jina_ai/jina_client.py
================================================
import logging
import os
import requests
logger = logging.getLogger(__name__)
class JinaClient:
def crawl(self, url: str, return_format: str = "html", timeout: int = 10) -> str:
headers = {
"Content-Type": "application/json",
"X-Return-Format": return_format,
"X-Timeout": str(timeout),
}
if os.getenv("JINA_API_KEY"):
headers["Authorization"] = f"Bearer {os.getenv('JINA_API_KEY')}"
else:
logger.warning("Jina API key is not set. Provide your own key to access a higher rate limit. See https://jina.ai/reader for more information.")
data = {"url": url}
try:
response = requests.post("https://r.jina.ai/", headers=headers, json=data)
if response.status_code != 200:
error_message = f"Jina API returned status {response.status_code}: {response.text}"
logger.error(error_message)
return f"Error: {error_message}"
if not response.text or not response.text.strip():
error_message = "Jina API returned empty response"
logger.error(error_message)
return f"Error: {error_message}"
return response.text
except Exception as e:
error_message = f"Request to Jina API failed: {str(e)}"
logger.error(error_message)
return f"Error: {error_message}"
================================================
FILE: backend/packages/harness/deerflow/community/jina_ai/tools.py
================================================
from langchain.tools import tool
from deerflow.community.jina_ai.jina_client import JinaClient
from deerflow.config import get_app_config
from deerflow.utils.readability import ReadabilityExtractor
readability_extractor = ReadabilityExtractor()
@tool("web_fetch", parse_docstring=True)
def web_fetch_tool(url: str) -> str:
"""Fetch the contents of a web page at a given URL.
Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
Do NOT add www. to URLs that do NOT have them.
URLs must include the schema: https://example.com is a valid URL while example.com is an invalid URL.
Args:
url: The URL to fetch the contents of.
"""
jina_client = JinaClient()
timeout = 10
config = get_app_config().get_tool_config("web_fetch")
if config is not None and "timeout" in config.model_extra:
timeout = config.model_extra.get("timeout")
html_content = jina_client.crawl(url, return_format="html", timeout=timeout)
article = readability_extractor.extract_article(html_content)
return article.to_markdown()[:4096]
================================================
FILE: backend/packages/harness/deerflow/community/tavily/tools.py
================================================
import json
from langchain.tools import tool
from tavily import TavilyClient
from deerflow.config import get_app_config
def _get_tavily_client() -> TavilyClient:
config = get_app_config().get_tool_config("web_search")
api_key = None
if config is not None and "api_key" in config.model_extra:
api_key = config.model_extra.get("api_key")
return TavilyClient(api_key=api_key)
@tool("web_search", parse_docstring=True)
def web_search_tool(query: str) -> str:
"""Search the web.
Args:
query: The query to search for.
"""
config = get_app_config().get_tool_config("web_search")
max_results = 5
if config is not None and "max_results" in config.model_extra:
max_results = config.model_extra.get("max_results")
client = _get_tavily_client()
res = client.search(query, max_results=max_results)
normalized_results = [
{
"title": result["title"],
"url": result["url"],
"snippet": result["content"],
}
for result in res["results"]
]
json_results = json.dumps(normalized_results, indent=2, ensure_ascii=False)
return json_results
@tool("web_fetch", parse_docstring=True)
def web_fetch_tool(url: str) -> str:
"""Fetch the contents of a web page at a given URL.
Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
Do NOT add www. to URLs that do NOT have them.
URLs must include the schema: https://example.com is a valid URL while example.com is an invalid URL.
Args:
url: The URL to fetch the contents of.
"""
client = _get_tavily_client()
res = client.extract([url])
if "failed_results" in res and len(res["failed_results"]) > 0:
return f"Error: {res['failed_results'][0]['error']}"
elif "results" in res and len(res["results"]) > 0:
result = res["results"][0]
return f"# {result['title']}\n\n{result['raw_content'][:4096]}"
else:
return "Error: No results found"
================================================
FILE: backend/packages/harness/deerflow/config/__init__.py
================================================
from .app_config import get_app_config
from .extensions_config import ExtensionsConfig, get_extensions_config
from .memory_config import MemoryConfig, get_memory_config
from .paths import Paths, get_paths
from .skills_config import SkillsConfig
from .tracing_config import get_tracing_config, is_tracing_enabled
__all__ = [
"get_app_config",
"Paths",
"get_paths",
"SkillsConfig",
"ExtensionsConfig",
"get_extensions_config",
"MemoryConfig",
"get_memory_config",
"get_tracing_config",
"is_tracing_enabled",
]
================================================
FILE: backend/packages/harness/deerflow/config/agents_config.py
================================================
"""Configuration and loaders for custom agents."""
import logging
import re
from typing import Any
import yaml
from pydantic import BaseModel
from deerflow.config.paths import get_paths
logger = logging.getLogger(__name__)
SOUL_FILENAME = "SOUL.md"
AGENT_NAME_PATTERN = re.compile(r"^[A-Za-z0-9-]+$")
class AgentConfig(BaseModel):
"""Configuration for a custom agent."""
name: str
description: str = ""
model: str | None = None
tool_groups: list[str] | None = None
def load_agent_config(name: str | None) -> AgentConfig | None:
"""Load the custom or default agent's config from its directory.
Args:
name: The agent name.
Returns:
AgentConfig instance.
Raises:
FileNotFoundError: If the agent directory or config.yaml does not exist.
ValueError: If config.yaml cannot be parsed.
"""
if name is None:
return None
if not AGENT_NAME_PATTERN.match(name):
raise ValueError(f"Invalid agent name '{name}'. Must match pattern: {AGENT_NAME_PATTERN.pattern}")
agent_dir = get_paths().agent_dir(name)
config_file = agent_dir / "config.yaml"
if not agent_dir.exists():
raise FileNotFoundError(f"Agent directory not found: {agent_dir}")
if not config_file.exists():
raise FileNotFoundError(f"Agent config not found: {config_file}")
try:
with open(config_file, encoding="utf-8") as f:
data: dict[str, Any] = yaml.safe_load(f) or {}
except yaml.YAMLError as e:
raise ValueError(f"Failed to parse agent config {config_file}: {e}") from e
# Ensure name is set from directory name if not in file
if "name" not in data:
data["name"] = name
# Strip unknown fields before passing to Pydantic (e.g. legacy prompt_file)
known_fields = set(AgentConfig.model_fields.keys())
data = {k: v for k, v in data.items() if k in known_fields}
return AgentConfig(**data)
def load_agent_soul(agent_name: str | None) -> str | None:
"""Read the SOUL.md file for a custom agent, if it exists.
SOUL.md defines the agent's personality, values, and behavioral guardrails.
It is injected into the lead agent's system prompt as additional context.
Args:
agent_name: The name of the agent or None for the default agent.
Returns:
The SOUL.md content as a string, or None if the file does not exist.
"""
agent_dir = get_paths().agent_dir(agent_name) if agent_name else get_paths().base_dir
soul_path = agent_dir / SOUL_FILENAME
if not soul_path.exists():
return None
content = soul_path.read_text(encoding="utf-8").strip()
return content or None
def list_custom_agents() -> list[AgentConfig]:
"""Scan the agents directory and return all valid custom agents.
Returns:
List of AgentConfig for each valid agent directory found.
"""
agents_dir = get_paths().agents_dir
if not agents_dir.exists():
return []
agents: list[AgentConfig] = []
for entry in sorted(agents_dir.iterdir()):
if not entry.is_dir():
continue
config_file = entry / "config.yaml"
if not config_file.exists():
logger.debug(f"Skipping {entry.name}: no config.yaml")
continue
try:
agent_cfg = load_agent_config(entry.name)
agents.append(agent_cfg)
except Exception as e:
logger.warning(f"Skipping agent '{entry.name}': {e}")
return agents
================================================
FILE: backend/packages/harness/deerflow/config/app_config.py
================================================
import logging
import os
from pathlib import Path
from typing import Any, Self
import yaml
from dotenv import load_dotenv
from pydantic import BaseModel, ConfigDict, Field
from deerflow.config.checkpointer_config import CheckpointerConfig, load_checkpointer_config_from_dict
from deerflow.config.extensions_config import ExtensionsConfig
from deerflow.config.memory_config import load_memory_config_from_dict
from deerflow.config.model_config import ModelConfig
from deerflow.config.sandbox_config import SandboxConfig
from deerflow.config.skills_config import SkillsConfig
from deerflow.config.subagents_config import load_subagents_config_from_dict
from deerflow.config.summarization_config import load_summarization_config_from_dict
from deerflow.config.title_config import load_title_config_from_dict
from deerflow.config.tool_config import ToolConfig, ToolGroupConfig
from deerflow.config.tool_search_config import ToolSearchConfig, load_tool_search_config_from_dict
load_dotenv()
logger = logging.getLogger(__name__)
class AppConfig(BaseModel):
"""Config for the DeerFlow application"""
models: list[ModelConfig] = Field(default_factory=list, description="Available models")
sandbox: SandboxConfig = Field(description="Sandbox configuration")
tools: list[ToolConfig] = Field(default_factory=list, description="Available tools")
tool_groups: list[ToolGroupConfig] = Field(default_factory=list, description="Available tool groups")
skills: SkillsConfig = Field(default_factory=SkillsConfig, description="Skills configuration")
extensions: ExtensionsConfig = Field(default_factory=ExtensionsConfig, description="Extensions configuration (MCP servers and skills state)")
tool_search: ToolSearchConfig = Field(default_factory=ToolSearchConfig, description="Tool search / deferred loading configuration")
model_config = ConfigDict(extra="allow", frozen=False)
checkpointer: CheckpointerConfig | None = Field(default=None, description="Checkpointer configuration")
@classmethod
def resolve_config_path(cls, config_path: str | None = None) -> Path:
"""Resolve the config file path.
Priority:
1. If provided `config_path` argument, use it.
2. If provided `DEER_FLOW_CONFIG_PATH` environment variable, use it.
3. Otherwise, first check the `config.yaml` in the current directory, then fallback to `config.yaml` in the parent directory.
"""
if config_path:
path = Path(config_path)
if not Path.exists(path):
raise FileNotFoundError(f"Config file specified by param `config_path` not found at {path}")
return path
elif os.getenv("DEER_FLOW_CONFIG_PATH"):
path = Path(os.getenv("DEER_FLOW_CONFIG_PATH"))
if not Path.exists(path):
raise FileNotFoundError(f"Config file specified by environment variable `DEER_FLOW_CONFIG_PATH` not found at {path}")
return path
else:
# Check if the config.yaml is in the current directory
path = Path(os.getcwd()) / "config.yaml"
if not path.exists():
# Check if the config.yaml is in the parent directory of CWD
path = Path(os.getcwd()).parent / "config.yaml"
if not path.exists():
raise FileNotFoundError("`config.yaml` file not found at the current directory nor its parent directory")
return path
@classmethod
def from_file(cls, config_path: str | None = None) -> Self:
"""Load config from YAML file.
See `resolve_config_path` for more details.
Args:
config_path: Path to the config file.
Returns:
AppConfig: The loaded config.
"""
resolved_path = cls.resolve_config_path(config_path)
with open(resolved_path, encoding="utf-8") as f:
config_data = yaml.safe_load(f) or {}
# Check config version before processing
cls._check_config_version(config_data, resolved_path)
config_data = cls.resolve_env_variables(config_data)
# Load title config if present
if "title" in config_data:
load_title_config_from_dict(config_data["title"])
# Load summarization config if present
if "summarization" in config_data:
load_summarization_config_from_dict(config_data["summarization"])
# Load memory config if present
if "memory" in config_data:
load_memory_config_from_dict(config_data["memory"])
# Load subagents config if present
if "subagents" in config_data:
load_subagents_config_from_dict(config_data["subagents"])
# Load tool_search config if present
if "tool_search" in config_data:
load_tool_search_config_from_dict(config_data["tool_search"])
# Load checkpointer config if present
if "checkpointer" in config_data:
load_checkpointer_config_from_dict(config_data["checkpointer"])
# Load extensions config separately (it's in a different file)
extensions_config = ExtensionsConfig.from_file()
config_data["extensions"] = extensions_config.model_dump()
result = cls.model_validate(config_data)
return result
@classmethod
def _check_config_version(cls, config_data: dict, config_path: Path) -> None:
"""Check if the user's config.yaml is outdated compared to config.example.yaml.
Emits a warning if the user's config_version is lower than the example's.
Missing config_version is treated as version 0 (pre-versioning).
"""
try:
user_version = int(config_data.get("config_version", 0))
except (TypeError, ValueError):
user_version = 0
# Find config.example.yaml by searching config.yaml's directory and its parents
example_path = None
search_dir = config_path.parent
for _ in range(5): # search up to 5 levels
candidate = search_dir / "config.example.yaml"
if candidate.exists():
example_path = candidate
break
parent = search_dir.parent
if parent == search_dir:
break
search_dir = parent
if example_path is None:
return
try:
with open(example_path, encoding="utf-8") as f:
example_data = yaml.safe_load(f)
raw = example_data.get("config_version", 0) if example_data else 0
try:
example_version = int(raw)
except (TypeError, ValueError):
example_version = 0
except Exception:
return
if user_version < example_version:
logger.warning(
"Your config.yaml (version %d) is outdated — the latest version is %d. "
"Run `make config-upgrade` to merge new fields into your config.",
user_version,
example_version,
)
@classmethod
def resolve_env_variables(cls, config: Any) -> Any:
"""Recursively resolve environment variables in the config.
Environment variables are resolved using the `os.getenv` function. Example: $OPENAI_API_KEY
Args:
config: The config to resolve environment variables in.
Returns:
The config with environment variables resolved.
"""
if isinstance(config, str):
if config.startswith("$"):
env_value = os.getenv(config[1:])
if env_value is None:
raise ValueError(f"Environment variable {config[1:]} not found for config value {config}")
return env_value
return config
elif isinstance(config, dict):
return {k: cls.resolve_env_variables(v) for k, v in config.items()}
elif isinstance(config, list):
return [cls.resolve_env_variables(item) for item in config]
return config
def get_model_config(self, name: str) -> ModelConfig | None:
"""Get the model config by name.
Args:
name: The name of the model to get the config for.
Returns:
The model config if found, otherwise None.
"""
return next((model for model in self.models if model.name == name), None)
def get_tool_config(self, name: str) -> ToolConfig | None:
"""Get the tool config by name.
Args:
name: The name of the tool to get the config for.
Returns:
The tool config if found, otherwise None.
"""
return next((tool for tool in self.tools if tool.name == name), None)
def get_tool_group_config(self, name: str) -> ToolGroupConfig | None:
"""Get the tool group config by name.
Args:
name: The name of the tool group to get the config for.
Returns:
The tool group config if found, otherwise None.
"""
return next((group for group in self.tool_groups if group.name == name), None)
_app_config: AppConfig | None = None
_app_config_path: Path | None = None
_app_config_mtime: float | None = None
_app_config_is_custom = False
def _get_config_mtime(config_path: Path) -> float | None:
"""Get the modification time of a config file if it exists."""
try:
return config_path.stat().st_mtime
except OSError:
return None
def _load_and_cache_app_config(config_path: str | None = None) -> AppConfig:
"""Load config from disk and refresh cache metadata."""
global _app_config, _app_config_path, _app_config_mtime, _app_config_is_custom
resolved_path = AppConfig.resolve_config_path(config_path)
_app_config = AppConfig.from_file(str(resolved_path))
_app_config_path = resolved_path
_app_config_mtime = _get_config_mtime(resolved_path)
_app_config_is_custom = False
return _app_config
def get_app_config() -> AppConfig:
"""Get the DeerFlow config instance.
Returns a cached singleton instance and automatically reloads it when the
underlying config file path or modification time changes. Use
`reload_app_config()` to force a reload, or `reset_app_config()` to clear
the cache.
"""
global _app_config, _app_config_path, _app_config_mtime
if _app_config is not None and _app_config_is_custom:
return _app_config
resolved_path = AppConfig.resolve_config_path()
current_mtime = _get_config_mtime(resolved_path)
should_reload = (
_app_config is None
or _app_config_path != resolved_path
or _app_config_mtime != current_mtime
)
if should_reload:
if (
_app_config_path == resolved_path
and _app_config_mtime is not None
and current_mtime is not None
and _app_config_mtime != current_mtime
):
logger.info(
"Config file has been modified (mtime: %s -> %s), reloading AppConfig",
_app_config_mtime,
current_mtime,
)
_load_and_cache_app_config(str(resolved_path))
return _app_config
def reload_app_config(config_path: str | None = None) -> AppConfig:
"""Reload the config from file and update the cached instance.
This is useful when the config file has been modified and you want
to pick up the changes without restarting the application.
Args:
config_path: Optional path to config file. If not provided,
uses the default resolution strategy.
Returns:
The newly loaded AppConfig instance.
"""
return _load_and_cache_app_config(config_path)
def reset_app_config() -> None:
"""Reset the cached config instance.
This clears the singleton cache, causing the next call to
`get_app_config()` to reload from file. Useful for testing
or when switching between different configurations.
"""
global _app_config, _app_config_path, _app_config_mtime, _app_config_is_custom
_app_config = None
_app_config_path = None
_app_config_mtime = None
_app_config_is_custom = False
def set_app_config(config: AppConfig) -> None:
"""Set a custom config instance.
This allows injecting a custom or mock config for testing purposes.
Args:
config: The AppConfig instance to use.
"""
global _app_config, _app_config_path, _app_config_mtime, _app_config_is_custom
_app_config = config
_app_config_path = None
_app_config_mtime = None
_app_config_is_custom = True
================================================
FILE: backend/packages/harness/deerflow/config/checkpointer_config.py
================================================
"""Configuration for LangGraph checkpointer."""
from typing import Literal
from pydantic import BaseModel, Field
CheckpointerType = Literal["memory", "sqlite", "postgres"]
class CheckpointerConfig(BaseModel):
"""Configuration for LangGraph state persistence checkpointer."""
type: CheckpointerType = Field(
description="Checkpointer backend type. "
"'memory' is in-process only (lost on restart). "
"'sqlite' persists to a local file (requires langgraph-checkpoint-sqlite). "
"'postgres' persists to PostgreSQL (requires langgraph-checkpoint-postgres)."
)
connection_string: str | None = Field(
default=None,
description="Connection string for sqlite (file path) or postgres (DSN). "
"Required for sqlite and postgres types. "
"For sqlite, use a file path like '.deer-flow/checkpoints.db' or ':memory:' for in-memory. "
"For postgres, use a DSN like 'postgresql://user:pass@localhost:5432/db'.",
)
# Global configuration instance — None means no checkpointer is configured.
_checkpointer_config: CheckpointerConfig | None = None
def get_checkpointer_config() -> CheckpointerConfig | None:
"""Get the current checkpointer configuration, or None if not configured."""
return _checkpointer_config
def set_checkpointer_config(config: CheckpointerConfig | None) -> None:
"""Set the checkpointer configuration."""
global _checkpointer_config
_checkpointer_config = config
def load_checkpointer_config_from_dict(config_dict: dict) -> None:
"""Load checkpointer configuration from a dictionary."""
global _checkpointer_config
_checkpointer_config = CheckpointerConfig(**config_dict)
================================================
FILE: backend/packages/harness/deerflow/config/extensions_config.py
================================================
"""Unified extensions configuration for MCP servers and skills."""
import json
import os
from pathlib import Path
from typing import Any, Literal
from pydantic import BaseModel, ConfigDict, Field
class McpOAuthConfig(BaseModel):
"""OAuth configuration for an MCP server (HTTP/SSE transports)."""
enabled: bool = Field(default=True, description="Whether OAuth token injection is enabled")
token_url: str = Field(description="OAuth token endpoint URL")
grant_type: Literal["client_credentials", "refresh_token"] = Field(
default="client_credentials",
description="OAuth grant type",
)
client_id: str | None = Field(default=None, description="OAuth client ID")
client_secret: str | None = Field(default=None, description="OAuth client secret")
refresh_token: str | None = Field(default=None, description="OAuth refresh token (for refresh_token grant)")
scope: str | None = Field(default=None, description="OAuth scope")
audience: str | None = Field(default=None, description="OAuth audience (provider-specific)")
token_field: str = Field(default="access_token", description="Field name containing access token in token response")
token_type_field: str = Field(default="token_type", description="Field name containing token type in token response")
expires_in_field: str = Field(default="expires_in", description="Field name containing expiry (seconds) in token response")
default_token_type: str = Field(default="Bearer", description="Default token type when missing in token response")
refresh_skew_seconds: int = Field(default=60, description="Refresh token this many seconds before expiry")
extra_token_params: dict[str, str] = Field(default_factory=dict, description="Additional form params sent to token endpoint")
model_config = ConfigDict(extra="allow")
class McpServerConfig(BaseModel):
"""Configuration for a single MCP server."""
enabled: bool = Field(default=True, description="Whether this MCP server is enabled")
type: str = Field(default="stdio", description="Transport type: 'stdio', 'sse', or 'http'")
command: str | None = Field(default=None, description="Command to execute to start the MCP server (for stdio type)")
args: list[str] = Field(default_factory=list, description="Arguments to pass to the command (for stdio type)")
env: dict[str, str] = Field(default_factory=dict, description="Environment variables for the MCP server")
url: str | None = Field(default=None, description="URL of the MCP server (for sse or http type)")
headers: dict[str, str] = Field(default_factory=dict, description="HTTP headers to send (for sse or http type)")
oauth: McpOAuthConfig | None = Field(default=None, description="OAuth configuration (for sse or http type)")
description: str = Field(default="", description="Human-readable description of what this MCP server provides")
model_config = ConfigDict(extra="allow")
class SkillStateConfig(BaseModel):
"""Configuration for a single skill's state."""
enabled: bool = Field(default=True, description="Whether this skill is enabled")
class ExtensionsConfig(BaseModel):
"""Unified configuration for MCP servers and skills."""
mcp_servers: dict[str, McpServerConfig] = Field(
default_factory=dict,
description="Map of MCP server name to configuration",
alias="mcpServers",
)
skills: dict[str, SkillStateConfig] = Field(
default_factory=dict,
description="Map of skill name to state configuration",
)
model_config = ConfigDict(extra="allow", populate_by_name=True)
@classmethod
def resolve_config_path(cls, config_path: str | None = None) -> Path | None:
"""Resolve the extensions config file path.
Priority:
1. If provided `config_path` argument, use it.
2. If provided `DEER_FLOW_EXTENSIONS_CONFIG_PATH` environment variable, use it.
3. Otherwise, check for `extensions_config.json` in the current directory, then in the parent directory.
4. For backward compatibility, also check for `mcp_config.json` if `extensions_config.json` is not found.
5. If not found, return None (extensions are optional).
Args:
config_path: Optional path to extensions config file.
Returns:
Path to the extensions config file if found, otherwise None.
"""
if config_path:
path = Path(config_path)
if not path.exists():
raise FileNotFoundError(f"Extensions config file specified by param `config_path` not found at {path}")
return path
elif os.getenv("DEER_FLOW_EXTENSIONS_CONFIG_PATH"):
path = Path(os.getenv("DEER_FLOW_EXTENSIONS_CONFIG_PATH"))
if not path.exists():
raise FileNotFoundError(f"Extensions config file specified by environment variable `DEER_FLOW_EXTENSIONS_CONFIG_PATH` not found at {path}")
return path
else:
# Check if the extensions_config.json is in the current directory
path = Path(os.getcwd()) / "extensions_config.json"
if path.exists():
return path
# Check if the extensions_config.json is in the parent directory of CWD
path = Path(os.getcwd()).parent / "extensions_config.json"
if path.exists():
return path
# Backward compatibility: check for mcp_config.json
path = Path(os.getcwd()) / "mcp_config.json"
if path.exists():
return path
path = Path(os.getcwd()).parent / "mcp_config.json"
if path.exists():
return path
# Extensions are optional, so return None if not found
return None
@classmethod
def from_file(cls, config_path: str | None = None) -> "ExtensionsConfig":
"""Load extensions config from JSON file.
See `resolve_config_path` for more details.
Args:
config_path: Path to the extensions config file.
Returns:
ExtensionsConfig: The loaded config, or empty config if file not found.
"""
resolved_path = cls.resolve_config_path(config_path)
if resolved_path is None:
# Return empty config if extensions config file is not found
return cls(mcp_servers={}, skills={})
try:
with open(resolved_path, encoding="utf-8") as f:
config_data = json.load(f)
cls.resolve_env_variables(config_data)
return cls.model_validate(config_data)
except json.JSONDecodeError as e:
raise ValueError(f"Extensions config file at {resolved_path} is not valid JSON: {e}") from e
except Exception as e:
raise RuntimeError(f"Failed to load extensions config from {resolved_path}: {e}") from e
@classmethod
def resolve_env_variables(cls, config: dict[str, Any]) -> dict[str, Any]:
"""Recursively resolve environment variables in the config.
Environment variables are resolved using the `os.getenv` function. Example: $OPENAI_API_KEY
Args:
config: The config to resolve environment variables in.
Returns:
The config with environment variables resolved.
"""
for key, value in config.items():
if isinstance(value, str):
if value.startswith("$"):
env_value = os.getenv(value[1:])
if env_value is None:
# Unresolved placeholder — store empty string so downstream
# consumers (e.g. MCP servers) don't receive the literal "$VAR"
# token as an actual environment value.
config[key] = ""
else:
config[key] = env_value
else:
config[key] = value
elif isinstance(value, dict):
config[key] = cls.resolve_env_variables(value)
elif isinstance(value, list):
config[key] = [cls.resolve_env_variables(item) if isinstance(item, dict) else item for item in value]
return config
def get_enabled_mcp_servers(self) -> dict[str, McpServerConfig]:
"""Get only the enabled MCP servers.
Returns:
Dictionary of enabled MCP servers.
"""
return {name: config for name, config in self.mcp_servers.items() if config.enabled}
def is_skill_enabled(self, skill_name: str, skill_category: str) -> bool:
"""Check if a skill is enabled.
Args:
skill_name: Name of the skill
skill_category: Category of the skill
Returns:
True if enabled, False otherwise
"""
skill_config = self.skills.get(skill_name)
if skill_config is None:
# Default to enable for public & custom skill
return skill_category in ("public", "custom")
return skill_config.enabled
_extensions_config: ExtensionsConfig | None = None
def get_extensions_config() -> ExtensionsConfig:
"""Get the extensions config instance.
Returns a cached singleton instance. Use `reload_extensions_config()` to reload
from file, or `reset_extensions_config()` to clear the cache.
Returns:
The cached ExtensionsConfig instance.
"""
global _extensions_config
if _extensions_config is None:
_extensions_config = ExtensionsConfig.from_file()
return _extensions_config
def reload_extensions_config(config_path: str | None = None) -> ExtensionsConfig:
"""Reload the extensions config from file and update the cached instance.
This is useful when the config file has been modified and you want
to pick up the changes without restarting the application.
Args:
config_path: Optional path to extensions config file. If not provided,
uses the default resolution strategy.
Returns:
The newly loaded ExtensionsConfig instance.
"""
global _extensions_config
_extensions_config = ExtensionsConfig.from_file(config_path)
return _extensions_config
def reset_extensions_config() -> None:
"""Reset the cached extensions config instance.
This clears the singleton cache, causing the next call to
`get_extensions_config()` to reload from file. Useful for testing
or when switching between different configurations.
"""
global _extensions_config
_extensions_config = None
def set_extensions_config(config: ExtensionsConfig) -> None:
"""Set a custom extensions config instance.
This allows injecting a custom or mock config for testing purposes.
Args:
config: The ExtensionsConfig instance to use.
"""
global _extensions_config
_extensions_config = config
================================================
FILE: backend/packages/harness/deerflow/config/memory_config.py
================================================
"""Configuration for memory mechanism."""
from pydantic import BaseModel, Field
class MemoryConfig(BaseModel):
"""Configuration for global memory mechanism."""
enabled: bool = Field(
default=True,
description="Whether to enable memory mechanism",
)
storage_path: str = Field(
default="",
description=(
"Path to store memory data. "
"If empty, defaults to `{base_dir}/memory.json` (see Paths.memory_file). "
"Absolute paths are used as-is. "
"Relative paths are resolved against `Paths.base_dir` "
"(not the backend working directory). "
"Note: if you previously set this to `.deer-flow/memory.json`, "
"the file will now be resolved as `{base_dir}/.deer-flow/memory.json`; "
"migrate existing data or use an absolute path to preserve the old location."
),
)
debounce_seconds: int = Field(
default=30,
ge=1,
le=300,
description="Seconds to wait before processing queued updates (debounce)",
)
model_name: str | None = Field(
default=None,
description="Model name to use for memory updates (None = use default model)",
)
max_facts: int = Field(
default=100,
ge=10,
le=500,
description="Maximum number of facts to store",
)
fact_confidence_threshold: float = Field(
default=0.7,
ge=0.0,
le=1.0,
description="Minimum confidence threshold for storing facts",
)
injection_enabled: bool = Field(
default=True,
description="Whether to inject memory into system prompt",
)
max_injection_tokens: int = Field(
default=2000,
ge=100,
le=8000,
description="Maximum tokens to use for memory injection",
)
# Global configuration instance
_memory_config: MemoryConfig = MemoryConfig()
def get_memory_config() -> MemoryConfig:
"""Get the current memory configuration."""
return _memory_config
def set_memory_config(config: MemoryConfig) -> None:
"""Set the memory configuration."""
global _memory_config
_memory_config = config
def load_memory_config_from_dict(config_dict: dict) -> None:
"""Load memory configuration from a dictionary."""
global _memory_config
_memory_config = MemoryConfig(**config_dict)
================================================
FILE: backend/packages/harness/deerflow/config/model_config.py
================================================
from pydantic import BaseModel, ConfigDict, Field
class ModelConfig(BaseModel):
"""Config section for a model"""
name: str = Field(..., description="Unique name for the model")
display_name: str | None = Field(..., default_factory=lambda: None, description="Display name for the model")
description: str | None = Field(..., default_factory=lambda: None, description="Description for the model")
use: str = Field(
...,
description="Class path of the model provider(e.g. langchain_openai.ChatOpenAI)",
)
model: str = Field(..., description="Model name")
model_config = ConfigDict(extra="allow")
use_responses_api: bool | None = Field(
default=None,
description="Whether to route OpenAI ChatOpenAI calls through the /v1/responses API",
)
output_version: str | None = Field(
default=None,
description="Structured output version for OpenAI responses content, e.g. responses/v1",
)
supports_thinking: bool = Field(default_factory=lambda: False, description="Whether the model supports thinking")
supports_reasoning_effort: bool = Field(default_factory=lambda: False, description="Whether the model supports reasoning effort")
when_thinking_enabled: dict | None = Field(
default_factory=lambda: None,
description="Extra settings to be passed to the model when thinking is enabled",
)
supports_vision: bool = Field(default_factory=lambda: False, description="Whether the model supports vision/image inputs")
thinking: dict | None = Field(
default_factory=lambda: None,
description=(
"Thinking settings for the model. If provided, these settings will be passed to the model when thinking is enabled. "
"This is a shortcut for `when_thinking_enabled` and will be merged with `when_thinking_enabled` if both are provided."
),
)
================================================
FILE: backend/packages/harness/deerflow/config/paths.py
================================================
import os
import re
from pathlib import Path
# Virtual path prefix seen by agents inside the sandbox
VIRTUAL_PATH_PREFIX = "/mnt/user-data"
_SAFE_THREAD_ID_RE = re.compile(r"^[A-Za-z0-9_\-]+$")
class Paths:
"""
Centralized path configuration for DeerFlow application data.
Directory layout (host side):
{base_dir}/
├── memory.json
├── USER.md <-- global user profile (injected into all agents)
├── agents/
│ └── {agent_name}/
│ ├── config.yaml
│ ├── SOUL.md <-- agent personality/identity (injected alongside lead prompt)
│ └── memory.json
└── threads/
└── {thread_id}/
└── user-data/ <-- mounted as /mnt/user-data/ inside sandbox
├── workspace/ <-- /mnt/user-data/workspace/
├── uploads/ <-- /mnt/user-data/uploads/
└── outputs/ <-- /mnt/user-data/outputs/
BaseDir resolution (in priority order):
1. Constructor argument `base_dir`
2. DEER_FLOW_HOME environment variable
3. Local dev fallback: cwd/.deer-flow (when cwd is the backend/ dir)
4. Default: $HOME/.deer-flow
"""
def __init__(self, base_dir: str | Path | None = None) -> None:
self._base_dir = Path(base_dir).resolve() if base_dir is not None else None
@property
def host_base_dir(self) -> Path:
"""Host-visible base dir for Docker volume mount sources.
When running inside Docker with a mounted Docker socket (DooD), the Docker
daemon runs on the host and resolves mount paths against the host filesystem.
Set DEER_FLOW_HOST_BASE_DIR to the host-side path that corresponds to this
container's base_dir so that sandbox container volume mounts work correctly.
Falls back to base_dir when the env var is not set (native/local execution).
"""
if env := os.getenv("DEER_FLOW_HOST_BASE_DIR"):
return Path(env)
return self.base_dir
@property
def base_dir(self) -> Path:
"""Root directory for all application data."""
if self._base_dir is not None:
return self._base_dir
if env_home := os.getenv("DEER_FLOW_HOME"):
return Path(env_home).resolve()
cwd = Path.cwd()
if cwd.name == "backend" or (cwd / "pyproject.toml").exists():
return cwd / ".deer-flow"
return Path.home() / ".deer-flow"
@property
def memory_file(self) -> Path:
"""Path to the persisted memory file: `{base_dir}/memory.json`."""
return self.base_dir / "memory.json"
@property
def user_md_file(self) -> Path:
"""Path to the global user profile file: `{base_dir}/USER.md`."""
return self.base_dir / "USER.md"
@property
def agents_dir(self) -> Path:
"""Root directory for all custom agents: `{base_dir}/agents/`."""
return self.base_dir / "agents"
def agent_dir(self, name: str) -> Path:
"""Directory for a specific agent: `{base_dir}/agents/{name}/`."""
return self.agents_dir / name.lower()
def agent_memory_file(self, name: str) -> Path:
"""Per-agent memory file: `{base_dir}/agents/{name}/memory.json`."""
return self.agent_dir(name) / "memory.json"
def thread_dir(self, thread_id: str) -> Path:
"""
Host path for a thread's data: `{base_dir}/threads/{thread_id}/`
This directory contains a `user-data/` subdirectory that is mounted
as `/mnt/user-data/` inside the sandbox.
Raises:
ValueError: If `thread_id` contains unsafe characters (path separators
or `..`) that could cause directory traversal.
"""
if not _SAFE_THREAD_ID_RE.match(thread_id):
raise ValueError(f"Invalid thread_id {thread_id!r}: only alphanumeric characters, hyphens, and underscores are allowed.")
return self.base_dir / "threads" / thread_id
def sandbox_work_dir(self, thread_id: str) -> Path:
"""
Host path for the agent's workspace directory.
Host: `{base_dir}/threads/{thread_id}/user-data/workspace/`
Sandbox: `/mnt/user-data/workspace/`
"""
return self.thread_dir(thread_id) / "user-data" / "workspace"
def sandbox_uploads_dir(self, thread_id: str) -> Path:
"""
Host path for user-uploaded files.
Host: `{base_dir}/threads/{thread_id}/user-data/uploads/`
Sandbox: `/mnt/user-data/uploads/`
"""
return self.thread_dir(thread_id) / "user-data" / "uploads"
def sandbox_outputs_dir(self, thread_id: str) -> Path:
"""
Host path for agent-generated artifacts.
Host: `{base_dir}/threads/{thread_id}/user-data/outputs/`
Sandbox: `/mnt/user-data/outputs/`
"""
return self.thread_dir(thread_id) / "user-data" / "outputs"
def sandbox_user_data_dir(self, thread_id: str) -> Path:
"""
Host path for the user-data root.
Host: `{base_dir}/threads/{thread_id}/user-data/`
Sandbox: `/mnt/user-data/`
"""
return self.thread_dir(thread_id) / "user-data"
def ensure_thread_dirs(self, thread_id: str) -> None:
"""Create all standard sandbox directories for a thread.
Directories are created with mode 0o777 so that sandbox containers
(which may run as a different UID than the host backend process) can
write to the volume-mounted paths without "Permission denied" errors.
The explicit chmod() call is necessary because Path.mkdir(mode=...) is
subject to the process umask and may not yield the intended permissions.
"""
for d in [
self.sandbox_work_dir(thread_id),
self.sandbox_uploads_dir(thread_id),
self.sandbox_outputs_dir(thread_id),
]:
d.mkdir(parents=True, exist_ok=True)
d.chmod(0o777)
def resolve_virtual_path(self, thread_id: str, virtual_path: str) -> Path:
"""Resolve a sandbox virtual path to the actual host filesystem path.
Args:
thread_id: The thread ID.
virtual_path: Virtual path as seen inside the sandbox, e.g.
``/mnt/user-data/outputs/report.pdf``.
Leading slashes are stripped before matching.
Returns:
The resolved absolute host filesystem path.
Raises:
ValueError: If the path does not start with the expected virtual
prefix or a path-traversal attempt is detected.
"""
stripped = virtual_path.lstrip("/")
prefix = VIRTUAL_PATH_PREFIX.lstrip("/")
# Require an exact segment-boundary match to avoid prefix confusion
# (e.g. reject paths like "mnt/user-dataX/...").
if stripped != prefix and not stripped.startswith(prefix + "/"):
raise ValueError(f"Path must start with /{prefix}")
relative = stripped[len(prefix) :].lstrip("/")
base = self.sandbox_user_data_dir(thread_id).resolve()
actual = (base / relative).resolve()
try:
actual.relative_to(base)
except ValueError:
raise ValueError("Access denied: path traversal detected")
return actual
# ── Singleton ────────────────────────────────────────────────────────────
_paths: Paths | None = None
def get_paths() -> Paths:
"""Return the global Paths singleton (lazy-initialized)."""
global _paths
if _paths is None:
_paths = Paths()
return _paths
def resolve_path(path: str) -> Path:
"""Resolve *path* to an absolute ``Path``.
Relative paths are resolved relative to the application base directory.
Absolute paths are returned as-is (after normalisation).
"""
p = Path(path)
if not p.is_absolute():
p = get_paths().base_dir / path
return p.resolve()
================================================
FILE: backend/packages/harness/deerflow/config/sandbox_config.py
================================================
from pydantic import BaseModel, ConfigDict, Field
class VolumeMountConfig(BaseModel):
"""Configuration for a volume mount."""
host_path: str = Field(..., description="Path on the host machine")
container_path: str = Field(..., description="Path inside the container")
read_only: bool = Field(default=False, description="Whether the mount is read-only")
class SandboxConfig(BaseModel):
"""Config section for a sandbox.
Common options:
use: Class path of the sandbox provider (required)
AioSandboxProvider specific options:
image: Docker image to use (default: enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest)
port: Base port for sandbox containers (default: 8080)
replicas: Maximum number of concurrent sandbox containers (default: 3). When the limit is reached the least-recently-used sandbox is evicted to make room.
container_prefix: Prefix for container names (default: deer-flow-sandbox)
idle_timeout: Idle timeout in seconds before sandbox is released (default: 600 = 10 minutes). Set to 0 to disable.
mounts: List of volume mounts to share directories with the container
environment: Environment variables to inject into the container (values starting with $ are resolved from host env)
"""
use: str = Field(
...,
description="Class path of the sandbox provider (e.g. deerflow.sandbox.local:LocalSandboxProvider)",
)
image: str | None = Field(
default=None,
description="Docker image to use for the sandbox container",
)
port: int | None = Field(
default=None,
description="Base port for sandbox containers",
)
replicas: int | None = Field(
default=None,
description="Maximum number of concurrent sandbox containers (default: 3). When the limit is reached the least-recently-used sandbox is evicted to make room.",
)
container_prefix: str | None = Field(
default=None,
description="Prefix for container names",
)
idle_timeout: int | None = Field(
default=None,
description="Idle timeout in seconds before sandbox is released (default: 600 = 10 minutes). Set to 0 to disable.",
)
mounts: list[VolumeMountConfig] = Field(
default_factory=list,
description="List of volume mounts to share directories between host and container",
)
environment: dict[str, str] = Field(
default_factory=dict,
description="Environment variables to inject into the sandbox container. Values starting with $ will be resolved from host environment variables.",
)
model_config = ConfigDict(extra="allow")
================================================
FILE: backend/packages/harness/deerflow/config/skills_config.py
================================================
from pathlib import Path
from pydantic import BaseModel, Field
class SkillsConfig(BaseModel):
"""Configuration for skills system"""
path: str | None = Field(
default=None,
description="Path to skills directory. If not specified, defaults to ../skills relative to backend directory",
)
container_path: str = Field(
default="/mnt/skills",
description="Path where skills are mounted in the sandbox container",
)
def get_skills_path(self) -> Path:
"""
Get the resolved skills directory path.
Returns:
Path to the skills directory
"""
if self.path:
# Use configured path (can be absolute or relative)
path = Path(self.path)
if not path.is_absolute():
# If relative, resolve from current working directory
path = Path.cwd() / path
return path.resolve()
else:
# Default: ../skills relative to backend directory
from deerflow.skills.loader import get_skills_root_path
return get_skills_root_path()
def get_skill_container_path(self, skill_name: str, category: str = "public") -> str:
"""
Get the full container path for a specific skill.
Args:
skill_name: Name of the skill (directory name)
category: Category of the skill (public or custom)
Returns:
Full path to the skill in the container
"""
return f"{self.container_path}/{category}/{skill_name}"
================================================
FILE: backend/packages/harness/deerflow/config/subagents_config.py
================================================
"""Configuration for the subagent system loaded from config.yaml."""
import logging
from pydantic import BaseModel, Field
logger = logging.getLogger(__name__)
class SubagentOverrideConfig(BaseModel):
"""Per-agent configuration overrides."""
timeout_seconds: int | None = Field(
default=None,
ge=1,
description="Timeout in seconds for this subagent (None = use global default)",
)
class SubagentsAppConfig(BaseModel):
"""Configuration for the subagent system."""
timeout_seconds: int = Field(
default=900,
ge=1,
description="Default timeout in seconds for all subagents (default: 900 = 15 minutes)",
)
agents: dict[str, SubagentOverrideConfig] = Field(
default_factory=dict,
description="Per-agent configuration overrides keyed by agent name",
)
def get_timeout_for(self, agent_name: str) -> int:
"""Get the effective timeout for a specific agent.
Args:
agent_name: The name of the subagent.
Returns:
The timeout in seconds, using per-agent override if set, otherwise global default.
"""
override = self.agents.get(agent_name)
if override is not None and override.timeout_seconds is not None:
return override.timeout_seconds
return self.timeout_seconds
_subagents_config: SubagentsAppConfig = SubagentsAppConfig()
def get_subagents_app_config() -> SubagentsAppConfig:
"""Get the current subagents configuration."""
return _subagents_config
def load_subagents_config_from_dict(config_dict: dict) -> None:
"""Load subagents configuration from a dictionary."""
global _subagents_config
_subagents_config = SubagentsAppConfig(**config_dict)
overrides_summary = {name: f"{override.timeout_seconds}s" for name, override in _subagents_config.agents.items() if override.timeout_seconds is not None}
if overrides_summary:
logger.info(f"Subagents config loaded: default timeout={_subagents_config.timeout_seconds}s, per-agent overrides={overrides_summary}")
else:
logger.info(f"Subagents config loaded: default timeout={_subagents_config.timeout_seconds}s, no per-agent overrides")
================================================
FILE: backend/packages/harness/deerflow/config/summarization_config.py
================================================
"""Configuration for conversation summarization."""
from typing import Literal
from pydantic import BaseModel, Field
ContextSizeType = Literal["fraction", "tokens", "messages"]
class ContextSize(BaseModel):
"""Context size specification for trigger or keep parameters."""
type: ContextSizeType = Field(description="Type of context size specification")
value: int | float = Field(description="Value for the context size specification")
def to_tuple(self) -> tuple[ContextSizeType, int | float]:
"""Convert to tuple format expected by SummarizationMiddleware."""
return (self.type, self.value)
class SummarizationConfig(BaseModel):
"""Configuration for automatic conversation summarization."""
enabled: bool = Field(
default=False,
description="Whether to enable automatic conversation summarization",
)
model_name: str | None = Field(
default=None,
description="Model name to use for summarization (None = use a lightweight model)",
)
trigger: ContextSize | list[ContextSize] | None = Field(
default=None,
description="One or more thresholds that trigger summarization. When any threshold is met, summarization runs. "
"Examples: {'type': 'messages', 'value': 50} triggers at 50 messages, "
"{'type': 'tokens', 'value': 4000} triggers at 4000 tokens, "
"{'type': 'fraction', 'value': 0.8} triggers at 80% of model's max input tokens",
)
keep: ContextSize = Field(
default_factory=lambda: ContextSize(type="messages", value=20),
description="Context retention policy after summarization. Specifies how much history to preserve. "
"Examples: {'type': 'messages', 'value': 20} keeps 20 messages, "
"{'type': 'tokens', 'value': 3000} keeps 3000 tokens, "
"{'type': 'fraction', 'value': 0.3} keeps 30% of model's max input tokens",
)
trim_tokens_to_summarize: int | None = Field(
default=4000,
description="Maximum tokens to keep when preparing messages for summarization. Pass null to skip trimming.",
)
summary_prompt: str | None = Field(
default=None,
description="Custom prompt template for generating summaries. If not provided, uses the default LangChain prompt.",
)
# Global configuration instance
_summarization_config: SummarizationConfig = SummarizationConfig()
def get_summarization_config() -> SummarizationConfig:
"""Get the current summarization configuration."""
return _summarization_config
def set_summarization_config(config: SummarizationConfig) -> None:
"""Set the summarization configuration."""
global _summarization_config
_summarization_config = config
def load_summarization_config_from_dict(config_dict: dict) -> None:
"""Load summarization configuration from a dictionary."""
global _summarization_config
_summarization_config = SummarizationConfig(**config_dict)
================================================
FILE: backend/packages/harness/deerflow/config/title_config.py
================================================
"""Configuration for automatic thread title generation."""
from pydantic import BaseModel, Field
class TitleConfig(BaseModel):
"""Configuration for automatic thread title generation."""
enabled: bool = Field(
default=True,
description="Whether to enable automatic title generation",
)
max_words: int = Field(
default=6,
ge=1,
le=20,
description="Maximum number of words in the generated title",
)
max_chars: int = Field(
default=60,
ge=10,
le=200,
description="Maximum number of characters in the generated title",
)
model_name: str | None = Field(
default=None,
description="Model name to use for title generation (None = use default model)",
)
prompt_template: str = Field(
default=("Generate a concise title (max {max_words} words) for this conversation.\nUser: {user_msg}\nAssistant: {assistant_msg}\n\nReturn ONLY the title, no quotes, no explanation."),
description="Prompt template for title generation",
)
# Global configuration instance
_title_config: TitleConfig = TitleConfig()
def get_title_config() -> TitleConfig:
"""Get the current title configuration."""
return _title_config
def set_title_config(config: TitleConfig) -> None:
"""Set the title configuration."""
global _title_config
_title_config = config
def load_title_config_from_dict(config_dict: dict) -> None:
"""Load title configuration from a dictionary."""
global _title_config
_title_config = TitleConfig(**config_dict)
================================================
FILE: backend/packages/harness/deerflow/config/tool_config.py
================================================
from pydantic import BaseModel, ConfigDict, Field
class ToolGroupConfig(BaseModel):
"""Config section for a tool group"""
name: str = Field(..., description="Unique name for the tool group")
model_config = ConfigDict(extra="allow")
class ToolConfig(BaseModel):
"""Config section for a tool"""
name: str = Field(..., description="Unique name for the tool")
group: str = Field(..., description="Group name for the tool")
use: str = Field(
...,
description="Variable name of the tool provider(e.g. deerflow.sandbox.tools:bash_tool)",
)
model_config = ConfigDict(extra="allow")
================================================
FILE: backend/packages/harness/deerflow/config/tool_search_config.py
================================================
"""Configuration for deferred tool loading via tool_search."""
from pydantic import BaseModel, Field
class ToolSearchConfig(BaseModel):
"""Configuration for deferred tool loading via tool_search.
When enabled, MCP tools are not loaded into the agent's context directly.
Instead, they are listed by name in the system prompt and discoverable
via the tool_search tool at runtime.
"""
enabled: bool = Field(
default=False,
description="Defer tools and enable tool_search",
)
_tool_search_config: ToolSearchConfig | None = None
def get_tool_search_config() -> ToolSearchConfig:
"""Get the tool search config, loading from AppConfig if needed."""
global _tool_search_config
if _tool_search_config is None:
_tool_search_config = ToolSearchConfig()
return _tool_search_config
def load_tool_search_config_from_dict(data: dict) -> ToolSearchConfig:
"""Load tool search config from a dict (called during AppConfig loading)."""
global _tool_search_config
_tool_search_config = ToolSearchConfig.model_validate(data)
return _tool_search_config
================================================
FILE: backend/packages/harness/deerflow/config/tracing_config.py
================================================
import logging
import os
import threading
from pydantic import BaseModel, Field
logger = logging.getLogger(__name__)
_config_lock = threading.Lock()
class TracingConfig(BaseModel):
"""Configuration for LangSmith tracing."""
enabled: bool = Field(...)
api_key: str | None = Field(...)
project: str = Field(...)
endpoint: str = Field(...)
@property
def is_configured(self) -> bool:
"""Check if tracing is fully configured (enabled and has API key)."""
return self.enabled and bool(self.api_key)
_tracing_config: TracingConfig | None = None
_TRUTHY_VALUES = {"1", "true", "yes", "on"}
def _env_flag_preferred(*names: str) -> bool:
"""Return the boolean value of the first env var that is present and non-empty.
Accepted truthy values (case-insensitive): ``1``, ``true``, ``yes``, ``on``.
Any other non-empty value is treated as falsy. If none of the named
variables is set, returns ``False``.
"""
for name in names:
value = os.environ.get(name)
if value is not None and value.strip():
return value.strip().lower() in _TRUTHY_VALUES
return False
def _first_env_value(*names: str) -> str | None:
"""Return the first non-empty environment value from candidate names."""
for name in names:
value = os.environ.get(name)
if value and value.strip():
return value.strip()
return None
def get_tracing_config() -> TracingConfig:
"""Get the current tracing configuration from environment variables.
``LANGSMITH_*`` variables take precedence over their legacy ``LANGCHAIN_*``
counterparts. For boolean flags (``enabled``), the *first* variable that is
present and non-empty in the priority list is the sole authority – its value
is parsed and returned without consulting the remaining candidates. Accepted
truthy values are ``1``, ``true``, ``yes``, and ``on`` (case-insensitive);
any other non-empty value is treated as falsy.
Priority order:
enabled : LANGSMITH_TRACING > LANGCHAIN_TRACING_V2 > LANGCHAIN_TRACING
api_key : LANGSMITH_API_KEY > LANGCHAIN_API_KEY
project : LANGSMITH_PROJECT > LANGCHAIN_PROJECT (default: "deer-flow")
endpoint : LANGSMITH_ENDPOINT > LANGCHAIN_ENDPOINT (default: https://api.smith.langchain.com)
Returns:
TracingConfig with current settings.
"""
global _tracing_config
if _tracing_config is not None:
return _tracing_config
with _config_lock:
if _tracing_config is not None: # Double-check after acquiring lock
return _tracing_config
_tracing_config = TracingConfig(
# Keep compatibility with both legacy LANGCHAIN_* and newer LANGSMITH_* variables.
enabled=_env_flag_preferred("LANGSMITH_TRACING", "LANGCHAIN_TRACING_V2", "LANGCHAIN_TRACING"),
api_key=_first_env_value("LANGSMITH_API_KEY", "LANGCHAIN_API_KEY"),
project=_first_env_value("LANGSMITH_PROJECT", "LANGCHAIN_PROJECT") or "deer-flow",
endpoint=_first_env_value("LANGSMITH_ENDPOINT", "LANGCHAIN_ENDPOINT") or "https://api.smith.langchain.com",
)
return _tracing_config
def is_tracing_enabled() -> bool:
"""Check if LangSmith tracing is enabled and configured.
Returns:
True if tracing is enabled and has an API key.
"""
return get_tracing_config().is_configured
================================================
FILE: backend/packages/harness/deerflow/mcp/__init__.py
================================================
"""MCP (Model Context Protocol) integration using langchain-mcp-adapters."""
from .cache import get_cached_mcp_tools, initialize_mcp_tools, reset_mcp_tools_cache
from .client import build_server_params, build_servers_config
from .tools import get_mcp_tools
__all__ = [
"build_server_params",
"build_servers_config",
"get_mcp_tools",
"initialize_mcp_tools",
"get_cached_mcp_tools",
"reset_mcp_tools_cache",
]
================================================
FILE: backend/packages/harness/deerflow/mcp/cache.py
================================================
"""Cache for MCP tools to avoid repeated loading."""
import asyncio
import logging
import os
from langchain_core.tools import BaseTool
logger = logging.getLogger(__name__)
_mcp_tools_cache: list[BaseTool] | None = None
_cache_initialized = False
_initialization_lock = asyncio.Lock()
_config_mtime: float | None = None # Track config file modification time
def _get_config_mtime() -> float | None:
"""Get the modification time of the extensions config file.
Returns:
The modification time as a float, or None if the file doesn't exist.
"""
from deerflow.config.extensions_config import ExtensionsConfig
config_path = ExtensionsConfig.resolve_config_path()
if config_path and config_path.exists():
return os.path.getmtime(config_path)
return None
def _is_cache_stale() -> bool:
"""Check if the cache is stale due to config file changes.
Returns:
True if the cache should be invalidated, False otherwise.
"""
global _config_mtime
if not _cache_initialized:
return False # Not initialized yet, not stale
current_mtime = _get_config_mtime()
# If we couldn't get mtime before or now, assume not stale
if _config_mtime is None or current_mtime is None:
return False
# If the config file has been modified since we cached, it's stale
if current_mtime > _config_mtime:
logger.info(f"MCP config file has been modified (mtime: {_config_mtime} -> {current_mtime}), cache is stale")
return True
return False
async def initialize_mcp_tools() -> list[BaseTool]:
"""Initialize and cache MCP tools.
This should be called once at application startup.
Returns:
List of LangChain tools from all enabled MCP servers.
"""
global _mcp_tools_cache, _cache_initialized, _config_mtime
async with _initialization_lock:
if _cache_initialized:
logger.info("MCP tools already initialized")
return _mcp_tools_cache or []
from deerflow.mcp.tools import get_mcp_tools
logger.info("Initializing MCP tools...")
_mcp_tools_cache = await get_mcp_tools()
_cache_initialized = True
_config_mtime = _get_config_mtime() # Record config file mtime
logger.info(f"MCP tools initialized: {len(_mcp_tools_cache)} tool(s) loaded (config mtime: {_config_mtime})")
return _mcp_tools_cache
def get_cached_mcp_tools() -> list[BaseTool]:
"""Get cached MCP tools with lazy initialization.
If tools are not initialized, automatically initializes them.
This ensures MCP tools work in both FastAPI and LangGraph Studio contexts.
Also checks if the config file has been modified since last initialization,
and re-initializes if needed. This ensures that changes made through the
Gateway API (which runs in a separate process) are reflected in the
LangGraph Server.
Returns:
List of cached MCP tools.
"""
global _cache_initialized
# Check if cache is stale due to config file changes
if _is_cache_stale():
logger.info("MCP cache is stale, resetting for re-initialization...")
reset_mcp_tools_cache()
if not _cache_initialized:
logger.info("MCP tools not initialized, performing lazy initialization...")
try:
# Try to initialize in the current event loop
loop = asyncio.get_event_loop()
if loop.is_running():
# If loop is already running (e.g., in LangGraph Studio),
# we need to create a new loop in a thread
import concurrent.futures
with concurrent.futures.ThreadPoolExecutor() as executor:
future = executor.submit(asyncio.run, initialize_mcp_tools())
future.result()
else:
# If no loop is running, we can use the current loop
loop.run_until_complete(initialize_mcp_tools())
except RuntimeError:
# No event loop exists, create one
asyncio.run(initialize_mcp_tools())
except Exception as e:
logger.error(f"Failed to lazy-initialize MCP tools: {e}")
return []
return _mcp_tools_cache or []
def reset_mcp_tools_cache() -> None:
"""Reset the MCP tools cache.
This is useful for testing or when you want to reload MCP tools.
"""
global _mcp_tools_cache, _cache_initialized, _config_mtime
_mcp_tools_cache = None
_cache_initialized = False
_config_mtime = None
logger.info("MCP tools cache reset")
================================================
FILE: backend/packages/harness/deerflow/mcp/client.py
================================================
"""MCP client using langchain-mcp-adapters."""
import logging
from typing import Any
from deerflow.config.extensions_config import ExtensionsConfig, McpServerConfig
logger = logging.getLogger(__name__)
def build_server_params(server_name: str, config: McpServerConfig) -> dict[str, Any]:
"""Build server parameters for MultiServerMCPClient.
Args:
server_name: Name of the MCP server.
config: Configuration for the MCP server.
Returns:
Dictionary of server parameters for langchain-mcp-adapters.
"""
transport_type = config.type or "stdio"
params: dict[str, Any] = {"transport": transport_type}
if transport_type == "stdio":
if not config.command:
raise ValueError(f"MCP server '{server_name}' with stdio transport requires 'command' field")
params["command"] = config.command
params["args"] = config.args
# Add environment variables if present
if config.env:
params["env"] = config.env
elif transport_type in ("sse", "http"):
if not config.url:
raise ValueError(f"MCP server '{server_name}' with {transport_type} transport requires 'url' field")
params["url"] = config.url
# Add headers if present
if config.headers:
params["headers"] = config.headers
else:
raise ValueError(f"MCP server '{server_name}' has unsupported transport type: {transport_type}")
return params
def build_servers_config(extensions_config: ExtensionsConfig) -> dict[str, dict[str, Any]]:
"""Build servers configuration for MultiServerMCPClient.
Args:
extensions_config: Extensions configuration containing all MCP servers.
Returns:
Dictionary mapping server names to their parameters.
"""
enabled_servers = extensions_config.get_enabled_mcp_servers()
if not enabled_servers:
logger.info("No enabled MCP servers found")
return {}
servers_config = {}
for server_name, server_config in enabled_servers.items():
try:
servers_config[server_name] = build_server_params(server_name, server_config)
logger.info(f"Configured MCP server: {server_name}")
except Exception as e:
logger.error(f"Failed to configure MCP server '{server_name}': {e}")
return servers_config
================================================
FILE: backend/packages/harness/deerflow/mcp/oauth.py
================================================
"""OAuth token support for MCP HTTP/SSE servers."""
from __future__ import annotations
import asyncio
import logging
from dataclasses import dataclass
from datetime import UTC, datetime, timedelta
from typing import Any
from deerflow.config.extensions_config import ExtensionsConfig, McpOAuthConfig
logger = logging.getLogger(__name__)
@dataclass
class _OAuthToken:
"""Cached OAuth token."""
access_token: str
token_type: str
expires_at: datetime
class OAuthTokenManager:
"""Acquire/cache/refresh OAuth tokens for MCP servers."""
def __init__(self, oauth_by_server: dict[str, McpOAuthConfig]):
self._oauth_by_server = oauth_by_server
self._tokens: dict[str, _OAuthToken] = {}
self._locks: dict[str, asyncio.Lock] = {name: asyncio.Lock() for name in oauth_by_server}
@classmethod
def from_extensions_config(cls, extensions_config: ExtensionsConfig) -> OAuthTokenManager:
oauth_by_server: dict[str, McpOAuthConfig] = {}
for server_name, server_config in extensions_config.get_enabled_mcp_servers().items():
if server_config.oauth and server_config.oauth.enabled:
oauth_by_server[server_name] = server_config.oauth
return cls(oauth_by_server)
def has_oauth_servers(self) -> bool:
return bool(self._oauth_by_server)
def oauth_server_names(self) -> list[str]:
return list(self._oauth_by_server.keys())
async def get_authorization_header(self, server_name: str) -> str | None:
oauth = self._oauth_by_server.get(server_name)
if not oauth:
return None
token = self._tokens.get(server_name)
if token and not self._is_expiring(token, oauth):
return f"{token.token_type} {token.access_token}"
lock = self._locks[server_name]
async with lock:
token = self._tokens.get(server_name)
if token and not self._is_expiring(token, oauth):
return f"{token.token_type} {token.access_token}"
fresh = await self._fetch_token(oauth)
self._tokens[server_name] = fresh
logger.info(f"Refreshed OAuth access token for MCP server: {server_name}")
return f"{fresh.token_type} {fresh.access_token}"
@staticmethod
def _is_expiring(token: _OAuthToken, oauth: McpOAuthConfig) -> bool:
now = datetime.now(UTC)
return token.expires_at <= now + timedelta(seconds=max(oauth.refresh_skew_seconds, 0))
async def _fetch_token(self, oauth: McpOAuthConfig) -> _OAuthToken:
import httpx # pyright: ignore[reportMissingImports]
data: dict[str, str] = {
"grant_type": oauth.grant_type,
**oauth.extra_token_params,
}
if oauth.scope:
data["scope"] = oauth.scope
if oauth.audience:
data["audience"] = oauth.audience
if oauth.grant_type == "client_credentials":
if not oauth.client_id or not oauth.client_secret:
raise ValueError("OAuth client_credentials requires client_id and client_secret")
data["client_id"] = oauth.client_id
data["client_secret"] = oauth.client_secret
elif oauth.grant_type == "refresh_token":
if not oauth.refresh_token:
raise ValueError("OAuth refresh_token grant requires refresh_token")
data["refresh_token"] = oauth.refresh_token
if oauth.client_id:
data["client_id"] = oauth.client_id
if oauth.client_secret:
data["client_secret"] = oauth.client_secret
else:
raise ValueError(f"Unsupported OAuth grant type: {oauth.grant_type}")
async with httpx.AsyncClient(timeout=15.0) as client:
response = await client.post(oauth.token_url, data=data)
response.raise_for_status()
payload = response.json()
access_token = payload.get(oauth.token_field)
if not access_token:
raise ValueError(f"OAuth token response missing '{oauth.token_field}'")
token_type = str(payload.get(oauth.token_type_field, oauth.default_token_type) or oauth.default_token_type)
expires_in_raw = payload.get(oauth.expires_in_field, 3600)
try:
expires_in = int(expires_in_raw)
except (TypeError, ValueError):
expires_in = 3600
expires_at = datetime.now(UTC) + timedelta(seconds=max(expires_in, 1))
return _OAuthToken(access_token=access_token, token_type=token_type, expires_at=expires_at)
def build_oauth_tool_interceptor(extensions_config: ExtensionsConfig) -> Any | None:
"""Build a tool interceptor that injects OAuth Authorization headers."""
token_manager = OAuthTokenManager.from_extensions_config(extensions_config)
if not token_manager.has_oauth_servers():
return None
async def oauth_interceptor(request: Any, handler: Any) -> Any:
header = await token_manager.get_authorization_header(request.server_name)
if not header:
return await handler(request)
updated_headers = dict(request.headers or {})
updated_headers["Authorization"] = header
return await handler(request.override(headers=updated_headers))
return oauth_interceptor
async def get_initial_oauth_headers(extensions_config: ExtensionsConfig) -> dict[str, str]:
"""Get initial OAuth Authorization headers for MCP server connections."""
token_manager = OAuthTokenManager.from_extensions_config(extensions_config)
if not token_manager.has_oauth_servers():
return {}
headers: dict[str, str] = {}
for server_name in token_manager.oauth_server_names():
headers[server_name] = await token_manager.get_authorization_header(server_name) or ""
return {name: value for name, value in headers.items() if value}
================================================
FILE: backend/packages/harness/deerflow/mcp/tools.py
================================================
"""Load MCP tools using langchain-mcp-adapters."""
import logging
from langchain_core.tools import BaseTool
from deerflow.config.extensions_config import ExtensionsConfig
from deerflow.mcp.client import build_servers_config
from deerflow.mcp.oauth import build_oauth_tool_interceptor, get_initial_oauth_headers
logger = logging.getLogger(__name__)
async def get_mcp_tools() -> list[BaseTool]:
"""Get all tools from enabled MCP servers.
Returns:
List of LangChain tools from all enabled MCP servers.
"""
try:
from langchain_mcp_adapters.client import MultiServerMCPClient
except ImportError:
logger.warning("langchain-mcp-adapters not installed. Install it to enable MCP tools: pip install langchain-mcp-adapters")
return []
# NOTE: We use ExtensionsConfig.from_file() instead of get_extensions_config()
# to always read the latest configuration from disk. This ensures that changes
# made through the Gateway API (which runs in a separate process) are immediately
# reflected when initializing MCP tools.
extensions_config = ExtensionsConfig.from_file()
servers_config = build_servers_config(extensions_config)
if not servers_config:
logger.info("No enabled MCP servers configured")
return []
try:
# Create the multi-server MCP client
logger.info(f"Initializing MCP client with {len(servers_config)} server(s)")
# Inject initial OAuth headers for server connections (tool discovery/session init)
initial_oauth_headers = await get_initial_oauth_headers(extensions_config)
for server_name, auth_header in initial_oauth_headers.items():
if server_name not in servers_config:
continue
if servers_config[server_name].get("transport") in ("sse", "http"):
existing_headers = dict(servers_config[server_name].get("headers", {}))
existing_headers["Authorization"] = auth_header
servers_config[server_name]["headers"] = existing_headers
tool_interceptors = []
oauth_interceptor = build_oauth_tool_interceptor(extensions_config)
if oauth_interceptor is not None:
tool_interceptors.append(oauth_interceptor)
client = MultiServerMCPClient(servers_config, tool_interceptors=tool_interceptors, tool_name_prefix=True)
# Get all tools from all servers
tools = await client.get_tools()
logger.info(f"Successfully loaded {len(tools)} tool(s) from MCP servers")
return tools
except Exception as e:
logger.error(f"Failed to load MCP tools: {e}", exc_info=True)
return []
================================================
FILE: backend/packages/harness/deerflow/models/__init__.py
================================================
from .factory import create_chat_model
__all__ = ["create_chat_model"]
================================================
FILE: backend/packages/harness/deerflow/models/claude_provider.py
================================================
"""Custom Claude provider with OAuth Bearer auth, prompt caching, and smart thinking.
Supports two authentication modes:
1. Standard API key (x-api-key header) — default ChatAnthropic behavior
2. Claude Code OAuth token (Authorization: Bearer header)
- Detected by sk-ant-oat prefix
- Requires anthropic-beta: oauth-2025-04-20,claude-code-20250219
Auto-loads credentials from explicit runtime handoff:
- $ANTHROPIC_API_KEY environment variable
- $CLAUDE_CODE_OAUTH_TOKEN or $ANTHROPIC_AUTH_TOKEN
- $CLAUDE_CODE_OAUTH_TOKEN_FILE_DESCRIPTOR
- $CLAUDE_CODE_CREDENTIALS_PATH
- ~/.claude/.credentials.json
"""
import logging
import time
from typing import Any
import anthropic
from langchain_anthropic import ChatAnthropic
from langchain_core.messages import BaseMessage
logger = logging.getLogger(__name__)
MAX_RETRIES = 3
THINKING_BUDGET_RATIO = 0.8
class ClaudeChatModel(ChatAnthropic):
"""ChatAnthropic with OAuth Bearer auth, prompt caching, and smart thinking.
Config example:
- name: claude-sonnet-4.6
use: deerflow.models.claude_provider:ClaudeChatModel
model: claude-sonnet-4-6
max_tokens: 16384
enable_prompt_caching: true
"""
# Custom fields
enable_prompt_caching: bool = True
prompt_cache_size: int = 3
auto_thinking_budget: bool = True
retry_max_attempts: int = MAX_RETRIES
_is_oauth: bool = False
_oauth_access_token: str = ""
model_config = {"arbitrary_types_allowed": True}
def _validate_retry_config(self) -> None:
if self.retry_max_attempts < 1:
raise ValueError("retry_max_attempts must be >= 1")
def model_post_init(self, __context: Any) -> None:
"""Auto-load credentials and configure OAuth if needed."""
from pydantic import SecretStr
from deerflow.models.credential_loader import (
OAUTH_ANTHROPIC_BETAS,
is_oauth_token,
load_claude_code_credential,
)
self._validate_retry_config()
# Extract actual key value (SecretStr.str() returns '**********')
current_key = ""
if self.anthropic_api_key:
if hasattr(self.anthropic_api_key, "get_secret_value"):
current_key = self.anthropic_api_key.get_secret_value()
else:
current_key = str(self.anthropic_api_key)
# Try the explicit Claude Code OAuth handoff sources if no valid key.
if not current_key or current_key in ("your-anthropic-api-key",):
cred = load_claude_code_credential()
if cred:
current_key = cred.access_token
logger.info(f"Using Claude Code CLI credential (source: {cred.source})")
else:
logger.warning("No Anthropic API key or explicit Claude Code OAuth credential found.")
# Detect OAuth token and configure Bearer auth
if is_oauth_token(current_key):
self._is_oauth = True
self._oauth_access_token = current_key
# Set the token as api_key temporarily (will be swapped to auth_token on client)
self.anthropic_api_key = SecretStr(current_key)
# Add required beta headers for OAuth
self.default_headers = {
**(self.default_headers or {}),
"anthropic-beta": OAUTH_ANTHROPIC_BETAS,
}
# OAuth tokens have a limit of 4 cache_control blocks — disable prompt caching
self.enable_prompt_caching = False
logger.info("OAuth token detected — will use Authorization: Bearer header")
else:
if current_key:
self.anthropic_api_key = SecretStr(current_key)
# Ensure api_key is SecretStr
if isinstance(self.anthropic_api_key, str):
self.anthropic_api_key = SecretStr(self.anthropic_api_key)
super().model_post_init(__context)
# Patch clients immediately after creation for OAuth Bearer auth.
# This must happen after super() because clients are lazily created.
if self._is_oauth:
self._patch_client_oauth(self._client)
self._patch_client_oauth(self._async_client)
def _patch_client_oauth(self, client: Any) -> None:
"""Swap api_key → auth_token on an Anthropic SDK client for OAuth Bearer auth."""
if hasattr(client, "api_key") and hasattr(client, "auth_token"):
client.api_key = None
client.auth_token = self._oauth_access_token
def _get_request_payload(
self,
input_: Any,
*,
stop: list[str] | None = None,
**kwargs: Any,
) -> dict:
"""Override to inject prompt caching and thinking budget."""
payload = super()._get_request_payload(input_, stop=stop, **kwargs)
if self.enable_prompt_caching:
self._apply_prompt_caching(payload)
if self.auto_thinking_budget:
self._apply_thinking_budget(payload)
return payload
def _apply_prompt_caching(self, payload: dict) -> None:
"""Apply ephemeral cache_control to system and recent messages."""
# Cache system messages
system = payload.get("system")
if system and isinstance(system, list):
for block in system:
if isinstance(block, dict) and block.get("type") == "text":
block["cache_control"] = {"type": "ephemeral"}
elif system and isinstance(system, str):
payload["system"] = [
{
"type": "text",
"text": system,
"cache_control": {"type": "ephemeral"},
}
]
# Cache recent messages
messages = payload.get("messages", [])
cache_start = max(0, len(messages) - self.prompt_cache_size)
for i in range(cache_start, len(messages)):
msg = messages[i]
if not isinstance(msg, dict):
continue
content = msg.get("content")
if isinstance(content, list):
for block in content:
if isinstance(block, dict):
block["cache_control"] = {"type": "ephemeral"}
elif isinstance(content, str) and content:
msg["content"] = [
{
"type": "text",
"text": content,
"cache_control": {"type": "ephemeral"},
}
]
# Cache the last tool definition
tools = payload.get("tools", [])
if tools and isinstance(tools[-1], dict):
tools[-1]["cache_control"] = {"type": "ephemeral"}
def _apply_thinking_budget(self, payload: dict) -> None:
"""Auto-allocate thinking budget (80% of max_tokens)."""
thinking = payload.get("thinking")
if not thinking or not isinstance(thinking, dict):
return
if thinking.get("type") != "enabled":
return
if thinking.get("budget_tokens"):
return
max_tokens = payload.get("max_tokens", 8192)
thinking["budget_tokens"] = int(max_tokens * THINKING_BUDGET_RATIO)
def _generate(self, messages: list[BaseMessage], stop: list[str] | None = None, **kwargs: Any) -> Any:
"""Override with OAuth patching and retry logic."""
if self._is_oauth:
self._patch_client_oauth(self._client)
last_error = None
for attempt in range(1, self.retry_max_attempts + 1):
try:
return super()._generate(messages, stop=stop, **kwargs)
except anthropic.RateLimitError as e:
last_error = e
if attempt >= self.retry_max_attempts:
raise
wait_ms = self._calc_backoff_ms(attempt, e)
logger.warning(f"Rate limited, retrying attempt {attempt}/{self.retry_max_attempts} after {wait_ms}ms")
time.sleep(wait_ms / 1000)
except anthropic.InternalServerError as e:
last_error = e
if attempt >= self.retry_max_attempts:
raise
wait_ms = self._calc_backoff_ms(attempt, e)
logger.warning(f"Server error, retrying attempt {attempt}/{self.retry_max_attempts} after {wait_ms}ms")
time.sleep(wait_ms / 1000)
raise last_error
async def _agenerate(self, messages: list[BaseMessage], stop: list[str] | None = None, **kwargs: Any) -> Any:
"""Async override with OAuth patching and retry logic."""
import asyncio
if self._is_oauth:
self._patch_client_oauth(self._async_client)
last_error = None
for attempt in range(1, self.retry_max_attempts + 1):
try:
return await super()._agenerate(messages, stop=stop, **kwargs)
except anthropic.RateLimitError as e:
last_error = e
if attempt >= self.retry_max_attempts:
raise
wait_ms = self._calc_backoff_ms(attempt, e)
logger.warning(f"Rate limited, retrying attempt {attempt}/{self.retry_max_attempts} after {wait_ms}ms")
await asyncio.sleep(wait_ms / 1000)
except anthropic.InternalServerError as e:
last_error = e
if attempt >= self.retry_max_attempts:
raise
wait_ms = self._calc_backoff_ms(attempt, e)
logger.warning(f"Server error, retrying attempt {attempt}/{self.retry_max_attempts} after {wait_ms}ms")
await asyncio.sleep(wait_ms / 1000)
raise last_error
@staticmethod
def _calc_backoff_ms(attempt: int, error: Exception) -> int:
"""Exponential backoff with a fixed 20% buffer."""
backoff_ms = 2000 * (1 << (attempt - 1))
jitter_ms = int(backoff_ms * 0.2)
total_ms = backoff_ms + jitter_ms
if hasattr(error, "response") and error.response is not None:
retry_after = error.response.headers.get("Retry-After")
if retry_after:
try:
total_ms = int(retry_after) * 1000
except (ValueError, TypeError):
pass
return total_ms
================================================
FILE: backend/packages/harness/deerflow/models/credential_loader.py
================================================
"""Auto-load credentials from Claude Code CLI and Codex CLI.
Implements two credential strategies:
1. Claude Code OAuth token from explicit env vars or an exported credentials file
- Uses Authorization: Bearer header (NOT x-api-key)
- Requires anthropic-beta: oauth-2025-04-20,claude-code-20250219
- Supports $CLAUDE_CODE_OAUTH_TOKEN, $CLAUDE_CODE_OAUTH_TOKEN_FILE_DESCRIPTOR, and $ANTHROPIC_AUTH_TOKEN
- Override path with $CLAUDE_CODE_CREDENTIALS_PATH
2. Codex CLI token from ~/.codex/auth.json
- Uses chatgpt.com/backend-api/codex/responses endpoint
- Supports both legacy top-level tokens and current nested tokens shape
- Override path with $CODEX_AUTH_PATH
"""
import json
import logging
import os
import time
from dataclasses import dataclass
from pathlib import Path
from typing import Any
logger = logging.getLogger(__name__)
# Required beta headers for Claude Code OAuth tokens
OAUTH_ANTHROPIC_BETAS = "oauth-2025-04-20,claude-code-20250219,interleaved-thinking-2025-05-14"
def is_oauth_token(token: str) -> bool:
"""Check if a token is a Claude Code OAuth token (not a standard API key)."""
return isinstance(token, str) and "sk-ant-oat" in token
@dataclass
class ClaudeCodeCredential:
"""Claude Code CLI OAuth credential."""
access_token: str
refresh_token: str = ""
expires_at: int = 0
source: str = ""
@property
def is_expired(self) -> bool:
if self.expires_at <= 0:
return False
return time.time() * 1000 > self.expires_at - 60_000 # 1 min buffer
@dataclass
class CodexCliCredential:
"""Codex CLI credential."""
access_token: str
account_id: str = ""
source: str = ""
def _resolve_credential_path(env_var: str, default_relative_path: str) -> Path:
configured_path = os.getenv(env_var)
if configured_path:
return Path(configured_path).expanduser()
return Path.home() / default_relative_path
def _load_json_file(path: Path, label: str) -> dict[str, Any] | None:
if not path.exists():
logger.debug(f"{label} not found: {path}")
return None
if path.is_dir():
logger.warning(f"{label} path is a directory, expected a file: {path}")
return None
try:
return json.loads(path.read_text())
except (json.JSONDecodeError, OSError) as e:
logger.warning(f"Failed to read {label}: {e}")
return None
def _read_secret_from_file_descriptor(env_var: str) -> str | None:
fd_value = os.getenv(env_var)
if not fd_value:
return None
try:
fd = int(fd_value)
except ValueError:
logger.warning(f"{env_var} must be an integer file descriptor, got: {fd_value}")
return None
try:
secret = Path(f"/dev/fd/{fd}").read_text().strip()
except OSError as e:
logger.warning(f"Failed to read {env_var}: {e}")
return None
return secret or None
def _credential_from_direct_token(access_token: str, source: str) -> ClaudeCodeCredential | None:
token = access_token.strip()
if not token:
return None
return ClaudeCodeCredential(access_token=token, source=source)
def _iter_claude_code_credential_paths() -> list[Path]:
paths: list[Path] = []
override_path = os.getenv("CLAUDE_CODE_CREDENTIALS_PATH")
if override_path:
paths.append(Path(override_path).expanduser())
default_path = Path.home() / ".claude/.credentials.json"
if not paths or paths[-1] != default_path:
paths.append(default_path)
return paths
def _extract_claude_code_credential(data: dict[str, Any], source: str) -> ClaudeCodeCredential | None:
oauth = data.get("claudeAiOauth", {})
access_token = oauth.get("accessToken", "")
if not access_token:
logger.debug("Claude Code credentials container exists but no accessToken found")
return None
cred = ClaudeCodeCredential(
access_token=access_token,
refresh_token=oauth.get("refreshToken", ""),
expires_at=oauth.get("expiresAt", 0),
source=source,
)
if cred.is_expired:
logger.warning("Claude Code OAuth token is expired. Run 'claude' to refresh.")
return None
return cred
def load_claude_code_credential() -> ClaudeCodeCredential | None:
"""Load OAuth credential from explicit Claude Code handoff sources.
Lookup order:
1. $CLAUDE_CODE_OAUTH_TOKEN or $ANTHROPIC_AUTH_TOKEN
2. $CLAUDE_CODE_OAUTH_TOKEN_FILE_DESCRIPTOR
3. $CLAUDE_CODE_CREDENTIALS_PATH
4. ~/.claude/.credentials.json
Exported credentials files contain:
{
"claudeAiOauth": {
"accessToken": "sk-ant-oat01-...",
"refreshToken": "sk-ant-ort01-...",
"expiresAt": 1773430695128,
"scopes": ["user:inference", ...],
...
}
}
"""
direct_token = os.getenv("CLAUDE_CODE_OAUTH_TOKEN") or os.getenv("ANTHROPIC_AUTH_TOKEN")
if direct_token:
cred = _credential_from_direct_token(direct_token, "claude-cli-env")
if cred:
logger.info("Loaded Claude Code OAuth credential from environment")
return cred
fd_token = _read_secret_from_file_descriptor("CLAUDE_CODE_OAUTH_TOKEN_FILE_DESCRIPTOR")
if fd_token:
cred = _credential_from_direct_token(fd_token, "claude-cli-fd")
if cred:
logger.info("Loaded Claude Code OAuth credential from file descriptor")
return cred
override_path = os.getenv("CLAUDE_CODE_CREDENTIALS_PATH")
override_path_obj = Path(override_path).expanduser() if override_path else None
for cred_path in _iter_claude_code_credential_paths():
data = _load_json_file(cred_path, "Claude Code credentials")
if data is None:
continue
cred = _extract_claude_code_credential(data, "claude-cli-file")
if cred:
source_label = "override path" if override_path_obj is not None and cred_path == override_path_obj else "plaintext file"
logger.info(f"Loaded Claude Code OAuth credential from {source_label} (expires_at={cred.expires_at})")
return cred
return None
def load_codex_cli_credential() -> CodexCliCredential | None:
"""Load credential from Codex CLI (~/.codex/auth.json)."""
cred_path = _resolve_credential_path("CODEX_AUTH_PATH", ".codex/auth.json")
data = _load_json_file(cred_path, "Codex CLI credentials")
if data is None:
return None
tokens = data.get("tokens", {})
if not isinstance(tokens, dict):
tokens = {}
access_token = data.get("access_token") or data.get("token") or tokens.get("access_token", "")
account_id = data.get("account_id") or tokens.get("account_id", "")
if not access_token:
logger.debug("Codex CLI credentials file exists but no token found")
return None
logger.info("Loaded Codex CLI credential")
return CodexCliCredential(
access_token=access_token,
account_id=account_id,
source="codex-cli",
)
================================================
FILE: backend/packages/harness/deerflow/models/factory.py
================================================
import logging
from langchain.chat_models import BaseChatModel
from deerflow.config import get_app_config, get_tracing_config, is_tracing_enabled
from deerflow.reflection import resolve_class
logger = logging.getLogger(__name__)
def create_chat_model(name: str | None = None, thinking_enabled: bool = False, **kwargs) -> BaseChatModel:
"""Create a chat model instance from the config.
Args:
name: The name of the model to create. If None, the first model in the config will be used.
Returns:
A chat model instance.
"""
config = get_app_config()
if name is None:
name = config.models[0].name
model_config = config.get_model_config(name)
if model_config is None:
raise ValueError(f"Model {name} not found in config") from None
model_class = resolve_class(model_config.use, BaseChatModel)
model_settings_from_config = model_config.model_dump(
exclude_none=True,
exclude={
"use",
"name",
"display_name",
"description",
"supports_thinking",
"supports_reasoning_effort",
"when_thinking_enabled",
"thinking",
"supports_vision",
},
)
# Compute effective when_thinking_enabled by merging in the `thinking` shortcut field.
# The `thinking` shortcut is equivalent to setting when_thinking_enabled["thinking"].
has_thinking_settings = (model_config.when_thinking_enabled is not None) or (model_config.thinking is not None)
effective_wte: dict = dict(model_config.when_thinking_enabled) if model_config.when_thinking_enabled else {}
if model_config.thinking is not None:
merged_thinking = {**(effective_wte.get("thinking") or {}), **model_config.thinking}
effective_wte = {**effective_wte, "thinking": merged_thinking}
if thinking_enabled and has_thinking_settings:
if not model_config.supports_thinking:
raise ValueError(f"Model {name} does not support thinking. Set `supports_thinking` to true in the `config.yaml` to enable thinking.") from None
if effective_wte:
model_settings_from_config.update(effective_wte)
if not thinking_enabled and has_thinking_settings:
if effective_wte.get("extra_body", {}).get("thinking", {}).get("type"):
# OpenAI-compatible gateway: thinking is nested under extra_body
kwargs.update({"extra_body": {"thinking": {"type": "disabled"}}})
kwargs.update({"reasoning_effort": "minimal"})
elif effective_wte.get("thinking", {}).get("type"):
# Native langchain_anthropic: thinking is a direct constructor parameter
kwargs.update({"thinking": {"type": "disabled"}})
if not model_config.supports_reasoning_effort and "reasoning_effort" in kwargs:
del kwargs["reasoning_effort"]
# For Codex Responses API models: map thinking mode to reasoning_effort
from deerflow.models.openai_codex_provider import CodexChatModel
if issubclass(model_class, CodexChatModel):
# The ChatGPT Codex endpoint currently rejects max_tokens/max_output_tokens.
model_settings_from_config.pop("max_tokens", None)
# Use explicit reasoning_effort from frontend if provided (low/medium/high)
explicit_effort = kwargs.pop("reasoning_effort", None)
if not thinking_enabled:
model_settings_from_config["reasoning_effort"] = "none"
elif explicit_effort and explicit_effort in ("low", "medium", "high", "xhigh"):
model_settings_from_config["reasoning_effort"] = explicit_effort
elif "reasoning_effort" not in model_settings_from_config:
model_settings_from_config["reasoning_effort"] = "medium"
model_instance = model_class(**kwargs, **model_settings_from_config)
if is_tracing_enabled():
try:
from langchain_core.tracers.langchain import LangChainTracer
tracing_config = get_tracing_config()
tracer = LangChainTracer(
project_name=tracing_config.project,
)
existing_callbacks = model_instance.callbacks or []
model_instance.callbacks = [*existing_callbacks, tracer]
logger.debug(f"LangSmith tracing attached to model '{name}' (project='{tracing_config.project}')")
except Exception as e:
logger.warning(f"Failed to attach LangSmith tracing to model '{name}': {e}")
return model_instance
================================================
FILE: backend/packages/harness/deerflow/models/openai_codex_provider.py
================================================
"""Custom OpenAI Codex provider using ChatGPT Codex Responses API.
Uses Codex CLI OAuth tokens with chatgpt.com/backend-api/codex/responses endpoint.
This is the same endpoint that the Codex CLI uses internally.
Supports:
- Auto-load credentials from ~/.codex/auth.json
- Responses API format (not Chat Completions)
- Tool calling
- Streaming (required by the endpoint)
- Retry with exponential backoff
"""
import json
import logging
import time
from typing import Any
import httpx
from langchain_core.callbacks import CallbackManagerForLLMRun
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage, ToolMessage
from langchain_core.outputs import ChatGeneration, ChatResult
from deerflow.models.credential_loader import CodexCliCredential, load_codex_cli_credential
logger = logging.getLogger(__name__)
CODEX_BASE_URL = "https://chatgpt.com/backend-api/codex"
MAX_RETRIES = 3
class CodexChatModel(BaseChatModel):
"""LangChain chat model using ChatGPT Codex Responses API.
Config example:
- name: gpt-5.4
use: deerflow.models.openai_codex_provider:CodexChatModel
model: gpt-5.4
reasoning_effort: medium
"""
model: str = "gpt-5.4"
reasoning_effort: str = "medium"
retry_max_attempts: int = MAX_RETRIES
_access_token: str = ""
_account_id: str = ""
model_config = {"arbitrary_types_allowed": True}
@property
def _llm_type(self) -> str:
return "codex-responses"
def _validate_retry_config(self) -> None:
if self.retry_max_attempts < 1:
raise ValueError("retry_max_attempts must be >= 1")
def model_post_init(self, __context: Any) -> None:
"""Auto-load Codex CLI credentials."""
self._validate_retry_config()
cred = self._load_codex_auth()
if cred:
self._access_token = cred.access_token
self._account_id = cred.account_id
logger.info(f"Using Codex CLI credential (account: {self._account_id[:8]}...)")
else:
raise ValueError("Codex CLI credential not found. Expected ~/.codex/auth.json or CODEX_AUTH_PATH.")
super().model_post_init(__context)
def _load_codex_auth(self) -> CodexCliCredential | None:
"""Load access_token and account_id from Codex CLI auth."""
return load_codex_cli_credential()
@classmethod
def _normalize_content(cls, content: Any) -> str:
"""Flatten LangChain content blocks into plain text for Codex."""
if isinstance(content, str):
return content
if isinstance(content, list):
parts = [cls._normalize_content(item) for item in content]
return "\n".join(part for part in parts if part)
if isinstance(content, dict):
for key in ("text", "output"):
value = content.get(key)
if isinstance(value, str):
return value
nested_content = content.get("content")
if nested_content is not None:
return cls._normalize_content(nested_content)
try:
return json.dumps(content, ensure_ascii=False)
except TypeError:
return str(content)
try:
return json.dumps(content, ensure_ascii=False)
except TypeError:
return str(content)
def _convert_messages(self, messages: list[BaseMessage]) -> tuple[str, list[dict]]:
"""Convert LangChain messages to Responses API format.
Returns (instructions, input_items).
"""
instructions_parts: list[str] = []
input_items = []
for msg in messages:
if isinstance(msg, SystemMessage):
content = self._normalize_content(msg.content)
if content:
instructions_parts.append(content)
elif isinstance(msg, HumanMessage):
content = self._normalize_content(msg.content)
input_items.append({"role": "user", "content": content})
elif isinstance(msg, AIMessage):
if msg.content:
content = self._normalize_content(msg.content)
input_items.append({"role": "assistant", "content": content})
if msg.tool_calls:
for tc in msg.tool_calls:
input_items.append(
{
"type": "function_call",
"name": tc["name"],
"arguments": json.dumps(tc["args"]) if isinstance(tc["args"], dict) else tc["args"],
"call_id": tc["id"],
}
)
elif isinstance(msg, ToolMessage):
input_items.append(
{
"type": "function_call_output",
"call_id": msg.tool_call_id,
"output": self._normalize_content(msg.content),
}
)
instructions = "\n\n".join(instructions_parts) or "You are a helpful assistant."
return instructions, input_items
def _convert_tools(self, tools: list[dict]) -> list[dict]:
"""Convert LangChain tool format to Responses API format."""
responses_tools = []
for tool in tools:
if tool.get("type") == "function" and "function" in tool:
fn = tool["function"]
responses_tools.append(
{
"type": "function",
"name": fn["name"],
"description": fn.get("description", ""),
"parameters": fn.get("parameters", {}),
}
)
elif "name" in tool:
responses_tools.append(
{
"type": "function",
"name": tool["name"],
"description": tool.get("description", ""),
"parameters": tool.get("parameters", {}),
}
)
return responses_tools
def _call_codex_api(self, messages: list[BaseMessage], tools: list[dict] | None = None) -> dict:
"""Call the Codex Responses API and return the completed response."""
instructions, input_items = self._convert_messages(messages)
payload = {
"model": self.model,
"instructions": instructions,
"input": input_items,
"store": False,
"stream": True,
"reasoning": {"effort": self.reasoning_effort, "summary": "detailed"} if self.reasoning_effort != "none" else {"effort": "none"},
}
if tools:
payload["tools"] = self._convert_tools(tools)
headers = {
"Authorization": f"Bearer {self._access_token}",
"ChatGPT-Account-ID": self._account_id,
"Content-Type": "application/json",
"Accept": "text/event-stream",
"originator": "codex_cli_rs",
}
last_error = None
for attempt in range(1, self.retry_max_attempts + 1):
try:
return self._stream_response(headers, payload)
except httpx.HTTPStatusError as e:
last_error = e
if e.response.status_code in (429, 500, 529):
if attempt >= self.retry_max_attempts:
raise
wait_ms = 2000 * (1 << (attempt - 1))
logger.warning(f"Codex API error {e.response.status_code}, retrying {attempt}/{self.retry_max_attempts} after {wait_ms}ms")
time.sleep(wait_ms / 1000)
else:
raise
except Exception:
raise
raise last_error
def _stream_response(self, headers: dict, payload: dict) -> dict:
"""Stream SSE from Codex API and collect the final response."""
completed_response = None
with httpx.Client(timeout=300) as client:
with client.stream("POST", f"{CODEX_BASE_URL}/responses", headers=headers, json=payload) as resp:
resp.raise_for_status()
for line in resp.iter_lines():
data = self._parse_sse_data_line(line)
if data and data.get("type") == "response.completed":
completed_response = data["response"]
if not completed_response:
raise RuntimeError("Codex API stream ended without response.completed event")
return completed_response
@staticmethod
def _parse_sse_data_line(line: str) -> dict[str, Any] | None:
"""Parse a data line from the SSE stream, skipping terminal markers."""
if not line.startswith("data:"):
return None
raw_data = line[5:].strip()
if not raw_data or raw_data == "[DONE]":
return None
try:
data = json.loads(raw_data)
except json.JSONDecodeError:
logger.debug(f"Skipping non-JSON Codex SSE frame: {raw_data}")
return None
return data if isinstance(data, dict) else None
def _parse_tool_call_arguments(self, output_item: dict[str, Any]) -> tuple[dict[str, Any] | None, dict[str, Any] | None]:
"""Parse function-call arguments, surfacing malformed payloads safely."""
raw_arguments = output_item.get("arguments", "{}")
if isinstance(raw_arguments, dict):
return raw_arguments, None
normalized_arguments = raw_arguments or "{}"
try:
parsed_arguments = json.loads(normalized_arguments)
except (TypeError, json.JSONDecodeError) as exc:
return None, {
"type": "invalid_tool_call",
"name": output_item.get("name"),
"args": str(raw_arguments),
"id": output_item.get("call_id"),
"error": f"Failed to parse tool arguments: {exc}",
}
if not isinstance(parsed_arguments, dict):
return None, {
"type": "invalid_tool_call",
"name": output_item.get("name"),
"args": str(raw_arguments),
"id": output_item.get("call_id"),
"error": "Tool arguments must decode to a JSON object.",
}
return parsed_arguments, None
def _parse_response(self, response: dict) -> ChatResult:
"""Parse Codex Responses API response into LangChain ChatResult."""
content = ""
tool_calls = []
invalid_tool_calls = []
reasoning_content = ""
for output_item in response.get("output", []):
if output_item.get("type") == "reasoning":
# Extract reasoning summary text
for summary_item in output_item.get("summary", []):
if isinstance(summary_item, dict) and summary_item.get("type") == "summary_text":
reasoning_content += summary_item.get("text", "")
elif isinstance(summary_item, str):
reasoning_content += summary_item
elif output_item.get("type") == "message":
for part in output_item.get("content", []):
if part.get("type") == "output_text":
content += part.get("text", "")
elif output_item.get("type") == "function_call":
parsed_arguments, invalid_tool_call = self._parse_tool_call_arguments(output_item)
if invalid_tool_call:
invalid_tool_calls.append(invalid_tool_call)
continue
tool_calls.append(
{
"name": output_item["name"],
"args": parsed_arguments or {},
"id": output_item.get("call_id", ""),
"type": "tool_call",
}
)
usage = response.get("usage", {})
additional_kwargs = {}
if reasoning_content:
additional_kwargs["reasoning_content"] = reasoning_content
message = AIMessage(
content=content,
tool_calls=tool_calls if tool_calls else [],
invalid_tool_calls=invalid_tool_calls,
additional_kwargs=additional_kwargs,
response_metadata={
"model": response.get("model", self.model),
"usage": usage,
},
)
return ChatResult(
generations=[ChatGeneration(message=message)],
llm_output={
"token_usage": {
"prompt_tokens": usage.get("input_tokens", 0),
"completion_tokens": usage.get("output_tokens", 0),
"total_tokens": usage.get("total_tokens", 0),
},
"model_name": response.get("model", self.model),
},
)
def _generate(
self,
messages: list[BaseMessage],
stop: list[str] | None = None,
run_manager: CallbackManagerForLLMRun | None = None,
**kwargs: Any,
) -> ChatResult:
"""Generate a response using Codex Responses API."""
tools = kwargs.get("tools", None)
response = self._call_codex_api(messages, tools=tools)
return self._parse_response(response)
def bind_tools(self, tools: list, **kwargs: Any) -> Any:
"""Bind tools for function calling."""
from langchain_core.runnables import RunnableBinding
from langchain_core.tools import BaseTool
from langchain_core.utils.function_calling import convert_to_openai_function
formatted_tools = []
for tool in tools:
if isinstance(tool, BaseTool):
try:
fn = convert_to_openai_function(tool)
formatted_tools.append(
{
"type": "function",
"name": fn["name"],
"description": fn.get("description", ""),
"parameters": fn.get("parameters", {}),
}
)
except Exception:
formatted_tools.append(
{
"type": "function",
"name": tool.name,
"description": tool.description,
"parameters": {"type": "object", "properties": {}},
}
)
elif isinstance(tool, dict):
if "function" in tool:
fn = tool["function"]
formatted_tools.append(
{
"type": "function",
"name": fn["name"],
"description": fn.get("description", ""),
"parameters": fn.get("parameters", {}),
}
)
else:
formatted_tools.append(tool)
return RunnableBinding(bound=self, kwargs={"tools": formatted_tools}, **kwargs)
================================================
FILE: backend/packages/harness/deerflow/models/patched_deepseek.py
================================================
"""Patched ChatDeepSeek that preserves reasoning_content in multi-turn conversations.
This module provides a patched version of ChatDeepSeek that properly handles
reasoning_content when sending messages back to the API. The original implementation
stores reasoning_content in additional_kwargs but doesn't include it when making
subsequent API calls, which causes errors with APIs that require reasoning_content
on all assistant messages when thinking mode is enabled.
"""
from typing import Any
from langchain_core.language_models import LanguageModelInput
from langchain_core.messages import AIMessage
from langchain_deepseek import ChatDeepSeek
class PatchedChatDeepSeek(ChatDeepSeek):
"""ChatDeepSeek with proper reasoning_content preservation.
When using thinking/reasoning enabled models, the API expects reasoning_content
to be present on ALL assistant messages in multi-turn conversations. This patched
version ensures reasoning_content from additional_kwargs is included in the
request payload.
"""
def _get_request_payload(
self,
input_: LanguageModelInput,
*,
stop: list[str] | None = None,
**kwargs: Any,
) -> dict:
"""Get request payload with reasoning_content preserved.
Overrides the parent method to inject reasoning_content from
additional_kwargs into assistant messages in the payload.
"""
# Get the original messages before conversion
original_messages = self._convert_input(input_).to_messages()
# Call parent to get the base payload
payload = super()._get_request_payload(input_, stop=stop, **kwargs)
# Match payload messages with original messages to restore reasoning_content
payload_messages = payload.get("messages", [])
# The payload messages and original messages should be in the same order
# Iterate through both and match by position
if len(payload_messages) == len(original_messages):
for payload_msg, orig_msg in zip(payload_messages, original_messages):
if payload_msg.get("role") == "assistant" and isinstance(orig_msg, AIMessage):
reasoning_content = orig_msg.additional_kwargs.get("reasoning_content")
if reasoning_content is not None:
payload_msg["reasoning_content"] = reasoning_content
else:
# Fallback: match by counting assistant messages
ai_messages = [m for m in original_messages if isinstance(m, AIMessage)]
assistant_payloads = [(i, m) for i, m in enumerate(payload_messages) if m.get("role") == "assistant"]
for (idx, payload_msg), ai_msg in zip(assistant_payloads, ai_messages):
reasoning_content = ai_msg.additional_kwargs.get("reasoning_content")
if reasoning_content is not None:
payload_messages[idx]["reasoning_content"] = reasoning_content
return payload
================================================
FILE: backend/packages/harness/deerflow/models/patched_minimax.py
================================================
"""Patched ChatOpenAI adapter for MiniMax reasoning output.
MiniMax's OpenAI-compatible chat completions API can return structured
``reasoning_details`` when ``extra_body.reasoning_split=true`` is enabled.
``langchain_openai.ChatOpenAI`` currently ignores that field, so DeerFlow's
frontend never receives reasoning content in the shape it expects.
This adapter preserves ``reasoning_split`` in the request payload and maps the
provider-specific reasoning field into ``additional_kwargs.reasoning_content``,
which DeerFlow already understands.
"""
from __future__ import annotations
import re
from collections.abc import Mapping
from typing import Any
from langchain_core.language_models import LanguageModelInput
from langchain_core.messages import AIMessage, AIMessageChunk
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_openai import ChatOpenAI
from langchain_openai.chat_models.base import (
_convert_delta_to_message_chunk,
_create_usage_metadata,
)
_THINK_TAG_RE = re.compile(r"\s*(.*?)\s*", re.DOTALL)
def _extract_reasoning_text(
reasoning_details: Any,
*,
strip_parts: bool = True,
) -> str | None:
if not isinstance(reasoning_details, list):
return None
parts: list[str] = []
for item in reasoning_details:
if not isinstance(item, Mapping):
continue
text = item.get("text")
if isinstance(text, str):
normalized = text.strip() if strip_parts else text
if normalized.strip():
parts.append(normalized)
return "\n\n".join(parts) if parts else None
def _strip_inline_think_tags(content: str) -> tuple[str, str | None]:
reasoning_parts: list[str] = []
def _replace(match: re.Match[str]) -> str:
reasoning = match.group(1).strip()
if reasoning:
reasoning_parts.append(reasoning)
return ""
cleaned = _THINK_TAG_RE.sub(_replace, content).strip()
reasoning = "\n\n".join(reasoning_parts) if reasoning_parts else None
return cleaned, reasoning
def _merge_reasoning(*values: str | None) -> str | None:
merged: list[str] = []
for value in values:
if not value:
continue
normalized = value.strip()
if normalized and normalized not in merged:
merged.append(normalized)
return "\n\n".join(merged) if merged else None
def _with_reasoning_content(
message: AIMessage | AIMessageChunk,
reasoning: str | None,
*,
preserve_whitespace: bool = False,
):
if not reasoning:
return message
additional_kwargs = dict(message.additional_kwargs)
if preserve_whitespace:
existing = additional_kwargs.get("reasoning_content")
additional_kwargs["reasoning_content"] = (
f"{existing}{reasoning}" if isinstance(existing, str) else reasoning
)
else:
additional_kwargs["reasoning_content"] = _merge_reasoning(
additional_kwargs.get("reasoning_content"),
reasoning,
)
return message.model_copy(update={"additional_kwargs": additional_kwargs})
class PatchedChatMiniMax(ChatOpenAI):
"""ChatOpenAI adapter that preserves MiniMax reasoning output."""
def _get_request_payload(
self,
input_: LanguageModelInput,
*,
stop: list[str] | None = None,
**kwargs: Any,
) -> dict:
payload = super()._get_request_payload(input_, stop=stop, **kwargs)
extra_body = payload.get("extra_body")
if isinstance(extra_body, dict):
payload["extra_body"] = {
**extra_body,
"reasoning_split": True,
}
else:
payload["extra_body"] = {"reasoning_split": True}
return payload
def _convert_chunk_to_generation_chunk(
self,
chunk: dict,
default_chunk_class: type,
base_generation_info: dict | None,
) -> ChatGenerationChunk | None:
if chunk.get("type") == "content.delta":
return None
token_usage = chunk.get("usage")
choices = chunk.get("choices", []) or chunk.get("chunk", {}).get("choices", [])
usage_metadata = (
_create_usage_metadata(token_usage, chunk.get("service_tier"))
if token_usage
else None
)
if len(choices) == 0:
generation_chunk = ChatGenerationChunk(
message=default_chunk_class(content="", usage_metadata=usage_metadata),
generation_info=base_generation_info,
)
if self.output_version == "v1":
generation_chunk.message.content = []
generation_chunk.message.response_metadata["output_version"] = "v1"
return generation_chunk
choice = choices[0]
delta = choice.get("delta")
if delta is None:
return None
message_chunk = _convert_delta_to_message_chunk(delta, default_chunk_class)
generation_info = {**base_generation_info} if base_generation_info else {}
if finish_reason := choice.get("finish_reason"):
generation_info["finish_reason"] = finish_reason
if model_name := chunk.get("model"):
generation_info["model_name"] = model_name
if system_fingerprint := chunk.get("system_fingerprint"):
generation_info["system_fingerprint"] = system_fingerprint
if service_tier := chunk.get("service_tier"):
generation_info["service_tier"] = service_tier
logprobs = choice.get("logprobs")
if logprobs:
generation_info["logprobs"] = logprobs
reasoning = _extract_reasoning_text(
delta.get("reasoning_details"),
strip_parts=False,
)
if isinstance(message_chunk, AIMessageChunk):
if usage_metadata:
message_chunk.usage_metadata = usage_metadata
if reasoning:
message_chunk = _with_reasoning_content(
message_chunk,
reasoning,
preserve_whitespace=True,
)
message_chunk.response_metadata["model_provider"] = "openai"
return ChatGenerationChunk(
message=message_chunk,
generation_info=generation_info or None,
)
def _create_chat_result(
self,
response: dict | Any,
generation_info: dict | None = None,
) -> ChatResult:
result = super()._create_chat_result(response, generation_info)
response_dict = response if isinstance(response, dict) else response.model_dump()
choices = response_dict.get("choices", [])
generations: list[ChatGeneration] = []
for index, generation in enumerate(result.generations):
choice = choices[index] if index < len(choices) else {}
message = generation.message
if isinstance(message, AIMessage):
content = message.content if isinstance(message.content, str) else None
cleaned_content = content
inline_reasoning = None
if isinstance(content, str):
cleaned_content, inline_reasoning = _strip_inline_think_tags(content)
choice_message = choice.get("message", {}) if isinstance(choice, Mapping) else {}
split_reasoning = _extract_reasoning_text(choice_message.get("reasoning_details"))
merged_reasoning = _merge_reasoning(split_reasoning, inline_reasoning)
updated_message = message
if cleaned_content is not None and cleaned_content != message.content:
updated_message = updated_message.model_copy(update={"content": cleaned_content})
if merged_reasoning:
updated_message = _with_reasoning_content(updated_message, merged_reasoning)
generation = ChatGeneration(
message=updated_message,
generation_info=generation.generation_info,
)
generations.append(generation)
return ChatResult(generations=generations, llm_output=result.llm_output)
================================================
FILE: backend/packages/harness/deerflow/reflection/__init__.py
================================================
from .resolvers import resolve_class, resolve_variable
__all__ = ["resolve_class", "resolve_variable"]
================================================
FILE: backend/packages/harness/deerflow/reflection/resolvers.py
================================================
from importlib import import_module
MODULE_TO_PACKAGE_HINTS = {
"langchain_google_genai": "langchain-google-genai",
"langchain_anthropic": "langchain-anthropic",
"langchain_openai": "langchain-openai",
"langchain_deepseek": "langchain-deepseek",
}
def _build_missing_dependency_hint(module_path: str, err: ImportError) -> str:
"""Build an actionable hint when module import fails."""
module_root = module_path.split(".", 1)[0]
missing_module = getattr(err, "name", None) or module_root
# Prefer provider package hints for known integrations, even when the import
# error is triggered by a transitive dependency (e.g. `google`).
package_name = MODULE_TO_PACKAGE_HINTS.get(module_root)
if package_name is None:
package_name = MODULE_TO_PACKAGE_HINTS.get(missing_module, missing_module.replace("_", "-"))
return f"Missing dependency '{missing_module}'. Install it with `uv add {package_name}` (or `pip install {package_name}`), then restart DeerFlow."
def resolve_variable[T](
variable_path: str,
expected_type: type[T] | tuple[type, ...] | None = None,
) -> T:
"""Resolve a variable from a path.
Args:
variable_path: The path to the variable (e.g. "parent_package_name.sub_package_name.module_name:variable_name").
expected_type: Optional type or tuple of types to validate the resolved variable against.
If provided, uses isinstance() to check if the variable is an instance of the expected type(s).
Returns:
The resolved variable.
Raises:
ImportError: If the module path is invalid or the attribute doesn't exist.
ValueError: If the resolved variable doesn't pass the validation checks.
"""
try:
module_path, variable_name = variable_path.rsplit(":", 1)
except ValueError as err:
raise ImportError(f"{variable_path} doesn't look like a variable path. Example: parent_package_name.sub_package_name.module_name:variable_name") from err
try:
module = import_module(module_path)
except ImportError as err:
module_root = module_path.split(".", 1)[0]
err_name = getattr(err, "name", None)
if isinstance(err, ModuleNotFoundError) or err_name == module_root:
hint = _build_missing_dependency_hint(module_path, err)
raise ImportError(f"Could not import module {module_path}. {hint}") from err
# Preserve the original ImportError message for non-missing-module failures.
raise ImportError(f"Error importing module {module_path}: {err}") from err
try:
variable = getattr(module, variable_name)
except AttributeError as err:
raise ImportError(f"Module {module_path} does not define a {variable_name} attribute/class") from err
# Type validation
if expected_type is not None:
if not isinstance(variable, expected_type):
type_name = expected_type.__name__ if isinstance(expected_type, type) else " or ".join(t.__name__ for t in expected_type)
raise ValueError(f"{variable_path} is not an instance of {type_name}, got {type(variable).__name__}")
return variable
def resolve_class[T](class_path: str, base_class: type[T] | None = None) -> type[T]:
"""Resolve a class from a module path and class name.
Args:
class_path: The path to the class (e.g. "langchain_openai:ChatOpenAI").
base_class: The base class to check if the resolved class is a subclass of.
Returns:
The resolved class.
Raises:
ImportError: If the module path is invalid or the attribute doesn't exist.
ValueError: If the resolved object is not a class or not a subclass of base_class.
"""
model_class = resolve_variable(class_path, expected_type=type)
if not isinstance(model_class, type):
raise ValueError(f"{class_path} is not a valid class")
if base_class is not None and not issubclass(model_class, base_class):
raise ValueError(f"{class_path} is not a subclass of {base_class.__name__}")
return model_class
================================================
FILE: backend/packages/harness/deerflow/sandbox/__init__.py
================================================
from .sandbox import Sandbox
from .sandbox_provider import SandboxProvider, get_sandbox_provider
__all__ = [
"Sandbox",
"SandboxProvider",
"get_sandbox_provider",
]
================================================
FILE: backend/packages/harness/deerflow/sandbox/exceptions.py
================================================
"""Sandbox-related exceptions with structured error information."""
class SandboxError(Exception):
"""Base exception for all sandbox-related errors."""
def __init__(self, message: str, details: dict | None = None):
super().__init__(message)
self.message = message
self.details = details or {}
def __str__(self) -> str:
if self.details:
detail_str = ", ".join(f"{k}={v}" for k, v in self.details.items())
return f"{self.message} ({detail_str})"
return self.message
class SandboxNotFoundError(SandboxError):
"""Raised when a sandbox cannot be found or is not available."""
def __init__(self, message: str = "Sandbox not found", sandbox_id: str | None = None):
details = {"sandbox_id": sandbox_id} if sandbox_id else None
super().__init__(message, details)
self.sandbox_id = sandbox_id
class SandboxRuntimeError(SandboxError):
"""Raised when sandbox runtime is not available or misconfigured."""
pass
class SandboxCommandError(SandboxError):
"""Raised when a command execution fails in the sandbox."""
def __init__(self, message: str, command: str | None = None, exit_code: int | None = None):
details = {}
if command:
details["command"] = command[:100] + "..." if len(command) > 100 else command
if exit_code is not None:
details["exit_code"] = exit_code
super().__init__(message, details)
self.command = command
self.exit_code = exit_code
class SandboxFileError(SandboxError):
"""Raised when a file operation fails in the sandbox."""
def __init__(self, message: str, path: str | None = None, operation: str | None = None):
details = {}
if path:
details["path"] = path
if operation:
details["operation"] = operation
super().__init__(message, details)
self.path = path
self.operation = operation
class SandboxPermissionError(SandboxFileError):
"""Raised when a permission error occurs during file operations."""
pass
class SandboxFileNotFoundError(SandboxFileError):
"""Raised when a file or directory is not found."""
pass
================================================
FILE: backend/packages/harness/deerflow/sandbox/local/__init__.py
================================================
from .local_sandbox_provider import LocalSandboxProvider
__all__ = ["LocalSandboxProvider"]
================================================
FILE: backend/packages/harness/deerflow/sandbox/local/list_dir.py
================================================
import fnmatch
from pathlib import Path
IGNORE_PATTERNS = [
# Version Control
".git",
".svn",
".hg",
".bzr",
# Dependencies
"node_modules",
"__pycache__",
".venv",
"venv",
".env",
"env",
".tox",
".nox",
".eggs",
"*.egg-info",
"site-packages",
# Build outputs
"dist",
"build",
".next",
".nuxt",
".output",
".turbo",
"target",
"out",
# IDE & Editor
".idea",
".vscode",
"*.swp",
"*.swo",
"*~",
".project",
".classpath",
".settings",
# OS generated
".DS_Store",
"Thumbs.db",
"desktop.ini",
"*.lnk",
# Logs & temp files
"*.log",
"*.tmp",
"*.temp",
"*.bak",
"*.cache",
".cache",
"logs",
# Coverage & test artifacts
".coverage",
"coverage",
".nyc_output",
"htmlcov",
".pytest_cache",
".mypy_cache",
".ruff_cache",
]
def _should_ignore(name: str) -> bool:
"""Check if a file/directory name matches any ignore pattern."""
for pattern in IGNORE_PATTERNS:
if fnmatch.fnmatch(name, pattern):
return True
return False
def list_dir(path: str, max_depth: int = 2) -> list[str]:
"""
List files and directories up to max_depth levels deep.
Args:
path: The root directory path to list.
max_depth: Maximum depth to traverse (default: 2).
1 = only direct children, 2 = children + grandchildren, etc.
Returns:
A list of absolute paths for files and directories,
excluding items matching IGNORE_PATTERNS.
"""
result: list[str] = []
root_path = Path(path).resolve()
if not root_path.is_dir():
return result
def _traverse(current_path: Path, current_depth: int) -> None:
"""Recursively traverse directories up to max_depth."""
if current_depth > max_depth:
return
try:
for item in current_path.iterdir():
if _should_ignore(item.name):
continue
post_fix = "/" if item.is_dir() else ""
result.append(str(item.resolve()) + post_fix)
# Recurse into subdirectories if not at max depth
if item.is_dir() and current_depth < max_depth:
_traverse(item, current_depth + 1)
except PermissionError:
pass
_traverse(root_path, 1)
return sorted(result)
================================================
FILE: backend/packages/harness/deerflow/sandbox/local/local_sandbox.py
================================================
import os
import shutil
import subprocess
from deerflow.sandbox.local.list_dir import list_dir
from deerflow.sandbox.sandbox import Sandbox
class LocalSandbox(Sandbox):
def __init__(self, id: str):
"""
Initialize local sandbox.
Args:
id: Sandbox identifier
"""
super().__init__(id)
@staticmethod
def _get_shell() -> str:
"""Detect available shell executable with fallback.
Returns the first available shell in order of preference:
/bin/zsh → /bin/bash → /bin/sh → first `sh` found on PATH.
Raises a RuntimeError if no suitable shell is found.
"""
for shell in ("/bin/zsh", "/bin/bash", "/bin/sh"):
if os.path.isfile(shell) and os.access(shell, os.X_OK):
return shell
shell_from_path = shutil.which("sh")
if shell_from_path is not None:
return shell_from_path
raise RuntimeError("No suitable shell executable found. Tried /bin/zsh, /bin/bash, /bin/sh, and `sh` on PATH.")
def execute_command(self, command: str) -> str:
result = subprocess.run(
command,
executable=self._get_shell(),
shell=True,
capture_output=True,
text=True,
timeout=600,
)
output = result.stdout
if result.stderr:
output += f"\nStd Error:\n{result.stderr}" if output else result.stderr
if result.returncode != 0:
output += f"\nExit Code: {result.returncode}"
return output if output else "(no output)"
def list_dir(self, path: str, max_depth=2) -> list[str]:
return list_dir(path, max_depth)
def read_file(self, path: str) -> str:
with open(path, encoding="utf-8") as f:
return f.read()
def write_file(self, path: str, content: str, append: bool = False) -> None:
dir_path = os.path.dirname(path)
if dir_path:
os.makedirs(dir_path, exist_ok=True)
mode = "a" if append else "w"
with open(path, mode, encoding="utf-8") as f:
f.write(content)
def update_file(self, path: str, content: bytes) -> None:
dir_path = os.path.dirname(path)
if dir_path:
os.makedirs(dir_path, exist_ok=True)
with open(path, "wb") as f:
f.write(content)
================================================
FILE: backend/packages/harness/deerflow/sandbox/local/local_sandbox_provider.py
================================================
from deerflow.sandbox.local.local_sandbox import LocalSandbox
from deerflow.sandbox.sandbox import Sandbox
from deerflow.sandbox.sandbox_provider import SandboxProvider
_singleton: LocalSandbox | None = None
class LocalSandboxProvider(SandboxProvider):
def acquire(self, thread_id: str | None = None) -> str:
global _singleton
if _singleton is None:
_singleton = LocalSandbox("local")
return _singleton.id
def get(self, sandbox_id: str) -> Sandbox | None:
if sandbox_id == "local":
if _singleton is None:
self.acquire()
return _singleton
return None
def release(self, sandbox_id: str) -> None:
# LocalSandbox uses singleton pattern - no cleanup needed.
# Note: This method is intentionally not called by SandboxMiddleware
# to allow sandbox reuse across multiple turns in a thread.
# For Docker-based providers (e.g., AioSandboxProvider), cleanup
# happens at application shutdown via the shutdown() method.
pass
================================================
FILE: backend/packages/harness/deerflow/sandbox/middleware.py
================================================
import logging
from typing import NotRequired, override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langgraph.runtime import Runtime
from deerflow.agents.thread_state import SandboxState, ThreadDataState
from deerflow.sandbox import get_sandbox_provider
logger = logging.getLogger(__name__)
class SandboxMiddlewareState(AgentState):
"""Compatible with the `ThreadState` schema."""
sandbox: NotRequired[SandboxState | None]
thread_data: NotRequired[ThreadDataState | None]
class SandboxMiddleware(AgentMiddleware[SandboxMiddlewareState]):
"""Create a sandbox environment and assign it to an agent.
Lifecycle Management:
- With lazy_init=True (default): Sandbox is acquired on first tool call
- With lazy_init=False: Sandbox is acquired on first agent invocation (before_agent)
- Sandbox is reused across multiple turns within the same thread
- Sandbox is NOT released after each agent call to avoid wasteful recreation
- Cleanup happens at application shutdown via SandboxProvider.shutdown()
"""
state_schema = SandboxMiddlewareState
def __init__(self, lazy_init: bool = True):
"""Initialize sandbox middleware.
Args:
lazy_init: If True, defer sandbox acquisition until first tool call.
If False, acquire sandbox eagerly in before_agent().
Default is True for optimal performance.
"""
super().__init__()
self._lazy_init = lazy_init
def _acquire_sandbox(self, thread_id: str) -> str:
provider = get_sandbox_provider()
sandbox_id = provider.acquire(thread_id)
logger.info(f"Acquiring sandbox {sandbox_id}")
return sandbox_id
@override
def before_agent(self, state: SandboxMiddlewareState, runtime: Runtime) -> dict | None:
# Skip acquisition if lazy_init is enabled
if self._lazy_init:
return super().before_agent(state, runtime)
# Eager initialization (original behavior)
if "sandbox" not in state or state["sandbox"] is None:
thread_id = runtime.context["thread_id"]
sandbox_id = self._acquire_sandbox(thread_id)
logger.info(f"Assigned sandbox {sandbox_id} to thread {thread_id}")
return {"sandbox": {"sandbox_id": sandbox_id}}
return super().before_agent(state, runtime)
@override
def after_agent(self, state: SandboxMiddlewareState, runtime: Runtime) -> dict | None:
sandbox = state.get("sandbox")
if sandbox is not None:
sandbox_id = sandbox["sandbox_id"]
logger.info(f"Releasing sandbox {sandbox_id}")
get_sandbox_provider().release(sandbox_id)
return None
if runtime.context.get("sandbox_id") is not None:
sandbox_id = runtime.context.get("sandbox_id")
logger.info(f"Releasing sandbox {sandbox_id} from context")
get_sandbox_provider().release(sandbox_id)
return None
# No sandbox to release
return super().after_agent(state, runtime)
================================================
FILE: backend/packages/harness/deerflow/sandbox/sandbox.py
================================================
from abc import ABC, abstractmethod
class Sandbox(ABC):
"""Abstract base class for sandbox environments"""
_id: str
def __init__(self, id: str):
self._id = id
@property
def id(self) -> str:
return self._id
@abstractmethod
def execute_command(self, command: str) -> str:
"""Execute bash command in sandbox.
Args:
command: The command to execute.
Returns:
The standard or error output of the command.
"""
pass
@abstractmethod
def read_file(self, path: str) -> str:
"""Read the content of a file.
Args:
path: The absolute path of the file to read.
Returns:
The content of the file.
"""
pass
@abstractmethod
def list_dir(self, path: str, max_depth=2) -> list[str]:
"""List the contents of a directory.
Args:
path: The absolute path of the directory to list.
max_depth: The maximum depth to traverse. Default is 2.
Returns:
The contents of the directory.
"""
pass
@abstractmethod
def write_file(self, path: str, content: str, append: bool = False) -> None:
"""Write content to a file.
Args:
path: The absolute path of the file to write to.
content: The text content to write to the file.
append: Whether to append the content to the file. If False, the file will be created or overwritten.
"""
pass
@abstractmethod
def update_file(self, path: str, content: bytes) -> None:
"""Update a file with binary content.
Args:
path: The absolute path of the file to update.
content: The binary content to write to the file.
"""
pass
================================================
FILE: backend/packages/harness/deerflow/sandbox/sandbox_provider.py
================================================
from abc import ABC, abstractmethod
from deerflow.config import get_app_config
from deerflow.reflection import resolve_class
from deerflow.sandbox.sandbox import Sandbox
class SandboxProvider(ABC):
"""Abstract base class for sandbox providers"""
@abstractmethod
def acquire(self, thread_id: str | None = None) -> str:
"""Acquire a sandbox environment and return its ID.
Returns:
The ID of the acquired sandbox environment.
"""
pass
@abstractmethod
def get(self, sandbox_id: str) -> Sandbox | None:
"""Get a sandbox environment by ID.
Args:
sandbox_id: The ID of the sandbox environment to retain.
"""
pass
@abstractmethod
def release(self, sandbox_id: str) -> None:
"""Release a sandbox environment.
Args:
sandbox_id: The ID of the sandbox environment to destroy.
"""
pass
_default_sandbox_provider: SandboxProvider | None = None
def get_sandbox_provider(**kwargs) -> SandboxProvider:
"""Get the sandbox provider singleton.
Returns a cached singleton instance. Use `reset_sandbox_provider()` to clear
the cache, or `shutdown_sandbox_provider()` to properly shutdown and clear.
Returns:
A sandbox provider instance.
"""
global _default_sandbox_provider
if _default_sandbox_provider is None:
config = get_app_config()
cls = resolve_class(config.sandbox.use, SandboxProvider)
_default_sandbox_provider = cls(**kwargs)
return _default_sandbox_provider
def reset_sandbox_provider() -> None:
"""Reset the sandbox provider singleton.
This clears the cached instance without calling shutdown.
The next call to `get_sandbox_provider()` will create a new instance.
Useful for testing or when switching configurations.
Note: If the provider has active sandboxes, they will be orphaned.
Use `shutdown_sandbox_provider()` for proper cleanup.
"""
global _default_sandbox_provider
_default_sandbox_provider = None
def shutdown_sandbox_provider() -> None:
"""Shutdown and reset the sandbox provider.
This properly shuts down the provider (releasing all sandboxes)
before clearing the singleton. Call this when the application
is shutting down or when you need to completely reset the sandbox system.
"""
global _default_sandbox_provider
if _default_sandbox_provider is not None:
if hasattr(_default_sandbox_provider, "shutdown"):
_default_sandbox_provider.shutdown()
_default_sandbox_provider = None
def set_sandbox_provider(provider: SandboxProvider) -> None:
"""Set a custom sandbox provider instance.
This allows injecting a custom or mock provider for testing purposes.
Args:
provider: The SandboxProvider instance to use.
"""
global _default_sandbox_provider
_default_sandbox_provider = provider
================================================
FILE: backend/packages/harness/deerflow/sandbox/tools.py
================================================
import re
from pathlib import Path
from langchain.tools import ToolRuntime, tool
from langgraph.typing import ContextT
from deerflow.agents.thread_state import ThreadDataState, ThreadState
from deerflow.config.paths import VIRTUAL_PATH_PREFIX
from deerflow.sandbox.exceptions import (
SandboxError,
SandboxNotFoundError,
SandboxRuntimeError,
)
from deerflow.sandbox.sandbox import Sandbox
from deerflow.sandbox.sandbox_provider import get_sandbox_provider
_ABSOLUTE_PATH_PATTERN = re.compile(r"(?()]+)")
_LOCAL_BASH_SYSTEM_PATH_PREFIXES = (
"/bin/",
"/usr/bin/",
"/usr/sbin/",
"/sbin/",
"/opt/homebrew/bin/",
"/dev/",
)
_DEFAULT_SKILLS_CONTAINER_PATH = "/mnt/skills"
def _get_skills_container_path() -> str:
"""Get the skills container path from config, with fallback to default.
Result is cached after the first successful config load. If config loading
fails the default is returned *without* caching so that a later call can
pick up the real value once the config is available.
"""
cached = getattr(_get_skills_container_path, "_cached", None)
if cached is not None:
return cached
try:
from deerflow.config import get_app_config
value = get_app_config().skills.container_path
_get_skills_container_path._cached = value # type: ignore[attr-defined]
return value
except Exception:
return _DEFAULT_SKILLS_CONTAINER_PATH
def _get_skills_host_path() -> str | None:
"""Get the skills host filesystem path from config.
Returns None if the skills directory does not exist or config cannot be
loaded. Only successful lookups are cached; failures are retried on the
next call so that a transiently unavailable skills directory does not
permanently disable skills access.
"""
cached = getattr(_get_skills_host_path, "_cached", None)
if cached is not None:
return cached
try:
from deerflow.config import get_app_config
config = get_app_config()
skills_path = config.skills.get_skills_path()
if skills_path.exists():
value = str(skills_path)
_get_skills_host_path._cached = value # type: ignore[attr-defined]
return value
except Exception:
pass
return None
def _is_skills_path(path: str) -> bool:
"""Check if a path is under the skills container path."""
skills_prefix = _get_skills_container_path()
return path == skills_prefix or path.startswith(f"{skills_prefix}/")
def _resolve_skills_path(path: str) -> str:
"""Resolve a virtual skills path to a host filesystem path.
Args:
path: Virtual skills path (e.g. /mnt/skills/public/bootstrap/SKILL.md)
Returns:
Resolved host path.
Raises:
FileNotFoundError: If skills directory is not configured or doesn't exist.
"""
skills_container = _get_skills_container_path()
skills_host = _get_skills_host_path()
if skills_host is None:
raise FileNotFoundError(f"Skills directory not available for path: {path}")
if path == skills_container:
return skills_host
relative = path[len(skills_container):].lstrip("/")
return str(Path(skills_host) / relative) if relative else skills_host
def _path_variants(path: str) -> set[str]:
return {path, path.replace("\\", "/"), path.replace("/", "\\")}
def _sanitize_error(error: Exception, runtime: "ToolRuntime[ContextT, ThreadState] | None" = None) -> str:
"""Sanitize an error message to avoid leaking host filesystem paths.
In local-sandbox mode, resolved host paths in the error string are masked
back to their virtual equivalents so that user-visible output never exposes
the host directory layout.
"""
msg = f"{type(error).__name__}: {error}"
if runtime is not None and is_local_sandbox(runtime):
thread_data = get_thread_data(runtime)
msg = mask_local_paths_in_output(msg, thread_data)
return msg
def replace_virtual_path(path: str, thread_data: ThreadDataState | None) -> str:
"""Replace virtual /mnt/user-data paths with actual thread data paths.
Mapping:
/mnt/user-data/workspace/* -> thread_data['workspace_path']/*
/mnt/user-data/uploads/* -> thread_data['uploads_path']/*
/mnt/user-data/outputs/* -> thread_data['outputs_path']/*
Args:
path: The path that may contain virtual path prefix.
thread_data: The thread data containing actual paths.
Returns:
The path with virtual prefix replaced by actual path.
"""
if thread_data is None:
return path
mappings = _thread_virtual_to_actual_mappings(thread_data)
if not mappings:
return path
# Longest-prefix-first replacement with segment-boundary checks.
for virtual_base, actual_base in sorted(mappings.items(), key=lambda item: len(item[0]), reverse=True):
if path == virtual_base:
return actual_base
if path.startswith(f"{virtual_base}/"):
rest = path[len(virtual_base) :].lstrip("/")
return str(Path(actual_base) / rest) if rest else actual_base
return path
def _thread_virtual_to_actual_mappings(thread_data: ThreadDataState) -> dict[str, str]:
"""Build virtual-to-actual path mappings for a thread."""
mappings: dict[str, str] = {}
workspace = thread_data.get("workspace_path")
uploads = thread_data.get("uploads_path")
outputs = thread_data.get("outputs_path")
if workspace:
mappings[f"{VIRTUAL_PATH_PREFIX}/workspace"] = workspace
if uploads:
mappings[f"{VIRTUAL_PATH_PREFIX}/uploads"] = uploads
if outputs:
mappings[f"{VIRTUAL_PATH_PREFIX}/outputs"] = outputs
# Also map the virtual root when all known dirs share the same parent.
actual_dirs = [Path(p) for p in (workspace, uploads, outputs) if p]
if actual_dirs:
common_parent = str(Path(actual_dirs[0]).parent)
if all(str(path.parent) == common_parent for path in actual_dirs):
mappings[VIRTUAL_PATH_PREFIX] = common_parent
return mappings
def _thread_actual_to_virtual_mappings(thread_data: ThreadDataState) -> dict[str, str]:
"""Build actual-to-virtual mappings for output masking."""
return {actual: virtual for virtual, actual in _thread_virtual_to_actual_mappings(thread_data).items()}
def mask_local_paths_in_output(output: str, thread_data: ThreadDataState | None) -> str:
"""Mask host absolute paths from local sandbox output using virtual paths.
Handles both user-data paths (per-thread) and skills paths (global).
"""
result = output
# Mask skills host paths
skills_host = _get_skills_host_path()
skills_container = _get_skills_container_path()
if skills_host:
raw_base = str(Path(skills_host))
resolved_base = str(Path(skills_host).resolve())
for base in _path_variants(raw_base) | _path_variants(resolved_base):
escaped = re.escape(base).replace(r"\\", r"[/\\]")
pattern = re.compile(escaped + r"(?:[/\\][^\s\"';&|<>()]*)?")
def replace_skills(match: re.Match, _base: str = base) -> str:
matched_path = match.group(0)
if matched_path == _base:
return skills_container
relative = matched_path[len(_base):].lstrip("/\\")
return f"{skills_container}/{relative}" if relative else skills_container
result = pattern.sub(replace_skills, result)
# Mask user-data host paths
if thread_data is None:
return result
mappings = _thread_actual_to_virtual_mappings(thread_data)
if not mappings:
return result
for actual_base, virtual_base in sorted(mappings.items(), key=lambda item: len(item[0]), reverse=True):
raw_base = str(Path(actual_base))
resolved_base = str(Path(actual_base).resolve())
for base in _path_variants(raw_base) | _path_variants(resolved_base):
escaped_actual = re.escape(base).replace(r"\\", r"[/\\]")
pattern = re.compile(escaped_actual + r"(?:[/\\][^\s\"';&|<>()]*)?")
def replace_match(match: re.Match, _base: str = base, _virtual: str = virtual_base) -> str:
matched_path = match.group(0)
if matched_path == _base:
return _virtual
relative = matched_path[len(_base):].lstrip("/\\")
return f"{_virtual}/{relative}" if relative else _virtual
result = pattern.sub(replace_match, result)
return result
def _reject_path_traversal(path: str) -> None:
"""Reject paths that contain '..' segments to prevent directory traversal."""
# Normalise to forward slashes, then check for '..' segments.
normalised = path.replace("\\", "/")
for segment in normalised.split("/"):
if segment == "..":
raise PermissionError("Access denied: path traversal detected")
def validate_local_tool_path(path: str, thread_data: ThreadDataState | None, *, read_only: bool = False) -> None:
"""Validate that a virtual path is allowed for local-sandbox access.
This function is a security gate — it checks whether *path* may be
accessed and raises on violation. It does **not** resolve the virtual
path to a host path; callers are responsible for resolution via
``_resolve_and_validate_user_data_path`` or ``_resolve_skills_path``.
Allowed virtual-path families:
- ``/mnt/user-data/*`` — always allowed (read + write)
- ``/mnt/skills/*`` — allowed only when *read_only* is True
Args:
path: The virtual path to validate.
thread_data: Thread data (must be present for local sandbox).
read_only: When True, skills paths are permitted.
Raises:
SandboxRuntimeError: If thread data is missing.
PermissionError: If the path is not allowed or contains traversal.
"""
if thread_data is None:
raise SandboxRuntimeError("Thread data not available for local sandbox")
_reject_path_traversal(path)
# Skills paths — read-only access only
if _is_skills_path(path):
if not read_only:
raise PermissionError(f"Write access to skills path is not allowed: {path}")
return
# User-data paths
if path.startswith(f"{VIRTUAL_PATH_PREFIX}/"):
return
raise PermissionError(f"Only paths under {VIRTUAL_PATH_PREFIX}/ or {_get_skills_container_path()}/ are allowed")
def _validate_resolved_user_data_path(resolved: Path, thread_data: ThreadDataState) -> None:
"""Verify that a resolved host path stays inside allowed per-thread roots.
Raises PermissionError if the path escapes workspace/uploads/outputs.
"""
allowed_roots = [
Path(p).resolve()
for p in (
thread_data.get("workspace_path"),
thread_data.get("uploads_path"),
thread_data.get("outputs_path"),
)
if p is not None
]
if not allowed_roots:
raise SandboxRuntimeError("No allowed local sandbox directories configured")
for root in allowed_roots:
try:
resolved.relative_to(root)
return
except ValueError:
continue
raise PermissionError("Access denied: path traversal detected")
def _resolve_and_validate_user_data_path(path: str, thread_data: ThreadDataState) -> str:
"""Resolve a /mnt/user-data virtual path and validate it stays in bounds.
Returns the resolved host path string.
"""
resolved_str = replace_virtual_path(path, thread_data)
resolved = Path(resolved_str).resolve()
_validate_resolved_user_data_path(resolved, thread_data)
return str(resolved)
def validate_local_bash_command_paths(command: str, thread_data: ThreadDataState | None) -> None:
"""Validate absolute paths in local-sandbox bash commands.
In local mode, commands must use virtual paths under /mnt/user-data for
user data access. Skills paths under /mnt/skills are allowed for reading.
A small allowlist of common system path prefixes is kept for executable
and device references (e.g. /bin/sh, /dev/null).
"""
if thread_data is None:
raise SandboxRuntimeError("Thread data not available for local sandbox")
unsafe_paths: list[str] = []
for absolute_path in _ABSOLUTE_PATH_PATTERN.findall(command):
if absolute_path == VIRTUAL_PATH_PREFIX or absolute_path.startswith(f"{VIRTUAL_PATH_PREFIX}/"):
_reject_path_traversal(absolute_path)
continue
# Allow skills container path (resolved by tools.py before passing to sandbox)
if _is_skills_path(absolute_path):
_reject_path_traversal(absolute_path)
continue
if any(
absolute_path == prefix.rstrip("/") or absolute_path.startswith(prefix)
for prefix in _LOCAL_BASH_SYSTEM_PATH_PREFIXES
):
continue
unsafe_paths.append(absolute_path)
if unsafe_paths:
unsafe = ", ".join(sorted(dict.fromkeys(unsafe_paths)))
raise PermissionError(f"Unsafe absolute paths in command: {unsafe}. Use paths under {VIRTUAL_PATH_PREFIX}")
def replace_virtual_paths_in_command(command: str, thread_data: ThreadDataState | None) -> str:
"""Replace all virtual paths (/mnt/user-data and /mnt/skills) in a command string.
Args:
command: The command string that may contain virtual paths.
thread_data: The thread data containing actual paths.
Returns:
The command with all virtual paths replaced.
"""
result = command
# Replace skills paths
skills_container = _get_skills_container_path()
skills_host = _get_skills_host_path()
if skills_host and skills_container in result:
skills_pattern = re.compile(rf"{re.escape(skills_container)}(/[^\s\"';&|<>()]*)?")
def replace_skills_match(match: re.Match) -> str:
return _resolve_skills_path(match.group(0))
result = skills_pattern.sub(replace_skills_match, result)
# Replace user-data paths
if VIRTUAL_PATH_PREFIX in result and thread_data is not None:
pattern = re.compile(rf"{re.escape(VIRTUAL_PATH_PREFIX)}(/[^\s\"';&|<>()]*)?")
def replace_user_data_match(match: re.Match) -> str:
return replace_virtual_path(match.group(0), thread_data)
result = pattern.sub(replace_user_data_match, result)
return result
def get_thread_data(runtime: ToolRuntime[ContextT, ThreadState] | None) -> ThreadDataState | None:
"""Extract thread_data from runtime state."""
if runtime is None:
return None
if runtime.state is None:
return None
return runtime.state.get("thread_data")
def is_local_sandbox(runtime: ToolRuntime[ContextT, ThreadState] | None) -> bool:
"""Check if the current sandbox is a local sandbox.
Path replacement is only needed for local sandbox since aio sandbox
already has /mnt/user-data mounted in the container.
"""
if runtime is None:
return False
if runtime.state is None:
return False
sandbox_state = runtime.state.get("sandbox")
if sandbox_state is None:
return False
return sandbox_state.get("sandbox_id") == "local"
def sandbox_from_runtime(runtime: ToolRuntime[ContextT, ThreadState] | None = None) -> Sandbox:
"""Extract sandbox instance from tool runtime.
DEPRECATED: Use ensure_sandbox_initialized() for lazy initialization support.
This function assumes sandbox is already initialized and will raise error if not.
Raises:
SandboxRuntimeError: If runtime is not available or sandbox state is missing.
SandboxNotFoundError: If sandbox with the given ID cannot be found.
"""
if runtime is None:
raise SandboxRuntimeError("Tool runtime not available")
if runtime.state is None:
raise SandboxRuntimeError("Tool runtime state not available")
sandbox_state = runtime.state.get("sandbox")
if sandbox_state is None:
raise SandboxRuntimeError("Sandbox state not initialized in runtime")
sandbox_id = sandbox_state.get("sandbox_id")
if sandbox_id is None:
raise SandboxRuntimeError("Sandbox ID not found in state")
sandbox = get_sandbox_provider().get(sandbox_id)
if sandbox is None:
raise SandboxNotFoundError(f"Sandbox with ID '{sandbox_id}' not found", sandbox_id=sandbox_id)
runtime.context["sandbox_id"] = sandbox_id # Ensure sandbox_id is in context for downstream use
return sandbox
def ensure_sandbox_initialized(runtime: ToolRuntime[ContextT, ThreadState] | None = None) -> Sandbox:
"""Ensure sandbox is initialized, acquiring lazily if needed.
On first call, acquires a sandbox from the provider and stores it in runtime state.
Subsequent calls return the existing sandbox.
Thread-safety is guaranteed by the provider's internal locking mechanism.
Args:
runtime: Tool runtime containing state and context.
Returns:
Initialized sandbox instance.
Raises:
SandboxRuntimeError: If runtime is not available or thread_id is missing.
SandboxNotFoundError: If sandbox acquisition fails.
"""
if runtime is None:
raise SandboxRuntimeError("Tool runtime not available")
if runtime.state is None:
raise SandboxRuntimeError("Tool runtime state not available")
# Check if sandbox already exists in state
sandbox_state = runtime.state.get("sandbox")
if sandbox_state is not None:
sandbox_id = sandbox_state.get("sandbox_id")
if sandbox_id is not None:
sandbox = get_sandbox_provider().get(sandbox_id)
if sandbox is not None:
runtime.context["sandbox_id"] = sandbox_id # Ensure sandbox_id is in context for releasing in after_agent
return sandbox
# Sandbox was released, fall through to acquire new one
# Lazy acquisition: get thread_id and acquire sandbox
thread_id = runtime.context.get("thread_id")
if thread_id is None:
raise SandboxRuntimeError("Thread ID not available in runtime context")
provider = get_sandbox_provider()
sandbox_id = provider.acquire(thread_id)
# Update runtime state - this persists across tool calls
runtime.state["sandbox"] = {"sandbox_id": sandbox_id}
# Retrieve and return the sandbox
sandbox = provider.get(sandbox_id)
if sandbox is None:
raise SandboxNotFoundError("Sandbox not found after acquisition", sandbox_id=sandbox_id)
runtime.context["sandbox_id"] = sandbox_id # Ensure sandbox_id is in context for releasing in after_agent
return sandbox
def ensure_thread_directories_exist(runtime: ToolRuntime[ContextT, ThreadState] | None) -> None:
"""Ensure thread data directories (workspace, uploads, outputs) exist.
This function is called lazily when any sandbox tool is first used.
For local sandbox, it creates the directories on the filesystem.
For other sandboxes (like aio), directories are already mounted in the container.
Args:
runtime: Tool runtime containing state and context.
"""
if runtime is None:
return
# Only create directories for local sandbox
if not is_local_sandbox(runtime):
return
thread_data = get_thread_data(runtime)
if thread_data is None:
return
# Check if directories have already been created
if runtime.state.get("thread_directories_created"):
return
# Create the three directories
import os
for key in ["workspace_path", "uploads_path", "outputs_path"]:
path = thread_data.get(key)
if path:
os.makedirs(path, exist_ok=True)
# Mark as created to avoid redundant operations
runtime.state["thread_directories_created"] = True
@tool("bash", parse_docstring=True)
def bash_tool(runtime: ToolRuntime[ContextT, ThreadState], description: str, command: str) -> str:
"""Execute a bash command in a Linux environment.
- Use `python` to run Python code.
- Prefer a thread-local virtual environment in `/mnt/user-data/workspace/.venv`.
- Use `python -m pip` (inside the virtual environment) to install Python packages.
Args:
description: Explain why you are running this command in short words. ALWAYS PROVIDE THIS PARAMETER FIRST.
command: The bash command to execute. Always use absolute paths for files and directories.
"""
try:
sandbox = ensure_sandbox_initialized(runtime)
ensure_thread_directories_exist(runtime)
thread_data = get_thread_data(runtime)
if is_local_sandbox(runtime):
validate_local_bash_command_paths(command, thread_data)
command = replace_virtual_paths_in_command(command, thread_data)
output = sandbox.execute_command(command)
return mask_local_paths_in_output(output, thread_data)
return sandbox.execute_command(command)
except SandboxError as e:
return f"Error: {e}"
except PermissionError as e:
return f"Error: {e}"
except Exception as e:
return f"Error: Unexpected error executing command: {_sanitize_error(e, runtime)}"
@tool("ls", parse_docstring=True)
def ls_tool(runtime: ToolRuntime[ContextT, ThreadState], description: str, path: str) -> str:
"""List the contents of a directory up to 2 levels deep in tree format.
Args:
description: Explain why you are listing this directory in short words. ALWAYS PROVIDE THIS PARAMETER FIRST.
path: The **absolute** path to the directory to list.
"""
try:
sandbox = ensure_sandbox_initialized(runtime)
ensure_thread_directories_exist(runtime)
requested_path = path
if is_local_sandbox(runtime):
thread_data = get_thread_data(runtime)
validate_local_tool_path(path, thread_data, read_only=True)
if _is_skills_path(path):
path = _resolve_skills_path(path)
else:
path = _resolve_and_validate_user_data_path(path, thread_data)
children = sandbox.list_dir(path)
if not children:
return "(empty)"
return "\n".join(children)
except SandboxError as e:
return f"Error: {e}"
except FileNotFoundError:
return f"Error: Directory not found: {requested_path}"
except PermissionError:
return f"Error: Permission denied: {requested_path}"
except Exception as e:
return f"Error: Unexpected error listing directory: {_sanitize_error(e, runtime)}"
@tool("read_file", parse_docstring=True)
def read_file_tool(
runtime: ToolRuntime[ContextT, ThreadState],
description: str,
path: str,
start_line: int | None = None,
end_line: int | None = None,
) -> str:
"""Read the contents of a text file. Use this to examine source code, configuration files, logs, or any text-based file.
Args:
description: Explain why you are reading this file in short words. ALWAYS PROVIDE THIS PARAMETER FIRST.
path: The **absolute** path to the file to read.
start_line: Optional starting line number (1-indexed, inclusive). Use with end_line to read a specific range.
end_line: Optional ending line number (1-indexed, inclusive). Use with start_line to read a specific range.
"""
try:
sandbox = ensure_sandbox_initialized(runtime)
ensure_thread_directories_exist(runtime)
requested_path = path
if is_local_sandbox(runtime):
thread_data = get_thread_data(runtime)
validate_local_tool_path(path, thread_data, read_only=True)
if _is_skills_path(path):
path = _resolve_skills_path(path)
else:
path = _resolve_and_validate_user_data_path(path, thread_data)
content = sandbox.read_file(path)
if not content:
return "(empty)"
if start_line is not None and end_line is not None:
content = "\n".join(content.splitlines()[start_line - 1 : end_line])
return content
except SandboxError as e:
return f"Error: {e}"
except FileNotFoundError:
return f"Error: File not found: {requested_path}"
except PermissionError:
return f"Error: Permission denied reading file: {requested_path}"
except IsADirectoryError:
return f"Error: Path is a directory, not a file: {requested_path}"
except Exception as e:
return f"Error: Unexpected error reading file: {_sanitize_error(e, runtime)}"
@tool("write_file", parse_docstring=True)
def write_file_tool(
runtime: ToolRuntime[ContextT, ThreadState],
description: str,
path: str,
content: str,
append: bool = False,
) -> str:
"""Write text content to a file.
Args:
description: Explain why you are writing to this file in short words. ALWAYS PROVIDE THIS PARAMETER FIRST.
path: The **absolute** path to the file to write to. ALWAYS PROVIDE THIS PARAMETER SECOND.
content: The content to write to the file. ALWAYS PROVIDE THIS PARAMETER THIRD.
"""
try:
sandbox = ensure_sandbox_initialized(runtime)
ensure_thread_directories_exist(runtime)
requested_path = path
if is_local_sandbox(runtime):
thread_data = get_thread_data(runtime)
validate_local_tool_path(path, thread_data)
path = _resolve_and_validate_user_data_path(path, thread_data)
sandbox.write_file(path, content, append)
return "OK"
except SandboxError as e:
return f"Error: {e}"
except PermissionError:
return f"Error: Permission denied writing to file: {requested_path}"
except IsADirectoryError:
return f"Error: Path is a directory, not a file: {requested_path}"
except OSError as e:
return f"Error: Failed to write file '{requested_path}': {_sanitize_error(e, runtime)}"
except Exception as e:
return f"Error: Unexpected error writing file: {_sanitize_error(e, runtime)}"
@tool("str_replace", parse_docstring=True)
def str_replace_tool(
runtime: ToolRuntime[ContextT, ThreadState],
description: str,
path: str,
old_str: str,
new_str: str,
replace_all: bool = False,
) -> str:
"""Replace a substring in a file with another substring.
If `replace_all` is False (default), the substring to replace must appear **exactly once** in the file.
Args:
description: Explain why you are replacing the substring in short words. ALWAYS PROVIDE THIS PARAMETER FIRST.
path: The **absolute** path to the file to replace the substring in. ALWAYS PROVIDE THIS PARAMETER SECOND.
old_str: The substring to replace. ALWAYS PROVIDE THIS PARAMETER THIRD.
new_str: The new substring. ALWAYS PROVIDE THIS PARAMETER FOURTH.
replace_all: Whether to replace all occurrences of the substring. If False, only the first occurrence will be replaced. Default is False.
"""
try:
sandbox = ensure_sandbox_initialized(runtime)
ensure_thread_directories_exist(runtime)
requested_path = path
if is_local_sandbox(runtime):
thread_data = get_thread_data(runtime)
validate_local_tool_path(path, thread_data)
path = _resolve_and_validate_user_data_path(path, thread_data)
content = sandbox.read_file(path)
if not content:
return "OK"
if old_str not in content:
return f"Error: String to replace not found in file: {requested_path}"
if replace_all:
content = content.replace(old_str, new_str)
else:
content = content.replace(old_str, new_str, 1)
sandbox.write_file(path, content)
return "OK"
except SandboxError as e:
return f"Error: {e}"
except FileNotFoundError:
return f"Error: File not found: {requested_path}"
except PermissionError:
return f"Error: Permission denied accessing file: {requested_path}"
except Exception as e:
return f"Error: Unexpected error replacing string: {_sanitize_error(e, runtime)}"
================================================
FILE: backend/packages/harness/deerflow/skills/__init__.py
================================================
from .loader import get_skills_root_path, load_skills
from .types import Skill
from .validation import ALLOWED_FRONTMATTER_PROPERTIES, _validate_skill_frontmatter
__all__ = ["load_skills", "get_skills_root_path", "Skill", "ALLOWED_FRONTMATTER_PROPERTIES", "_validate_skill_frontmatter"]
================================================
FILE: backend/packages/harness/deerflow/skills/loader.py
================================================
import os
from pathlib import Path
from .parser import parse_skill_file
from .types import Skill
def get_skills_root_path() -> Path:
"""
Get the root path of the skills directory.
Returns:
Path to the skills directory (deer-flow/skills)
"""
# loader.py lives at packages/harness/deerflow/skills/loader.py — 5 parents up reaches backend/
backend_dir = Path(__file__).resolve().parent.parent.parent.parent.parent
# skills directory is sibling to backend directory
skills_dir = backend_dir.parent / "skills"
return skills_dir
def load_skills(skills_path: Path | None = None, use_config: bool = True, enabled_only: bool = False) -> list[Skill]:
"""
Load all skills from the skills directory.
Scans both public and custom skill directories, parsing SKILL.md files
to extract metadata. The enabled state is determined by the skills_state_config.json file.
Args:
skills_path: Optional custom path to skills directory.
If not provided and use_config is True, uses path from config.
Otherwise defaults to deer-flow/skills
use_config: Whether to load skills path from config (default: True)
enabled_only: If True, only return enabled skills (default: False)
Returns:
List of Skill objects, sorted by name
"""
if skills_path is None:
if use_config:
try:
from deerflow.config import get_app_config
config = get_app_config()
skills_path = config.skills.get_skills_path()
except Exception:
# Fallback to default if config fails
skills_path = get_skills_root_path()
else:
skills_path = get_skills_root_path()
if not skills_path.exists():
return []
skills = []
# Scan public and custom directories
for category in ["public", "custom"]:
category_path = skills_path / category
if not category_path.exists() or not category_path.is_dir():
continue
for current_root, dir_names, file_names in os.walk(category_path):
# Keep traversal deterministic and skip hidden directories.
dir_names[:] = sorted(name for name in dir_names if not name.startswith("."))
if "SKILL.md" not in file_names:
continue
skill_file = Path(current_root) / "SKILL.md"
relative_path = skill_file.parent.relative_to(category_path)
skill = parse_skill_file(skill_file, category=category, relative_path=relative_path)
if skill:
skills.append(skill)
# Load skills state configuration and update enabled status
# NOTE: We use ExtensionsConfig.from_file() instead of get_extensions_config()
# to always read the latest configuration from disk. This ensures that changes
# made through the Gateway API (which runs in a separate process) are immediately
# reflected in the LangGraph Server when loading skills.
try:
from deerflow.config.extensions_config import ExtensionsConfig
extensions_config = ExtensionsConfig.from_file()
for skill in skills:
skill.enabled = extensions_config.is_skill_enabled(skill.name, skill.category)
except Exception as e:
# If config loading fails, default to all enabled
print(f"Warning: Failed to load extensions config: {e}")
# Filter by enabled status if requested
if enabled_only:
skills = [skill for skill in skills if skill.enabled]
# Sort by name for consistent ordering
skills.sort(key=lambda s: s.name)
return skills
================================================
FILE: backend/packages/harness/deerflow/skills/parser.py
================================================
import re
from pathlib import Path
from .types import Skill
def parse_skill_file(skill_file: Path, category: str, relative_path: Path | None = None) -> Skill | None:
"""
Parse a SKILL.md file and extract metadata.
Args:
skill_file: Path to the SKILL.md file
category: Category of the skill ('public' or 'custom')
Returns:
Skill object if parsing succeeds, None otherwise
"""
if not skill_file.exists() or skill_file.name != "SKILL.md":
return None
try:
content = skill_file.read_text(encoding="utf-8")
# Extract YAML front matter
# Pattern: ---\nkey: value\n---
front_matter_match = re.match(r"^---\s*\n(.*?)\n---\s*\n", content, re.DOTALL)
if not front_matter_match:
return None
front_matter = front_matter_match.group(1)
# Parse YAML front matter (simple key-value parsing)
metadata = {}
for line in front_matter.split("\n"):
line = line.strip()
if not line:
continue
if ":" in line:
key, value = line.split(":", 1)
metadata[key.strip()] = value.strip()
# Extract required fields
name = metadata.get("name")
description = metadata.get("description")
if not name or not description:
return None
license_text = metadata.get("license")
return Skill(
name=name,
description=description,
license=license_text,
skill_dir=skill_file.parent,
skill_file=skill_file,
relative_path=relative_path or Path(skill_file.parent.name),
category=category,
enabled=True, # Default to enabled, actual state comes from config file
)
except Exception as e:
print(f"Error parsing skill file {skill_file}: {e}")
return None
================================================
FILE: backend/packages/harness/deerflow/skills/types.py
================================================
from dataclasses import dataclass
from pathlib import Path
@dataclass
class Skill:
"""Represents a skill with its metadata and file path"""
name: str
description: str
license: str | None
skill_dir: Path
skill_file: Path
relative_path: Path # Relative path from category root to skill directory
category: str # 'public' or 'custom'
enabled: bool = False # Whether this skill is enabled
@property
def skill_path(self) -> str:
"""Returns the relative path from the category root (skills/{category}) to this skill's directory"""
path = self.relative_path.as_posix()
return "" if path == "." else path
def get_container_path(self, container_base_path: str = "/mnt/skills") -> str:
"""
Get the full path to this skill in the container.
Args:
container_base_path: Base path where skills are mounted in the container
Returns:
Full container path to the skill directory
"""
category_base = f"{container_base_path}/{self.category}"
skill_path = self.skill_path
if skill_path:
return f"{category_base}/{skill_path}"
return category_base
def get_container_file_path(self, container_base_path: str = "/mnt/skills") -> str:
"""
Get the full path to this skill's main file (SKILL.md) in the container.
Args:
container_base_path: Base path where skills are mounted in the container
Returns:
Full container path to the skill's SKILL.md file
"""
return f"{self.get_container_path(container_base_path)}/SKILL.md"
def __repr__(self) -> str:
return f"Skill(name={self.name!r}, description={self.description!r}, category={self.category!r})"
================================================
FILE: backend/packages/harness/deerflow/skills/validation.py
================================================
"""Skill frontmatter validation utilities.
Pure-logic validation of SKILL.md frontmatter — no FastAPI or HTTP dependencies.
"""
import re
from pathlib import Path
import yaml
# Allowed properties in SKILL.md frontmatter
ALLOWED_FRONTMATTER_PROPERTIES = {"name", "description", "license", "allowed-tools", "metadata", "compatibility", "version", "author"}
def _validate_skill_frontmatter(skill_dir: Path) -> tuple[bool, str, str | None]:
"""Validate a skill directory's SKILL.md frontmatter.
Args:
skill_dir: Path to the skill directory containing SKILL.md.
Returns:
Tuple of (is_valid, message, skill_name).
"""
skill_md = skill_dir / "SKILL.md"
if not skill_md.exists():
return False, "SKILL.md not found", None
content = skill_md.read_text(encoding="utf-8")
if not content.startswith("---"):
return False, "No YAML frontmatter found", None
# Extract frontmatter
match = re.match(r"^---\n(.*?)\n---", content, re.DOTALL)
if not match:
return False, "Invalid frontmatter format", None
frontmatter_text = match.group(1)
# Parse YAML frontmatter
try:
frontmatter = yaml.safe_load(frontmatter_text)
if not isinstance(frontmatter, dict):
return False, "Frontmatter must be a YAML dictionary", None
except yaml.YAMLError as e:
return False, f"Invalid YAML in frontmatter: {e}", None
# Check for unexpected properties
unexpected_keys = set(frontmatter.keys()) - ALLOWED_FRONTMATTER_PROPERTIES
if unexpected_keys:
return False, f"Unexpected key(s) in SKILL.md frontmatter: {', '.join(sorted(unexpected_keys))}", None
# Check required fields
if "name" not in frontmatter:
return False, "Missing 'name' in frontmatter", None
if "description" not in frontmatter:
return False, "Missing 'description' in frontmatter", None
# Validate name
name = frontmatter.get("name", "")
if not isinstance(name, str):
return False, f"Name must be a string, got {type(name).__name__}", None
name = name.strip()
if not name:
return False, "Name cannot be empty", None
# Check naming convention (hyphen-case: lowercase with hyphens)
if not re.match(r"^[a-z0-9-]+$", name):
return False, f"Name '{name}' should be hyphen-case (lowercase letters, digits, and hyphens only)", None
if name.startswith("-") or name.endswith("-") or "--" in name:
return False, f"Name '{name}' cannot start/end with hyphen or contain consecutive hyphens", None
if len(name) > 64:
return False, f"Name is too long ({len(name)} characters). Maximum is 64 characters.", None
# Validate description
description = frontmatter.get("description", "")
if not isinstance(description, str):
return False, f"Description must be a string, got {type(description).__name__}", None
description = description.strip()
if description:
if "<" in description or ">" in description:
return False, "Description cannot contain angle brackets (< or >)", None
if len(description) > 1024:
return False, f"Description is too long ({len(description)} characters). Maximum is 1024 characters.", None
return True, "Skill is valid!", name
================================================
FILE: backend/packages/harness/deerflow/subagents/__init__.py
================================================
from .config import SubagentConfig
from .executor import SubagentExecutor, SubagentResult
from .registry import get_subagent_config, list_subagents
__all__ = [
"SubagentConfig",
"SubagentExecutor",
"SubagentResult",
"get_subagent_config",
"list_subagents",
]
================================================
FILE: backend/packages/harness/deerflow/subagents/builtins/__init__.py
================================================
"""Built-in subagent configurations."""
from .bash_agent import BASH_AGENT_CONFIG
from .general_purpose import GENERAL_PURPOSE_CONFIG
__all__ = [
"GENERAL_PURPOSE_CONFIG",
"BASH_AGENT_CONFIG",
]
# Registry of built-in subagents
BUILTIN_SUBAGENTS = {
"general-purpose": GENERAL_PURPOSE_CONFIG,
"bash": BASH_AGENT_CONFIG,
}
================================================
FILE: backend/packages/harness/deerflow/subagents/builtins/bash_agent.py
================================================
"""Bash command execution subagent configuration."""
from deerflow.subagents.config import SubagentConfig
BASH_AGENT_CONFIG = SubagentConfig(
name="bash",
description="""Command execution specialist for running bash commands in a separate context.
Use this subagent when:
- You need to run a series of related bash commands
- Terminal operations like git, npm, docker, etc.
- Command output is verbose and would clutter main context
- Build, test, or deployment operations
Do NOT use for simple single commands - use bash tool directly instead.""",
system_prompt="""You are a bash command execution specialist. Execute the requested commands carefully and report results clearly.
- Execute commands one at a time when they depend on each other
- Use parallel execution when commands are independent
- Report both stdout and stderr when relevant
- Handle errors gracefully and explain what went wrong
- Use absolute paths for file operations
- Be cautious with destructive operations (rm, overwrite, etc.)
For each command or group of commands:
1. What was executed
2. The result (success/failure)
3. Relevant output (summarized if verbose)
4. Any errors or warnings
You have access to the sandbox environment:
- User uploads: `/mnt/user-data/uploads`
- User workspace: `/mnt/user-data/workspace`
- Output files: `/mnt/user-data/outputs`
""",
tools=["bash", "ls", "read_file", "write_file", "str_replace"], # Sandbox tools only
disallowed_tools=["task", "ask_clarification", "present_files"],
model="inherit",
max_turns=30,
)
================================================
FILE: backend/packages/harness/deerflow/subagents/builtins/general_purpose.py
================================================
"""General-purpose subagent configuration."""
from deerflow.subagents.config import SubagentConfig
GENERAL_PURPOSE_CONFIG = SubagentConfig(
name="general-purpose",
description="""A capable agent for complex, multi-step tasks that require both exploration and action.
Use this subagent when:
- The task requires both exploration and modification
- Complex reasoning is needed to interpret results
- Multiple dependent steps must be executed
- The task would benefit from isolated context management
Do NOT use for simple, single-step operations.""",
system_prompt="""You are a general-purpose subagent working on a delegated task. Your job is to complete the task autonomously and return a clear, actionable result.
- Focus on completing the delegated task efficiently
- Use available tools as needed to accomplish the goal
- Think step by step but act decisively
- If you encounter issues, explain them clearly in your response
- Return a concise summary of what you accomplished
- Do NOT ask for clarification - work with the information provided
When you complete the task, provide:
1. A brief summary of what was accomplished
2. Key findings or results
3. Any relevant file paths, data, or artifacts created
4. Issues encountered (if any)
5. Citations: Use `[citation:Title](URL)` format for external sources
You have access to the same sandbox environment as the parent agent:
- User uploads: `/mnt/user-data/uploads`
- User workspace: `/mnt/user-data/workspace`
- Output files: `/mnt/user-data/outputs`
""",
tools=None, # Inherit all tools from parent
disallowed_tools=["task", "ask_clarification", "present_files"], # Prevent nesting and clarification
model="inherit",
max_turns=50,
)
================================================
FILE: backend/packages/harness/deerflow/subagents/config.py
================================================
"""Subagent configuration definitions."""
from dataclasses import dataclass, field
@dataclass
class SubagentConfig:
"""Configuration for a subagent.
Attributes:
name: Unique identifier for the subagent.
description: When Claude should delegate to this subagent.
system_prompt: The system prompt that guides the subagent's behavior.
tools: Optional list of tool names to allow. If None, inherits all tools.
disallowed_tools: Optional list of tool names to deny.
model: Model to use - 'inherit' uses parent's model.
max_turns: Maximum number of agent turns before stopping.
timeout_seconds: Maximum execution time in seconds (default: 900 = 15 minutes).
"""
name: str
description: str
system_prompt: str
tools: list[str] | None = None
disallowed_tools: list[str] | None = field(default_factory=lambda: ["task"])
model: str = "inherit"
max_turns: int = 50
timeout_seconds: int = 900
================================================
FILE: backend/packages/harness/deerflow/subagents/executor.py
================================================
"""Subagent execution engine."""
import asyncio
import logging
import threading
import uuid
from concurrent.futures import Future, ThreadPoolExecutor
from concurrent.futures import TimeoutError as FuturesTimeoutError
from dataclasses import dataclass
from datetime import datetime
from enum import Enum
from typing import Any
from langchain.agents import create_agent
from langchain.tools import BaseTool
from langchain_core.messages import AIMessage, HumanMessage
from langchain_core.runnables import RunnableConfig
from deerflow.agents.thread_state import SandboxState, ThreadDataState, ThreadState
from deerflow.models import create_chat_model
from deerflow.subagents.config import SubagentConfig
logger = logging.getLogger(__name__)
class SubagentStatus(Enum):
"""Status of a subagent execution."""
PENDING = "pending"
RUNNING = "running"
COMPLETED = "completed"
FAILED = "failed"
TIMED_OUT = "timed_out"
@dataclass
class SubagentResult:
"""Result of a subagent execution.
Attributes:
task_id: Unique identifier for this execution.
trace_id: Trace ID for distributed tracing (links parent and subagent logs).
status: Current status of the execution.
result: The final result message (if completed).
error: Error message (if failed).
started_at: When execution started.
completed_at: When execution completed.
ai_messages: List of complete AI messages (as dicts) generated during execution.
"""
task_id: str
trace_id: str
status: SubagentStatus
result: str | None = None
error: str | None = None
started_at: datetime | None = None
completed_at: datetime | None = None
ai_messages: list[dict[str, Any]] | None = None
def __post_init__(self):
"""Initialize mutable defaults."""
if self.ai_messages is None:
self.ai_messages = []
# Global storage for background task results
_background_tasks: dict[str, SubagentResult] = {}
_background_tasks_lock = threading.Lock()
# Thread pool for background task scheduling and orchestration
_scheduler_pool = ThreadPoolExecutor(max_workers=3, thread_name_prefix="subagent-scheduler-")
# Thread pool for actual subagent execution (with timeout support)
# Larger pool to avoid blocking when scheduler submits execution tasks
_execution_pool = ThreadPoolExecutor(max_workers=3, thread_name_prefix="subagent-exec-")
def _filter_tools(
all_tools: list[BaseTool],
allowed: list[str] | None,
disallowed: list[str] | None,
) -> list[BaseTool]:
"""Filter tools based on subagent configuration.
Args:
all_tools: List of all available tools.
allowed: Optional allowlist of tool names. If provided, only these tools are included.
disallowed: Optional denylist of tool names. These tools are always excluded.
Returns:
Filtered list of tools.
"""
filtered = all_tools
# Apply allowlist if specified
if allowed is not None:
allowed_set = set(allowed)
filtered = [t for t in filtered if t.name in allowed_set]
# Apply denylist
if disallowed is not None:
disallowed_set = set(disallowed)
filtered = [t for t in filtered if t.name not in disallowed_set]
return filtered
def _get_model_name(config: SubagentConfig, parent_model: str | None) -> str | None:
"""Resolve the model name for a subagent.
Args:
config: Subagent configuration.
parent_model: The parent agent's model name.
Returns:
Model name to use, or None to use default.
"""
if config.model == "inherit":
return parent_model
return config.model
class SubagentExecutor:
"""Executor for running subagents."""
def __init__(
self,
config: SubagentConfig,
tools: list[BaseTool],
parent_model: str | None = None,
sandbox_state: SandboxState | None = None,
thread_data: ThreadDataState | None = None,
thread_id: str | None = None,
trace_id: str | None = None,
):
"""Initialize the executor.
Args:
config: Subagent configuration.
tools: List of all available tools (will be filtered).
parent_model: The parent agent's model name for inheritance.
sandbox_state: Sandbox state from parent agent.
thread_data: Thread data from parent agent.
thread_id: Thread ID for sandbox operations.
trace_id: Trace ID from parent for distributed tracing.
"""
self.config = config
self.parent_model = parent_model
self.sandbox_state = sandbox_state
self.thread_data = thread_data
self.thread_id = thread_id
# Generate trace_id if not provided (for top-level calls)
self.trace_id = trace_id or str(uuid.uuid4())[:8]
# Filter tools based on config
self.tools = _filter_tools(
tools,
config.tools,
config.disallowed_tools,
)
logger.info(f"[trace={self.trace_id}] SubagentExecutor initialized: {config.name} with {len(self.tools)} tools")
def _create_agent(self):
"""Create the agent instance."""
model_name = _get_model_name(self.config, self.parent_model)
model = create_chat_model(name=model_name, thinking_enabled=False)
from deerflow.agents.middlewares.tool_error_handling_middleware import build_subagent_runtime_middlewares
# Reuse shared middleware composition with lead agent.
middlewares = build_subagent_runtime_middlewares(lazy_init=True)
return create_agent(
model=model,
tools=self.tools,
middleware=middlewares,
system_prompt=self.config.system_prompt,
state_schema=ThreadState,
)
def _build_initial_state(self, task: str) -> dict[str, Any]:
"""Build the initial state for agent execution.
Args:
task: The task description.
Returns:
Initial state dictionary.
"""
state: dict[str, Any] = {
"messages": [HumanMessage(content=task)],
}
# Pass through sandbox and thread data from parent
if self.sandbox_state is not None:
state["sandbox"] = self.sandbox_state
if self.thread_data is not None:
state["thread_data"] = self.thread_data
return state
async def _aexecute(self, task: str, result_holder: SubagentResult | None = None) -> SubagentResult:
"""Execute a task asynchronously.
Args:
task: The task description for the subagent.
result_holder: Optional pre-created result object to update during execution.
Returns:
SubagentResult with the execution result.
"""
if result_holder is not None:
# Use the provided result holder (for async execution with real-time updates)
result = result_holder
else:
# Create a new result for synchronous execution
task_id = str(uuid.uuid4())[:8]
result = SubagentResult(
task_id=task_id,
trace_id=self.trace_id,
status=SubagentStatus.RUNNING,
started_at=datetime.now(),
)
try:
agent = self._create_agent()
state = self._build_initial_state(task)
# Build config with thread_id for sandbox access and recursion limit
run_config: RunnableConfig = {
"recursion_limit": self.config.max_turns,
}
context = {}
if self.thread_id:
run_config["configurable"] = {"thread_id": self.thread_id}
context["thread_id"] = self.thread_id
logger.info(f"[trace={self.trace_id}] Subagent {self.config.name} starting async execution with max_turns={self.config.max_turns}")
# Use stream instead of invoke to get real-time updates
# This allows us to collect AI messages as they are generated
final_state = None
async for chunk in agent.astream(state, config=run_config, context=context, stream_mode="values"): # type: ignore[arg-type]
final_state = chunk
# Extract AI messages from the current state
messages = chunk.get("messages", [])
if messages:
last_message = messages[-1]
# Check if this is a new AI message
if isinstance(last_message, AIMessage):
# Convert message to dict for serialization
message_dict = last_message.model_dump()
# Only add if it's not already in the list (avoid duplicates)
# Check by comparing message IDs if available, otherwise compare full dict
message_id = message_dict.get("id")
is_duplicate = False
if message_id:
is_duplicate = any(msg.get("id") == message_id for msg in result.ai_messages)
else:
is_duplicate = message_dict in result.ai_messages
if not is_duplicate:
result.ai_messages.append(message_dict)
logger.info(f"[trace={self.trace_id}] Subagent {self.config.name} captured AI message #{len(result.ai_messages)}")
logger.info(f"[trace={self.trace_id}] Subagent {self.config.name} completed async execution")
if final_state is None:
logger.warning(f"[trace={self.trace_id}] Subagent {self.config.name} no final state")
result.result = "No response generated"
else:
# Extract the final message - find the last AIMessage
messages = final_state.get("messages", [])
logger.info(f"[trace={self.trace_id}] Subagent {self.config.name} final messages count: {len(messages)}")
# Find the last AIMessage in the conversation
last_ai_message = None
for msg in reversed(messages):
if isinstance(msg, AIMessage):
last_ai_message = msg
break
if last_ai_message is not None:
content = last_ai_message.content
# Handle both str and list content types for the final result
if isinstance(content, str):
result.result = content
elif isinstance(content, list):
# Extract text from list of content blocks for final result only.
# Concatenate raw string chunks directly, but preserve separation
# between full text blocks for readability.
text_parts = []
pending_str_parts = []
for block in content:
if isinstance(block, str):
pending_str_parts.append(block)
elif isinstance(block, dict):
if pending_str_parts:
text_parts.append("".join(pending_str_parts))
pending_str_parts.clear()
text_val = block.get("text")
if isinstance(text_val, str):
text_parts.append(text_val)
if pending_str_parts:
text_parts.append("".join(pending_str_parts))
result.result = "\n".join(text_parts) if text_parts else "No text content in response"
else:
result.result = str(content)
elif messages:
# Fallback: use the last message if no AIMessage found
last_message = messages[-1]
logger.warning(f"[trace={self.trace_id}] Subagent {self.config.name} no AIMessage found, using last message: {type(last_message)}")
raw_content = last_message.content if hasattr(last_message, "content") else str(last_message)
if isinstance(raw_content, str):
result.result = raw_content
elif isinstance(raw_content, list):
parts = []
pending_str_parts = []
for block in raw_content:
if isinstance(block, str):
pending_str_parts.append(block)
elif isinstance(block, dict):
if pending_str_parts:
parts.append("".join(pending_str_parts))
pending_str_parts.clear()
text_val = block.get("text")
if isinstance(text_val, str):
parts.append(text_val)
if pending_str_parts:
parts.append("".join(pending_str_parts))
result.result = "\n".join(parts) if parts else "No text content in response"
else:
result.result = str(raw_content)
else:
logger.warning(f"[trace={self.trace_id}] Subagent {self.config.name} no messages in final state")
result.result = "No response generated"
result.status = SubagentStatus.COMPLETED
result.completed_at = datetime.now()
except Exception as e:
logger.exception(f"[trace={self.trace_id}] Subagent {self.config.name} async execution failed")
result.status = SubagentStatus.FAILED
result.error = str(e)
result.completed_at = datetime.now()
return result
def execute(self, task: str, result_holder: SubagentResult | None = None) -> SubagentResult:
"""Execute a task synchronously (wrapper around async execution).
This method runs the async execution in a new event loop, allowing
asynchronous tools (like MCP tools) to be used within the thread pool.
Args:
task: The task description for the subagent.
result_holder: Optional pre-created result object to update during execution.
Returns:
SubagentResult with the execution result.
"""
# Run the async execution in a new event loop
# This is necessary because:
# 1. We may have async-only tools (like MCP tools)
# 2. We're running inside a ThreadPoolExecutor which doesn't have an event loop
#
# Note: _aexecute() catches all exceptions internally, so this outer
# try-except only handles asyncio.run() failures (e.g., if called from
# an async context where an event loop already exists). Subagent execution
# errors are handled within _aexecute() and returned as FAILED status.
try:
return asyncio.run(self._aexecute(task, result_holder))
except Exception as e:
logger.exception(f"[trace={self.trace_id}] Subagent {self.config.name} execution failed")
# Create a result with error if we don't have one
if result_holder is not None:
result = result_holder
else:
result = SubagentResult(
task_id=str(uuid.uuid4())[:8],
trace_id=self.trace_id,
status=SubagentStatus.FAILED,
)
result.status = SubagentStatus.FAILED
result.error = str(e)
result.completed_at = datetime.now()
return result
def execute_async(self, task: str, task_id: str | None = None) -> str:
"""Start a task execution in the background.
Args:
task: The task description for the subagent.
task_id: Optional task ID to use. If not provided, a random UUID will be generated.
Returns:
Task ID that can be used to check status later.
"""
# Use provided task_id or generate a new one
if task_id is None:
task_id = str(uuid.uuid4())[:8]
# Create initial pending result
result = SubagentResult(
task_id=task_id,
trace_id=self.trace_id,
status=SubagentStatus.PENDING,
)
logger.info(f"[trace={self.trace_id}] Subagent {self.config.name} starting async execution, task_id={task_id}, timeout={self.config.timeout_seconds}s")
with _background_tasks_lock:
_background_tasks[task_id] = result
# Submit to scheduler pool
def run_task():
with _background_tasks_lock:
_background_tasks[task_id].status = SubagentStatus.RUNNING
_background_tasks[task_id].started_at = datetime.now()
result_holder = _background_tasks[task_id]
try:
# Submit execution to execution pool with timeout
# Pass result_holder so execute() can update it in real-time
execution_future: Future = _execution_pool.submit(self.execute, task, result_holder)
try:
# Wait for execution with timeout
exec_result = execution_future.result(timeout=self.config.timeout_seconds)
with _background_tasks_lock:
_background_tasks[task_id].status = exec_result.status
_background_tasks[task_id].result = exec_result.result
_background_tasks[task_id].error = exec_result.error
_background_tasks[task_id].completed_at = datetime.now()
_background_tasks[task_id].ai_messages = exec_result.ai_messages
except FuturesTimeoutError:
logger.error(f"[trace={self.trace_id}] Subagent {self.config.name} execution timed out after {self.config.timeout_seconds}s")
with _background_tasks_lock:
_background_tasks[task_id].status = SubagentStatus.TIMED_OUT
_background_tasks[task_id].error = f"Execution timed out after {self.config.timeout_seconds} seconds"
_background_tasks[task_id].completed_at = datetime.now()
# Cancel the future (best effort - may not stop the actual execution)
execution_future.cancel()
except Exception as e:
logger.exception(f"[trace={self.trace_id}] Subagent {self.config.name} async execution failed")
with _background_tasks_lock:
_background_tasks[task_id].status = SubagentStatus.FAILED
_background_tasks[task_id].error = str(e)
_background_tasks[task_id].completed_at = datetime.now()
_scheduler_pool.submit(run_task)
return task_id
MAX_CONCURRENT_SUBAGENTS = 3
def get_background_task_result(task_id: str) -> SubagentResult | None:
"""Get the result of a background task.
Args:
task_id: The task ID returned by execute_async.
Returns:
SubagentResult if found, None otherwise.
"""
with _background_tasks_lock:
return _background_tasks.get(task_id)
def list_background_tasks() -> list[SubagentResult]:
"""List all background tasks.
Returns:
List of all SubagentResult instances.
"""
with _background_tasks_lock:
return list(_background_tasks.values())
def cleanup_background_task(task_id: str) -> None:
"""Remove a completed task from background tasks.
Should be called by task_tool after it finishes polling and returns the result.
This prevents memory leaks from accumulated completed tasks.
Only removes tasks that are in a terminal state (COMPLETED/FAILED/TIMED_OUT)
to avoid race conditions with the background executor still updating the task entry.
Args:
task_id: The task ID to remove.
"""
with _background_tasks_lock:
result = _background_tasks.get(task_id)
if result is None:
# Nothing to clean up; may have been removed already.
logger.debug("Requested cleanup for unknown background task %s", task_id)
return
# Only clean up tasks that are in a terminal state to avoid races with
# the background executor still updating the task entry.
is_terminal_status = result.status in {
SubagentStatus.COMPLETED,
SubagentStatus.FAILED,
SubagentStatus.TIMED_OUT,
}
if is_terminal_status or result.completed_at is not None:
del _background_tasks[task_id]
logger.debug("Cleaned up background task: %s", task_id)
else:
logger.debug(
"Skipping cleanup for non-terminal background task %s (status=%s)",
task_id,
result.status.value if hasattr(result.status, "value") else result.status,
)
================================================
FILE: backend/packages/harness/deerflow/subagents/registry.py
================================================
"""Subagent registry for managing available subagents."""
import logging
from dataclasses import replace
from deerflow.subagents.builtins import BUILTIN_SUBAGENTS
from deerflow.subagents.config import SubagentConfig
logger = logging.getLogger(__name__)
def get_subagent_config(name: str) -> SubagentConfig | None:
"""Get a subagent configuration by name, with config.yaml overrides applied.
Args:
name: The name of the subagent.
Returns:
SubagentConfig if found (with any config.yaml overrides applied), None otherwise.
"""
config = BUILTIN_SUBAGENTS.get(name)
if config is None:
return None
# Apply timeout override from config.yaml (lazy import to avoid circular deps)
from deerflow.config.subagents_config import get_subagents_app_config
app_config = get_subagents_app_config()
effective_timeout = app_config.get_timeout_for(name)
if effective_timeout != config.timeout_seconds:
logger.debug(f"Subagent '{name}': timeout overridden by config.yaml ({config.timeout_seconds}s -> {effective_timeout}s)")
config = replace(config, timeout_seconds=effective_timeout)
return config
def list_subagents() -> list[SubagentConfig]:
"""List all available subagent configurations (with config.yaml overrides applied).
Returns:
List of all registered SubagentConfig instances.
"""
return [get_subagent_config(name) for name in BUILTIN_SUBAGENTS]
def get_subagent_names() -> list[str]:
"""Get all available subagent names.
Returns:
List of subagent names.
"""
return list(BUILTIN_SUBAGENTS.keys())
================================================
FILE: backend/packages/harness/deerflow/tools/__init__.py
================================================
from .tools import get_available_tools
__all__ = ["get_available_tools"]
================================================
FILE: backend/packages/harness/deerflow/tools/builtins/__init__.py
================================================
from .clarification_tool import ask_clarification_tool
from .present_file_tool import present_file_tool
from .setup_agent_tool import setup_agent
from .task_tool import task_tool
from .view_image_tool import view_image_tool
__all__ = [
"setup_agent",
"present_file_tool",
"ask_clarification_tool",
"view_image_tool",
"task_tool",
]
================================================
FILE: backend/packages/harness/deerflow/tools/builtins/clarification_tool.py
================================================
from typing import Literal
from langchain.tools import tool
@tool("ask_clarification", parse_docstring=True, return_direct=True)
def ask_clarification_tool(
question: str,
clarification_type: Literal[
"missing_info",
"ambiguous_requirement",
"approach_choice",
"risk_confirmation",
"suggestion",
],
context: str | None = None,
options: list[str] | None = None,
) -> str:
"""Ask the user for clarification when you need more information to proceed.
Use this tool when you encounter situations where you cannot proceed without user input:
- **Missing information**: Required details not provided (e.g., file paths, URLs, specific requirements)
- **Ambiguous requirements**: Multiple valid interpretations exist
- **Approach choices**: Several valid approaches exist and you need user preference
- **Risky operations**: Destructive actions that need explicit confirmation (e.g., deleting files, modifying production)
- **Suggestions**: You have a recommendation but want user approval before proceeding
The execution will be interrupted and the question will be presented to the user.
Wait for the user's response before continuing.
When to use ask_clarification:
- You need information that wasn't provided in the user's request
- The requirement can be interpreted in multiple ways
- Multiple valid implementation approaches exist
- You're about to perform a potentially dangerous operation
- You have a recommendation but need user approval
Best practices:
- Ask ONE clarification at a time for clarity
- Be specific and clear in your question
- Don't make assumptions when clarification is needed
- For risky operations, ALWAYS ask for confirmation
- After calling this tool, execution will be interrupted automatically
Args:
question: The clarification question to ask the user. Be specific and clear.
clarification_type: The type of clarification needed (missing_info, ambiguous_requirement, approach_choice, risk_confirmation, suggestion).
context: Optional context explaining why clarification is needed. Helps the user understand the situation.
options: Optional list of choices (for approach_choice or suggestion types). Present clear options for the user to choose from.
"""
# This is a placeholder implementation
# The actual logic is handled by ClarificationMiddleware which intercepts this tool call
# and interrupts execution to present the question to the user
return "Clarification request processed by middleware"
================================================
FILE: backend/packages/harness/deerflow/tools/builtins/present_file_tool.py
================================================
from pathlib import Path
from typing import Annotated
from langchain.tools import InjectedToolCallId, ToolRuntime, tool
from langchain_core.messages import ToolMessage
from langgraph.types import Command
from langgraph.typing import ContextT
from deerflow.agents.thread_state import ThreadState
from deerflow.config.paths import VIRTUAL_PATH_PREFIX, get_paths
OUTPUTS_VIRTUAL_PREFIX = f"{VIRTUAL_PATH_PREFIX}/outputs"
def _normalize_presented_filepath(
runtime: ToolRuntime[ContextT, ThreadState],
filepath: str,
) -> str:
"""Normalize a presented file path to the `/mnt/user-data/outputs/*` contract.
Accepts either:
- A virtual sandbox path such as `/mnt/user-data/outputs/report.md`
- A host-side thread outputs path such as
`/app/backend/.deer-flow/threads//user-data/outputs/report.md`
Returns:
The normalized virtual path.
Raises:
ValueError: If runtime metadata is missing or the path is outside the
current thread's outputs directory.
"""
if runtime.state is None:
raise ValueError("Thread runtime state is not available")
thread_id = runtime.context.get("thread_id")
if not thread_id:
raise ValueError("Thread ID is not available in runtime context")
thread_data = runtime.state.get("thread_data") or {}
outputs_path = thread_data.get("outputs_path")
if not outputs_path:
raise ValueError("Thread outputs path is not available in runtime state")
outputs_dir = Path(outputs_path).resolve()
stripped = filepath.lstrip("/")
virtual_prefix = VIRTUAL_PATH_PREFIX.lstrip("/")
if stripped == virtual_prefix or stripped.startswith(virtual_prefix + "/"):
actual_path = get_paths().resolve_virtual_path(thread_id, filepath)
else:
actual_path = Path(filepath).expanduser().resolve()
try:
relative_path = actual_path.relative_to(outputs_dir)
except ValueError as exc:
raise ValueError(f"Only files in {OUTPUTS_VIRTUAL_PREFIX} can be presented: {filepath}") from exc
return f"{OUTPUTS_VIRTUAL_PREFIX}/{relative_path.as_posix()}"
@tool("present_files", parse_docstring=True)
def present_file_tool(
runtime: ToolRuntime[ContextT, ThreadState],
filepaths: list[str],
tool_call_id: Annotated[str, InjectedToolCallId],
) -> Command:
"""Make files visible to the user for viewing and rendering in the client interface.
When to use the present_files tool:
- Making any file available for the user to view, download, or interact with
- Presenting multiple related files at once
- After creating files that should be presented to the user
When NOT to use the present_files tool:
- When you only need to read file contents for your own processing
- For temporary or intermediate files not meant for user viewing
Notes:
- You should call this tool after creating files and moving them to the `/mnt/user-data/outputs` directory.
- This tool can be safely called in parallel with other tools. State updates are handled by a reducer to prevent conflicts.
Args:
filepaths: List of absolute file paths to present to the user. **Only** files in `/mnt/user-data/outputs` can be presented.
"""
try:
normalized_paths = [_normalize_presented_filepath(runtime, filepath) for filepath in filepaths]
except ValueError as exc:
return Command(
update={"messages": [ToolMessage(f"Error: {exc}", tool_call_id=tool_call_id)]},
)
# The merge_artifacts reducer will handle merging and deduplication
return Command(
update={
"artifacts": normalized_paths,
"messages": [ToolMessage("Successfully presented files", tool_call_id=tool_call_id)],
},
)
================================================
FILE: backend/packages/harness/deerflow/tools/builtins/setup_agent_tool.py
================================================
import logging
import yaml
from langchain_core.messages import ToolMessage
from langchain_core.tools import tool
from langgraph.prebuilt import ToolRuntime
from langgraph.types import Command
from deerflow.config.paths import get_paths
logger = logging.getLogger(__name__)
@tool
def setup_agent(
soul: str,
description: str,
runtime: ToolRuntime,
) -> Command:
"""Setup the custom DeerFlow agent.
Args:
soul: Full SOUL.md content defining the agent's personality and behavior.
description: One-line description of what the agent does.
"""
agent_name: str | None = runtime.context.get("agent_name")
try:
paths = get_paths()
agent_dir = paths.agent_dir(agent_name) if agent_name else paths.base_dir
agent_dir.mkdir(parents=True, exist_ok=True)
if agent_name:
# If agent_name is provided, we are creating a custom agent in the agents/ directory
config_data: dict = {"name": agent_name}
if description:
config_data["description"] = description
config_file = agent_dir / "config.yaml"
with open(config_file, "w", encoding="utf-8") as f:
yaml.dump(config_data, f, default_flow_style=False, allow_unicode=True)
soul_file = agent_dir / "SOUL.md"
soul_file.write_text(soul, encoding="utf-8")
logger.info(f"[agent_creator] Created agent '{agent_name}' at {agent_dir}")
return Command(
update={
"created_agent_name": agent_name,
"messages": [ToolMessage(content=f"Agent '{agent_name}' created successfully!", tool_call_id=runtime.tool_call_id)],
}
)
except Exception as e:
import shutil
if agent_name and agent_dir.exists():
# Cleanup the custom agent directory only if it was created but an error occurred during setup
shutil.rmtree(agent_dir)
logger.error(f"[agent_creator] Failed to create agent '{agent_name}': {e}", exc_info=True)
return Command(update={"messages": [ToolMessage(content=f"Error: {e}", tool_call_id=runtime.tool_call_id)]})
================================================
FILE: backend/packages/harness/deerflow/tools/builtins/task_tool.py
================================================
"""Task tool for delegating work to subagents."""
import logging
import time
import uuid
from dataclasses import replace
from typing import Annotated, Literal
from langchain.tools import InjectedToolCallId, ToolRuntime, tool
from langgraph.config import get_stream_writer
from langgraph.typing import ContextT
from deerflow.agents.lead_agent.prompt import get_skills_prompt_section
from deerflow.agents.thread_state import ThreadState
from deerflow.subagents import SubagentExecutor, get_subagent_config
from deerflow.subagents.executor import SubagentStatus, cleanup_background_task, get_background_task_result
logger = logging.getLogger(__name__)
@tool("task", parse_docstring=True)
def task_tool(
runtime: ToolRuntime[ContextT, ThreadState],
description: str,
prompt: str,
subagent_type: Literal["general-purpose", "bash"],
tool_call_id: Annotated[str, InjectedToolCallId],
max_turns: int | None = None,
) -> str:
"""Delegate a task to a specialized subagent that runs in its own context.
Subagents help you:
- Preserve context by keeping exploration and implementation separate
- Handle complex multi-step tasks autonomously
- Execute commands or operations in isolated contexts
Available subagent types:
- **general-purpose**: A capable agent for complex, multi-step tasks that require
both exploration and action. Use when the task requires complex reasoning,
multiple dependent steps, or would benefit from isolated context.
- **bash**: Command execution specialist for running bash commands. Use for
git operations, build processes, or when command output would be verbose.
When to use this tool:
- Complex tasks requiring multiple steps or tools
- Tasks that produce verbose output
- When you want to isolate context from the main conversation
- Parallel research or exploration tasks
When NOT to use this tool:
- Simple, single-step operations (use tools directly)
- Tasks requiring user interaction or clarification
Args:
description: A short (3-5 word) description of the task for logging/display. ALWAYS PROVIDE THIS PARAMETER FIRST.
prompt: The task description for the subagent. Be specific and clear about what needs to be done. ALWAYS PROVIDE THIS PARAMETER SECOND.
subagent_type: The type of subagent to use. ALWAYS PROVIDE THIS PARAMETER THIRD.
max_turns: Optional maximum number of agent turns. Defaults to subagent's configured max.
"""
# Get subagent configuration
config = get_subagent_config(subagent_type)
if config is None:
return f"Error: Unknown subagent type '{subagent_type}'. Available: general-purpose, bash"
# Build config overrides
overrides: dict = {}
skills_section = get_skills_prompt_section()
if skills_section:
overrides["system_prompt"] = config.system_prompt + "\n\n" + skills_section
if max_turns is not None:
overrides["max_turns"] = max_turns
if overrides:
config = replace(config, **overrides)
# Extract parent context from runtime
sandbox_state = None
thread_data = None
thread_id = None
parent_model = None
trace_id = None
if runtime is not None:
sandbox_state = runtime.state.get("sandbox")
thread_data = runtime.state.get("thread_data")
thread_id = runtime.context.get("thread_id")
# Try to get parent model from configurable
metadata = runtime.config.get("metadata", {})
parent_model = metadata.get("model_name")
# Get or generate trace_id for distributed tracing
trace_id = metadata.get("trace_id") or str(uuid.uuid4())[:8]
# Get available tools (excluding task tool to prevent nesting)
# Lazy import to avoid circular dependency
from deerflow.tools import get_available_tools
# Subagents should not have subagent tools enabled (prevent recursive nesting)
tools = get_available_tools(model_name=parent_model, subagent_enabled=False)
# Create executor
executor = SubagentExecutor(
config=config,
tools=tools,
parent_model=parent_model,
sandbox_state=sandbox_state,
thread_data=thread_data,
thread_id=thread_id,
trace_id=trace_id,
)
# Start background execution (always async to prevent blocking)
# Use tool_call_id as task_id for better traceability
task_id = executor.execute_async(prompt, task_id=tool_call_id)
# Poll for task completion in backend (removes need for LLM to poll)
poll_count = 0
last_status = None
last_message_count = 0 # Track how many AI messages we've already sent
# Polling timeout: execution timeout + 60s buffer, checked every 5s
max_poll_count = (config.timeout_seconds + 60) // 5
logger.info(f"[trace={trace_id}] Started background task {task_id} (subagent={subagent_type}, timeout={config.timeout_seconds}s, polling_limit={max_poll_count} polls)")
writer = get_stream_writer()
# Send Task Started message'
writer({"type": "task_started", "task_id": task_id, "description": description})
while True:
result = get_background_task_result(task_id)
if result is None:
logger.error(f"[trace={trace_id}] Task {task_id} not found in background tasks")
writer({"type": "task_failed", "task_id": task_id, "error": "Task disappeared from background tasks"})
cleanup_background_task(task_id)
return f"Error: Task {task_id} disappeared from background tasks"
# Log status changes for debugging
if result.status != last_status:
logger.info(f"[trace={trace_id}] Task {task_id} status: {result.status.value}")
last_status = result.status
# Check for new AI messages and send task_running events
current_message_count = len(result.ai_messages)
if current_message_count > last_message_count:
# Send task_running event for each new message
for i in range(last_message_count, current_message_count):
message = result.ai_messages[i]
writer(
{
"type": "task_running",
"task_id": task_id,
"message": message,
"message_index": i + 1, # 1-based index for display
"total_messages": current_message_count,
}
)
logger.info(f"[trace={trace_id}] Task {task_id} sent message #{i + 1}/{current_message_count}")
last_message_count = current_message_count
# Check if task completed, failed, or timed out
if result.status == SubagentStatus.COMPLETED:
writer({"type": "task_completed", "task_id": task_id, "result": result.result})
logger.info(f"[trace={trace_id}] Task {task_id} completed after {poll_count} polls")
cleanup_background_task(task_id)
return f"Task Succeeded. Result: {result.result}"
elif result.status == SubagentStatus.FAILED:
writer({"type": "task_failed", "task_id": task_id, "error": result.error})
logger.error(f"[trace={trace_id}] Task {task_id} failed: {result.error}")
cleanup_background_task(task_id)
return f"Task failed. Error: {result.error}"
elif result.status == SubagentStatus.TIMED_OUT:
writer({"type": "task_timed_out", "task_id": task_id, "error": result.error})
logger.warning(f"[trace={trace_id}] Task {task_id} timed out: {result.error}")
cleanup_background_task(task_id)
return f"Task timed out. Error: {result.error}"
# Still running, wait before next poll
time.sleep(5) # Poll every 5 seconds
poll_count += 1
# Polling timeout as a safety net (in case thread pool timeout doesn't work)
# Set to execution timeout + 60s buffer, in 5s poll intervals
# This catches edge cases where the background task gets stuck
# Note: We don't call cleanup_background_task here because the task may
# still be running in the background. The cleanup will happen when the
# executor completes and sets a terminal status.
if poll_count > max_poll_count:
timeout_minutes = config.timeout_seconds // 60
logger.error(f"[trace={trace_id}] Task {task_id} polling timed out after {poll_count} polls (should have been caught by thread pool timeout)")
writer({"type": "task_timed_out", "task_id": task_id})
return f"Task polling timed out after {timeout_minutes} minutes. This may indicate the background task is stuck. Status: {result.status.value}"
================================================
FILE: backend/packages/harness/deerflow/tools/builtins/tool_search.py
================================================
"""Tool search — deferred tool discovery at runtime.
Contains:
- DeferredToolRegistry: stores deferred tools and handles regex search
- tool_search: the LangChain tool the agent calls to discover deferred tools
The agent sees deferred tool names in but cannot
call them until it fetches their full schema via the tool_search tool.
Source-agnostic: no mention of MCP or tool origin.
"""
import json
import logging
import re
from dataclasses import dataclass
from langchain.tools import BaseTool
from langchain_core.tools import tool
from langchain_core.utils.function_calling import convert_to_openai_function
logger = logging.getLogger(__name__)
MAX_RESULTS = 5 # Max tools returned per search
# ── Registry ──
@dataclass
class DeferredToolEntry:
"""Lightweight metadata for a deferred tool (no full schema in context)."""
name: str
description: str
tool: BaseTool # Full tool object, returned only on search match
class DeferredToolRegistry:
"""Registry of deferred tools, searchable by regex pattern."""
def __init__(self):
self._entries: list[DeferredToolEntry] = []
def register(self, tool: BaseTool) -> None:
self._entries.append(
DeferredToolEntry(
name=tool.name,
description=tool.description or "",
tool=tool,
)
)
def search(self, query: str) -> list[BaseTool]:
"""Search deferred tools by regex pattern against name + description.
Supports three query forms (aligned with Claude Code):
- "select:name1,name2" — exact name match
- "+keyword rest" — name must contain keyword, rank by rest
- "keyword query" — regex match against name + description
Returns:
List of matched BaseTool objects (up to MAX_RESULTS).
"""
if query.startswith("select:"):
names = {n.strip() for n in query[7:].split(",")}
return [e.tool for e in self._entries if e.name in names][:MAX_RESULTS]
if query.startswith("+"):
parts = query[1:].split(None, 1)
required = parts[0].lower()
candidates = [e for e in self._entries if required in e.name.lower()]
if len(parts) > 1:
candidates.sort(
key=lambda e: _regex_score(parts[1], e),
reverse=True,
)
return [e.tool for e in candidates][:MAX_RESULTS]
# General regex search
try:
regex = re.compile(query, re.IGNORECASE)
except re.error:
regex = re.compile(re.escape(query), re.IGNORECASE)
scored = []
for entry in self._entries:
searchable = f"{entry.name} {entry.description}"
if regex.search(searchable):
score = 2 if regex.search(entry.name) else 1
scored.append((score, entry))
scored.sort(key=lambda x: x[0], reverse=True)
return [entry.tool for _, entry in scored][:MAX_RESULTS]
@property
def entries(self) -> list[DeferredToolEntry]:
return list(self._entries)
def __len__(self) -> int:
return len(self._entries)
def _regex_score(pattern: str, entry: DeferredToolEntry) -> int:
try:
regex = re.compile(pattern, re.IGNORECASE)
except re.error:
regex = re.compile(re.escape(pattern), re.IGNORECASE)
return len(regex.findall(f"{entry.name} {entry.description}"))
# ── Singleton ──
_registry: DeferredToolRegistry | None = None
def get_deferred_registry() -> DeferredToolRegistry | None:
return _registry
def set_deferred_registry(registry: DeferredToolRegistry) -> None:
global _registry
_registry = registry
def reset_deferred_registry() -> None:
"""Reset the deferred registry singleton. Useful for testing."""
global _registry
_registry = None
# ── Tool ──
@tool
def tool_search(query: str) -> str:
"""Fetches full schema definitions for deferred tools so they can be called.
Deferred tools appear by name in in the system
prompt. Until fetched, only the name is known — there is no parameter
schema, so the tool cannot be invoked. This tool takes a query, matches
it against the deferred tool list, and returns the matched tools' complete
definitions. Once a tool's schema appears in that result, it is callable.
Query forms:
- "select:Read,Edit,Grep" — fetch these exact tools by name
- "notebook jupyter" — keyword search, up to max_results best matches
- "+slack send" — require "slack" in the name, rank by remaining terms
Args:
query: Query to find deferred tools. Use "select:" for
direct selection, or keywords to search.
Returns:
Matched tool definitions as JSON array.
"""
registry = get_deferred_registry()
if registry is None:
return "No deferred tools available."
matched_tools = registry.search(query)
if not matched_tools:
return f"No tools found matching: {query}"
# Use LangChain's built-in serialization to produce OpenAI function format.
# This is model-agnostic: all LLMs understand this standard schema.
tool_defs = [convert_to_openai_function(t) for t in matched_tools[:MAX_RESULTS]]
return json.dumps(tool_defs, indent=2, ensure_ascii=False)
================================================
FILE: backend/packages/harness/deerflow/tools/builtins/view_image_tool.py
================================================
import base64
import mimetypes
from pathlib import Path
from typing import Annotated
from langchain.tools import InjectedToolCallId, ToolRuntime, tool
from langchain_core.messages import ToolMessage
from langgraph.types import Command
from langgraph.typing import ContextT
from deerflow.agents.thread_state import ThreadState
from deerflow.sandbox.tools import get_thread_data, replace_virtual_path
@tool("view_image", parse_docstring=True)
def view_image_tool(
runtime: ToolRuntime[ContextT, ThreadState],
image_path: str,
tool_call_id: Annotated[str, InjectedToolCallId],
) -> Command:
"""Read an image file.
Use this tool to read an image file and make it available for display.
When to use the view_image tool:
- When you need to view an image file.
When NOT to use the view_image tool:
- For non-image files (use present_files instead)
- For multiple files at once (use present_files instead)
Args:
image_path: Absolute path to the image file. Common formats supported: jpg, jpeg, png, webp.
"""
# Replace virtual path with actual path
# /mnt/user-data/* paths are mapped to thread-specific directories
thread_data = get_thread_data(runtime)
actual_path = replace_virtual_path(image_path, thread_data)
# Validate that the path is absolute
path = Path(actual_path)
if not path.is_absolute():
return Command(
update={"messages": [ToolMessage(f"Error: Path must be absolute, got: {image_path}", tool_call_id=tool_call_id)]},
)
# Validate that the file exists
if not path.exists():
return Command(
update={"messages": [ToolMessage(f"Error: Image file not found: {image_path}", tool_call_id=tool_call_id)]},
)
# Validate that it's a file (not a directory)
if not path.is_file():
return Command(
update={"messages": [ToolMessage(f"Error: Path is not a file: {image_path}", tool_call_id=tool_call_id)]},
)
# Validate image extension
valid_extensions = {".jpg", ".jpeg", ".png", ".webp"}
if path.suffix.lower() not in valid_extensions:
return Command(
update={"messages": [ToolMessage(f"Error: Unsupported image format: {path.suffix}. Supported formats: {', '.join(valid_extensions)}", tool_call_id=tool_call_id)]},
)
# Detect MIME type from file extension
mime_type, _ = mimetypes.guess_type(actual_path)
if mime_type is None:
# Fallback to default MIME types for common image formats
extension_to_mime = {
".jpg": "image/jpeg",
".jpeg": "image/jpeg",
".png": "image/png",
".webp": "image/webp",
}
mime_type = extension_to_mime.get(path.suffix.lower(), "application/octet-stream")
# Read image file and convert to base64
try:
with open(actual_path, "rb") as f:
image_data = f.read()
image_base64 = base64.b64encode(image_data).decode("utf-8")
except Exception as e:
return Command(
update={"messages": [ToolMessage(f"Error reading image file: {str(e)}", tool_call_id=tool_call_id)]},
)
# Update viewed_images in state
# The merge_viewed_images reducer will handle merging with existing images
new_viewed_images = {image_path: {"base64": image_base64, "mime_type": mime_type}}
return Command(
update={"viewed_images": new_viewed_images, "messages": [ToolMessage("Successfully read image", tool_call_id=tool_call_id)]},
)
================================================
FILE: backend/packages/harness/deerflow/tools/tools.py
================================================
import logging
from langchain.tools import BaseTool
from deerflow.config import get_app_config
from deerflow.reflection import resolve_variable
from deerflow.tools.builtins import ask_clarification_tool, present_file_tool, task_tool, view_image_tool
from deerflow.tools.builtins.tool_search import reset_deferred_registry
logger = logging.getLogger(__name__)
BUILTIN_TOOLS = [
present_file_tool,
ask_clarification_tool,
]
SUBAGENT_TOOLS = [
task_tool,
# task_status_tool is no longer exposed to LLM (backend handles polling internally)
]
def get_available_tools(
groups: list[str] | None = None,
include_mcp: bool = True,
model_name: str | None = None,
subagent_enabled: bool = False,
) -> list[BaseTool]:
"""Get all available tools from config.
Note: MCP tools should be initialized at application startup using
`initialize_mcp_tools()` from deerflow.mcp module.
Args:
groups: Optional list of tool groups to filter by.
include_mcp: Whether to include tools from MCP servers (default: True).
model_name: Optional model name to determine if vision tools should be included.
subagent_enabled: Whether to include subagent tools (task, task_status).
Returns:
List of available tools.
"""
config = get_app_config()
loaded_tools = [resolve_variable(tool.use, BaseTool) for tool in config.tools if groups is None or tool.group in groups]
# Conditionally add tools based on config
builtin_tools = BUILTIN_TOOLS.copy()
# Add subagent tools only if enabled via runtime parameter
if subagent_enabled:
builtin_tools.extend(SUBAGENT_TOOLS)
logger.info("Including subagent tools (task)")
# If no model_name specified, use the first model (default)
if model_name is None and config.models:
model_name = config.models[0].name
# Add view_image_tool only if the model supports vision
model_config = config.get_model_config(model_name) if model_name else None
if model_config is not None and model_config.supports_vision:
builtin_tools.append(view_image_tool)
logger.info(f"Including view_image_tool for model '{model_name}' (supports_vision=True)")
# Get cached MCP tools if enabled
# NOTE: We use ExtensionsConfig.from_file() instead of config.extensions
# to always read the latest configuration from disk. This ensures that changes
# made through the Gateway API (which runs in a separate process) are immediately
# reflected when loading MCP tools.
mcp_tools = []
# Reset deferred registry upfront to prevent stale state from previous calls
reset_deferred_registry()
if include_mcp:
try:
from deerflow.config.extensions_config import ExtensionsConfig
from deerflow.mcp.cache import get_cached_mcp_tools
extensions_config = ExtensionsConfig.from_file()
if extensions_config.get_enabled_mcp_servers():
mcp_tools = get_cached_mcp_tools()
if mcp_tools:
logger.info(f"Using {len(mcp_tools)} cached MCP tool(s)")
# When tool_search is enabled, register MCP tools in the
# deferred registry and add tool_search to builtin tools.
if config.tool_search.enabled:
from deerflow.tools.builtins.tool_search import DeferredToolRegistry, set_deferred_registry
from deerflow.tools.builtins.tool_search import tool_search as tool_search_tool
registry = DeferredToolRegistry()
for t in mcp_tools:
registry.register(t)
set_deferred_registry(registry)
builtin_tools.append(tool_search_tool)
logger.info(f"Tool search active: {len(mcp_tools)} tools deferred")
except ImportError:
logger.warning("MCP module not available. Install 'langchain-mcp-adapters' package to enable MCP tools.")
except Exception as e:
logger.error(f"Failed to get cached MCP tools: {e}")
logger.info(f"Total tools loaded: {len(loaded_tools)}, built-in tools: {len(builtin_tools)}, MCP tools: {len(mcp_tools)}")
return loaded_tools + builtin_tools + mcp_tools
================================================
FILE: backend/packages/harness/deerflow/utils/file_conversion.py
================================================
"""File conversion utilities.
Converts document files (PDF, PPT, Excel, Word) to Markdown using markitdown.
No FastAPI or HTTP dependencies — pure utility functions.
"""
import logging
from pathlib import Path
logger = logging.getLogger(__name__)
# File extensions that should be converted to markdown
CONVERTIBLE_EXTENSIONS = {
".pdf",
".ppt",
".pptx",
".xls",
".xlsx",
".doc",
".docx",
}
async def convert_file_to_markdown(file_path: Path) -> Path | None:
"""Convert a file to markdown using markitdown.
Args:
file_path: Path to the file to convert.
Returns:
Path to the markdown file if conversion was successful, None otherwise.
"""
try:
from markitdown import MarkItDown
md = MarkItDown()
result = md.convert(str(file_path))
# Save as .md file with same name
md_path = file_path.with_suffix(".md")
md_path.write_text(result.text_content, encoding="utf-8")
logger.info(f"Converted {file_path.name} to markdown: {md_path.name}")
return md_path
except Exception as e:
logger.error(f"Failed to convert {file_path.name} to markdown: {e}")
return None
================================================
FILE: backend/packages/harness/deerflow/utils/network.py
================================================
"""Thread-safe network utilities."""
import socket
import threading
from contextlib import contextmanager
class PortAllocator:
"""Thread-safe port allocator that prevents port conflicts in concurrent environments.
This class maintains a set of reserved ports and uses a lock to ensure that
port allocation is atomic. Once a port is allocated, it remains reserved until
explicitly released.
Usage:
allocator = PortAllocator()
# Option 1: Manual allocation and release
port = allocator.allocate(start_port=8080)
try:
# Use the port...
finally:
allocator.release(port)
# Option 2: Context manager (recommended)
with allocator.allocate_context(start_port=8080) as port:
# Use the port...
# Port is automatically released when exiting the context
"""
def __init__(self):
self._lock = threading.Lock()
self._reserved_ports: set[int] = set()
def _is_port_available(self, port: int) -> bool:
"""Check if a port is available for binding.
Args:
port: The port number to check.
Returns:
True if the port is available, False otherwise.
"""
if port in self._reserved_ports:
return False
# Bind to 0.0.0.0 (wildcard) rather than localhost so that the check
# mirrors exactly what Docker does. Docker binds to 0.0.0.0:PORT;
# checking only 127.0.0.1 can falsely report a port as available even
# when Docker already occupies it on the wildcard address.
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
try:
s.bind(("0.0.0.0", port))
return True
except OSError:
return False
def allocate(self, start_port: int = 8080, max_range: int = 100) -> int:
"""Allocate an available port in a thread-safe manner.
This method is thread-safe. It finds an available port, marks it as reserved,
and returns it. The port remains reserved until release() is called.
Args:
start_port: The port number to start searching from.
max_range: Maximum number of ports to search.
Returns:
An available port number.
Raises:
RuntimeError: If no available port is found in the specified range.
"""
with self._lock:
for port in range(start_port, start_port + max_range):
if self._is_port_available(port):
self._reserved_ports.add(port)
return port
raise RuntimeError(f"No available port found in range {start_port}-{start_port + max_range}")
def release(self, port: int) -> None:
"""Release a previously allocated port.
Args:
port: The port number to release.
"""
with self._lock:
self._reserved_ports.discard(port)
@contextmanager
def allocate_context(self, start_port: int = 8080, max_range: int = 100):
"""Context manager for port allocation with automatic release.
Args:
start_port: The port number to start searching from.
max_range: Maximum number of ports to search.
Yields:
An available port number.
"""
port = self.allocate(start_port, max_range)
try:
yield port
finally:
self.release(port)
# Global port allocator instance for shared use across the application
_global_port_allocator = PortAllocator()
def get_free_port(start_port: int = 8080, max_range: int = 100) -> int:
"""Get a free port in a thread-safe manner.
This function uses a global port allocator to ensure that concurrent calls
don't return the same port. The port is marked as reserved until release_port()
is called.
Args:
start_port: The port number to start searching from.
max_range: Maximum number of ports to search.
Returns:
An available port number.
Raises:
RuntimeError: If no available port is found in the specified range.
"""
return _global_port_allocator.allocate(start_port, max_range)
def release_port(port: int) -> None:
"""Release a previously allocated port.
Args:
port: The port number to release.
"""
_global_port_allocator.release(port)
================================================
FILE: backend/packages/harness/deerflow/utils/readability.py
================================================
import logging
import re
import subprocess
from urllib.parse import urljoin
from markdownify import markdownify as md
from readabilipy import simple_json_from_html_string
logger = logging.getLogger(__name__)
class Article:
url: str
def __init__(self, title: str, html_content: str):
self.title = title
self.html_content = html_content
def to_markdown(self, including_title: bool = True) -> str:
markdown = ""
if including_title:
markdown += f"# {self.title}\n\n"
if self.html_content is None or not str(self.html_content).strip():
markdown += "*No content available*\n"
else:
markdown += md(self.html_content)
return markdown
def to_message(self) -> list[dict]:
image_pattern = r"!\[.*?\]\((.*?)\)"
content: list[dict[str, str]] = []
markdown = self.to_markdown()
if not markdown or not markdown.strip():
return [{"type": "text", "text": "No content available"}]
parts = re.split(image_pattern, markdown)
for i, part in enumerate(parts):
if i % 2 == 1:
image_url = urljoin(self.url, part.strip())
content.append({"type": "image_url", "image_url": {"url": image_url}})
else:
text_part = part.strip()
if text_part:
content.append({"type": "text", "text": text_part})
# If after processing all parts, content is still empty, provide a fallback message.
if not content:
content = [{"type": "text", "text": "No content available"}]
return content
class ReadabilityExtractor:
def extract_article(self, html: str) -> Article:
try:
article = simple_json_from_html_string(html, use_readability=True)
except (subprocess.CalledProcessError, FileNotFoundError) as exc:
stderr = getattr(exc, "stderr", None)
if isinstance(stderr, bytes):
stderr = stderr.decode(errors="replace")
stderr_info = f"; stderr={stderr.strip()}" if isinstance(stderr, str) and stderr.strip() else ""
logger.warning(
"Readability.js extraction failed with %s%s; falling back to pure-Python extraction",
type(exc).__name__,
stderr_info,
exc_info=True,
)
article = simple_json_from_html_string(html, use_readability=False)
html_content = article.get("content")
if not html_content or not str(html_content).strip():
html_content = "No content could be extracted from this page"
title = article.get("title")
if not title or not str(title).strip():
title = "Untitled"
return Article(title=title, html_content=html_content)
================================================
FILE: backend/packages/harness/pyproject.toml
================================================
[project]
name = "deerflow-harness"
version = "0.1.0"
description = "DeerFlow agent harness framework"
requires-python = ">=3.12"
dependencies = [
"agent-sandbox>=0.0.19",
"dotenv>=0.9.9",
"httpx>=0.28.0",
"kubernetes>=30.0.0",
"langchain>=1.2.3",
"langchain-anthropic>=1.3.4",
"langchain-deepseek>=1.0.1",
"langchain-mcp-adapters>=0.1.0",
"langchain-openai>=1.1.7",
"langgraph>=1.0.6",
"langgraph-api>=0.7.0,<0.8.0",
"langgraph-cli>=0.4.14",
"langgraph-runtime-inmem>=0.22.1",
"markdownify>=1.2.2",
"markitdown[all,xlsx]>=0.0.1a2",
"pydantic>=2.12.5",
"pyyaml>=6.0.3",
"readabilipy>=0.3.0",
"tavily-python>=0.7.17",
"firecrawl-py>=1.15.0",
"tiktoken>=0.8.0",
"ddgs>=9.10.0",
"duckdb>=1.4.4",
"langchain-google-genai>=4.2.1",
"langgraph-checkpoint-sqlite>=3.0.3",
"langgraph-sdk>=0.1.51",
]
[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"
[tool.hatch.build.targets.wheel]
packages = ["deerflow"]
================================================
FILE: backend/pyproject.toml
================================================
[project]
name = "deer-flow"
version = "0.1.0"
description = "LangGraph-based AI agent system with sandbox execution capabilities"
readme = "README.md"
requires-python = ">=3.12"
dependencies = [
"deerflow-harness",
"fastapi>=0.115.0",
"httpx>=0.28.0",
"python-multipart>=0.0.20",
"sse-starlette>=2.1.0",
"uvicorn[standard]>=0.34.0",
"lark-oapi>=1.4.0",
"slack-sdk>=3.33.0",
"python-telegram-bot>=21.0",
"langgraph-sdk>=0.1.51",
"markdown-to-mrkdwn>=0.3.1",
]
[dependency-groups]
dev = ["pytest>=8.0.0", "ruff>=0.14.11"]
[tool.uv.workspace]
members = ["packages/harness"]
[tool.uv.sources]
deerflow-harness = { workspace = true }
================================================
FILE: backend/ruff.toml
================================================
line-length = 240
target-version = "py312"
[lint]
select = ["E", "F", "I", "UP"]
ignore = []
[lint.isort]
known-first-party = ["deerflow", "app"]
[format]
quote-style = "double"
indent-style = "space"
================================================
FILE: backend/tests/conftest.py
================================================
"""Test configuration for the backend test suite.
Sets up sys.path and pre-mocks modules that would cause circular import
issues when unit-testing lightweight config/registry code in isolation.
"""
import sys
from pathlib import Path
from unittest.mock import MagicMock
# Make 'app' and 'deerflow' importable from any working directory
sys.path.insert(0, str(Path(__file__).parent.parent))
# Break the circular import chain that exists in production code:
# deerflow.subagents.__init__
# -> .executor (SubagentExecutor, SubagentResult)
# -> deerflow.agents.thread_state
# -> deerflow.agents.__init__
# -> lead_agent.agent
# -> subagent_limit_middleware
# -> deerflow.subagents.executor <-- circular!
#
# By injecting a mock for deerflow.subagents.executor *before* any test module
# triggers the import, __init__.py's "from .executor import ..." succeeds
# immediately without running the real executor module.
_executor_mock = MagicMock()
_executor_mock.SubagentExecutor = MagicMock
_executor_mock.SubagentResult = MagicMock
_executor_mock.SubagentStatus = MagicMock
_executor_mock.MAX_CONCURRENT_SUBAGENTS = 3
_executor_mock.get_background_task_result = MagicMock()
sys.modules["deerflow.subagents.executor"] = _executor_mock
================================================
FILE: backend/tests/test_app_config_reload.py
================================================
from __future__ import annotations
import json
import os
from pathlib import Path
import yaml
from deerflow.config.app_config import get_app_config, reset_app_config
def _write_config(path: Path, *, model_name: str, supports_thinking: bool) -> None:
path.write_text(
yaml.safe_dump(
{
"sandbox": {"use": "deerflow.sandbox.local:LocalSandboxProvider"},
"models": [
{
"name": model_name,
"use": "langchain_openai:ChatOpenAI",
"model": "gpt-test",
"supports_thinking": supports_thinking,
}
],
}
),
encoding="utf-8",
)
def _write_extensions_config(path: Path) -> None:
path.write_text(json.dumps({"mcpServers": {}, "skills": {}}), encoding="utf-8")
def test_get_app_config_reloads_when_file_changes(tmp_path, monkeypatch):
config_path = tmp_path / "config.yaml"
extensions_path = tmp_path / "extensions_config.json"
_write_extensions_config(extensions_path)
_write_config(config_path, model_name="first-model", supports_thinking=False)
monkeypatch.setenv("DEER_FLOW_CONFIG_PATH", str(config_path))
monkeypatch.setenv("DEER_FLOW_EXTENSIONS_CONFIG_PATH", str(extensions_path))
reset_app_config()
try:
initial = get_app_config()
assert initial.models[0].supports_thinking is False
_write_config(config_path, model_name="first-model", supports_thinking=True)
next_mtime = config_path.stat().st_mtime + 5
os.utime(config_path, (next_mtime, next_mtime))
reloaded = get_app_config()
assert reloaded.models[0].supports_thinking is True
assert reloaded is not initial
finally:
reset_app_config()
def test_get_app_config_reloads_when_config_path_changes(tmp_path, monkeypatch):
config_a = tmp_path / "config-a.yaml"
config_b = tmp_path / "config-b.yaml"
extensions_path = tmp_path / "extensions_config.json"
_write_extensions_config(extensions_path)
_write_config(config_a, model_name="model-a", supports_thinking=False)
_write_config(config_b, model_name="model-b", supports_thinking=True)
monkeypatch.setenv("DEER_FLOW_EXTENSIONS_CONFIG_PATH", str(extensions_path))
monkeypatch.setenv("DEER_FLOW_CONFIG_PATH", str(config_a))
reset_app_config()
try:
first = get_app_config()
assert first.models[0].name == "model-a"
monkeypatch.setenv("DEER_FLOW_CONFIG_PATH", str(config_b))
second = get_app_config()
assert second.models[0].name == "model-b"
assert second is not first
finally:
reset_app_config()
================================================
FILE: backend/tests/test_artifacts_router.py
================================================
import asyncio
from pathlib import Path
from starlette.requests import Request
import app.gateway.routers.artifacts as artifacts_router
def test_get_artifact_reads_utf8_text_file_on_windows_locale(tmp_path, monkeypatch) -> None:
artifact_path = tmp_path / "note.txt"
text = "Curly quotes: \u201cutf8\u201d"
artifact_path.write_text(text, encoding="utf-8")
original_read_text = Path.read_text
def read_text_with_gbk_default(self, *args, **kwargs):
kwargs.setdefault("encoding", "gbk")
return original_read_text(self, *args, **kwargs)
monkeypatch.setattr(Path, "read_text", read_text_with_gbk_default)
monkeypatch.setattr(artifacts_router, "resolve_thread_virtual_path", lambda _thread_id, _path: artifact_path)
request = Request({"type": "http", "method": "GET", "path": "/", "headers": [], "query_string": b""})
response = asyncio.run(artifacts_router.get_artifact("thread-1", "mnt/user-data/outputs/note.txt", request))
assert bytes(response.body).decode("utf-8") == text
assert response.media_type == "text/plain"
================================================
FILE: backend/tests/test_channel_file_attachments.py
================================================
"""Tests for channel file attachment support (ResolvedAttachment, resolution, send_file)."""
from __future__ import annotations
import asyncio
from pathlib import Path
from unittest.mock import MagicMock, patch
from app.channels.base import Channel
from app.channels.message_bus import MessageBus, OutboundMessage, ResolvedAttachment
def _run(coro):
"""Run an async coroutine synchronously."""
loop = asyncio.new_event_loop()
try:
return loop.run_until_complete(coro)
finally:
loop.close()
# ---------------------------------------------------------------------------
# ResolvedAttachment tests
# ---------------------------------------------------------------------------
class TestResolvedAttachment:
def test_basic_construction(self, tmp_path):
f = tmp_path / "test.pdf"
f.write_bytes(b"PDF content")
att = ResolvedAttachment(
virtual_path="/mnt/user-data/outputs/test.pdf",
actual_path=f,
filename="test.pdf",
mime_type="application/pdf",
size=11,
is_image=False,
)
assert att.filename == "test.pdf"
assert att.is_image is False
assert att.size == 11
def test_image_detection(self, tmp_path):
f = tmp_path / "photo.png"
f.write_bytes(b"\x89PNG")
att = ResolvedAttachment(
virtual_path="/mnt/user-data/outputs/photo.png",
actual_path=f,
filename="photo.png",
mime_type="image/png",
size=4,
is_image=True,
)
assert att.is_image is True
# ---------------------------------------------------------------------------
# OutboundMessage.attachments field tests
# ---------------------------------------------------------------------------
class TestOutboundMessageAttachments:
def test_default_empty_attachments(self):
msg = OutboundMessage(
channel_name="test",
chat_id="c1",
thread_id="t1",
text="hello",
)
assert msg.attachments == []
def test_attachments_populated(self, tmp_path):
f = tmp_path / "file.txt"
f.write_text("content")
att = ResolvedAttachment(
virtual_path="/mnt/user-data/outputs/file.txt",
actual_path=f,
filename="file.txt",
mime_type="text/plain",
size=7,
is_image=False,
)
msg = OutboundMessage(
channel_name="test",
chat_id="c1",
thread_id="t1",
text="hello",
attachments=[att],
)
assert len(msg.attachments) == 1
assert msg.attachments[0].filename == "file.txt"
# ---------------------------------------------------------------------------
# _resolve_attachments tests
# ---------------------------------------------------------------------------
class TestResolveAttachments:
def test_resolves_existing_file(self, tmp_path):
"""Successfully resolves a virtual path to an existing file."""
from app.channels.manager import _resolve_attachments
# Create the directory structure: threads/{thread_id}/user-data/outputs/
thread_id = "test-thread-123"
outputs_dir = tmp_path / "threads" / thread_id / "user-data" / "outputs"
outputs_dir.mkdir(parents=True)
test_file = outputs_dir / "report.pdf"
test_file.write_bytes(b"%PDF-1.4 fake content")
mock_paths = MagicMock()
mock_paths.resolve_virtual_path.return_value = test_file
mock_paths.sandbox_outputs_dir.return_value = outputs_dir
with patch("deerflow.config.paths.get_paths", return_value=mock_paths):
result = _resolve_attachments(thread_id, ["/mnt/user-data/outputs/report.pdf"])
assert len(result) == 1
assert result[0].filename == "report.pdf"
assert result[0].mime_type == "application/pdf"
assert result[0].is_image is False
assert result[0].size == len(b"%PDF-1.4 fake content")
def test_resolves_image_file(self, tmp_path):
"""Images are detected by MIME type."""
from app.channels.manager import _resolve_attachments
thread_id = "test-thread"
outputs_dir = tmp_path / "threads" / thread_id / "user-data" / "outputs"
outputs_dir.mkdir(parents=True)
img = outputs_dir / "chart.png"
img.write_bytes(b"\x89PNG fake image")
mock_paths = MagicMock()
mock_paths.resolve_virtual_path.return_value = img
mock_paths.sandbox_outputs_dir.return_value = outputs_dir
with patch("deerflow.config.paths.get_paths", return_value=mock_paths):
result = _resolve_attachments(thread_id, ["/mnt/user-data/outputs/chart.png"])
assert len(result) == 1
assert result[0].is_image is True
assert result[0].mime_type == "image/png"
def test_skips_missing_file(self, tmp_path):
"""Missing files are skipped with a warning."""
from app.channels.manager import _resolve_attachments
outputs_dir = tmp_path / "outputs"
outputs_dir.mkdir()
mock_paths = MagicMock()
mock_paths.resolve_virtual_path.return_value = outputs_dir / "nonexistent.txt"
mock_paths.sandbox_outputs_dir.return_value = outputs_dir
with patch("deerflow.config.paths.get_paths", return_value=mock_paths):
result = _resolve_attachments("t1", ["/mnt/user-data/outputs/nonexistent.txt"])
assert result == []
def test_skips_invalid_path(self):
"""Invalid paths (ValueError from resolve) are skipped."""
from app.channels.manager import _resolve_attachments
mock_paths = MagicMock()
mock_paths.resolve_virtual_path.side_effect = ValueError("bad path")
with patch("deerflow.config.paths.get_paths", return_value=mock_paths):
result = _resolve_attachments("t1", ["/invalid/path"])
assert result == []
def test_rejects_uploads_path(self):
"""Paths under /mnt/user-data/uploads/ are rejected (security)."""
from app.channels.manager import _resolve_attachments
mock_paths = MagicMock()
with patch("deerflow.config.paths.get_paths", return_value=mock_paths):
result = _resolve_attachments("t1", ["/mnt/user-data/uploads/secret.pdf"])
assert result == []
mock_paths.resolve_virtual_path.assert_not_called()
def test_rejects_workspace_path(self):
"""Paths under /mnt/user-data/workspace/ are rejected (security)."""
from app.channels.manager import _resolve_attachments
mock_paths = MagicMock()
with patch("deerflow.config.paths.get_paths", return_value=mock_paths):
result = _resolve_attachments("t1", ["/mnt/user-data/workspace/config.py"])
assert result == []
mock_paths.resolve_virtual_path.assert_not_called()
def test_rejects_path_traversal_escape(self, tmp_path):
"""Paths that escape the outputs directory after resolution are rejected."""
from app.channels.manager import _resolve_attachments
thread_id = "t1"
outputs_dir = tmp_path / "threads" / thread_id / "user-data" / "outputs"
outputs_dir.mkdir(parents=True)
# Simulate a resolved path that escapes outside the outputs directory
escaped_file = tmp_path / "threads" / thread_id / "user-data" / "uploads" / "stolen.txt"
escaped_file.parent.mkdir(parents=True, exist_ok=True)
escaped_file.write_text("sensitive")
mock_paths = MagicMock()
mock_paths.resolve_virtual_path.return_value = escaped_file
mock_paths.sandbox_outputs_dir.return_value = outputs_dir
with patch("deerflow.config.paths.get_paths", return_value=mock_paths):
result = _resolve_attachments(thread_id, ["/mnt/user-data/outputs/../uploads/stolen.txt"])
assert result == []
def test_multiple_artifacts_partial_resolution(self, tmp_path):
"""Mixed valid/invalid artifacts: only valid ones are returned."""
from app.channels.manager import _resolve_attachments
thread_id = "t1"
outputs_dir = tmp_path / "outputs"
outputs_dir.mkdir()
good_file = outputs_dir / "data.csv"
good_file.write_text("a,b,c")
mock_paths = MagicMock()
mock_paths.sandbox_outputs_dir.return_value = outputs_dir
def resolve_side_effect(tid, vpath):
if "data.csv" in vpath:
return good_file
return tmp_path / "missing.txt"
mock_paths.resolve_virtual_path.side_effect = resolve_side_effect
with patch("deerflow.config.paths.get_paths", return_value=mock_paths):
result = _resolve_attachments(
thread_id,
["/mnt/user-data/outputs/data.csv", "/mnt/user-data/outputs/missing.txt"],
)
assert len(result) == 1
assert result[0].filename == "data.csv"
# ---------------------------------------------------------------------------
# Channel base class _on_outbound with attachments
# ---------------------------------------------------------------------------
class _DummyChannel(Channel):
"""Concrete channel for testing the base class behavior."""
def __init__(self, bus):
super().__init__(name="dummy", bus=bus, config={})
self.sent_messages: list[OutboundMessage] = []
self.sent_files: list[tuple[OutboundMessage, ResolvedAttachment]] = []
async def start(self):
pass
async def stop(self):
pass
async def send(self, msg: OutboundMessage) -> None:
self.sent_messages.append(msg)
async def send_file(self, msg: OutboundMessage, attachment: ResolvedAttachment) -> bool:
self.sent_files.append((msg, attachment))
return True
class TestBaseChannelOnOutbound:
def test_send_file_called_for_each_attachment(self, tmp_path):
"""_on_outbound sends text first, then uploads each attachment."""
bus = MessageBus()
ch = _DummyChannel(bus)
f1 = tmp_path / "a.txt"
f1.write_text("aaa")
f2 = tmp_path / "b.png"
f2.write_bytes(b"\x89PNG")
att1 = ResolvedAttachment("/mnt/user-data/outputs/a.txt", f1, "a.txt", "text/plain", 3, False)
att2 = ResolvedAttachment("/mnt/user-data/outputs/b.png", f2, "b.png", "image/png", 4, True)
msg = OutboundMessage(
channel_name="dummy",
chat_id="c1",
thread_id="t1",
text="Here are your files",
attachments=[att1, att2],
)
_run(ch._on_outbound(msg))
assert len(ch.sent_messages) == 1
assert len(ch.sent_files) == 2
assert ch.sent_files[0][1].filename == "a.txt"
assert ch.sent_files[1][1].filename == "b.png"
def test_no_attachments_no_send_file(self):
"""When there are no attachments, send_file is not called."""
bus = MessageBus()
ch = _DummyChannel(bus)
msg = OutboundMessage(
channel_name="dummy",
chat_id="c1",
thread_id="t1",
text="No files here",
)
_run(ch._on_outbound(msg))
assert len(ch.sent_messages) == 1
assert len(ch.sent_files) == 0
def test_send_file_failure_does_not_block_others(self, tmp_path):
"""If one attachment upload fails, remaining attachments still get sent."""
bus = MessageBus()
ch = _DummyChannel(bus)
# Override send_file to fail on first call, succeed on second
call_count = 0
original_send_file = ch.send_file
async def flaky_send_file(msg, att):
nonlocal call_count
call_count += 1
if call_count == 1:
raise RuntimeError("upload failed")
return await original_send_file(msg, att)
ch.send_file = flaky_send_file # type: ignore
f1 = tmp_path / "fail.txt"
f1.write_text("x")
f2 = tmp_path / "ok.txt"
f2.write_text("y")
att1 = ResolvedAttachment("/mnt/user-data/outputs/fail.txt", f1, "fail.txt", "text/plain", 1, False)
att2 = ResolvedAttachment("/mnt/user-data/outputs/ok.txt", f2, "ok.txt", "text/plain", 1, False)
msg = OutboundMessage(
channel_name="dummy",
chat_id="c1",
thread_id="t1",
text="files",
attachments=[att1, att2],
)
_run(ch._on_outbound(msg))
# First upload failed, second succeeded
assert len(ch.sent_files) == 1
assert ch.sent_files[0][1].filename == "ok.txt"
def test_send_raises_skips_file_uploads(self, tmp_path):
"""When send() raises, file uploads are skipped entirely."""
bus = MessageBus()
ch = _DummyChannel(bus)
async def failing_send(msg):
raise RuntimeError("network error")
ch.send = failing_send # type: ignore
f = tmp_path / "a.pdf"
f.write_bytes(b"%PDF")
att = ResolvedAttachment("/mnt/user-data/outputs/a.pdf", f, "a.pdf", "application/pdf", 4, False)
msg = OutboundMessage(
channel_name="dummy",
chat_id="c1",
thread_id="t1",
text="Here is the file",
attachments=[att],
)
_run(ch._on_outbound(msg))
# send() raised, so send_file should never be called
assert len(ch.sent_files) == 0
def test_default_send_file_returns_false(self):
"""The base Channel.send_file returns False by default."""
class MinimalChannel(Channel):
async def start(self):
pass
async def stop(self):
pass
async def send(self, msg):
pass
bus = MessageBus()
ch = MinimalChannel(name="minimal", bus=bus, config={})
att = ResolvedAttachment("/x", Path("/x"), "x", "text/plain", 0, False)
msg = OutboundMessage(channel_name="minimal", chat_id="c", thread_id="t", text="t")
result = _run(ch.send_file(msg, att))
assert result is False
# ---------------------------------------------------------------------------
# ChannelManager artifact resolution integration
# ---------------------------------------------------------------------------
class TestManagerArtifactResolution:
def test_handle_chat_populates_attachments(self):
"""Verify _resolve_attachments is importable and works with the manager module."""
from app.channels.manager import _resolve_attachments
# Basic smoke test: empty artifacts returns empty list
mock_paths = MagicMock()
with patch("deerflow.config.paths.get_paths", return_value=mock_paths):
result = _resolve_attachments("t1", [])
assert result == []
def test_format_artifact_text_for_unresolved(self):
"""_format_artifact_text produces expected output."""
from app.channels.manager import _format_artifact_text
assert "report.pdf" in _format_artifact_text(["/mnt/user-data/outputs/report.pdf"])
result = _format_artifact_text(["/mnt/user-data/outputs/a.txt", "/mnt/user-data/outputs/b.txt"])
assert "a.txt" in result
assert "b.txt" in result
================================================
FILE: backend/tests/test_channels.py
================================================
"""Tests for the IM channel system (MessageBus, ChannelStore, ChannelManager)."""
from __future__ import annotations
import asyncio
import json
import tempfile
from pathlib import Path
from types import SimpleNamespace
from unittest.mock import AsyncMock, MagicMock
import pytest
from app.channels.base import Channel
from app.channels.message_bus import InboundMessage, InboundMessageType, MessageBus, OutboundMessage
from app.channels.store import ChannelStore
def _run(coro):
"""Run an async coroutine synchronously."""
loop = asyncio.new_event_loop()
try:
return loop.run_until_complete(coro)
finally:
loop.close()
async def _wait_for(condition, *, timeout=5.0, interval=0.05):
"""Poll *condition* until it returns True, or raise after *timeout* seconds."""
import time
deadline = time.monotonic() + timeout
while time.monotonic() < deadline:
if condition():
return
await asyncio.sleep(interval)
raise TimeoutError(f"Condition not met within {timeout}s")
# ---------------------------------------------------------------------------
# MessageBus tests
# ---------------------------------------------------------------------------
class TestMessageBus:
def test_publish_and_get_inbound(self):
bus = MessageBus()
async def go():
msg = InboundMessage(
channel_name="test",
chat_id="chat1",
user_id="user1",
text="hello",
)
await bus.publish_inbound(msg)
result = await bus.get_inbound()
assert result.text == "hello"
assert result.channel_name == "test"
assert result.chat_id == "chat1"
_run(go())
def test_inbound_queue_is_fifo(self):
bus = MessageBus()
async def go():
for i in range(3):
await bus.publish_inbound(InboundMessage(channel_name="test", chat_id="c", user_id="u", text=f"msg{i}"))
for i in range(3):
msg = await bus.get_inbound()
assert msg.text == f"msg{i}"
_run(go())
def test_outbound_callback(self):
bus = MessageBus()
received = []
async def callback(msg):
received.append(msg)
async def go():
bus.subscribe_outbound(callback)
out = OutboundMessage(channel_name="test", chat_id="c1", thread_id="t1", text="reply")
await bus.publish_outbound(out)
assert len(received) == 1
assert received[0].text == "reply"
_run(go())
def test_unsubscribe_outbound(self):
bus = MessageBus()
received = []
async def callback(msg):
received.append(msg)
async def go():
bus.subscribe_outbound(callback)
bus.unsubscribe_outbound(callback)
out = OutboundMessage(channel_name="test", chat_id="c1", thread_id="t1", text="reply")
await bus.publish_outbound(out)
assert len(received) == 0
_run(go())
def test_outbound_error_does_not_crash(self):
bus = MessageBus()
async def bad_callback(msg):
raise ValueError("boom")
received = []
async def good_callback(msg):
received.append(msg)
async def go():
bus.subscribe_outbound(bad_callback)
bus.subscribe_outbound(good_callback)
out = OutboundMessage(channel_name="test", chat_id="c1", thread_id="t1", text="reply")
await bus.publish_outbound(out)
assert len(received) == 1
_run(go())
def test_inbound_message_defaults(self):
msg = InboundMessage(channel_name="test", chat_id="c", user_id="u", text="hi")
assert msg.msg_type == InboundMessageType.CHAT
assert msg.thread_ts is None
assert msg.files == []
assert msg.metadata == {}
assert msg.created_at > 0
def test_outbound_message_defaults(self):
msg = OutboundMessage(channel_name="test", chat_id="c", thread_id="t", text="hi")
assert msg.artifacts == []
assert msg.is_final is True
assert msg.thread_ts is None
assert msg.metadata == {}
# ---------------------------------------------------------------------------
# ChannelStore tests
# ---------------------------------------------------------------------------
class TestChannelStore:
@pytest.fixture
def store(self, tmp_path):
return ChannelStore(path=tmp_path / "store.json")
def test_set_and_get_thread_id(self, store):
store.set_thread_id("slack", "ch1", "thread-abc", user_id="u1")
assert store.get_thread_id("slack", "ch1") == "thread-abc"
def test_get_nonexistent_returns_none(self, store):
assert store.get_thread_id("slack", "nonexistent") is None
def test_remove(self, store):
store.set_thread_id("slack", "ch1", "t1")
assert store.remove("slack", "ch1") is True
assert store.get_thread_id("slack", "ch1") is None
def test_remove_nonexistent_returns_false(self, store):
assert store.remove("slack", "nope") is False
def test_list_entries_all(self, store):
store.set_thread_id("slack", "ch1", "t1")
store.set_thread_id("feishu", "ch2", "t2")
entries = store.list_entries()
assert len(entries) == 2
def test_list_entries_filtered(self, store):
store.set_thread_id("slack", "ch1", "t1")
store.set_thread_id("feishu", "ch2", "t2")
entries = store.list_entries(channel_name="slack")
assert len(entries) == 1
assert entries[0]["channel_name"] == "slack"
def test_persistence(self, tmp_path):
path = tmp_path / "store.json"
store1 = ChannelStore(path=path)
store1.set_thread_id("slack", "ch1", "t1")
store2 = ChannelStore(path=path)
assert store2.get_thread_id("slack", "ch1") == "t1"
def test_update_preserves_created_at(self, store):
store.set_thread_id("slack", "ch1", "t1")
entries = store.list_entries()
created_at = entries[0]["created_at"]
store.set_thread_id("slack", "ch1", "t2")
entries = store.list_entries()
assert entries[0]["created_at"] == created_at
assert entries[0]["thread_id"] == "t2"
assert entries[0]["updated_at"] >= created_at
def test_corrupt_file_handled(self, tmp_path):
path = tmp_path / "store.json"
path.write_text("not json", encoding="utf-8")
store = ChannelStore(path=path)
assert store.get_thread_id("x", "y") is None
# ---------------------------------------------------------------------------
# Channel base class tests
# ---------------------------------------------------------------------------
class DummyChannel(Channel):
"""Concrete test implementation of Channel."""
def __init__(self, bus, config=None):
super().__init__(name="dummy", bus=bus, config=config or {})
self.sent_messages: list[OutboundMessage] = []
self._running = False
async def start(self):
self._running = True
self.bus.subscribe_outbound(self._on_outbound)
async def stop(self):
self._running = False
self.bus.unsubscribe_outbound(self._on_outbound)
async def send(self, msg: OutboundMessage):
self.sent_messages.append(msg)
class TestChannelBase:
def test_make_inbound(self):
bus = MessageBus()
ch = DummyChannel(bus)
msg = ch._make_inbound(
chat_id="c1",
user_id="u1",
text="hello",
msg_type=InboundMessageType.COMMAND,
)
assert msg.channel_name == "dummy"
assert msg.chat_id == "c1"
assert msg.text == "hello"
assert msg.msg_type == InboundMessageType.COMMAND
def test_on_outbound_routes_to_channel(self):
bus = MessageBus()
ch = DummyChannel(bus)
async def go():
await ch.start()
msg = OutboundMessage(channel_name="dummy", chat_id="c1", thread_id="t1", text="hi")
await bus.publish_outbound(msg)
assert len(ch.sent_messages) == 1
_run(go())
def test_on_outbound_ignores_other_channels(self):
bus = MessageBus()
ch = DummyChannel(bus)
async def go():
await ch.start()
msg = OutboundMessage(channel_name="other", chat_id="c1", thread_id="t1", text="hi")
await bus.publish_outbound(msg)
assert len(ch.sent_messages) == 0
_run(go())
# ---------------------------------------------------------------------------
# _extract_response_text tests
# ---------------------------------------------------------------------------
class TestExtractResponseText:
def test_string_content(self):
from app.channels.manager import _extract_response_text
result = {"messages": [{"type": "ai", "content": "hello"}]}
assert _extract_response_text(result) == "hello"
def test_list_content_blocks(self):
from app.channels.manager import _extract_response_text
result = {"messages": [{"type": "ai", "content": [{"type": "text", "text": "hello"}, {"type": "text", "text": " world"}]}]}
assert _extract_response_text(result) == "hello world"
def test_picks_last_ai_message(self):
from app.channels.manager import _extract_response_text
result = {
"messages": [
{"type": "ai", "content": "first"},
{"type": "human", "content": "question"},
{"type": "ai", "content": "second"},
]
}
assert _extract_response_text(result) == "second"
def test_empty_messages(self):
from app.channels.manager import _extract_response_text
assert _extract_response_text({"messages": []}) == ""
def test_no_ai_messages(self):
from app.channels.manager import _extract_response_text
result = {"messages": [{"type": "human", "content": "hi"}]}
assert _extract_response_text(result) == ""
def test_list_result(self):
from app.channels.manager import _extract_response_text
result = [{"type": "ai", "content": "from list"}]
assert _extract_response_text(result) == "from list"
def test_skips_empty_ai_content(self):
from app.channels.manager import _extract_response_text
result = {
"messages": [
{"type": "ai", "content": ""},
{"type": "ai", "content": "actual response"},
]
}
assert _extract_response_text(result) == "actual response"
def test_clarification_tool_message(self):
from app.channels.manager import _extract_response_text
result = {
"messages": [
{"type": "human", "content": "健身"},
{"type": "ai", "content": "", "tool_calls": [{"name": "ask_clarification", "args": {"question": "您想了解哪方面？"}}]},
{"type": "tool", "name": "ask_clarification", "content": "您想了解哪方面？"},
]
}
assert _extract_response_text(result) == "您想了解哪方面？"
def test_clarification_over_empty_ai(self):
"""When AI content is empty but ask_clarification tool message exists, use the tool message."""
from app.channels.manager import _extract_response_text
result = {
"messages": [
{"type": "ai", "content": ""},
{"type": "tool", "name": "ask_clarification", "content": "Could you clarify?"},
]
}
assert _extract_response_text(result) == "Could you clarify?"
def test_does_not_leak_previous_turn_text(self):
"""When current turn AI has no text (only tool calls), do not return previous turn's text."""
from app.channels.manager import _extract_response_text
result = {
"messages": [
{"type": "human", "content": "hello"},
{"type": "ai", "content": "Hi there!"},
{"type": "human", "content": "export data"},
{
"type": "ai",
"content": "",
"tool_calls": [{"name": "present_files", "args": {"filepaths": ["/mnt/user-data/outputs/data.csv"]}}],
},
{"type": "tool", "name": "present_files", "content": "ok"},
]
}
# Should return "" (no text in current turn), NOT "Hi there!" from previous turn
assert _extract_response_text(result) == ""
# ---------------------------------------------------------------------------
# ChannelManager tests
# ---------------------------------------------------------------------------
def _make_mock_langgraph_client(thread_id="test-thread-123", run_result=None):
"""Create a mock langgraph_sdk async client."""
mock_client = MagicMock()
# threads.create() returns a Thread-like dict
mock_client.threads.create = AsyncMock(return_value={"thread_id": thread_id})
# threads.get() returns thread info (succeeds by default)
mock_client.threads.get = AsyncMock(return_value={"thread_id": thread_id})
# runs.wait() returns the final state with messages
if run_result is None:
run_result = {
"messages": [
{"type": "human", "content": "hi"},
{"type": "ai", "content": "Hello from agent!"},
]
}
mock_client.runs.wait = AsyncMock(return_value=run_result)
return mock_client
def _make_stream_part(event: str, data):
return SimpleNamespace(event=event, data=data)
def _make_async_iterator(items):
async def iterator():
for item in items:
yield item
return iterator()
class TestChannelManager:
def test_handle_chat_creates_thread(self):
from app.channels.manager import ChannelManager
async def go():
bus = MessageBus()
store = ChannelStore(path=Path(tempfile.mkdtemp()) / "store.json")
manager = ChannelManager(bus=bus, store=store)
outbound_received = []
async def capture_outbound(msg):
outbound_received.append(msg)
bus.subscribe_outbound(capture_outbound)
mock_client = _make_mock_langgraph_client()
manager._client = mock_client
await manager.start()
inbound = InboundMessage(channel_name="test", chat_id="chat1", user_id="user1", text="hi")
await bus.publish_inbound(inbound)
await _wait_for(lambda: len(outbound_received) >= 1)
await manager.stop()
# Thread should be created on the LangGraph Server
mock_client.threads.create.assert_called_once()
# Thread ID should be stored
thread_id = store.get_thread_id("test", "chat1")
assert thread_id == "test-thread-123"
# runs.wait should be called with the thread_id
mock_client.runs.wait.assert_called_once()
call_args = mock_client.runs.wait.call_args
assert call_args[0][0] == "test-thread-123" # thread_id
assert call_args[0][1] == "lead_agent" # assistant_id
assert call_args[1]["input"]["messages"][0]["content"] == "hi"
assert len(outbound_received) == 1
assert outbound_received[0].text == "Hello from agent!"
_run(go())
def test_handle_chat_uses_channel_session_overrides(self):
from app.channels.manager import ChannelManager
async def go():
bus = MessageBus()
store = ChannelStore(path=Path(tempfile.mkdtemp()) / "store.json")
manager = ChannelManager(
bus=bus,
store=store,
channel_sessions={
"telegram": {
"assistant_id": "mobile_agent",
"config": {"recursion_limit": 55},
"context": {
"thinking_enabled": False,
"subagent_enabled": True,
},
}
},
)
outbound_received = []
async def capture_outbound(msg):
outbound_received.append(msg)
bus.subscribe_outbound(capture_outbound)
mock_client = _make_mock_langgraph_client()
manager._client = mock_client
await manager.start()
inbound = InboundMessage(channel_name="telegram", chat_id="chat1", user_id="user1", text="hi")
await bus.publish_inbound(inbound)
await _wait_for(lambda: len(outbound_received) >= 1)
await manager.stop()
mock_client.runs.wait.assert_called_once()
call_args = mock_client.runs.wait.call_args
assert call_args[0][1] == "mobile_agent"
assert call_args[1]["config"]["recursion_limit"] == 55
assert call_args[1]["context"]["thinking_enabled"] is False
assert call_args[1]["context"]["subagent_enabled"] is True
_run(go())
def test_handle_chat_uses_user_session_overrides(self):
from app.channels.manager import ChannelManager
async def go():
bus = MessageBus()
store = ChannelStore(path=Path(tempfile.mkdtemp()) / "store.json")
manager = ChannelManager(
bus=bus,
store=store,
default_session={"context": {"is_plan_mode": True}},
channel_sessions={
"telegram": {
"assistant_id": "mobile_agent",
"config": {"recursion_limit": 55},
"context": {
"thinking_enabled": False,
"subagent_enabled": False,
},
"users": {
"vip-user": {
"assistant_id": "vip_agent",
"config": {"recursion_limit": 77},
"context": {
"thinking_enabled": True,
"subagent_enabled": True,
},
}
},
}
},
)
outbound_received = []
async def capture_outbound(msg):
outbound_received.append(msg)
bus.subscribe_outbound(capture_outbound)
mock_client = _make_mock_langgraph_client()
manager._client = mock_client
await manager.start()
inbound = InboundMessage(channel_name="telegram", chat_id="chat1", user_id="vip-user", text="hi")
await bus.publish_inbound(inbound)
await _wait_for(lambda: len(outbound_received) >= 1)
await manager.stop()
mock_client.runs.wait.assert_called_once()
call_args = mock_client.runs.wait.call_args
assert call_args[0][1] == "vip_agent"
assert call_args[1]["config"]["recursion_limit"] == 77
assert call_args[1]["context"]["thinking_enabled"] is True
assert call_args[1]["context"]["subagent_enabled"] is True
assert call_args[1]["context"]["is_plan_mode"] is True
_run(go())
def test_handle_feishu_chat_streams_multiple_outbound_updates(self, monkeypatch):
from app.channels.manager import ChannelManager
monkeypatch.setattr("app.channels.manager.STREAM_UPDATE_MIN_INTERVAL_SECONDS", 0.0)
async def go():
bus = MessageBus()
store = ChannelStore(path=Path(tempfile.mkdtemp()) / "store.json")
manager = ChannelManager(bus=bus, store=store)
outbound_received = []
async def capture_outbound(msg):
outbound_received.append(msg)
bus.subscribe_outbound(capture_outbound)
stream_events = [
_make_stream_part(
"messages-tuple",
[
{"id": "ai-1", "content": "Hello", "type": "AIMessageChunk"},
{"langgraph_node": "agent"},
],
),
_make_stream_part(
"messages-tuple",
[
{"id": "ai-1", "content": " world", "type": "AIMessageChunk"},
{"langgraph_node": "agent"},
],
),
_make_stream_part(
"values",
{
"messages": [
{"type": "human", "content": "hi"},
{"type": "ai", "content": "Hello world"},
],
"artifacts": [],
},
),
]
mock_client = _make_mock_langgraph_client()
mock_client.runs.stream = MagicMock(return_value=_make_async_iterator(stream_events))
manager._client = mock_client
await manager.start()
inbound = InboundMessage(
channel_name="feishu",
chat_id="chat1",
user_id="user1",
text="hi",
thread_ts="om-source-1",
)
await bus.publish_inbound(inbound)
await _wait_for(lambda: len(outbound_received) >= 3)
await manager.stop()
mock_client.runs.stream.assert_called_once()
assert [msg.text for msg in outbound_received] == ["Hello", "Hello world", "Hello world"]
assert [msg.is_final for msg in outbound_received] == [False, False, True]
assert all(msg.thread_ts == "om-source-1" for msg in outbound_received)
_run(go())
def test_handle_feishu_stream_error_still_sends_final(self, monkeypatch):
"""When the stream raises mid-way, a final outbound with is_final=True must still be published."""
from app.channels.manager import ChannelManager
monkeypatch.setattr("app.channels.manager.STREAM_UPDATE_MIN_INTERVAL_SECONDS", 0.0)
async def go():
bus = MessageBus()
store = ChannelStore(path=Path(tempfile.mkdtemp()) / "store.json")
manager = ChannelManager(bus=bus, store=store)
outbound_received = []
async def capture_outbound(msg):
outbound_received.append(msg)
bus.subscribe_outbound(capture_outbound)
async def _failing_stream():
yield _make_stream_part(
"messages-tuple",
[
{"id": "ai-1", "content": "Partial", "type": "AIMessageChunk"},
{"langgraph_node": "agent"},
],
)
raise ConnectionError("stream broken")
mock_client = _make_mock_langgraph_client()
mock_client.runs.stream = MagicMock(return_value=_failing_stream())
manager._client = mock_client
await manager.start()
inbound = InboundMessage(
channel_name="feishu",
chat_id="chat1",
user_id="user1",
text="hi",
thread_ts="om-source-1",
)
await bus.publish_inbound(inbound)
await _wait_for(lambda: any(m.is_final for m in outbound_received))
await manager.stop()
# Should have at least one intermediate and one final message
final_msgs = [m for m in outbound_received if m.is_final]
assert len(final_msgs) == 1
assert final_msgs[0].thread_ts == "om-source-1"
_run(go())
def test_handle_command_help(self):
from app.channels.manager import ChannelManager
async def go():
bus = MessageBus()
store = ChannelStore(path=Path(tempfile.mkdtemp()) / "store.json")
manager = ChannelManager(bus=bus, store=store)
outbound_received = []
async def capture_outbound(msg):
outbound_received.append(msg)
bus.subscribe_outbound(capture_outbound)
await manager.start()
inbound = InboundMessage(
channel_name="test",
chat_id="chat1",
user_id="user1",
text="/help",
msg_type=InboundMessageType.COMMAND,
)
await bus.publish_inbound(inbound)
await _wait_for(lambda: len(outbound_received) >= 1)
await manager.stop()
assert len(outbound_received) == 1
assert "/new" in outbound_received[0].text
assert "/help" in outbound_received[0].text
_run(go())
def test_handle_command_new(self):
from app.channels.manager import ChannelManager
async def go():
bus = MessageBus()
store = ChannelStore(path=Path(tempfile.mkdtemp()) / "store.json")
manager = ChannelManager(bus=bus, store=store)
store.set_thread_id("test", "chat1", "old-thread")
mock_client = _make_mock_langgraph_client(thread_id="new-thread-456")
manager._client = mock_client
outbound_received = []
async def capture_outbound(msg):
outbound_received.append(msg)
bus.subscribe_outbound(capture_outbound)
await manager.start()
inbound = InboundMessage(
channel_name="test",
chat_id="chat1",
user_id="user1",
text="/new",
msg_type=InboundMessageType.COMMAND,
)
await bus.publish_inbound(inbound)
await _wait_for(lambda: len(outbound_received) >= 1)
await manager.stop()
new_thread = store.get_thread_id("test", "chat1")
assert new_thread == "new-thread-456"
assert new_thread != "old-thread"
assert "New conversation started" in outbound_received[0].text
# threads.create should be called for /new
mock_client.threads.create.assert_called_once()
_run(go())
def test_each_topic_creates_new_thread(self):
"""Messages with distinct topic_ids should each create a new DeerFlow thread."""
from app.channels.manager import ChannelManager
async def go():
bus = MessageBus()
store = ChannelStore(path=Path(tempfile.mkdtemp()) / "store.json")
manager = ChannelManager(bus=bus, store=store)
# Return a different thread_id for each create call
thread_ids = iter(["thread-1", "thread-2"])
async def create_thread(**kwargs):
return {"thread_id": next(thread_ids)}
mock_client = _make_mock_langgraph_client()
mock_client.threads.create = AsyncMock(side_effect=create_thread)
manager._client = mock_client
outbound_received = []
async def capture(msg):
outbound_received.append(msg)
bus.subscribe_outbound(capture)
await manager.start()
# Send two messages with different topic_ids (e.g. group chat, each starts a new topic)
for i, text in enumerate(["first", "second"]):
await bus.publish_inbound(
InboundMessage(
channel_name="test",
chat_id="chat1",
user_id="user1",
text=text,
topic_id=f"topic-{i}",
)
)
await _wait_for(lambda: mock_client.runs.wait.call_count >= 2)
await manager.stop()
# threads.create should be called twice (different topics)
assert mock_client.threads.create.call_count == 2
# runs.wait should be called twice with different thread_ids
assert mock_client.runs.wait.call_count == 2
wait_thread_ids = [c[0][0] for c in mock_client.runs.wait.call_args_list]
assert "thread-1" in wait_thread_ids
assert "thread-2" in wait_thread_ids
_run(go())
def test_same_topic_reuses_thread(self):
"""Messages with the same topic_id should reuse the same DeerFlow thread."""
from app.channels.manager import ChannelManager
async def go():
bus = MessageBus()
store = ChannelStore(path=Path(tempfile.mkdtemp()) / "store.json")
manager = ChannelManager(bus=bus, store=store)
mock_client = _make_mock_langgraph_client(thread_id="topic-thread-1")
manager._client = mock_client
outbound_received = []
async def capture(msg):
outbound_received.append(msg)
bus.subscribe_outbound(capture)
await manager.start()
# Send two messages with the same topic_id (simulates replies in a thread)
for text in ["first message", "follow-up"]:
msg = InboundMessage(
channel_name="test",
chat_id="chat1",
user_id="user1",
text=text,
topic_id="topic-root-123",
)
await bus.publish_inbound(msg)
await _wait_for(lambda: mock_client.runs.wait.call_count >= 2)
await manager.stop()
# threads.create should be called only ONCE (second message reuses the thread)
mock_client.threads.create.assert_called_once()
# Both runs.wait calls should use the same thread_id
assert mock_client.runs.wait.call_count == 2
for call in mock_client.runs.wait.call_args_list:
assert call[0][0] == "topic-thread-1"
_run(go())
def test_none_topic_reuses_thread(self):
"""Messages with topic_id=None should reuse the same thread (e.g. Telegram private chat)."""
from app.channels.manager import ChannelManager
async def go():
bus = MessageBus()
store = ChannelStore(path=Path(tempfile.mkdtemp()) / "store.json")
manager = ChannelManager(bus=bus, store=store)
mock_client = _make_mock_langgraph_client(thread_id="private-thread-1")
manager._client = mock_client
outbound_received = []
async def capture(msg):
outbound_received.append(msg)
bus.subscribe_outbound(capture)
await manager.start()
# Send two messages with topic_id=None (simulates Telegram private chat)
for text in ["hello", "what did I just say?"]:
msg = InboundMessage(
channel_name="telegram",
chat_id="chat1",
user_id="user1",
text=text,
topic_id=None,
)
await bus.publish_inbound(msg)
await _wait_for(lambda: mock_client.runs.wait.call_count >= 2)
await manager.stop()
# threads.create should be called only ONCE (second message reuses the thread)
mock_client.threads.create.assert_called_once()
# Both runs.wait calls should use the same thread_id
assert mock_client.runs.wait.call_count == 2
for call in mock_client.runs.wait.call_args_list:
assert call[0][0] == "private-thread-1"
_run(go())
def test_different_topics_get_different_threads(self):
"""Messages with different topic_ids should create separate threads."""
from app.channels.manager import ChannelManager
async def go():
bus = MessageBus()
store = ChannelStore(path=Path(tempfile.mkdtemp()) / "store.json")
manager = ChannelManager(bus=bus, store=store)
thread_ids = iter(["thread-A", "thread-B"])
async def create_thread(**kwargs):
return {"thread_id": next(thread_ids)}
mock_client = _make_mock_langgraph_client()
mock_client.threads.create = AsyncMock(side_effect=create_thread)
manager._client = mock_client
bus.subscribe_outbound(lambda msg: None)
await manager.start()
# Send messages with different topic_ids
for topic in ["topic-1", "topic-2"]:
msg = InboundMessage(
channel_name="test",
chat_id="chat1",
user_id="user1",
text="hi",
topic_id=topic,
)
await bus.publish_inbound(msg)
await _wait_for(lambda: mock_client.runs.wait.call_count >= 2)
await manager.stop()
# threads.create called twice (different topics)
assert mock_client.threads.create.call_count == 2
# runs.wait used different thread_ids
wait_thread_ids = [c[0][0] for c in mock_client.runs.wait.call_args_list]
assert set(wait_thread_ids) == {"thread-A", "thread-B"}
_run(go())
def test_handle_command_bootstrap_with_text(self):
"""/bootstrap should route to chat with is_bootstrap=True in run_context."""
from app.channels.manager import ChannelManager
async def go():
bus = MessageBus()
store = ChannelStore(path=Path(tempfile.mkdtemp()) / "store.json")
manager = ChannelManager(bus=bus, store=store)
outbound_received = []
async def capture_outbound(msg):
outbound_received.append(msg)
bus.subscribe_outbound(capture_outbound)
mock_client = _make_mock_langgraph_client()
manager._client = mock_client
await manager.start()
inbound = InboundMessage(
channel_name="test",
chat_id="chat1",
user_id="user1",
text="/bootstrap setup my workspace",
msg_type=InboundMessageType.COMMAND,
)
await bus.publish_inbound(inbound)
await _wait_for(lambda: len(outbound_received) >= 1)
await manager.stop()
# Should go through the chat path (runs.wait), not the command reply path
mock_client.runs.wait.assert_called_once()
call_args = mock_client.runs.wait.call_args
# The text sent to the agent should be the part after /bootstrap
assert call_args[1]["input"]["messages"][0]["content"] == "setup my workspace"
# run_context should contain is_bootstrap=True
assert call_args[1]["context"]["is_bootstrap"] is True
# Normal context fields should still be present
assert "thread_id" in call_args[1]["context"]
# Should get the agent response (not a command reply)
assert outbound_received[0].text == "Hello from agent!"
_run(go())
def test_handle_command_bootstrap_without_text(self):
"""/bootstrap with no text should use a default message."""
from app.channels.manager import ChannelManager
async def go():
bus = MessageBus()
store = ChannelStore(path=Path(tempfile.mkdtemp()) / "store.json")
manager = ChannelManager(bus=bus, store=store)
outbound_received = []
async def capture_outbound(msg):
outbound_received.append(msg)
bus.subscribe_outbound(capture_outbound)
mock_client = _make_mock_langgraph_client()
manager._client = mock_client
await manager.start()
inbound = InboundMessage(
channel_name="test",
chat_id="chat1",
user_id="user1",
text="/bootstrap",
msg_type=InboundMessageType.COMMAND,
)
await bus.publish_inbound(inbound)
await _wait_for(lambda: len(outbound_received) >= 1)
await manager.stop()
mock_client.runs.wait.assert_called_once()
call_args = mock_client.runs.wait.call_args
# Default text should be used when no text is provided
assert call_args[1]["input"]["messages"][0]["content"] == "Initialize workspace"
assert call_args[1]["context"]["is_bootstrap"] is True
_run(go())
def test_handle_command_bootstrap_feishu_uses_streaming(self, monkeypatch):
"""/bootstrap from feishu should go through the streaming path."""
from app.channels.manager import ChannelManager
monkeypatch.setattr("app.channels.manager.STREAM_UPDATE_MIN_INTERVAL_SECONDS", 0.0)
async def go():
bus = MessageBus()
store = ChannelStore(path=Path(tempfile.mkdtemp()) / "store.json")
manager = ChannelManager(bus=bus, store=store)
outbound_received = []
async def capture_outbound(msg):
outbound_received.append(msg)
bus.subscribe_outbound(capture_outbound)
stream_events = [
_make_stream_part(
"values",
{
"messages": [
{"type": "human", "content": "hello"},
{"type": "ai", "content": "Bootstrap done"},
],
"artifacts": [],
},
),
]
mock_client = _make_mock_langgraph_client()
mock_client.runs.stream = MagicMock(return_value=_make_async_iterator(stream_events))
manager._client = mock_client
await manager.start()
inbound = InboundMessage(
channel_name="feishu",
chat_id="chat1",
user_id="user1",
text="/bootstrap hello",
msg_type=InboundMessageType.COMMAND,
thread_ts="om-source-1",
)
await bus.publish_inbound(inbound)
await _wait_for(lambda: any(m.is_final for m in outbound_received))
await manager.stop()
# Should use streaming path (runs.stream, not runs.wait)
mock_client.runs.stream.assert_called_once()
call_args = mock_client.runs.stream.call_args
assert call_args[1]["input"]["messages"][0]["content"] == "hello"
assert call_args[1]["context"]["is_bootstrap"] is True
# Final message should be published
final_msgs = [m for m in outbound_received if m.is_final]
assert len(final_msgs) == 1
assert final_msgs[0].text == "Bootstrap done"
_run(go())
def test_handle_command_bootstrap_creates_thread_if_needed(self):
"""/bootstrap should create a new thread when none exists."""
from app.channels.manager import ChannelManager
async def go():
bus = MessageBus()
store = ChannelStore(path=Path(tempfile.mkdtemp()) / "store.json")
manager = ChannelManager(bus=bus, store=store)
outbound_received = []
async def capture_outbound(msg):
outbound_received.append(msg)
bus.subscribe_outbound(capture_outbound)
mock_client = _make_mock_langgraph_client(thread_id="bootstrap-thread")
manager._client = mock_client
await manager.start()
inbound = InboundMessage(
channel_name="test",
chat_id="chat1",
user_id="user1",
text="/bootstrap init",
msg_type=InboundMessageType.COMMAND,
)
await bus.publish_inbound(inbound)
await _wait_for(lambda: len(outbound_received) >= 1)
await manager.stop()
# A thread should be created
mock_client.threads.create.assert_called_once()
assert store.get_thread_id("test", "chat1") == "bootstrap-thread"
_run(go())
def test_help_includes_bootstrap(self):
"""/help output should mention /bootstrap."""
from app.channels.manager import ChannelManager
async def go():
bus = MessageBus()
store = ChannelStore(path=Path(tempfile.mkdtemp()) / "store.json")
manager = ChannelManager(bus=bus, store=store)
outbound_received = []
async def capture(msg):
outbound_received.append(msg)
bus.subscribe_outbound(capture)
await manager.start()
inbound = InboundMessage(
channel_name="test",
chat_id="chat1",
user_id="user1",
text="/help",
msg_type=InboundMessageType.COMMAND,
)
await bus.publish_inbound(inbound)
await _wait_for(lambda: len(outbound_received) >= 1)
await manager.stop()
assert "/bootstrap" in outbound_received[0].text
_run(go())
# ---------------------------------------------------------------------------
# ChannelService tests
# ---------------------------------------------------------------------------
class TestExtractArtifacts:
def test_extracts_from_present_files_tool_call(self):
from app.channels.manager import _extract_artifacts
result = {
"messages": [
{"type": "human", "content": "generate report"},
{
"type": "ai",
"content": "Here is your report.",
"tool_calls": [
{"name": "present_files", "args": {"filepaths": ["/mnt/user-data/outputs/report.md"]}},
],
},
{"type": "tool", "name": "present_files", "content": "Successfully presented files"},
]
}
assert _extract_artifacts(result) == ["/mnt/user-data/outputs/report.md"]
def test_empty_when_no_present_files(self):
from app.channels.manager import _extract_artifacts
result = {
"messages": [
{"type": "human", "content": "hello"},
{"type": "ai", "content": "hello"},
]
}
assert _extract_artifacts(result) == []
def test_empty_for_list_result_no_tool_calls(self):
from app.channels.manager import _extract_artifacts
result = [{"type": "ai", "content": "hello"}]
assert _extract_artifacts(result) == []
def test_only_extracts_after_last_human_message(self):
"""Artifacts from previous turns (before the last human message) should be ignored."""
from app.channels.manager import _extract_artifacts
result = {
"messages": [
{"type": "human", "content": "make report"},
{
"type": "ai",
"content": "Created report.",
"tool_calls": [
{"name": "present_files", "args": {"filepaths": ["/mnt/user-data/outputs/report.md"]}},
],
},
{"type": "tool", "name": "present_files", "content": "ok"},
{"type": "human", "content": "add chart"},
{
"type": "ai",
"content": "Created chart.",
"tool_calls": [
{"name": "present_files", "args": {"filepaths": ["/mnt/user-data/outputs/chart.png"]}},
],
},
{"type": "tool", "name": "present_files", "content": "ok"},
]
}
# Should only return chart.png (from the last turn)
assert _extract_artifacts(result) == ["/mnt/user-data/outputs/chart.png"]
def test_multiple_files_in_single_call(self):
from app.channels.manager import _extract_artifacts
result = {
"messages": [
{"type": "human", "content": "export"},
{
"type": "ai",
"content": "Done.",
"tool_calls": [
{"name": "present_files", "args": {"filepaths": ["/mnt/user-data/outputs/a.txt", "/mnt/user-data/outputs/b.csv"]}},
],
},
]
}
assert _extract_artifacts(result) == ["/mnt/user-data/outputs/a.txt", "/mnt/user-data/outputs/b.csv"]
class TestFormatArtifactText:
def test_single_artifact(self):
from app.channels.manager import _format_artifact_text
text = _format_artifact_text(["/mnt/user-data/outputs/report.md"])
assert text == "Created File: 📎 report.md"
def test_multiple_artifacts(self):
from app.channels.manager import _format_artifact_text
text = _format_artifact_text(
["/mnt/user-data/outputs/a.txt", "/mnt/user-data/outputs/b.csv"],
)
assert text == "Created Files: 📎 a.txt、b.csv"
class TestHandleChatWithArtifacts:
def test_artifacts_appended_to_text(self):
from app.channels.manager import ChannelManager
async def go():
bus = MessageBus()
store = ChannelStore(path=Path(tempfile.mkdtemp()) / "store.json")
manager = ChannelManager(bus=bus, store=store)
run_result = {
"messages": [
{"type": "human", "content": "generate report"},
{
"type": "ai",
"content": "Here is your report.",
"tool_calls": [
{"name": "present_files", "args": {"filepaths": ["/mnt/user-data/outputs/report.md"]}},
],
},
{"type": "tool", "name": "present_files", "content": "ok"},
],
}
mock_client = _make_mock_langgraph_client(run_result=run_result)
manager._client = mock_client
outbound_received = []
bus.subscribe_outbound(lambda msg: outbound_received.append(msg))
await manager.start()
await bus.publish_inbound(
InboundMessage(
channel_name="test",
chat_id="c1",
user_id="u1",
text="generate report",
)
)
await _wait_for(lambda: len(outbound_received) >= 1)
await manager.stop()
assert len(outbound_received) == 1
assert "Here is your report." in outbound_received[0].text
assert "report.md" in outbound_received[0].text
assert outbound_received[0].artifacts == ["/mnt/user-data/outputs/report.md"]
_run(go())
def test_artifacts_only_no_text(self):
"""When agent produces artifacts but no text, the artifacts should be the response."""
from app.channels.manager import ChannelManager
async def go():
bus = MessageBus()
store = ChannelStore(path=Path(tempfile.mkdtemp()) / "store.json")
manager = ChannelManager(bus=bus, store=store)
run_result = {
"messages": [
{"type": "human", "content": "export data"},
{
"type": "ai",
"content": "",
"tool_calls": [
{"name": "present_files", "args": {"filepaths": ["/mnt/user-data/outputs/output.csv"]}},
],
},
{"type": "tool", "name": "present_files", "content": "ok"},
],
}
mock_client = _make_mock_langgraph_client(run_result=run_result)
manager._client = mock_client
outbound_received = []
bus.subscribe_outbound(lambda msg: outbound_received.append(msg))
await manager.start()
await bus.publish_inbound(
InboundMessage(
channel_name="test",
chat_id="c1",
user_id="u1",
text="export data",
)
)
await _wait_for(lambda: len(outbound_received) >= 1)
await manager.stop()
assert len(outbound_received) == 1
# Should NOT be the "(No response from agent)" fallback
assert outbound_received[0].text != "(No response from agent)"
assert "output.csv" in outbound_received[0].text
assert outbound_received[0].artifacts == ["/mnt/user-data/outputs/output.csv"]
_run(go())
def test_only_last_turn_artifacts_returned(self):
"""Only artifacts from the current turn's present_files calls should be included."""
from app.channels.manager import ChannelManager
async def go():
bus = MessageBus()
store = ChannelStore(path=Path(tempfile.mkdtemp()) / "store.json")
manager = ChannelManager(bus=bus, store=store)
# Turn 1: produces report.md
turn1_result = {
"messages": [
{"type": "human", "content": "make report"},
{
"type": "ai",
"content": "Created report.",
"tool_calls": [
{"name": "present_files", "args": {"filepaths": ["/mnt/user-data/outputs/report.md"]}},
],
},
{"type": "tool", "name": "present_files", "content": "ok"},
],
}
# Turn 2: accumulated messages include turn 1's artifacts, but only chart.png is new
turn2_result = {
"messages": [
{"type": "human", "content": "make report"},
{
"type": "ai",
"content": "Created report.",
"tool_calls": [
{"name": "present_files", "args": {"filepaths": ["/mnt/user-data/outputs/report.md"]}},
],
},
{"type": "tool", "name": "present_files", "content": "ok"},
{"type": "human", "content": "add chart"},
{
"type": "ai",
"content": "Created chart.",
"tool_calls": [
{"name": "present_files", "args": {"filepaths": ["/mnt/user-data/outputs/chart.png"]}},
],
},
{"type": "tool", "name": "present_files", "content": "ok"},
],
}
mock_client = _make_mock_langgraph_client(thread_id="thread-dup-test")
mock_client.runs.wait = AsyncMock(side_effect=[turn1_result, turn2_result])
manager._client = mock_client
outbound_received = []
bus.subscribe_outbound(lambda msg: outbound_received.append(msg))
await manager.start()
# Send two messages with the same topic_id (same thread)
for text in ["make report", "add chart"]:
msg = InboundMessage(
channel_name="test",
chat_id="c1",
user_id="u1",
text=text,
topic_id="topic-dup",
)
await bus.publish_inbound(msg)
await _wait_for(lambda: len(outbound_received) >= 2)
await manager.stop()
assert len(outbound_received) == 2
# Turn 1: should include report.md
assert "report.md" in outbound_received[0].text
assert outbound_received[0].artifacts == ["/mnt/user-data/outputs/report.md"]
# Turn 2: should include ONLY chart.png (report.md is from previous turn)
assert "chart.png" in outbound_received[1].text
assert "report.md" not in outbound_received[1].text
assert outbound_received[1].artifacts == ["/mnt/user-data/outputs/chart.png"]
_run(go())
class TestFeishuChannel:
def test_prepare_inbound_publishes_without_waiting_for_running_card(self):
from app.channels.feishu import FeishuChannel
async def go():
bus = MessageBus()
bus.publish_inbound = AsyncMock()
channel = FeishuChannel(bus, config={})
reply_started = asyncio.Event()
release_reply = asyncio.Event()
async def slow_reply(message_id: str, text: str) -> str:
reply_started.set()
await release_reply.wait()
return "om-running-card"
channel._add_reaction = AsyncMock()
channel._reply_card = AsyncMock(side_effect=slow_reply)
inbound = InboundMessage(
channel_name="feishu",
chat_id="chat-1",
user_id="user-1",
text="hello",
thread_ts="om-source-msg",
)
prepare_task = asyncio.create_task(channel._prepare_inbound("om-source-msg", inbound))
await _wait_for(lambda: bus.publish_inbound.await_count == 1)
await prepare_task
assert reply_started.is_set()
assert "om-source-msg" in channel._running_card_tasks
assert channel._reply_card.await_count == 1
release_reply.set()
await _wait_for(lambda: channel._running_card_ids.get("om-source-msg") == "om-running-card")
await _wait_for(lambda: "om-source-msg" not in channel._running_card_tasks)
_run(go())
def test_prepare_inbound_and_send_share_running_card_task(self):
from app.channels.feishu import FeishuChannel
async def go():
bus = MessageBus()
bus.publish_inbound = AsyncMock()
channel = FeishuChannel(bus, config={})
channel._api_client = MagicMock()
reply_started = asyncio.Event()
release_reply = asyncio.Event()
async def slow_reply(message_id: str, text: str) -> str:
reply_started.set()
await release_reply.wait()
return "om-running-card"
channel._add_reaction = AsyncMock()
channel._reply_card = AsyncMock(side_effect=slow_reply)
channel._update_card = AsyncMock()
inbound = InboundMessage(
channel_name="feishu",
chat_id="chat-1",
user_id="user-1",
text="hello",
thread_ts="om-source-msg",
)
prepare_task = asyncio.create_task(channel._prepare_inbound("om-source-msg", inbound))
await _wait_for(lambda: bus.publish_inbound.await_count == 1)
await _wait_for(reply_started.is_set)
send_task = asyncio.create_task(
channel.send(
OutboundMessage(
channel_name="feishu",
chat_id="chat-1",
thread_id="thread-1",
text="Hello",
is_final=False,
thread_ts="om-source-msg",
)
)
)
await asyncio.sleep(0)
assert channel._reply_card.await_count == 1
release_reply.set()
await prepare_task
await send_task
assert channel._reply_card.await_count == 1
channel._update_card.assert_awaited_once_with("om-running-card", "Hello")
assert "om-source-msg" not in channel._running_card_tasks
_run(go())
def test_streaming_reuses_single_running_card(self):
from lark_oapi.api.im.v1 import (
CreateMessageReactionRequest,
CreateMessageReactionRequestBody,
Emoji,
PatchMessageRequest,
PatchMessageRequestBody,
ReplyMessageRequest,
ReplyMessageRequestBody,
)
from app.channels.feishu import FeishuChannel
async def go():
bus = MessageBus()
channel = FeishuChannel(bus, config={})
channel._api_client = MagicMock()
channel._ReplyMessageRequest = ReplyMessageRequest
channel._ReplyMessageRequestBody = ReplyMessageRequestBody
channel._PatchMessageRequest = PatchMessageRequest
channel._PatchMessageRequestBody = PatchMessageRequestBody
channel._CreateMessageReactionRequest = CreateMessageReactionRequest
channel._CreateMessageReactionRequestBody = CreateMessageReactionRequestBody
channel._Emoji = Emoji
reply_response = MagicMock()
reply_response.data.message_id = "om-running-card"
channel._api_client.im.v1.message.reply = MagicMock(return_value=reply_response)
channel._api_client.im.v1.message.patch = MagicMock()
channel._api_client.im.v1.message_reaction.create = MagicMock()
await channel._send_running_reply("om-source-msg")
await channel.send(
OutboundMessage(
channel_name="feishu",
chat_id="chat-1",
thread_id="thread-1",
text="Hello",
is_final=False,
thread_ts="om-source-msg",
)
)
await channel.send(
OutboundMessage(
channel_name="feishu",
chat_id="chat-1",
thread_id="thread-1",
text="Hello world",
is_final=True,
thread_ts="om-source-msg",
)
)
assert channel._api_client.im.v1.message.reply.call_count == 1
assert channel._api_client.im.v1.message.patch.call_count == 2
assert channel._api_client.im.v1.message_reaction.create.call_count == 1
assert "om-source-msg" not in channel._running_card_ids
assert "om-source-msg" not in channel._running_card_tasks
first_patch_request = channel._api_client.im.v1.message.patch.call_args_list[0].args[0]
final_patch_request = channel._api_client.im.v1.message.patch.call_args_list[1].args[0]
assert first_patch_request.message_id == "om-running-card"
assert final_patch_request.message_id == "om-running-card"
assert json.loads(first_patch_request.body.content)["elements"][0]["content"] == "Hello"
assert json.loads(final_patch_request.body.content)["elements"][0]["content"] == "Hello world"
assert json.loads(final_patch_request.body.content)["config"]["update_multi"] is True
_run(go())
class TestChannelService:
def test_get_status_no_channels(self):
from app.channels.service import ChannelService
async def go():
service = ChannelService(channels_config={})
await service.start()
status = service.get_status()
assert status["service_running"] is True
for ch_status in status["channels"].values():
assert ch_status["enabled"] is False
assert ch_status["running"] is False
await service.stop()
_run(go())
def test_disabled_channels_are_skipped(self):
from app.channels.service import ChannelService
async def go():
service = ChannelService(
channels_config={
"feishu": {"enabled": False, "app_id": "x", "app_secret": "y"},
}
)
await service.start()
assert "feishu" not in service._channels
await service.stop()
_run(go())
def test_session_config_is_forwarded_to_manager(self):
from app.channels.service import ChannelService
service = ChannelService(
channels_config={
"session": {"context": {"thinking_enabled": False}},
"telegram": {
"enabled": False,
"session": {
"assistant_id": "mobile_agent",
"users": {
"vip": {
"assistant_id": "vip_agent",
}
},
},
},
}
)
assert service.manager._default_session["context"]["thinking_enabled"] is False
assert service.manager._channel_sessions["telegram"]["assistant_id"] == "mobile_agent"
assert service.manager._channel_sessions["telegram"]["users"]["vip"]["assistant_id"] == "vip_agent"
# ---------------------------------------------------------------------------
# Slack send retry tests
# ---------------------------------------------------------------------------
class TestSlackSendRetry:
def test_retries_on_failure_then_succeeds(self):
from app.channels.slack import SlackChannel
async def go():
bus = MessageBus()
ch = SlackChannel(bus=bus, config={"bot_token": "xoxb-test", "app_token": "xapp-test"})
mock_web = MagicMock()
call_count = 0
def post_message(**kwargs):
nonlocal call_count
call_count += 1
if call_count < 3:
raise ConnectionError("network error")
return MagicMock()
mock_web.chat_postMessage = post_message
ch._web_client = mock_web
msg = OutboundMessage(channel_name="slack", chat_id="C123", thread_id="t1", text="hello")
await ch.send(msg)
assert call_count == 3
_run(go())
def test_raises_after_all_retries_exhausted(self):
from app.channels.slack import SlackChannel
async def go():
bus = MessageBus()
ch = SlackChannel(bus=bus, config={"bot_token": "xoxb-test", "app_token": "xapp-test"})
mock_web = MagicMock()
mock_web.chat_postMessage = MagicMock(side_effect=ConnectionError("fail"))
ch._web_client = mock_web
msg = OutboundMessage(channel_name="slack", chat_id="C123", thread_id="t1", text="hello")
with pytest.raises(ConnectionError):
await ch.send(msg)
assert mock_web.chat_postMessage.call_count == 3
_run(go())
# ---------------------------------------------------------------------------
# Telegram send retry tests
# ---------------------------------------------------------------------------
class TestTelegramSendRetry:
def test_retries_on_failure_then_succeeds(self):
from app.channels.telegram import TelegramChannel
async def go():
bus = MessageBus()
ch = TelegramChannel(bus=bus, config={"bot_token": "test-token"})
mock_app = MagicMock()
mock_bot = AsyncMock()
call_count = 0
async def send_message(**kwargs):
nonlocal call_count
call_count += 1
if call_count < 3:
raise ConnectionError("network error")
result = MagicMock()
result.message_id = 999
return result
mock_bot.send_message = send_message
mock_app.bot = mock_bot
ch._application = mock_app
msg = OutboundMessage(channel_name="telegram", chat_id="12345", thread_id="t1", text="hello")
await ch.send(msg)
assert call_count == 3
_run(go())
def test_raises_after_all_retries_exhausted(self):
from app.channels.telegram import TelegramChannel
async def go():
bus = MessageBus()
ch = TelegramChannel(bus=bus, config={"bot_token": "test-token"})
mock_app = MagicMock()
mock_bot = AsyncMock()
mock_bot.send_message = AsyncMock(side_effect=ConnectionError("fail"))
mock_app.bot = mock_bot
ch._application = mock_app
msg = OutboundMessage(channel_name="telegram", chat_id="12345", thread_id="t1", text="hello")
with pytest.raises(ConnectionError):
await ch.send(msg)
assert mock_bot.send_message.call_count == 3
_run(go())
# ---------------------------------------------------------------------------
# Telegram private-chat thread context tests
# ---------------------------------------------------------------------------
def _make_telegram_update(chat_type: str, message_id: int, *, reply_to_message_id: int | None = None, text: str = "hello"):
"""Build a minimal mock telegram Update for testing _on_text / _cmd_generic."""
update = MagicMock()
update.effective_chat.type = chat_type
update.effective_chat.id = 100
update.effective_user.id = 42
update.message.text = text
update.message.message_id = message_id
if reply_to_message_id is not None:
reply_msg = MagicMock()
reply_msg.message_id = reply_to_message_id
update.message.reply_to_message = reply_msg
else:
update.message.reply_to_message = None
return update
class TestTelegramPrivateChatThread:
"""Verify that private chats use topic_id=None (single thread per chat)."""
def test_private_chat_no_reply_uses_none_topic(self):
from app.channels.telegram import TelegramChannel
async def go():
bus = MessageBus()
ch = TelegramChannel(bus=bus, config={"bot_token": "test-token"})
ch._main_loop = asyncio.get_event_loop()
update = _make_telegram_update("private", message_id=10)
await ch._on_text(update, None)
msg = await asyncio.wait_for(bus.get_inbound(), timeout=2)
assert msg.topic_id is None
_run(go())
def test_private_chat_with_reply_still_uses_none_topic(self):
from app.channels.telegram import TelegramChannel
async def go():
bus = MessageBus()
ch = TelegramChannel(bus=bus, config={"bot_token": "test-token"})
ch._main_loop = asyncio.get_event_loop()
update = _make_telegram_update("private", message_id=11, reply_to_message_id=5)
await ch._on_text(update, None)
msg = await asyncio.wait_for(bus.get_inbound(), timeout=2)
assert msg.topic_id is None
_run(go())
def test_group_chat_no_reply_uses_msg_id_as_topic(self):
from app.channels.telegram import TelegramChannel
async def go():
bus = MessageBus()
ch = TelegramChannel(bus=bus, config={"bot_token": "test-token"})
ch._main_loop = asyncio.get_event_loop()
update = _make_telegram_update("group", message_id=20)
await ch._on_text(update, None)
msg = await asyncio.wait_for(bus.get_inbound(), timeout=2)
assert msg.topic_id == "20"
_run(go())
def test_group_chat_reply_uses_reply_msg_id_as_topic(self):
from app.channels.telegram import TelegramChannel
async def go():
bus = MessageBus()
ch = TelegramChannel(bus=bus, config={"bot_token": "test-token"})
ch._main_loop = asyncio.get_event_loop()
update = _make_telegram_update("group", message_id=21, reply_to_message_id=15)
await ch._on_text(update, None)
msg = await asyncio.wait_for(bus.get_inbound(), timeout=2)
assert msg.topic_id == "15"
_run(go())
def test_supergroup_chat_uses_msg_id_as_topic(self):
from app.channels.telegram import TelegramChannel
async def go():
bus = MessageBus()
ch = TelegramChannel(bus=bus, config={"bot_token": "test-token"})
ch._main_loop = asyncio.get_event_loop()
update = _make_telegram_update("supergroup", message_id=25)
await ch._on_text(update, None)
msg = await asyncio.wait_for(bus.get_inbound(), timeout=2)
assert msg.topic_id == "25"
_run(go())
def test_cmd_generic_private_chat_uses_none_topic(self):
from app.channels.telegram import TelegramChannel
async def go():
bus = MessageBus()
ch = TelegramChannel(bus=bus, config={"bot_token": "test-token"})
ch._main_loop = asyncio.get_event_loop()
update = _make_telegram_update("private", message_id=30, text="/new")
await ch._cmd_generic(update, None)
msg = await asyncio.wait_for(bus.get_inbound(), timeout=2)
assert msg.topic_id is None
assert msg.msg_type == InboundMessageType.COMMAND
_run(go())
def test_cmd_generic_group_chat_uses_msg_id_as_topic(self):
from app.channels.telegram import TelegramChannel
async def go():
bus = MessageBus()
ch = TelegramChannel(bus=bus, config={"bot_token": "test-token"})
ch._main_loop = asyncio.get_event_loop()
update = _make_telegram_update("group", message_id=31, text="/status")
await ch._cmd_generic(update, None)
msg = await asyncio.wait_for(bus.get_inbound(), timeout=2)
assert msg.topic_id == "31"
assert msg.msg_type == InboundMessageType.COMMAND
_run(go())
def test_cmd_generic_group_chat_reply_uses_reply_msg_id_as_topic(self):
from app.channels.telegram import TelegramChannel
async def go():
bus = MessageBus()
ch = TelegramChannel(bus=bus, config={"bot_token": "test-token"})
ch._main_loop = asyncio.get_event_loop()
update = _make_telegram_update("group", message_id=32, reply_to_message_id=20, text="/status")
await ch._cmd_generic(update, None)
msg = await asyncio.wait_for(bus.get_inbound(), timeout=2)
assert msg.topic_id == "20"
assert msg.msg_type == InboundMessageType.COMMAND
_run(go())
class TestTelegramProcessingOrder:
"""Ensure 'working on it...' is sent before inbound is published."""
def test_running_reply_sent_before_publish(self):
from app.channels.telegram import TelegramChannel
async def go():
bus = MessageBus()
ch = TelegramChannel(bus=bus, config={"bot_token": "test-token"})
ch._main_loop = asyncio.get_event_loop()
order = []
async def mock_send_running_reply(chat_id, msg_id):
order.append("running_reply")
async def mock_publish_inbound(inbound):
order.append("publish_inbound")
ch._send_running_reply = mock_send_running_reply
ch.bus.publish_inbound = mock_publish_inbound
await ch._process_incoming_with_reply(chat_id="chat1", msg_id=123, inbound=InboundMessage(channel_name="telegram", chat_id="chat1", user_id="user1", text="hello"))
assert order == ["running_reply", "publish_inbound"]
_run(go())
# ---------------------------------------------------------------------------
# Slack markdown-to-mrkdwn conversion tests (via markdown_to_mrkdwn library)
# ---------------------------------------------------------------------------
class TestSlackMarkdownConversion:
"""Verify that the SlackChannel.send() path applies mrkdwn conversion."""
def test_bold_converted(self):
from app.channels.slack import _slack_md_converter
result = _slack_md_converter.convert("this is **bold** text")
assert "*bold*" in result
assert "**" not in result
def test_link_converted(self):
from app.channels.slack import _slack_md_converter
result = _slack_md_converter.convert("[click](https://example.com)")
assert "" in result
def test_heading_converted(self):
from app.channels.slack import _slack_md_converter
result = _slack_md_converter.convert("# Title")
assert "*Title*" in result
assert "#" not in result
================================================
FILE: backend/tests/test_checkpointer.py
================================================
"""Unit tests for checkpointer config and singleton factory."""
import sys
from unittest.mock import MagicMock, patch
import pytest
import deerflow.config.app_config as app_config_module
from deerflow.agents.checkpointer import get_checkpointer, reset_checkpointer
from deerflow.config.checkpointer_config import (
CheckpointerConfig,
get_checkpointer_config,
load_checkpointer_config_from_dict,
set_checkpointer_config,
)
@pytest.fixture(autouse=True)
def reset_state():
"""Reset singleton state before each test."""
app_config_module._app_config = None
set_checkpointer_config(None)
reset_checkpointer()
yield
app_config_module._app_config = None
set_checkpointer_config(None)
reset_checkpointer()
# ---------------------------------------------------------------------------
# Config tests
# ---------------------------------------------------------------------------
class TestCheckpointerConfig:
def test_load_memory_config(self):
load_checkpointer_config_from_dict({"type": "memory"})
config = get_checkpointer_config()
assert config is not None
assert config.type == "memory"
assert config.connection_string is None
def test_load_sqlite_config(self):
load_checkpointer_config_from_dict({"type": "sqlite", "connection_string": "/tmp/test.db"})
config = get_checkpointer_config()
assert config is not None
assert config.type == "sqlite"
assert config.connection_string == "/tmp/test.db"
def test_load_postgres_config(self):
load_checkpointer_config_from_dict({"type": "postgres", "connection_string": "postgresql://localhost/db"})
config = get_checkpointer_config()
assert config is not None
assert config.type == "postgres"
assert config.connection_string == "postgresql://localhost/db"
def test_default_connection_string_is_none(self):
config = CheckpointerConfig(type="memory")
assert config.connection_string is None
def test_set_config_to_none(self):
load_checkpointer_config_from_dict({"type": "memory"})
set_checkpointer_config(None)
assert get_checkpointer_config() is None
def test_invalid_type_raises(self):
with pytest.raises(Exception):
load_checkpointer_config_from_dict({"type": "unknown"})
# ---------------------------------------------------------------------------
# Factory tests
# ---------------------------------------------------------------------------
class TestGetCheckpointer:
def test_returns_in_memory_saver_when_not_configured(self):
"""get_checkpointer should return InMemorySaver when not configured."""
from langgraph.checkpoint.memory import InMemorySaver
with patch("deerflow.agents.checkpointer.provider.get_app_config", side_effect=FileNotFoundError):
cp = get_checkpointer()
assert cp is not None
assert isinstance(cp, InMemorySaver)
def test_memory_returns_in_memory_saver(self):
load_checkpointer_config_from_dict({"type": "memory"})
from langgraph.checkpoint.memory import InMemorySaver
cp = get_checkpointer()
assert isinstance(cp, InMemorySaver)
def test_memory_singleton(self):
load_checkpointer_config_from_dict({"type": "memory"})
cp1 = get_checkpointer()
cp2 = get_checkpointer()
assert cp1 is cp2
def test_reset_clears_singleton(self):
load_checkpointer_config_from_dict({"type": "memory"})
cp1 = get_checkpointer()
reset_checkpointer()
cp2 = get_checkpointer()
assert cp1 is not cp2
def test_sqlite_raises_when_package_missing(self):
load_checkpointer_config_from_dict({"type": "sqlite", "connection_string": "/tmp/test.db"})
with patch.dict(sys.modules, {"langgraph.checkpoint.sqlite": None}):
reset_checkpointer()
with pytest.raises(ImportError, match="langgraph-checkpoint-sqlite"):
get_checkpointer()
def test_postgres_raises_when_package_missing(self):
load_checkpointer_config_from_dict({"type": "postgres", "connection_string": "postgresql://localhost/db"})
with patch.dict(sys.modules, {"langgraph.checkpoint.postgres": None}):
reset_checkpointer()
with pytest.raises(ImportError, match="langgraph-checkpoint-postgres"):
get_checkpointer()
def test_postgres_raises_when_connection_string_missing(self):
load_checkpointer_config_from_dict({"type": "postgres"})
mock_saver = MagicMock()
mock_module = MagicMock()
mock_module.PostgresSaver = mock_saver
with patch.dict(sys.modules, {"langgraph.checkpoint.postgres": mock_module}):
reset_checkpointer()
with pytest.raises(ValueError, match="connection_string is required"):
get_checkpointer()
def test_sqlite_creates_saver(self):
"""SQLite checkpointer is created when package is available."""
load_checkpointer_config_from_dict({"type": "sqlite", "connection_string": "/tmp/test.db"})
mock_saver_instance = MagicMock()
mock_cm = MagicMock()
mock_cm.__enter__ = MagicMock(return_value=mock_saver_instance)
mock_cm.__exit__ = MagicMock(return_value=False)
mock_saver_cls = MagicMock()
mock_saver_cls.from_conn_string = MagicMock(return_value=mock_cm)
mock_module = MagicMock()
mock_module.SqliteSaver = mock_saver_cls
with patch.dict(sys.modules, {"langgraph.checkpoint.sqlite": mock_module}):
reset_checkpointer()
cp = get_checkpointer()
assert cp is mock_saver_instance
mock_saver_cls.from_conn_string.assert_called_once()
mock_saver_instance.setup.assert_called_once()
def test_postgres_creates_saver(self):
"""Postgres checkpointer is created when packages are available."""
load_checkpointer_config_from_dict({"type": "postgres", "connection_string": "postgresql://localhost/db"})
mock_saver_instance = MagicMock()
mock_cm = MagicMock()
mock_cm.__enter__ = MagicMock(return_value=mock_saver_instance)
mock_cm.__exit__ = MagicMock(return_value=False)
mock_saver_cls = MagicMock()
mock_saver_cls.from_conn_string = MagicMock(return_value=mock_cm)
mock_pg_module = MagicMock()
mock_pg_module.PostgresSaver = mock_saver_cls
with patch.dict(sys.modules, {"langgraph.checkpoint.postgres": mock_pg_module}):
reset_checkpointer()
cp = get_checkpointer()
assert cp is mock_saver_instance
mock_saver_cls.from_conn_string.assert_called_once_with("postgresql://localhost/db")
mock_saver_instance.setup.assert_called_once()
# ---------------------------------------------------------------------------
# app_config.py integration
# ---------------------------------------------------------------------------
class TestAppConfigLoadsCheckpointer:
def test_load_checkpointer_section(self):
"""load_checkpointer_config_from_dict populates the global config."""
set_checkpointer_config(None)
load_checkpointer_config_from_dict({"type": "memory"})
cfg = get_checkpointer_config()
assert cfg is not None
assert cfg.type == "memory"
# ---------------------------------------------------------------------------
# DeerFlowClient falls back to config checkpointer
# ---------------------------------------------------------------------------
class TestClientCheckpointerFallback:
def test_client_uses_config_checkpointer_when_none_provided(self):
"""DeerFlowClient._ensure_agent falls back to get_checkpointer() when checkpointer=None."""
from langgraph.checkpoint.memory import InMemorySaver
from deerflow.client import DeerFlowClient
load_checkpointer_config_from_dict({"type": "memory"})
captured_kwargs = {}
def fake_create_agent(**kwargs):
captured_kwargs.update(kwargs)
return MagicMock()
model_mock = MagicMock()
config_mock = MagicMock()
config_mock.models = [model_mock]
config_mock.get_model_config.return_value = MagicMock(supports_vision=False)
config_mock.checkpointer = None
with (
patch("deerflow.client.get_app_config", return_value=config_mock),
patch("deerflow.client.create_agent", side_effect=fake_create_agent),
patch("deerflow.client.create_chat_model", return_value=MagicMock()),
patch("deerflow.client._build_middlewares", return_value=[]),
patch("deerflow.client.apply_prompt_template", return_value=""),
patch("deerflow.client.DeerFlowClient._get_tools", return_value=[]),
):
client = DeerFlowClient(checkpointer=None)
config = client._get_runnable_config("test-thread")
client._ensure_agent(config)
assert "checkpointer" in captured_kwargs
assert isinstance(captured_kwargs["checkpointer"], InMemorySaver)
def test_client_explicit_checkpointer_takes_precedence(self):
"""An explicitly provided checkpointer is used even when config checkpointer is set."""
from deerflow.client import DeerFlowClient
load_checkpointer_config_from_dict({"type": "memory"})
explicit_cp = MagicMock()
captured_kwargs = {}
def fake_create_agent(**kwargs):
captured_kwargs.update(kwargs)
return MagicMock()
model_mock = MagicMock()
config_mock = MagicMock()
config_mock.models = [model_mock]
config_mock.get_model_config.return_value = MagicMock(supports_vision=False)
config_mock.checkpointer = None
with (
patch("deerflow.client.get_app_config", return_value=config_mock),
patch("deerflow.client.create_agent", side_effect=fake_create_agent),
patch("deerflow.client.create_chat_model", return_value=MagicMock()),
patch("deerflow.client._build_middlewares", return_value=[]),
patch("deerflow.client.apply_prompt_template", return_value=""),
patch("deerflow.client.DeerFlowClient._get_tools", return_value=[]),
):
client = DeerFlowClient(checkpointer=explicit_cp)
config = client._get_runnable_config("test-thread")
client._ensure_agent(config)
assert captured_kwargs["checkpointer"] is explicit_cp
================================================
FILE: backend/tests/test_checkpointer_none_fix.py
================================================
"""Test for issue #1016: checkpointer should not return None."""
from unittest.mock import MagicMock, patch
import pytest
from langgraph.checkpoint.memory import InMemorySaver
class TestCheckpointerNoneFix:
"""Tests that checkpointer context managers return InMemorySaver instead of None."""
@pytest.mark.anyio
async def test_async_make_checkpointer_returns_in_memory_saver_when_not_configured(self):
"""make_checkpointer should return InMemorySaver when config.checkpointer is None."""
from deerflow.agents.checkpointer.async_provider import make_checkpointer
# Mock get_app_config to return a config with checkpointer=None
mock_config = MagicMock()
mock_config.checkpointer = None
with patch("deerflow.agents.checkpointer.async_provider.get_app_config", return_value=mock_config):
async with make_checkpointer() as checkpointer:
# Should return InMemorySaver, not None
assert checkpointer is not None
assert isinstance(checkpointer, InMemorySaver)
# Should be able to call alist() without AttributeError
# This is what LangGraph does and what was failing in issue #1016
result = []
async for item in checkpointer.alist(config={"configurable": {"thread_id": "test"}}):
result.append(item)
# Empty list is expected for a fresh checkpointer
assert result == []
def test_sync_checkpointer_context_returns_in_memory_saver_when_not_configured(self):
"""checkpointer_context should return InMemorySaver when config.checkpointer is None."""
from deerflow.agents.checkpointer.provider import checkpointer_context
# Mock get_app_config to return a config with checkpointer=None
mock_config = MagicMock()
mock_config.checkpointer = None
with patch("deerflow.agents.checkpointer.provider.get_app_config", return_value=mock_config):
with checkpointer_context() as checkpointer:
# Should return InMemorySaver, not None
assert checkpointer is not None
assert isinstance(checkpointer, InMemorySaver)
# Should be able to call list() without AttributeError
result = list(checkpointer.list(config={"configurable": {"thread_id": "test"}}))
# Empty list is expected for a fresh checkpointer
assert result == []
================================================
FILE: backend/tests/test_cli_auth_providers.py
================================================
from __future__ import annotations
import json
import pytest
from langchain_core.messages import HumanMessage, SystemMessage
from deerflow.models.claude_provider import ClaudeChatModel
from deerflow.models.credential_loader import CodexCliCredential
from deerflow.models.openai_codex_provider import CodexChatModel
def test_codex_provider_rejects_non_positive_retry_attempts():
with pytest.raises(ValueError, match="retry_max_attempts must be >= 1"):
CodexChatModel(retry_max_attempts=0)
def test_codex_provider_requires_credentials(monkeypatch):
monkeypatch.setattr(CodexChatModel, "_load_codex_auth", lambda self: None)
with pytest.raises(ValueError, match="Codex CLI credential not found"):
CodexChatModel()
def test_codex_provider_concatenates_multiple_system_messages(monkeypatch):
monkeypatch.setattr(
CodexChatModel,
"_load_codex_auth",
lambda self: CodexCliCredential(access_token="token", account_id="acct"),
)
model = CodexChatModel()
instructions, input_items = model._convert_messages(
[
SystemMessage(content="First system prompt."),
SystemMessage(content="Second system prompt."),
HumanMessage(content="Hello"),
]
)
assert instructions == "First system prompt.\n\nSecond system prompt."
assert input_items == [{"role": "user", "content": "Hello"}]
def test_codex_provider_flattens_structured_text_blocks(monkeypatch):
monkeypatch.setattr(
CodexChatModel,
"_load_codex_auth",
lambda self: CodexCliCredential(access_token="token", account_id="acct"),
)
model = CodexChatModel()
instructions, input_items = model._convert_messages(
[
HumanMessage(content=[{"type": "text", "text": "Hello from blocks"}]),
]
)
assert instructions == "You are a helpful assistant."
assert input_items == [{"role": "user", "content": "Hello from blocks"}]
def test_claude_provider_rejects_non_positive_retry_attempts():
with pytest.raises(ValueError, match="retry_max_attempts must be >= 1"):
ClaudeChatModel(model="claude-sonnet-4-6", retry_max_attempts=0)
def test_codex_provider_skips_terminal_sse_markers(monkeypatch):
monkeypatch.setattr(
CodexChatModel,
"_load_codex_auth",
lambda self: CodexCliCredential(access_token="token", account_id="acct"),
)
model = CodexChatModel()
assert model._parse_sse_data_line("data: [DONE]") is None
assert model._parse_sse_data_line("event: response.completed") is None
def test_codex_provider_skips_non_json_sse_frames(monkeypatch):
monkeypatch.setattr(
CodexChatModel,
"_load_codex_auth",
lambda self: CodexCliCredential(access_token="token", account_id="acct"),
)
model = CodexChatModel()
assert model._parse_sse_data_line("data: not-json") is None
def test_codex_provider_marks_invalid_tool_call_arguments(monkeypatch):
monkeypatch.setattr(
CodexChatModel,
"_load_codex_auth",
lambda self: CodexCliCredential(access_token="token", account_id="acct"),
)
model = CodexChatModel()
result = model._parse_response(
{
"model": "gpt-5.4",
"output": [
{
"type": "function_call",
"name": "bash",
"arguments": "{invalid",
"call_id": "tc-1",
}
],
"usage": {},
}
)
message = result.generations[0].message
assert message.tool_calls == []
assert len(message.invalid_tool_calls) == 1
assert message.invalid_tool_calls[0]["type"] == "invalid_tool_call"
assert message.invalid_tool_calls[0]["name"] == "bash"
assert message.invalid_tool_calls[0]["args"] == "{invalid"
assert message.invalid_tool_calls[0]["id"] == "tc-1"
assert "Failed to parse tool arguments" in message.invalid_tool_calls[0]["error"]
def test_codex_provider_parses_valid_tool_arguments(monkeypatch):
monkeypatch.setattr(
CodexChatModel,
"_load_codex_auth",
lambda self: CodexCliCredential(access_token="token", account_id="acct"),
)
model = CodexChatModel()
result = model._parse_response(
{
"model": "gpt-5.4",
"output": [
{
"type": "function_call",
"name": "bash",
"arguments": json.dumps({"cmd": "pwd"}),
"call_id": "tc-1",
}
],
"usage": {},
}
)
assert result.generations[0].message.tool_calls == [
{"name": "bash", "args": {"cmd": "pwd"}, "id": "tc-1", "type": "tool_call"}
]
================================================
FILE: backend/tests/test_client.py
================================================
"""Tests for DeerFlowClient."""
import asyncio
import concurrent.futures
import json
import tempfile
import zipfile
from pathlib import Path
from unittest.mock import MagicMock, patch
import pytest
from langchain_core.messages import AIMessage, HumanMessage, ToolMessage # noqa: F401
from app.gateway.routers.mcp import McpConfigResponse
from app.gateway.routers.memory import MemoryConfigResponse, MemoryStatusResponse
from app.gateway.routers.models import ModelResponse, ModelsListResponse
from app.gateway.routers.skills import SkillInstallResponse, SkillResponse, SkillsListResponse
from app.gateway.routers.uploads import UploadResponse
from deerflow.client import DeerFlowClient
# ---------------------------------------------------------------------------
# Fixtures
# ---------------------------------------------------------------------------
@pytest.fixture
def mock_app_config():
"""Provide a minimal AppConfig mock."""
model = MagicMock()
model.name = "test-model"
model.model = "test-model"
model.supports_thinking = False
model.supports_reasoning_effort = False
model.model_dump.return_value = {"name": "test-model", "use": "langchain_openai:ChatOpenAI"}
config = MagicMock()
config.models = [model]
return config
@pytest.fixture
def client(mock_app_config):
"""Create a DeerFlowClient with mocked config loading."""
with patch("deerflow.client.get_app_config", return_value=mock_app_config):
return DeerFlowClient()
# ---------------------------------------------------------------------------
# __init__
# ---------------------------------------------------------------------------
class TestClientInit:
def test_default_params(self, client):
assert client._model_name is None
assert client._thinking_enabled is True
assert client._subagent_enabled is False
assert client._plan_mode is False
assert client._agent_name is None
assert client._checkpointer is None
assert client._agent is None
def test_custom_params(self, mock_app_config):
with patch("deerflow.client.get_app_config", return_value=mock_app_config):
c = DeerFlowClient(
model_name="gpt-4",
thinking_enabled=False,
subagent_enabled=True,
plan_mode=True,
agent_name="test-agent"
)
assert c._model_name == "gpt-4"
assert c._thinking_enabled is False
assert c._subagent_enabled is True
assert c._plan_mode is True
assert c._agent_name == "test-agent"
def test_invalid_agent_name(self, mock_app_config):
with patch("deerflow.client.get_app_config", return_value=mock_app_config):
with pytest.raises(ValueError, match="Invalid agent name"):
DeerFlowClient(agent_name="invalid name with spaces!")
with pytest.raises(ValueError, match="Invalid agent name"):
DeerFlowClient(agent_name="../path/traversal")
def test_custom_config_path(self, mock_app_config):
with (
patch("deerflow.client.reload_app_config") as mock_reload,
patch("deerflow.client.get_app_config", return_value=mock_app_config),
):
DeerFlowClient(config_path="/tmp/custom.yaml")
mock_reload.assert_called_once_with("/tmp/custom.yaml")
def test_checkpointer_stored(self, mock_app_config):
cp = MagicMock()
with patch("deerflow.client.get_app_config", return_value=mock_app_config):
c = DeerFlowClient(checkpointer=cp)
assert c._checkpointer is cp
# ---------------------------------------------------------------------------
# list_models / list_skills / get_memory
# ---------------------------------------------------------------------------
class TestConfigQueries:
def test_list_models(self, client):
result = client.list_models()
assert "models" in result
assert len(result["models"]) == 1
assert result["models"][0]["name"] == "test-model"
# Verify Gateway-aligned fields are present
assert "model" in result["models"][0]
assert "display_name" in result["models"][0]
assert "supports_thinking" in result["models"][0]
def test_list_skills(self, client):
skill = MagicMock()
skill.name = "web-search"
skill.description = "Search the web"
skill.license = "MIT"
skill.category = "public"
skill.enabled = True
with patch("deerflow.skills.loader.load_skills", return_value=[skill]) as mock_load:
result = client.list_skills()
mock_load.assert_called_once_with(enabled_only=False)
assert "skills" in result
assert len(result["skills"]) == 1
assert result["skills"][0] == {
"name": "web-search",
"description": "Search the web",
"license": "MIT",
"category": "public",
"enabled": True,
}
def test_list_skills_enabled_only(self, client):
with patch("deerflow.skills.loader.load_skills", return_value=[]) as mock_load:
client.list_skills(enabled_only=True)
mock_load.assert_called_once_with(enabled_only=True)
def test_get_memory(self, client):
memory = {"version": "1.0", "facts": []}
with patch("deerflow.agents.memory.updater.get_memory_data", return_value=memory) as mock_mem:
result = client.get_memory()
mock_mem.assert_called_once()
assert result == memory
# ---------------------------------------------------------------------------
# stream / chat
# ---------------------------------------------------------------------------
def _make_agent_mock(chunks: list[dict]):
"""Create a mock agent whose .stream() yields the given chunks."""
agent = MagicMock()
agent.stream.return_value = iter(chunks)
return agent
def _ai_events(events):
"""Filter messages-tuple events with type=ai and non-empty content."""
return [e for e in events if e.type == "messages-tuple" and e.data.get("type") == "ai" and e.data.get("content")]
def _tool_call_events(events):
"""Filter messages-tuple events with type=ai and tool_calls."""
return [e for e in events if e.type == "messages-tuple" and e.data.get("type") == "ai" and "tool_calls" in e.data]
def _tool_result_events(events):
"""Filter messages-tuple events with type=tool."""
return [e for e in events if e.type == "messages-tuple" and e.data.get("type") == "tool"]
class TestStream:
def test_basic_message(self, client):
"""stream() emits messages-tuple + values + end for a simple AI reply."""
ai = AIMessage(content="Hello!", id="ai-1")
chunks = [
{"messages": [HumanMessage(content="hi", id="h-1")]},
{"messages": [HumanMessage(content="hi", id="h-1"), ai]},
]
agent = _make_agent_mock(chunks)
with (
patch.object(client, "_ensure_agent"),
patch.object(client, "_agent", agent),
):
events = list(client.stream("hi", thread_id="t1"))
types = [e.type for e in events]
assert "messages-tuple" in types
assert "values" in types
assert types[-1] == "end"
msg_events = _ai_events(events)
assert msg_events[0].data["content"] == "Hello!"
def test_context_propagation(self, client):
"""stream() passes agent_name to the context."""
agent = _make_agent_mock([{"messages": [AIMessage(content="ok", id="ai-1")]}])
client._agent_name = "test-agent-1"
with (
patch.object(client, "_ensure_agent"),
patch.object(client, "_agent", agent),
):
list(client.stream("hi", thread_id="t1"))
# Verify context passed to agent.stream
agent.stream.assert_called_once()
call_kwargs = agent.stream.call_args.kwargs
assert call_kwargs["context"]["thread_id"] == "t1"
assert call_kwargs["context"]["agent_name"] == "test-agent-1"
def test_tool_call_and_result(self, client):
"""stream() emits messages-tuple events for tool calls and results."""
ai = AIMessage(content="", id="ai-1", tool_calls=[{"name": "bash", "args": {"cmd": "ls"}, "id": "tc-1"}])
tool = ToolMessage(content="file.txt", id="tm-1", tool_call_id="tc-1", name="bash")
ai2 = AIMessage(content="Here are the files.", id="ai-2")
chunks = [
{"messages": [HumanMessage(content="list files", id="h-1"), ai]},
{"messages": [HumanMessage(content="list files", id="h-1"), ai, tool]},
{"messages": [HumanMessage(content="list files", id="h-1"), ai, tool, ai2]},
]
agent = _make_agent_mock(chunks)
with (
patch.object(client, "_ensure_agent"),
patch.object(client, "_agent", agent),
):
events = list(client.stream("list files", thread_id="t2"))
assert len(_tool_call_events(events)) >= 1
assert len(_tool_result_events(events)) >= 1
assert len(_ai_events(events)) >= 1
assert events[-1].type == "end"
def test_values_event_with_title(self, client):
"""stream() emits values event containing title when present in state."""
ai = AIMessage(content="ok", id="ai-1")
chunks = [
{"messages": [HumanMessage(content="hi", id="h-1"), ai], "title": "Greeting"},
]
agent = _make_agent_mock(chunks)
with (
patch.object(client, "_ensure_agent"),
patch.object(client, "_agent", agent),
):
events = list(client.stream("hi", thread_id="t3"))
values_events = [e for e in events if e.type == "values"]
assert len(values_events) >= 1
assert values_events[-1].data["title"] == "Greeting"
assert "messages" in values_events[-1].data
def test_deduplication(self, client):
"""Messages with the same id are not emitted twice."""
ai = AIMessage(content="Hello!", id="ai-1")
chunks = [
{"messages": [HumanMessage(content="hi", id="h-1"), ai]},
{"messages": [HumanMessage(content="hi", id="h-1"), ai]}, # duplicate
]
agent = _make_agent_mock(chunks)
with (
patch.object(client, "_ensure_agent"),
patch.object(client, "_agent", agent),
):
events = list(client.stream("hi", thread_id="t4"))
msg_events = _ai_events(events)
assert len(msg_events) == 1
def test_auto_thread_id(self, client):
"""stream() auto-generates a thread_id if not provided."""
agent = _make_agent_mock([{"messages": [AIMessage(content="ok", id="ai-1")]}])
with (
patch.object(client, "_ensure_agent"),
patch.object(client, "_agent", agent),
):
events = list(client.stream("hi"))
# Should not raise; end event proves it completed
assert events[-1].type == "end"
def test_list_content_blocks(self, client):
"""stream() handles AIMessage with list-of-blocks content."""
ai = AIMessage(
content=[
{"type": "thinking", "thinking": "hmm"},
{"type": "text", "text": "result"},
],
id="ai-1",
)
chunks = [{"messages": [ai]}]
agent = _make_agent_mock(chunks)
with (
patch.object(client, "_ensure_agent"),
patch.object(client, "_agent", agent),
):
events = list(client.stream("hi", thread_id="t5"))
msg_events = _ai_events(events)
assert len(msg_events) == 1
assert msg_events[0].data["content"] == "result"
class TestChat:
def test_returns_last_message(self, client):
"""chat() returns the last AI message text."""
ai1 = AIMessage(content="thinking...", id="ai-1")
ai2 = AIMessage(content="final answer", id="ai-2")
chunks = [
{"messages": [HumanMessage(content="q", id="h-1"), ai1]},
{"messages": [HumanMessage(content="q", id="h-1"), ai1, ai2]},
]
agent = _make_agent_mock(chunks)
with (
patch.object(client, "_ensure_agent"),
patch.object(client, "_agent", agent),
):
result = client.chat("q", thread_id="t6")
assert result == "final answer"
def test_empty_response(self, client):
"""chat() returns empty string if no AI message produced."""
chunks = [{"messages": []}]
agent = _make_agent_mock(chunks)
with (
patch.object(client, "_ensure_agent"),
patch.object(client, "_agent", agent),
):
result = client.chat("q", thread_id="t7")
assert result == ""
# ---------------------------------------------------------------------------
# _extract_text
# ---------------------------------------------------------------------------
class TestExtractText:
def test_string(self):
assert DeerFlowClient._extract_text("hello") == "hello"
def test_list_text_blocks(self):
content = [
{"type": "text", "text": "first"},
{"type": "thinking", "thinking": "skip"},
{"type": "text", "text": "second"},
]
assert DeerFlowClient._extract_text(content) == "first\nsecond"
def test_list_plain_strings(self):
assert DeerFlowClient._extract_text(["a", "b"]) == "a\nb"
def test_empty_list(self):
assert DeerFlowClient._extract_text([]) == ""
def test_other_type(self):
assert DeerFlowClient._extract_text(42) == "42"
# ---------------------------------------------------------------------------
# _ensure_agent
# ---------------------------------------------------------------------------
class TestEnsureAgent:
def test_creates_agent(self, client):
"""_ensure_agent creates an agent on first call."""
mock_agent = MagicMock()
config = client._get_runnable_config("t1")
with (
patch("deerflow.client.create_chat_model"),
patch("deerflow.client.create_agent", return_value=mock_agent),
patch("deerflow.client._build_middlewares", return_value=[]) as mock_build_middlewares,
patch("deerflow.client.apply_prompt_template", return_value="prompt") as mock_apply_prompt,
patch.object(client, "_get_tools", return_value=[]),
):
client._agent_name = "custom-agent"
client._ensure_agent(config)
assert client._agent is mock_agent
# Verify agent_name propagation
mock_build_middlewares.assert_called_once()
assert mock_build_middlewares.call_args.kwargs.get("agent_name") == "custom-agent"
mock_apply_prompt.assert_called_once()
assert mock_apply_prompt.call_args.kwargs.get("agent_name") == "custom-agent"
def test_uses_default_checkpointer_when_available(self, client):
mock_agent = MagicMock()
mock_checkpointer = MagicMock()
config = client._get_runnable_config("t1")
with (
patch("deerflow.client.create_chat_model"),
patch("deerflow.client.create_agent", return_value=mock_agent) as mock_create_agent,
patch("deerflow.client._build_middlewares", return_value=[]),
patch("deerflow.client.apply_prompt_template", return_value="prompt"),
patch.object(client, "_get_tools", return_value=[]),
patch("deerflow.agents.checkpointer.get_checkpointer", return_value=mock_checkpointer),
):
client._ensure_agent(config)
assert mock_create_agent.call_args.kwargs["checkpointer"] is mock_checkpointer
def test_skips_default_checkpointer_when_unconfigured(self, client):
mock_agent = MagicMock()
config = client._get_runnable_config("t1")
with (
patch("deerflow.client.create_chat_model"),
patch("deerflow.client.create_agent", return_value=mock_agent) as mock_create_agent,
patch("deerflow.client._build_middlewares", return_value=[]),
patch("deerflow.client.apply_prompt_template", return_value="prompt"),
patch.object(client, "_get_tools", return_value=[]),
patch("deerflow.agents.checkpointer.get_checkpointer", return_value=None),
):
client._ensure_agent(config)
assert "checkpointer" not in mock_create_agent.call_args.kwargs
def test_reuses_agent_same_config(self, client):
"""_ensure_agent does not recreate if config key unchanged."""
mock_agent = MagicMock()
client._agent = mock_agent
client._agent_config_key = (None, True, False, False)
config = client._get_runnable_config("t1")
client._ensure_agent(config)
# Should still be the same mock — no recreation
assert client._agent is mock_agent
# ---------------------------------------------------------------------------
# get_model
# ---------------------------------------------------------------------------
class TestGetModel:
def test_found(self, client):
model_cfg = MagicMock()
model_cfg.name = "test-model"
model_cfg.model = "test-model"
model_cfg.display_name = "Test Model"
model_cfg.description = "A test model"
model_cfg.supports_thinking = True
model_cfg.supports_reasoning_effort = True
client._app_config.get_model_config.return_value = model_cfg
result = client.get_model("test-model")
assert result == {
"name": "test-model",
"model": "test-model",
"display_name": "Test Model",
"description": "A test model",
"supports_thinking": True,
"supports_reasoning_effort": True,
}
def test_not_found(self, client):
client._app_config.get_model_config.return_value = None
assert client.get_model("nonexistent") is None
# ---------------------------------------------------------------------------
# MCP config
# ---------------------------------------------------------------------------
class TestMcpConfig:
def test_get_mcp_config(self, client):
server = MagicMock()
server.model_dump.return_value = {"enabled": True, "type": "stdio"}
ext_config = MagicMock()
ext_config.mcp_servers = {"github": server}
with patch("deerflow.client.get_extensions_config", return_value=ext_config):
result = client.get_mcp_config()
assert "mcp_servers" in result
assert "github" in result["mcp_servers"]
assert result["mcp_servers"]["github"]["enabled"] is True
def test_update_mcp_config(self, client):
# Set up current config with skills
current_config = MagicMock()
current_config.skills = {}
reloaded_server = MagicMock()
reloaded_server.model_dump.return_value = {"enabled": True, "type": "sse"}
reloaded_config = MagicMock()
reloaded_config.mcp_servers = {"new-server": reloaded_server}
with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f:
json.dump({}, f)
tmp_path = Path(f.name)
try:
# Pre-set agent to verify it gets invalidated
client._agent = MagicMock()
with (
patch("deerflow.client.ExtensionsConfig.resolve_config_path", return_value=tmp_path),
patch("deerflow.client.get_extensions_config", return_value=current_config),
patch("deerflow.client.reload_extensions_config", return_value=reloaded_config),
):
result = client.update_mcp_config({"new-server": {"enabled": True, "type": "sse"}})
assert "mcp_servers" in result
assert "new-server" in result["mcp_servers"]
assert client._agent is None # M2: agent invalidated
# Verify file was actually written
with open(tmp_path) as f:
saved = json.load(f)
assert "mcpServers" in saved
finally:
tmp_path.unlink()
# ---------------------------------------------------------------------------
# Skills management
# ---------------------------------------------------------------------------
class TestSkillsManagement:
def _make_skill(self, name="test-skill", enabled=True):
s = MagicMock()
s.name = name
s.description = "A test skill"
s.license = "MIT"
s.category = "public"
s.enabled = enabled
return s
def test_get_skill_found(self, client):
skill = self._make_skill()
with patch("deerflow.skills.loader.load_skills", return_value=[skill]):
result = client.get_skill("test-skill")
assert result is not None
assert result["name"] == "test-skill"
def test_get_skill_not_found(self, client):
with patch("deerflow.skills.loader.load_skills", return_value=[]):
result = client.get_skill("nonexistent")
assert result is None
def test_update_skill(self, client):
skill = self._make_skill(enabled=True)
updated_skill = self._make_skill(enabled=False)
ext_config = MagicMock()
ext_config.mcp_servers = {}
ext_config.skills = {}
with tempfile.NamedTemporaryFile(mode="w", suffix=".json", delete=False) as f:
json.dump({}, f)
tmp_path = Path(f.name)
try:
# Pre-set agent to verify it gets invalidated
client._agent = MagicMock()
with (
patch("deerflow.skills.loader.load_skills", side_effect=[[skill], [updated_skill]]),
patch("deerflow.client.ExtensionsConfig.resolve_config_path", return_value=tmp_path),
patch("deerflow.client.get_extensions_config", return_value=ext_config),
patch("deerflow.client.reload_extensions_config"),
):
result = client.update_skill("test-skill", enabled=False)
assert result["enabled"] is False
assert client._agent is None # M2: agent invalidated
finally:
tmp_path.unlink()
def test_update_skill_not_found(self, client):
with patch("deerflow.skills.loader.load_skills", return_value=[]):
with pytest.raises(ValueError, match="not found"):
client.update_skill("nonexistent", enabled=True)
def test_install_skill(self, client):
with tempfile.TemporaryDirectory() as tmp:
tmp_path = Path(tmp)
# Create a valid .skill archive
skill_dir = tmp_path / "my-skill"
skill_dir.mkdir()
(skill_dir / "SKILL.md").write_text("---\nname: my-skill\ndescription: A skill\n---\nContent")
archive_path = tmp_path / "my-skill.skill"
with zipfile.ZipFile(archive_path, "w") as zf:
zf.write(skill_dir / "SKILL.md", "my-skill/SKILL.md")
skills_root = tmp_path / "skills"
(skills_root / "custom").mkdir(parents=True)
with (
patch("deerflow.skills.loader.get_skills_root_path", return_value=skills_root),
patch("deerflow.skills.validation._validate_skill_frontmatter", return_value=(True, "OK", "my-skill")),
):
result = client.install_skill(archive_path)
assert result["success"] is True
assert result["skill_name"] == "my-skill"
assert (skills_root / "custom" / "my-skill").exists()
def test_install_skill_not_found(self, client):
with pytest.raises(FileNotFoundError):
client.install_skill("/nonexistent/path.skill")
def test_install_skill_bad_extension(self, client):
with tempfile.NamedTemporaryFile(suffix=".zip", delete=False) as f:
tmp_path = Path(f.name)
try:
with pytest.raises(ValueError, match=".skill extension"):
client.install_skill(tmp_path)
finally:
tmp_path.unlink()
# ---------------------------------------------------------------------------
# Memory management
# ---------------------------------------------------------------------------
class TestMemoryManagement:
def test_reload_memory(self, client):
data = {"version": "1.0", "facts": []}
with patch("deerflow.agents.memory.updater.reload_memory_data", return_value=data):
result = client.reload_memory()
assert result == data
def test_get_memory_config(self, client):
config = MagicMock()
config.enabled = True
config.storage_path = ".deer-flow/memory.json"
config.debounce_seconds = 30
config.max_facts = 100
config.fact_confidence_threshold = 0.7
config.injection_enabled = True
config.max_injection_tokens = 2000
with patch("deerflow.config.memory_config.get_memory_config", return_value=config):
result = client.get_memory_config()
assert result["enabled"] is True
assert result["max_facts"] == 100
def test_get_memory_status(self, client):
config = MagicMock()
config.enabled = True
config.storage_path = ".deer-flow/memory.json"
config.debounce_seconds = 30
config.max_facts = 100
config.fact_confidence_threshold = 0.7
config.injection_enabled = True
config.max_injection_tokens = 2000
data = {"version": "1.0", "facts": []}
with (
patch("deerflow.config.memory_config.get_memory_config", return_value=config),
patch("deerflow.agents.memory.updater.get_memory_data", return_value=data),
):
result = client.get_memory_status()
assert "config" in result
assert "data" in result
# ---------------------------------------------------------------------------
# Uploads
# ---------------------------------------------------------------------------
class TestUploads:
def test_upload_files(self, client):
with tempfile.TemporaryDirectory() as tmp:
tmp_path = Path(tmp)
# Create a source file
src_file = tmp_path / "test.txt"
src_file.write_text("hello")
uploads_dir = tmp_path / "uploads"
uploads_dir.mkdir()
with patch.object(DeerFlowClient, "_get_uploads_dir", return_value=uploads_dir):
result = client.upload_files("thread-1", [src_file])
assert result["success"] is True
assert len(result["files"]) == 1
assert result["files"][0]["filename"] == "test.txt"
assert "artifact_url" in result["files"][0]
assert "message" in result
assert (uploads_dir / "test.txt").exists()
def test_upload_files_not_found(self, client):
with pytest.raises(FileNotFoundError):
client.upload_files("thread-1", ["/nonexistent/file.txt"])
def test_upload_files_rejects_directory_path(self, client):
with tempfile.TemporaryDirectory() as tmp:
with pytest.raises(ValueError, match="Path is not a file"):
client.upload_files("thread-1", [tmp])
def test_upload_files_reuses_single_executor_inside_event_loop(self, client):
with tempfile.TemporaryDirectory() as tmp:
tmp_path = Path(tmp)
uploads_dir = tmp_path / "uploads"
uploads_dir.mkdir()
first = tmp_path / "first.pdf"
second = tmp_path / "second.pdf"
first.write_bytes(b"%PDF-1.4 first")
second.write_bytes(b"%PDF-1.4 second")
created_executors = []
real_executor_cls = concurrent.futures.ThreadPoolExecutor
async def fake_convert(path: Path) -> Path:
md_path = path.with_suffix(".md")
md_path.write_text(f"converted {path.name}")
return md_path
class FakeExecutor:
def __init__(self, max_workers: int):
self.max_workers = max_workers
self.shutdown_calls = []
self._executor = real_executor_cls(max_workers=max_workers)
created_executors.append(self)
def submit(self, fn, *args, **kwargs):
return self._executor.submit(fn, *args, **kwargs)
def shutdown(self, wait: bool = True):
self.shutdown_calls.append(wait)
self._executor.shutdown(wait=wait)
async def call_upload() -> dict:
return client.upload_files("thread-async", [first, second])
with (
patch.object(DeerFlowClient, "_get_uploads_dir", return_value=uploads_dir),
patch("deerflow.utils.file_conversion.CONVERTIBLE_EXTENSIONS", {".pdf"}),
patch("deerflow.utils.file_conversion.convert_file_to_markdown", side_effect=fake_convert),
patch("concurrent.futures.ThreadPoolExecutor", FakeExecutor),
):
result = asyncio.run(call_upload())
assert result["success"] is True
assert len(result["files"]) == 2
assert len(created_executors) == 1
assert created_executors[0].max_workers == 1
assert created_executors[0].shutdown_calls == [True]
assert result["files"][0]["markdown_file"] == "first.md"
assert result["files"][1]["markdown_file"] == "second.md"
def test_list_uploads(self, client):
with tempfile.TemporaryDirectory() as tmp:
uploads_dir = Path(tmp)
(uploads_dir / "a.txt").write_text("a")
(uploads_dir / "b.txt").write_text("bb")
with patch.object(DeerFlowClient, "_get_uploads_dir", return_value=uploads_dir):
result = client.list_uploads("thread-1")
assert result["count"] == 2
assert len(result["files"]) == 2
names = {f["filename"] for f in result["files"]}
assert names == {"a.txt", "b.txt"}
# Verify artifact_url is present
for f in result["files"]:
assert "artifact_url" in f
def test_delete_upload(self, client):
with tempfile.TemporaryDirectory() as tmp:
uploads_dir = Path(tmp)
(uploads_dir / "delete-me.txt").write_text("gone")
with patch.object(DeerFlowClient, "_get_uploads_dir", return_value=uploads_dir):
result = client.delete_upload("thread-1", "delete-me.txt")
assert result["success"] is True
assert "delete-me.txt" in result["message"]
assert not (uploads_dir / "delete-me.txt").exists()
def test_delete_upload_not_found(self, client):
with tempfile.TemporaryDirectory() as tmp:
with patch.object(DeerFlowClient, "_get_uploads_dir", return_value=Path(tmp)):
with pytest.raises(FileNotFoundError):
client.delete_upload("thread-1", "nope.txt")
def test_delete_upload_path_traversal(self, client):
with tempfile.TemporaryDirectory() as tmp:
uploads_dir = Path(tmp)
with patch.object(DeerFlowClient, "_get_uploads_dir", return_value=uploads_dir):
with pytest.raises(PermissionError):
client.delete_upload("thread-1", "../../etc/passwd")
# ---------------------------------------------------------------------------
# Artifacts
# ---------------------------------------------------------------------------
class TestArtifacts:
def test_get_artifact(self, client):
with tempfile.TemporaryDirectory() as tmp:
user_data_dir = Path(tmp) / "user-data"
outputs = user_data_dir / "outputs"
outputs.mkdir(parents=True)
(outputs / "result.txt").write_text("artifact content")
mock_paths = MagicMock()
mock_paths.sandbox_user_data_dir.return_value = user_data_dir
with patch("deerflow.client.get_paths", return_value=mock_paths):
content, mime = client.get_artifact("t1", "mnt/user-data/outputs/result.txt")
assert content == b"artifact content"
assert "text" in mime
def test_get_artifact_not_found(self, client):
with tempfile.TemporaryDirectory() as tmp:
user_data_dir = Path(tmp) / "user-data"
user_data_dir.mkdir()
mock_paths = MagicMock()
mock_paths.sandbox_user_data_dir.return_value = user_data_dir
with patch("deerflow.client.get_paths", return_value=mock_paths):
with pytest.raises(FileNotFoundError):
client.get_artifact("t1", "mnt/user-data/outputs/nope.txt")
def test_get_artifact_bad_prefix(self, client):
with pytest.raises(ValueError, match="must start with"):
client.get_artifact("t1", "bad/path/file.txt")
def test_get_artifact_path_traversal(self, client):
with tempfile.TemporaryDirectory() as tmp:
user_data_dir = Path(tmp) / "user-data"
user_data_dir.mkdir()
mock_paths = MagicMock()
mock_paths.sandbox_user_data_dir.return_value = user_data_dir
with patch("deerflow.client.get_paths", return_value=mock_paths):
with pytest.raises(PermissionError):
client.get_artifact("t1", "mnt/user-data/../../../etc/passwd")
# ===========================================================================
# Scenario-based integration tests
# ===========================================================================
# These tests simulate realistic user workflows end-to-end, exercising
# multiple methods in sequence to verify they compose correctly.
class TestScenarioMultiTurnConversation:
"""Scenario: User has a multi-turn conversation within a single thread."""
def test_two_turn_conversation(self, client):
"""Two sequential chat() calls on the same thread_id produce
independent results (without checkpointer, each call is stateless)."""
ai1 = AIMessage(content="I'm a helpful assistant.", id="ai-1")
ai2 = AIMessage(content="Python is great!", id="ai-2")
agent = MagicMock()
agent.stream.side_effect = [
iter([{"messages": [HumanMessage(content="who are you?", id="h-1"), ai1]}]),
iter([{"messages": [HumanMessage(content="what language?", id="h-2"), ai2]}]),
]
with (
patch.object(client, "_ensure_agent"),
patch.object(client, "_agent", agent),
):
r1 = client.chat("who are you?", thread_id="thread-multi")
r2 = client.chat("what language?", thread_id="thread-multi")
assert r1 == "I'm a helpful assistant."
assert r2 == "Python is great!"
assert agent.stream.call_count == 2
def test_stream_collects_all_event_types_across_turns(self, client):
"""A full turn emits messages-tuple (tool_call, tool_result, ai text) + values + end."""
ai_tc = AIMessage(
content="",
id="ai-1",
tool_calls=[
{"name": "web_search", "args": {"query": "LangGraph"}, "id": "tc-1"},
],
)
tool_r = ToolMessage(content="LangGraph is a framework...", id="tm-1", tool_call_id="tc-1", name="web_search")
ai_final = AIMessage(content="LangGraph is a framework for building agents.", id="ai-2")
chunks = [
{"messages": [HumanMessage(content="search", id="h-1"), ai_tc]},
{"messages": [HumanMessage(content="search", id="h-1"), ai_tc, tool_r]},
{"messages": [HumanMessage(content="search", id="h-1"), ai_tc, tool_r, ai_final], "title": "LangGraph Search"},
]
agent = _make_agent_mock(chunks)
with (
patch.object(client, "_ensure_agent"),
patch.object(client, "_agent", agent),
):
events = list(client.stream("search", thread_id="t-full"))
# Verify expected event types
types = set(e.type for e in events)
assert types == {"messages-tuple", "values", "end"}
assert events[-1].type == "end"
# Verify tool_call data
tc_events = _tool_call_events(events)
assert len(tc_events) == 1
assert tc_events[0].data["tool_calls"][0]["name"] == "web_search"
assert tc_events[0].data["tool_calls"][0]["args"] == {"query": "LangGraph"}
# Verify tool_result data
tr_events = _tool_result_events(events)
assert len(tr_events) == 1
assert tr_events[0].data["tool_call_id"] == "tc-1"
assert "LangGraph" in tr_events[0].data["content"]
# Verify AI text
msg_events = _ai_events(events)
assert any("framework" in e.data["content"] for e in msg_events)
# Verify values event contains title
values_events = [e for e in events if e.type == "values"]
assert any(e.data.get("title") == "LangGraph Search" for e in values_events)
class TestScenarioToolChain:
"""Scenario: Agent chains multiple tool calls in sequence."""
def test_multi_tool_chain(self, client):
"""Agent calls bash → reads output → calls write_file → responds."""
ai_bash = AIMessage(
content="",
id="ai-1",
tool_calls=[
{"name": "bash", "args": {"cmd": "ls /mnt/user-data/workspace"}, "id": "tc-1"},
],
)
bash_result = ToolMessage(content="README.md\nsrc/", id="tm-1", tool_call_id="tc-1", name="bash")
ai_write = AIMessage(
content="",
id="ai-2",
tool_calls=[
{"name": "write_file", "args": {"path": "/mnt/user-data/outputs/listing.txt", "content": "README.md\nsrc/"}, "id": "tc-2"},
],
)
write_result = ToolMessage(content="File written successfully.", id="tm-2", tool_call_id="tc-2", name="write_file")
ai_final = AIMessage(content="I listed the workspace and saved the output.", id="ai-3")
chunks = [
{"messages": [HumanMessage(content="list and save", id="h-1"), ai_bash]},
{"messages": [HumanMessage(content="list and save", id="h-1"), ai_bash, bash_result]},
{"messages": [HumanMessage(content="list and save", id="h-1"), ai_bash, bash_result, ai_write]},
{"messages": [HumanMessage(content="list and save", id="h-1"), ai_bash, bash_result, ai_write, write_result]},
{"messages": [HumanMessage(content="list and save", id="h-1"), ai_bash, bash_result, ai_write, write_result, ai_final]},
]
agent = _make_agent_mock(chunks)
with (
patch.object(client, "_ensure_agent"),
patch.object(client, "_agent", agent),
):
events = list(client.stream("list and save", thread_id="t-chain"))
tool_calls = _tool_call_events(events)
tool_results = _tool_result_events(events)
messages = _ai_events(events)
assert len(tool_calls) == 2
assert tool_calls[0].data["tool_calls"][0]["name"] == "bash"
assert tool_calls[1].data["tool_calls"][0]["name"] == "write_file"
assert len(tool_results) == 2
assert len(messages) == 1
assert events[-1].type == "end"
class TestScenarioFileLifecycle:
"""Scenario: Upload files → list them → use in chat → download artifact."""
def test_upload_list_delete_lifecycle(self, client):
"""Upload → list → verify → delete → list again."""
with tempfile.TemporaryDirectory() as tmp:
tmp_path = Path(tmp)
uploads_dir = tmp_path / "uploads"
uploads_dir.mkdir()
# Create source files
(tmp_path / "report.txt").write_text("quarterly report data")
(tmp_path / "data.csv").write_text("a,b,c\n1,2,3")
with patch.object(DeerFlowClient, "_get_uploads_dir", return_value=uploads_dir):
# Step 1: Upload
result = client.upload_files(
"t-lifecycle",
[
tmp_path / "report.txt",
tmp_path / "data.csv",
],
)
assert result["success"] is True
assert len(result["files"]) == 2
assert {f["filename"] for f in result["files"]} == {"report.txt", "data.csv"}
# Step 2: List
listed = client.list_uploads("t-lifecycle")
assert listed["count"] == 2
assert all("virtual_path" in f for f in listed["files"])
# Step 3: Delete one
del_result = client.delete_upload("t-lifecycle", "report.txt")
assert del_result["success"] is True
# Step 4: Verify deletion
listed = client.list_uploads("t-lifecycle")
assert listed["count"] == 1
assert listed["files"][0]["filename"] == "data.csv"
def test_upload_then_read_artifact(self, client):
"""Upload a file, simulate agent producing artifact, read it back."""
with tempfile.TemporaryDirectory() as tmp:
tmp_path = Path(tmp)
uploads_dir = tmp_path / "uploads"
uploads_dir.mkdir()
user_data_dir = tmp_path / "user-data"
outputs_dir = user_data_dir / "outputs"
outputs_dir.mkdir(parents=True)
# Upload phase
src_file = tmp_path / "input.txt"
src_file.write_text("raw data to process")
with patch.object(DeerFlowClient, "_get_uploads_dir", return_value=uploads_dir):
uploaded = client.upload_files("t-artifact", [src_file])
assert len(uploaded["files"]) == 1
# Simulate agent writing an artifact
(outputs_dir / "analysis.json").write_text('{"result": "processed"}')
# Retrieve artifact
mock_paths = MagicMock()
mock_paths.sandbox_user_data_dir.return_value = user_data_dir
with patch("deerflow.client.get_paths", return_value=mock_paths):
content, mime = client.get_artifact("t-artifact", "mnt/user-data/outputs/analysis.json")
assert json.loads(content) == {"result": "processed"}
assert "json" in mime
class TestScenarioConfigManagement:
"""Scenario: Query and update configuration through a management session."""
def test_model_and_skill_discovery(self, client):
"""List models → get specific model → list skills → get specific skill."""
# List models
result = client.list_models()
assert len(result["models"]) >= 1
model_name = result["models"][0]["name"]
# Get specific model
model_cfg = MagicMock()
model_cfg.name = model_name
model_cfg.model = model_name
model_cfg.display_name = None
model_cfg.description = None
model_cfg.supports_thinking = False
model_cfg.supports_reasoning_effort = False
client._app_config.get_model_config.return_value = model_cfg
detail = client.get_model(model_name)
assert detail["name"] == model_name
# List skills
skill = MagicMock()
skill.name = "web-search"
skill.description = "Search the web"
skill.license = "MIT"
skill.category = "public"
skill.enabled = True
with patch("deerflow.skills.loader.load_skills", return_value=[skill]):
skills_result = client.list_skills()
assert len(skills_result["skills"]) == 1
# Get specific skill
with patch("deerflow.skills.loader.load_skills", return_value=[skill]):
detail = client.get_skill("web-search")
assert detail is not None
assert detail["enabled"] is True
def test_mcp_update_then_skill_toggle(self, client):
"""Update MCP config → toggle skill → verify both invalidate agent."""
with tempfile.TemporaryDirectory() as tmp:
config_file = Path(tmp) / "extensions_config.json"
config_file.write_text("{}")
# --- MCP update ---
current_config = MagicMock()
current_config.skills = {}
reloaded_server = MagicMock()
reloaded_server.model_dump.return_value = {"enabled": True, "type": "sse"}
reloaded_config = MagicMock()
reloaded_config.mcp_servers = {"my-mcp": reloaded_server}
client._agent = MagicMock() # Simulate existing agent
with (
patch("deerflow.client.ExtensionsConfig.resolve_config_path", return_value=config_file),
patch("deerflow.client.get_extensions_config", return_value=current_config),
patch("deerflow.client.reload_extensions_config", return_value=reloaded_config),
):
mcp_result = client.update_mcp_config({"my-mcp": {"enabled": True}})
assert "my-mcp" in mcp_result["mcp_servers"]
assert client._agent is None # Agent invalidated
# --- Skill toggle ---
skill = MagicMock()
skill.name = "code-gen"
skill.description = "Generate code"
skill.license = "MIT"
skill.category = "custom"
skill.enabled = True
toggled = MagicMock()
toggled.name = "code-gen"
toggled.description = "Generate code"
toggled.license = "MIT"
toggled.category = "custom"
toggled.enabled = False
ext_config = MagicMock()
ext_config.mcp_servers = {}
ext_config.skills = {}
client._agent = MagicMock() # Simulate re-created agent
with (
patch("deerflow.skills.loader.load_skills", side_effect=[[skill], [toggled]]),
patch("deerflow.client.ExtensionsConfig.resolve_config_path", return_value=config_file),
patch("deerflow.client.get_extensions_config", return_value=ext_config),
patch("deerflow.client.reload_extensions_config"),
):
skill_result = client.update_skill("code-gen", enabled=False)
assert skill_result["enabled"] is False
assert client._agent is None # Agent invalidated again
class TestScenarioAgentRecreation:
"""Scenario: Config changes trigger agent recreation at the right times."""
def test_different_model_triggers_rebuild(self, client):
"""Switching model_name between calls forces agent rebuild."""
agents_created = []
def fake_create_agent(**kwargs):
agent = MagicMock()
agents_created.append(agent)
return agent
config_a = client._get_runnable_config("t1", model_name="gpt-4")
config_b = client._get_runnable_config("t1", model_name="claude-3")
with (
patch("deerflow.client.create_chat_model"),
patch("deerflow.client.create_agent", side_effect=fake_create_agent),
patch("deerflow.client._build_middlewares", return_value=[]),
patch("deerflow.client.apply_prompt_template", return_value="prompt"),
patch.object(client, "_get_tools", return_value=[]),
):
client._ensure_agent(config_a)
first_agent = client._agent
client._ensure_agent(config_b)
second_agent = client._agent
assert len(agents_created) == 2
assert first_agent is not second_agent
def test_same_config_reuses_agent(self, client):
"""Repeated calls with identical config do not rebuild."""
agents_created = []
def fake_create_agent(**kwargs):
agent = MagicMock()
agents_created.append(agent)
return agent
config = client._get_runnable_config("t1", model_name="gpt-4")
with (
patch("deerflow.client.create_chat_model"),
patch("deerflow.client.create_agent", side_effect=fake_create_agent),
patch("deerflow.client._build_middlewares", return_value=[]),
patch("deerflow.client.apply_prompt_template", return_value="prompt"),
patch.object(client, "_get_tools", return_value=[]),
):
client._ensure_agent(config)
client._ensure_agent(config)
client._ensure_agent(config)
assert len(agents_created) == 1
def test_reset_agent_forces_rebuild(self, client):
"""reset_agent() clears cache, next call rebuilds."""
agents_created = []
def fake_create_agent(**kwargs):
agent = MagicMock()
agents_created.append(agent)
return agent
config = client._get_runnable_config("t1")
with (
patch("deerflow.client.create_chat_model"),
patch("deerflow.client.create_agent", side_effect=fake_create_agent),
patch("deerflow.client._build_middlewares", return_value=[]),
patch("deerflow.client.apply_prompt_template", return_value="prompt"),
patch.object(client, "_get_tools", return_value=[]),
):
client._ensure_agent(config)
client.reset_agent()
client._ensure_agent(config)
assert len(agents_created) == 2
def test_per_call_override_triggers_rebuild(self, client):
"""stream() with model_name override creates a different agent config."""
ai = AIMessage(content="ok", id="ai-1")
agent = _make_agent_mock([{"messages": [ai]}])
agents_created = []
def fake_ensure(config):
key = tuple(config.get("configurable", {}).get(k) for k in ["model_name", "thinking_enabled", "is_plan_mode", "subagent_enabled"])
agents_created.append(key)
client._agent = agent
with patch.object(client, "_ensure_agent", side_effect=fake_ensure):
list(client.stream("hi", thread_id="t1"))
list(client.stream("hi", thread_id="t1", model_name="other-model"))
# Two different config keys should have been created
assert len(agents_created) == 2
assert agents_created[0] != agents_created[1]
class TestScenarioThreadIsolation:
"""Scenario: Operations on different threads don't interfere."""
def test_uploads_isolated_per_thread(self, client):
"""Files uploaded to thread-A are not visible in thread-B."""
with tempfile.TemporaryDirectory() as tmp:
tmp_path = Path(tmp)
uploads_a = tmp_path / "thread-a" / "uploads"
uploads_b = tmp_path / "thread-b" / "uploads"
uploads_a.mkdir(parents=True)
uploads_b.mkdir(parents=True)
src_file = tmp_path / "secret.txt"
src_file.write_text("thread-a only")
def get_dir(thread_id):
return uploads_a if thread_id == "thread-a" else uploads_b
with patch.object(DeerFlowClient, "_get_uploads_dir", side_effect=get_dir):
client.upload_files("thread-a", [src_file])
files_a = client.list_uploads("thread-a")
files_b = client.list_uploads("thread-b")
assert files_a["count"] == 1
assert files_b["count"] == 0
def test_artifacts_isolated_per_thread(self, client):
"""Artifacts in thread-A are not accessible from thread-B."""
with tempfile.TemporaryDirectory() as tmp:
tmp_path = Path(tmp)
data_a = tmp_path / "thread-a"
data_b = tmp_path / "thread-b"
(data_a / "outputs").mkdir(parents=True)
(data_b / "outputs").mkdir(parents=True)
(data_a / "outputs" / "result.txt").write_text("thread-a artifact")
mock_paths = MagicMock()
mock_paths.sandbox_user_data_dir.side_effect = lambda tid: data_a if tid == "thread-a" else data_b
with patch("deerflow.client.get_paths", return_value=mock_paths):
content, _ = client.get_artifact("thread-a", "mnt/user-data/outputs/result.txt")
assert content == b"thread-a artifact"
with pytest.raises(FileNotFoundError):
client.get_artifact("thread-b", "mnt/user-data/outputs/result.txt")
class TestScenarioMemoryWorkflow:
"""Scenario: Memory query → reload → status check."""
def test_memory_full_lifecycle(self, client):
"""get_memory → reload → get_status covers the full memory API."""
initial_data = {"version": "1.0", "facts": [{"id": "f1", "content": "User likes Python"}]}
updated_data = {
"version": "1.0",
"facts": [
{"id": "f1", "content": "User likes Python"},
{"id": "f2", "content": "User prefers dark mode"},
],
}
config = MagicMock()
config.enabled = True
config.storage_path = ".deer-flow/memory.json"
config.debounce_seconds = 30
config.max_facts = 100
config.fact_confidence_threshold = 0.7
config.injection_enabled = True
config.max_injection_tokens = 2000
with patch("deerflow.agents.memory.updater.get_memory_data", return_value=initial_data):
mem = client.get_memory()
assert len(mem["facts"]) == 1
with patch("deerflow.agents.memory.updater.reload_memory_data", return_value=updated_data):
refreshed = client.reload_memory()
assert len(refreshed["facts"]) == 2
with (
patch("deerflow.config.memory_config.get_memory_config", return_value=config),
patch("deerflow.agents.memory.updater.get_memory_data", return_value=updated_data),
):
status = client.get_memory_status()
assert status["config"]["enabled"] is True
assert len(status["data"]["facts"]) == 2
class TestScenarioSkillInstallAndUse:
"""Scenario: Install a skill → verify it appears → toggle it."""
def test_install_then_toggle(self, client):
"""Install .skill archive → list to verify → disable → verify disabled."""
with tempfile.TemporaryDirectory() as tmp:
tmp_path = Path(tmp)
# Create .skill archive
skill_src = tmp_path / "my-analyzer"
skill_src.mkdir()
(skill_src / "SKILL.md").write_text("---\nname: my-analyzer\ndescription: Analyze code\nlicense: MIT\n---\nAnalysis skill")
archive = tmp_path / "my-analyzer.skill"
with zipfile.ZipFile(archive, "w") as zf:
zf.write(skill_src / "SKILL.md", "my-analyzer/SKILL.md")
skills_root = tmp_path / "skills"
(skills_root / "custom").mkdir(parents=True)
# Step 1: Install
with (
patch("deerflow.skills.loader.get_skills_root_path", return_value=skills_root),
patch("deerflow.skills.validation._validate_skill_frontmatter", return_value=(True, "OK", "my-analyzer")),
):
result = client.install_skill(archive)
assert result["success"] is True
assert (skills_root / "custom" / "my-analyzer" / "SKILL.md").exists()
# Step 2: List and find it
installed_skill = MagicMock()
installed_skill.name = "my-analyzer"
installed_skill.description = "Analyze code"
installed_skill.license = "MIT"
installed_skill.category = "custom"
installed_skill.enabled = True
with patch("deerflow.skills.loader.load_skills", return_value=[installed_skill]):
skills_result = client.list_skills()
assert any(s["name"] == "my-analyzer" for s in skills_result["skills"])
# Step 3: Disable it
disabled_skill = MagicMock()
disabled_skill.name = "my-analyzer"
disabled_skill.description = "Analyze code"
disabled_skill.license = "MIT"
disabled_skill.category = "custom"
disabled_skill.enabled = False
ext_config = MagicMock()
ext_config.mcp_servers = {}
ext_config.skills = {}
config_file = tmp_path / "extensions_config.json"
config_file.write_text("{}")
with (
patch("deerflow.skills.loader.load_skills", side_effect=[[installed_skill], [disabled_skill]]),
patch("deerflow.client.ExtensionsConfig.resolve_config_path", return_value=config_file),
patch("deerflow.client.get_extensions_config", return_value=ext_config),
patch("deerflow.client.reload_extensions_config"),
):
toggled = client.update_skill("my-analyzer", enabled=False)
assert toggled["enabled"] is False
class TestScenarioEdgeCases:
"""Scenario: Edge cases and error boundaries in realistic workflows."""
def test_empty_stream_response(self, client):
"""Agent produces no messages — only values + end events."""
agent = _make_agent_mock([{"messages": []}])
with (
patch.object(client, "_ensure_agent"),
patch.object(client, "_agent", agent),
):
events = list(client.stream("hi", thread_id="t-empty"))
# values event (empty messages) + end
assert len(events) == 2
assert events[0].type == "values"
assert events[-1].type == "end"
def test_chat_on_empty_response(self, client):
"""chat() returns empty string for no-message response."""
agent = _make_agent_mock([{"messages": []}])
with (
patch.object(client, "_ensure_agent"),
patch.object(client, "_agent", agent),
):
result = client.chat("hi", thread_id="t-empty-chat")
assert result == ""
def test_multiple_title_changes(self, client):
"""Title changes are carried in values events."""
ai = AIMessage(content="ok", id="ai-1")
chunks = [
{"messages": [ai], "title": "First Title"},
{"messages": [], "title": "First Title"}, # same title repeated
{"messages": [], "title": "Second Title"}, # different title
]
agent = _make_agent_mock(chunks)
with (
patch.object(client, "_ensure_agent"),
patch.object(client, "_agent", agent),
):
events = list(client.stream("hi", thread_id="t-titles"))
# Every chunk produces a values event with the title
values_events = [e for e in events if e.type == "values"]
assert len(values_events) == 3
assert values_events[0].data["title"] == "First Title"
assert values_events[1].data["title"] == "First Title"
assert values_events[2].data["title"] == "Second Title"
def test_concurrent_tool_calls_in_single_message(self, client):
"""Agent produces multiple tool_calls in one AIMessage — emitted as single messages-tuple."""
ai = AIMessage(
content="",
id="ai-1",
tool_calls=[
{"name": "web_search", "args": {"q": "a"}, "id": "tc-1"},
{"name": "web_search", "args": {"q": "b"}, "id": "tc-2"},
{"name": "bash", "args": {"cmd": "echo hi"}, "id": "tc-3"},
],
)
chunks = [{"messages": [ai]}]
agent = _make_agent_mock(chunks)
with (
patch.object(client, "_ensure_agent"),
patch.object(client, "_agent", agent),
):
events = list(client.stream("do things", thread_id="t-parallel"))
tc_events = _tool_call_events(events)
assert len(tc_events) == 1 # One messages-tuple event for the AIMessage
tool_calls = tc_events[0].data["tool_calls"]
assert len(tool_calls) == 3
assert {tc["id"] for tc in tool_calls} == {"tc-1", "tc-2", "tc-3"}
def test_upload_convertible_file_conversion_failure(self, client):
"""Upload a .pdf file where conversion fails — file still uploaded, no markdown."""
with tempfile.TemporaryDirectory() as tmp:
tmp_path = Path(tmp)
uploads_dir = tmp_path / "uploads"
uploads_dir.mkdir()
pdf_file = tmp_path / "doc.pdf"
pdf_file.write_bytes(b"%PDF-1.4 fake content")
with (
patch.object(DeerFlowClient, "_get_uploads_dir", return_value=uploads_dir),
patch("deerflow.utils.file_conversion.CONVERTIBLE_EXTENSIONS", {".pdf"}),
patch("deerflow.utils.file_conversion.convert_file_to_markdown", side_effect=Exception("conversion failed")),
):
result = client.upload_files("t-pdf-fail", [pdf_file])
assert result["success"] is True
assert len(result["files"]) == 1
assert result["files"][0]["filename"] == "doc.pdf"
assert "markdown_file" not in result["files"][0] # Conversion failed gracefully
assert (uploads_dir / "doc.pdf").exists() # File still uploaded
# ---------------------------------------------------------------------------
# Gateway conformance — validate client output against Gateway Pydantic models
# ---------------------------------------------------------------------------
class TestGatewayConformance:
"""Validate that DeerFlowClient return dicts conform to Gateway Pydantic response models.
Each test calls a client method, then parses the result through the
corresponding Gateway response model. If the client drifts (missing or
wrong-typed fields), Pydantic raises ``ValidationError`` and CI catches it.
"""
def test_list_models(self, mock_app_config):
model = MagicMock()
model.name = "test-model"
model.model = "gpt-test"
model.display_name = "Test Model"
model.description = "A test model"
model.supports_thinking = False
mock_app_config.models = [model]
with patch("deerflow.client.get_app_config", return_value=mock_app_config):
client = DeerFlowClient()
result = client.list_models()
parsed = ModelsListResponse(**result)
assert len(parsed.models) == 1
assert parsed.models[0].name == "test-model"
assert parsed.models[0].model == "gpt-test"
def test_get_model(self, mock_app_config):
model = MagicMock()
model.name = "test-model"
model.model = "gpt-test"
model.display_name = "Test Model"
model.description = "A test model"
model.supports_thinking = True
mock_app_config.models = [model]
mock_app_config.get_model_config.return_value = model
with patch("deerflow.client.get_app_config", return_value=mock_app_config):
client = DeerFlowClient()
result = client.get_model("test-model")
assert result is not None
parsed = ModelResponse(**result)
assert parsed.name == "test-model"
assert parsed.model == "gpt-test"
def test_list_skills(self, client):
skill = MagicMock()
skill.name = "web-search"
skill.description = "Search the web"
skill.license = "MIT"
skill.category = "public"
skill.enabled = True
with patch("deerflow.skills.loader.load_skills", return_value=[skill]):
result = client.list_skills()
parsed = SkillsListResponse(**result)
assert len(parsed.skills) == 1
assert parsed.skills[0].name == "web-search"
def test_get_skill(self, client):
skill = MagicMock()
skill.name = "web-search"
skill.description = "Search the web"
skill.license = "MIT"
skill.category = "public"
skill.enabled = True
with patch("deerflow.skills.loader.load_skills", return_value=[skill]):
result = client.get_skill("web-search")
assert result is not None
parsed = SkillResponse(**result)
assert parsed.name == "web-search"
def test_install_skill(self, client, tmp_path):
skill_dir = tmp_path / "my-skill"
skill_dir.mkdir()
(skill_dir / "SKILL.md").write_text("---\nname: my-skill\ndescription: A test skill\n---\nBody\n")
archive = tmp_path / "my-skill.skill"
with zipfile.ZipFile(archive, "w") as zf:
zf.write(skill_dir / "SKILL.md", "my-skill/SKILL.md")
custom_dir = tmp_path / "custom"
custom_dir.mkdir()
with patch("deerflow.skills.loader.get_skills_root_path", return_value=tmp_path):
result = client.install_skill(archive)
parsed = SkillInstallResponse(**result)
assert parsed.success is True
assert parsed.skill_name == "my-skill"
def test_get_mcp_config(self, client):
server = MagicMock()
server.model_dump.return_value = {
"enabled": True,
"type": "stdio",
"command": "npx",
"args": ["-y", "server"],
"env": {},
"url": None,
"headers": {},
"description": "test server",
}
ext_config = MagicMock()
ext_config.mcp_servers = {"test": server}
with patch("deerflow.client.get_extensions_config", return_value=ext_config):
result = client.get_mcp_config()
parsed = McpConfigResponse(**result)
assert "test" in parsed.mcp_servers
def test_update_mcp_config(self, client, tmp_path):
server = MagicMock()
server.model_dump.return_value = {
"enabled": True,
"type": "stdio",
"command": "npx",
"args": [],
"env": {},
"url": None,
"headers": {},
"description": "",
}
ext_config = MagicMock()
ext_config.mcp_servers = {"srv": server}
ext_config.skills = {}
config_file = tmp_path / "extensions_config.json"
config_file.write_text("{}")
with (
patch("deerflow.client.get_extensions_config", return_value=ext_config),
patch("deerflow.client.ExtensionsConfig.resolve_config_path", return_value=config_file),
patch("deerflow.client.reload_extensions_config", return_value=ext_config),
):
result = client.update_mcp_config({"srv": server.model_dump.return_value})
parsed = McpConfigResponse(**result)
assert "srv" in parsed.mcp_servers
def test_upload_files(self, client, tmp_path):
uploads_dir = tmp_path / "uploads"
uploads_dir.mkdir()
src_file = tmp_path / "hello.txt"
src_file.write_text("hello")
with patch.object(DeerFlowClient, "_get_uploads_dir", return_value=uploads_dir):
result = client.upload_files("t-conform", [src_file])
parsed = UploadResponse(**result)
assert parsed.success is True
assert len(parsed.files) == 1
def test_get_memory_config(self, client):
mem_cfg = MagicMock()
mem_cfg.enabled = True
mem_cfg.storage_path = ".deer-flow/memory.json"
mem_cfg.debounce_seconds = 30
mem_cfg.max_facts = 100
mem_cfg.fact_confidence_threshold = 0.7
mem_cfg.injection_enabled = True
mem_cfg.max_injection_tokens = 2000
with patch("deerflow.config.memory_config.get_memory_config", return_value=mem_cfg):
result = client.get_memory_config()
parsed = MemoryConfigResponse(**result)
assert parsed.enabled is True
assert parsed.max_facts == 100
def test_get_memory_status(self, client):
mem_cfg = MagicMock()
mem_cfg.enabled = True
mem_cfg.storage_path = ".deer-flow/memory.json"
mem_cfg.debounce_seconds = 30
mem_cfg.max_facts = 100
mem_cfg.fact_confidence_threshold = 0.7
mem_cfg.injection_enabled = True
mem_cfg.max_injection_tokens = 2000
memory_data = {
"version": "1.0",
"lastUpdated": "",
"user": {
"workContext": {"summary": "", "updatedAt": ""},
"personalContext": {"summary": "", "updatedAt": ""},
"topOfMind": {"summary": "", "updatedAt": ""},
},
"history": {
"recentMonths": {"summary": "", "updatedAt": ""},
"earlierContext": {"summary": "", "updatedAt": ""},
"longTermBackground": {"summary": "", "updatedAt": ""},
},
"facts": [],
}
with (
patch("deerflow.config.memory_config.get_memory_config", return_value=mem_cfg),
patch("deerflow.agents.memory.updater.get_memory_data", return_value=memory_data),
):
result = client.get_memory_status()
parsed = MemoryStatusResponse(**result)
assert parsed.config.enabled is True
assert parsed.data.version == "1.0"
================================================
FILE: backend/tests/test_client_live.py
================================================
"""Live integration tests for DeerFlowClient with real API.
These tests require a working config.yaml with valid API credentials.
They are skipped in CI and must be run explicitly:
PYTHONPATH=. uv run pytest tests/test_client_live.py -v -s
"""
import json
import os
from pathlib import Path
import pytest
from deerflow.client import DeerFlowClient, StreamEvent
# Skip entire module in CI or when no config.yaml exists
_skip_reason = None
if os.environ.get("CI"):
_skip_reason = "Live tests skipped in CI"
elif not Path(__file__).resolve().parents[2].joinpath("config.yaml").exists():
_skip_reason = "No config.yaml found — live tests require valid API credentials"
if _skip_reason:
pytest.skip(_skip_reason, allow_module_level=True)
# ---------------------------------------------------------------------------
# Fixtures
# ---------------------------------------------------------------------------
@pytest.fixture(scope="module")
def client():
"""Create a real DeerFlowClient (no mocks)."""
return DeerFlowClient(thinking_enabled=False)
@pytest.fixture
def thread_tmp(tmp_path):
"""Provide a unique thread_id + tmp directory for file operations."""
import uuid
tid = f"live-test-{uuid.uuid4().hex[:8]}"
return tid, tmp_path
# ===========================================================================
# Scenario 1: Basic chat — model responds coherently
# ===========================================================================
class TestLiveBasicChat:
def test_chat_returns_nonempty_string(self, client):
"""chat() returns a non-empty response from the real model."""
response = client.chat("Reply with exactly: HELLO")
assert isinstance(response, str)
assert len(response) > 0
print(f" chat response: {response}")
def test_chat_follows_instruction(self, client):
"""Model can follow a simple instruction."""
response = client.chat("What is 7 * 8? Reply with just the number.")
assert "56" in response
print(f" math response: {response}")
# ===========================================================================
# Scenario 2: Streaming — events arrive in correct order
# ===========================================================================
class TestLiveStreaming:
def test_stream_yields_messages_tuple_and_end(self, client):
"""stream() produces at least one messages-tuple event and ends with end."""
events = list(client.stream("Say hi in one word."))
types = [e.type for e in events]
assert "messages-tuple" in types, f"Expected 'messages-tuple' event, got: {types}"
assert "values" in types, f"Expected 'values' event, got: {types}"
assert types[-1] == "end"
for e in events:
assert isinstance(e, StreamEvent)
print(f" [{e.type}] {e.data}")
def test_stream_ai_content_nonempty(self, client):
"""Streamed messages-tuple AI events contain non-empty content."""
ai_messages = [e for e in client.stream("What color is the sky? One word.") if e.type == "messages-tuple" and e.data.get("type") == "ai" and e.data.get("content")]
assert len(ai_messages) >= 1
for m in ai_messages:
assert len(m.data.get("content", "")) > 0
# ===========================================================================
# Scenario 3: Tool use — agent calls a tool and returns result
# ===========================================================================
class TestLiveToolUse:
def test_agent_uses_bash_tool(self, client):
"""Agent uses bash tool when asked to run a command."""
events = list(client.stream("Use the bash tool to run: echo 'LIVE_TEST_OK'. Then tell me the output."))
types = [e.type for e in events]
print(f" event types: {types}")
for e in events:
print(f" [{e.type}] {e.data}")
# All message events are now messages-tuple
mt_events = [e for e in events if e.type == "messages-tuple"]
tc_events = [e for e in mt_events if e.data.get("type") == "ai" and "tool_calls" in e.data]
tr_events = [e for e in mt_events if e.data.get("type") == "tool"]
ai_events = [e for e in mt_events if e.data.get("type") == "ai" and e.data.get("content")]
assert len(tc_events) >= 1, f"Expected tool_call event, got types: {types}"
assert len(tr_events) >= 1, f"Expected tool result event, got types: {types}"
assert len(ai_events) >= 1
assert tc_events[0].data["tool_calls"][0]["name"] == "bash"
assert "LIVE_TEST_OK" in tr_events[0].data["content"]
def test_agent_uses_ls_tool(self, client):
"""Agent uses ls tool to list a directory."""
events = list(client.stream("Use the ls tool to list the contents of /mnt/user-data/workspace. Just report what you see."))
types = [e.type for e in events]
print(f" event types: {types}")
tc_events = [e for e in events if e.type == "messages-tuple" and e.data.get("type") == "ai" and "tool_calls" in e.data]
assert len(tc_events) >= 1
assert tc_events[0].data["tool_calls"][0]["name"] == "ls"
# ===========================================================================
# Scenario 4: Multi-tool chain — agent chains tools in sequence
# ===========================================================================
class TestLiveMultiToolChain:
def test_write_then_read(self, client):
"""Agent writes a file, then reads it back."""
events = list(client.stream("Step 1: Use write_file to write 'integration_test_content' to /mnt/user-data/outputs/live_test.txt. Step 2: Use read_file to read that file back. Step 3: Tell me the content you read."))
types = [e.type for e in events]
print(f" event types: {types}")
for e in events:
print(f" [{e.type}] {e.data}")
tc_events = [e for e in events if e.type == "messages-tuple" and e.data.get("type") == "ai" and "tool_calls" in e.data]
tool_names = [tc.data["tool_calls"][0]["name"] for tc in tc_events]
assert "write_file" in tool_names, f"Expected write_file, got: {tool_names}"
assert "read_file" in tool_names, f"Expected read_file, got: {tool_names}"
# Final AI message or tool result should mention the content
ai_events = [e for e in events if e.type == "messages-tuple" and e.data.get("type") == "ai" and e.data.get("content")]
tr_events = [e for e in events if e.type == "messages-tuple" and e.data.get("type") == "tool"]
final_text = ai_events[-1].data["content"] if ai_events else ""
assert "integration_test_content" in final_text.lower() or any("integration_test_content" in e.data.get("content", "") for e in tr_events)
# ===========================================================================
# Scenario 5: File upload lifecycle with real filesystem
# ===========================================================================
class TestLiveFileUpload:
def test_upload_list_delete(self, client, thread_tmp):
"""Upload → list → delete → verify deletion."""
thread_id, tmp_path = thread_tmp
# Create test files
f1 = tmp_path / "test_upload_a.txt"
f1.write_text("content A")
f2 = tmp_path / "test_upload_b.txt"
f2.write_text("content B")
# Upload
result = client.upload_files(thread_id, [f1, f2])
assert result["success"] is True
assert len(result["files"]) == 2
filenames = {r["filename"] for r in result["files"]}
assert filenames == {"test_upload_a.txt", "test_upload_b.txt"}
for r in result["files"]:
assert int(r["size"]) > 0
assert r["virtual_path"].startswith("/mnt/user-data/uploads/")
assert "artifact_url" in r
print(f" uploaded: {filenames}")
# List
listed = client.list_uploads(thread_id)
assert listed["count"] == 2
print(f" listed: {[f['filename'] for f in listed['files']]}")
# Delete one
del_result = client.delete_upload(thread_id, "test_upload_a.txt")
assert del_result["success"] is True
remaining = client.list_uploads(thread_id)
assert remaining["count"] == 1
assert remaining["files"][0]["filename"] == "test_upload_b.txt"
print(f" after delete: {[f['filename'] for f in remaining['files']]}")
# Delete the other
client.delete_upload(thread_id, "test_upload_b.txt")
empty = client.list_uploads(thread_id)
assert empty["count"] == 0
assert empty["files"] == []
def test_upload_nonexistent_file_raises(self, client):
with pytest.raises(FileNotFoundError):
client.upload_files("t-fail", ["/nonexistent/path/file.txt"])
# ===========================================================================
# Scenario 6: Configuration query — real config loading
# ===========================================================================
class TestLiveConfigQueries:
def test_list_models_returns_configured_model(self, client):
"""list_models() returns at least one configured model with Gateway-aligned fields."""
result = client.list_models()
assert "models" in result
assert len(result["models"]) >= 1
names = [m["name"] for m in result["models"]]
# Verify Gateway-aligned fields
for m in result["models"]:
assert "display_name" in m
assert "supports_thinking" in m
print(f" models: {names}")
def test_get_model_found(self, client):
"""get_model() returns details for the first configured model."""
result = client.list_models()
first_model_name = result["models"][0]["name"]
model = client.get_model(first_model_name)
assert model is not None
assert model["name"] == first_model_name
assert "display_name" in model
assert "supports_thinking" in model
print(f" model detail: {model}")
def test_get_model_not_found(self, client):
assert client.get_model("nonexistent-model-xyz") is None
def test_list_skills(self, client):
"""list_skills() runs without error."""
result = client.list_skills()
assert "skills" in result
assert isinstance(result["skills"], list)
print(f" skills count: {len(result['skills'])}")
for s in result["skills"][:3]:
print(f" - {s['name']}: {s['enabled']}")
# ===========================================================================
# Scenario 7: Artifact read after agent writes
# ===========================================================================
class TestLiveArtifact:
def test_get_artifact_after_write(self, client):
"""Agent writes a file → client reads it back via get_artifact()."""
import uuid
thread_id = f"live-artifact-{uuid.uuid4().hex[:8]}"
# Ask agent to write a file
events = list(
client.stream(
'Use write_file to create /mnt/user-data/outputs/artifact_test.json with content: {"status": "ok", "source": "live_test"}',
thread_id=thread_id,
)
)
# Verify write happened
tc_events = [e for e in events if e.type == "messages-tuple" and e.data.get("type") == "ai" and "tool_calls" in e.data]
assert any(any(tc["name"] == "write_file" for tc in e.data["tool_calls"]) for e in tc_events)
# Read artifact
content, mime = client.get_artifact(thread_id, "mnt/user-data/outputs/artifact_test.json")
data = json.loads(content)
assert data["status"] == "ok"
assert data["source"] == "live_test"
assert "json" in mime
print(f" artifact: {data}, mime: {mime}")
def test_get_artifact_not_found(self, client):
with pytest.raises(FileNotFoundError):
client.get_artifact("nonexistent-thread", "mnt/user-data/outputs/nope.txt")
# ===========================================================================
# Scenario 8: Per-call overrides
# ===========================================================================
class TestLiveOverrides:
def test_thinking_disabled_still_works(self, client):
"""Explicit thinking_enabled=False override produces a response."""
response = client.chat(
"Say OK.",
thinking_enabled=False,
)
assert len(response) > 0
print(f" response: {response}")
# ===========================================================================
# Scenario 9: Error resilience
# ===========================================================================
class TestLiveErrorResilience:
def test_delete_nonexistent_upload(self, client):
with pytest.raises(FileNotFoundError):
client.delete_upload("nonexistent-thread", "ghost.txt")
def test_bad_artifact_path(self, client):
with pytest.raises(ValueError):
client.get_artifact("t", "invalid/path")
def test_path_traversal_blocked(self, client):
with pytest.raises(PermissionError):
client.delete_upload("t", "../../etc/passwd")
================================================
FILE: backend/tests/test_config_version.py
================================================
"""Tests for config version check and upgrade logic."""
from __future__ import annotations
import logging
import tempfile
from pathlib import Path
import yaml
from deerflow.config.app_config import AppConfig
def _make_config_files(tmpdir: Path, user_config: dict, example_config: dict) -> Path:
"""Write user config.yaml and config.example.yaml to a temp dir, return config path."""
config_path = tmpdir / "config.yaml"
example_path = tmpdir / "config.example.yaml"
# Minimal valid config needs sandbox
defaults = {
"sandbox": {"use": "deerflow.sandbox.local:LocalSandboxProvider"},
}
for cfg in (user_config, example_config):
for k, v in defaults.items():
cfg.setdefault(k, v)
with open(config_path, "w", encoding="utf-8") as f:
yaml.dump(user_config, f)
with open(example_path, "w", encoding="utf-8") as f:
yaml.dump(example_config, f)
return config_path
def test_missing_version_treated_as_zero(caplog):
"""Config without config_version should be treated as version 0."""
with tempfile.TemporaryDirectory() as tmpdir:
config_path = _make_config_files(
Path(tmpdir),
user_config={}, # no config_version
example_config={"config_version": 1},
)
with caplog.at_level(logging.WARNING, logger="deerflow.config.app_config"):
AppConfig._check_config_version(
{"sandbox": {"use": "deerflow.sandbox.local:LocalSandboxProvider"}},
config_path,
)
assert "outdated" in caplog.text
assert "version 0" in caplog.text
assert "version is 1" in caplog.text
def test_matching_version_no_warning(caplog):
"""Config with matching version should not emit a warning."""
with tempfile.TemporaryDirectory() as tmpdir:
config_path = _make_config_files(
Path(tmpdir),
user_config={"config_version": 1},
example_config={"config_version": 1},
)
with caplog.at_level(logging.WARNING, logger="deerflow.config.app_config"):
AppConfig._check_config_version(
{"config_version": 1},
config_path,
)
assert "outdated" not in caplog.text
def test_outdated_version_emits_warning(caplog):
"""Config with lower version should emit a warning."""
with tempfile.TemporaryDirectory() as tmpdir:
config_path = _make_config_files(
Path(tmpdir),
user_config={"config_version": 1},
example_config={"config_version": 2},
)
with caplog.at_level(logging.WARNING, logger="deerflow.config.app_config"):
AppConfig._check_config_version(
{"config_version": 1},
config_path,
)
assert "outdated" in caplog.text
assert "version 1" in caplog.text
assert "version is 2" in caplog.text
def test_no_example_file_no_warning(caplog):
"""If config.example.yaml doesn't exist, no warning should be emitted."""
with tempfile.TemporaryDirectory() as tmpdir:
config_path = Path(tmpdir) / "config.yaml"
with open(config_path, "w", encoding="utf-8") as f:
yaml.dump({"sandbox": {"use": "test"}}, f)
# No config.example.yaml created
with caplog.at_level(logging.WARNING, logger="deerflow.config.app_config"):
AppConfig._check_config_version({}, config_path)
assert "outdated" not in caplog.text
def test_string_config_version_does_not_raise_type_error(caplog):
"""config_version stored as a YAML string should not raise TypeError on comparison."""
with tempfile.TemporaryDirectory() as tmpdir:
config_path = _make_config_files(
Path(tmpdir),
user_config={"config_version": "1"}, # string, as YAML can produce
example_config={"config_version": 2},
)
# Must not raise TypeError: '<' not supported between instances of 'str' and 'int'
AppConfig._check_config_version({"config_version": "1"}, config_path)
def test_newer_user_version_no_warning(caplog):
"""If user has a newer version than example (edge case), no warning."""
with tempfile.TemporaryDirectory() as tmpdir:
config_path = _make_config_files(
Path(tmpdir),
user_config={"config_version": 3},
example_config={"config_version": 2},
)
with caplog.at_level(logging.WARNING, logger="deerflow.config.app_config"):
AppConfig._check_config_version(
{"config_version": 3},
config_path,
)
assert "outdated" not in caplog.text
================================================
FILE: backend/tests/test_credential_loader.py
================================================
import json
import os
from deerflow.models.credential_loader import (
load_claude_code_credential,
load_codex_cli_credential,
)
def _clear_claude_code_env(monkeypatch) -> None:
for env_var in (
"CLAUDE_CODE_OAUTH_TOKEN",
"ANTHROPIC_AUTH_TOKEN",
"CLAUDE_CODE_OAUTH_TOKEN_FILE_DESCRIPTOR",
"CLAUDE_CODE_CREDENTIALS_PATH",
):
monkeypatch.delenv(env_var, raising=False)
def test_load_claude_code_credential_from_direct_env(monkeypatch):
_clear_claude_code_env(monkeypatch)
monkeypatch.setenv("CLAUDE_CODE_OAUTH_TOKEN", " sk-ant-oat01-env ")
cred = load_claude_code_credential()
assert cred is not None
assert cred.access_token == "sk-ant-oat01-env"
assert cred.refresh_token == ""
assert cred.source == "claude-cli-env"
def test_load_claude_code_credential_from_anthropic_auth_env(monkeypatch):
_clear_claude_code_env(monkeypatch)
monkeypatch.setenv("ANTHROPIC_AUTH_TOKEN", "sk-ant-oat01-anthropic-auth")
cred = load_claude_code_credential()
assert cred is not None
assert cred.access_token == "sk-ant-oat01-anthropic-auth"
assert cred.source == "claude-cli-env"
def test_load_claude_code_credential_from_file_descriptor(monkeypatch):
_clear_claude_code_env(monkeypatch)
read_fd, write_fd = os.pipe()
try:
os.write(write_fd, b"sk-ant-oat01-fd")
os.close(write_fd)
monkeypatch.setenv("CLAUDE_CODE_OAUTH_TOKEN_FILE_DESCRIPTOR", str(read_fd))
cred = load_claude_code_credential()
finally:
os.close(read_fd)
assert cred is not None
assert cred.access_token == "sk-ant-oat01-fd"
assert cred.refresh_token == ""
assert cred.source == "claude-cli-fd"
def test_load_claude_code_credential_from_override_path(tmp_path, monkeypatch):
_clear_claude_code_env(monkeypatch)
cred_path = tmp_path / "claude-credentials.json"
cred_path.write_text(
json.dumps(
{
"claudeAiOauth": {
"accessToken": "sk-ant-oat01-test",
"refreshToken": "sk-ant-ort01-test",
"expiresAt": 4_102_444_800_000,
}
}
)
)
monkeypatch.setenv("CLAUDE_CODE_CREDENTIALS_PATH", str(cred_path))
cred = load_claude_code_credential()
assert cred is not None
assert cred.access_token == "sk-ant-oat01-test"
assert cred.refresh_token == "sk-ant-ort01-test"
assert cred.source == "claude-cli-file"
def test_load_claude_code_credential_ignores_directory_path(tmp_path, monkeypatch):
_clear_claude_code_env(monkeypatch)
cred_dir = tmp_path / "claude-creds-dir"
cred_dir.mkdir()
monkeypatch.setenv("CLAUDE_CODE_CREDENTIALS_PATH", str(cred_dir))
assert load_claude_code_credential() is None
def test_load_claude_code_credential_falls_back_to_default_file_when_override_is_invalid(tmp_path, monkeypatch):
_clear_claude_code_env(monkeypatch)
monkeypatch.setenv("HOME", str(tmp_path))
cred_dir = tmp_path / "claude-creds-dir"
cred_dir.mkdir()
monkeypatch.setenv("CLAUDE_CODE_CREDENTIALS_PATH", str(cred_dir))
default_path = tmp_path / ".claude" / ".credentials.json"
default_path.parent.mkdir()
default_path.write_text(
json.dumps(
{
"claudeAiOauth": {
"accessToken": "sk-ant-oat01-default",
"refreshToken": "sk-ant-ort01-default",
"expiresAt": 4_102_444_800_000,
}
}
)
)
cred = load_claude_code_credential()
assert cred is not None
assert cred.access_token == "sk-ant-oat01-default"
assert cred.refresh_token == "sk-ant-ort01-default"
assert cred.source == "claude-cli-file"
def test_load_codex_cli_credential_supports_nested_tokens_shape(tmp_path, monkeypatch):
auth_path = tmp_path / "auth.json"
auth_path.write_text(
json.dumps(
{
"tokens": {
"access_token": "codex-access-token",
"account_id": "acct_123",
}
}
)
)
monkeypatch.setenv("CODEX_AUTH_PATH", str(auth_path))
cred = load_codex_cli_credential()
assert cred is not None
assert cred.access_token == "codex-access-token"
assert cred.account_id == "acct_123"
assert cred.source == "codex-cli"
def test_load_codex_cli_credential_supports_legacy_top_level_shape(tmp_path, monkeypatch):
auth_path = tmp_path / "auth.json"
auth_path.write_text(json.dumps({"access_token": "legacy-access-token"}))
monkeypatch.setenv("CODEX_AUTH_PATH", str(auth_path))
cred = load_codex_cli_credential()
assert cred is not None
assert cred.access_token == "legacy-access-token"
assert cred.account_id == ""
================================================
FILE: backend/tests/test_custom_agent.py
================================================
"""Tests for custom agent support."""
from __future__ import annotations
from pathlib import Path
from unittest.mock import patch
import pytest
import yaml
from fastapi.testclient import TestClient
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def _make_paths(base_dir: Path):
"""Return a Paths instance pointing to base_dir."""
from deerflow.config.paths import Paths
return Paths(base_dir=base_dir)
def _write_agent(base_dir: Path, name: str, config: dict, soul: str = "You are helpful.") -> None:
"""Write an agent directory with config.yaml and SOUL.md."""
agent_dir = base_dir / "agents" / name
agent_dir.mkdir(parents=True, exist_ok=True)
config_copy = dict(config)
if "name" not in config_copy:
config_copy["name"] = name
with open(agent_dir / "config.yaml", "w") as f:
yaml.dump(config_copy, f)
(agent_dir / "SOUL.md").write_text(soul, encoding="utf-8")
# ===========================================================================
# 1. Paths class – agent path methods
# ===========================================================================
class TestPaths:
def test_agents_dir(self, tmp_path):
paths = _make_paths(tmp_path)
assert paths.agents_dir == tmp_path / "agents"
def test_agent_dir(self, tmp_path):
paths = _make_paths(tmp_path)
assert paths.agent_dir("code-reviewer") == tmp_path / "agents" / "code-reviewer"
def test_agent_memory_file(self, tmp_path):
paths = _make_paths(tmp_path)
assert paths.agent_memory_file("code-reviewer") == tmp_path / "agents" / "code-reviewer" / "memory.json"
def test_user_md_file(self, tmp_path):
paths = _make_paths(tmp_path)
assert paths.user_md_file == tmp_path / "USER.md"
def test_paths_are_different_from_global(self, tmp_path):
paths = _make_paths(tmp_path)
assert paths.memory_file != paths.agent_memory_file("my-agent")
assert paths.memory_file == tmp_path / "memory.json"
assert paths.agent_memory_file("my-agent") == tmp_path / "agents" / "my-agent" / "memory.json"
# ===========================================================================
# 2. AgentConfig – Pydantic parsing
# ===========================================================================
class TestAgentConfig:
def test_minimal_config(self):
from deerflow.config.agents_config import AgentConfig
cfg = AgentConfig(name="my-agent")
assert cfg.name == "my-agent"
assert cfg.description == ""
assert cfg.model is None
assert cfg.tool_groups is None
def test_full_config(self):
from deerflow.config.agents_config import AgentConfig
cfg = AgentConfig(
name="code-reviewer",
description="Specialized for code review",
model="deepseek-v3",
tool_groups=["file:read", "bash"],
)
assert cfg.name == "code-reviewer"
assert cfg.model == "deepseek-v3"
assert cfg.tool_groups == ["file:read", "bash"]
def test_config_from_dict(self):
from deerflow.config.agents_config import AgentConfig
data = {"name": "test-agent", "description": "A test", "model": "gpt-4"}
cfg = AgentConfig(**data)
assert cfg.name == "test-agent"
assert cfg.model == "gpt-4"
assert cfg.tool_groups is None
# ===========================================================================
# 3. load_agent_config
# ===========================================================================
class TestLoadAgentConfig:
def test_load_valid_config(self, tmp_path):
config_dict = {"name": "code-reviewer", "description": "Code review agent", "model": "deepseek-v3"}
_write_agent(tmp_path, "code-reviewer", config_dict)
with patch("deerflow.config.agents_config.get_paths", return_value=_make_paths(tmp_path)):
from deerflow.config.agents_config import load_agent_config
cfg = load_agent_config("code-reviewer")
assert cfg.name == "code-reviewer"
assert cfg.description == "Code review agent"
assert cfg.model == "deepseek-v3"
def test_load_missing_agent_raises(self, tmp_path):
with patch("deerflow.config.agents_config.get_paths", return_value=_make_paths(tmp_path)):
from deerflow.config.agents_config import load_agent_config
with pytest.raises(FileNotFoundError):
load_agent_config("nonexistent-agent")
def test_load_missing_config_yaml_raises(self, tmp_path):
# Create directory without config.yaml
(tmp_path / "agents" / "broken-agent").mkdir(parents=True)
with patch("deerflow.config.agents_config.get_paths", return_value=_make_paths(tmp_path)):
from deerflow.config.agents_config import load_agent_config
with pytest.raises(FileNotFoundError):
load_agent_config("broken-agent")
def test_load_config_infers_name_from_dir(self, tmp_path):
"""Config without 'name' field should use directory name."""
agent_dir = tmp_path / "agents" / "inferred-name"
agent_dir.mkdir(parents=True)
(agent_dir / "config.yaml").write_text("description: My agent\n")
(agent_dir / "SOUL.md").write_text("Hello")
with patch("deerflow.config.agents_config.get_paths", return_value=_make_paths(tmp_path)):
from deerflow.config.agents_config import load_agent_config
cfg = load_agent_config("inferred-name")
assert cfg.name == "inferred-name"
def test_load_config_with_tool_groups(self, tmp_path):
config_dict = {"name": "restricted", "tool_groups": ["file:read", "file:write"]}
_write_agent(tmp_path, "restricted", config_dict)
with patch("deerflow.config.agents_config.get_paths", return_value=_make_paths(tmp_path)):
from deerflow.config.agents_config import load_agent_config
cfg = load_agent_config("restricted")
assert cfg.tool_groups == ["file:read", "file:write"]
def test_legacy_prompt_file_field_ignored(self, tmp_path):
"""Unknown fields like the old prompt_file should be silently ignored."""
agent_dir = tmp_path / "agents" / "legacy-agent"
agent_dir.mkdir(parents=True)
(agent_dir / "config.yaml").write_text("name: legacy-agent\nprompt_file: system.md\n")
(agent_dir / "SOUL.md").write_text("Soul content")
with patch("deerflow.config.agents_config.get_paths", return_value=_make_paths(tmp_path)):
from deerflow.config.agents_config import load_agent_config
cfg = load_agent_config("legacy-agent")
assert cfg.name == "legacy-agent"
# ===========================================================================
# 4. load_agent_soul
# ===========================================================================
class TestLoadAgentSoul:
def test_reads_soul_file(self, tmp_path):
expected_soul = "You are a specialized code review expert."
_write_agent(tmp_path, "code-reviewer", {"name": "code-reviewer"}, soul=expected_soul)
with patch("deerflow.config.agents_config.get_paths", return_value=_make_paths(tmp_path)):
from deerflow.config.agents_config import AgentConfig, load_agent_soul
cfg = AgentConfig(name="code-reviewer")
soul = load_agent_soul(cfg.name)
assert soul == expected_soul
def test_missing_soul_file_returns_none(self, tmp_path):
agent_dir = tmp_path / "agents" / "no-soul"
agent_dir.mkdir(parents=True)
(agent_dir / "config.yaml").write_text("name: no-soul\n")
# No SOUL.md created
with patch("deerflow.config.agents_config.get_paths", return_value=_make_paths(tmp_path)):
from deerflow.config.agents_config import AgentConfig, load_agent_soul
cfg = AgentConfig(name="no-soul")
soul = load_agent_soul(cfg.name)
assert soul is None
def test_empty_soul_file_returns_none(self, tmp_path):
agent_dir = tmp_path / "agents" / "empty-soul"
agent_dir.mkdir(parents=True)
(agent_dir / "config.yaml").write_text("name: empty-soul\n")
(agent_dir / "SOUL.md").write_text(" \n ")
with patch("deerflow.config.agents_config.get_paths", return_value=_make_paths(tmp_path)):
from deerflow.config.agents_config import AgentConfig, load_agent_soul
cfg = AgentConfig(name="empty-soul")
soul = load_agent_soul(cfg.name)
assert soul is None
# ===========================================================================
# 5. list_custom_agents
# ===========================================================================
class TestListCustomAgents:
def test_empty_when_no_agents_dir(self, tmp_path):
with patch("deerflow.config.agents_config.get_paths", return_value=_make_paths(tmp_path)):
from deerflow.config.agents_config import list_custom_agents
agents = list_custom_agents()
assert agents == []
def test_discovers_multiple_agents(self, tmp_path):
_write_agent(tmp_path, "agent-a", {"name": "agent-a"})
_write_agent(tmp_path, "agent-b", {"name": "agent-b", "description": "B"})
with patch("deerflow.config.agents_config.get_paths", return_value=_make_paths(tmp_path)):
from deerflow.config.agents_config import list_custom_agents
agents = list_custom_agents()
names = [a.name for a in agents]
assert "agent-a" in names
assert "agent-b" in names
def test_skips_dirs_without_config_yaml(self, tmp_path):
# Valid agent
_write_agent(tmp_path, "valid-agent", {"name": "valid-agent"})
# Invalid dir (no config.yaml)
(tmp_path / "agents" / "invalid-dir").mkdir(parents=True)
with patch("deerflow.config.agents_config.get_paths", return_value=_make_paths(tmp_path)):
from deerflow.config.agents_config import list_custom_agents
agents = list_custom_agents()
assert len(agents) == 1
assert agents[0].name == "valid-agent"
def test_skips_non_directory_entries(self, tmp_path):
# Create the agents dir with a file (not a dir)
agents_dir = tmp_path / "agents"
agents_dir.mkdir(parents=True)
(agents_dir / "not-a-dir.txt").write_text("hello")
_write_agent(tmp_path, "real-agent", {"name": "real-agent"})
with patch("deerflow.config.agents_config.get_paths", return_value=_make_paths(tmp_path)):
from deerflow.config.agents_config import list_custom_agents
agents = list_custom_agents()
assert len(agents) == 1
assert agents[0].name == "real-agent"
def test_returns_sorted_by_name(self, tmp_path):
_write_agent(tmp_path, "z-agent", {"name": "z-agent"})
_write_agent(tmp_path, "a-agent", {"name": "a-agent"})
_write_agent(tmp_path, "m-agent", {"name": "m-agent"})
with patch("deerflow.config.agents_config.get_paths", return_value=_make_paths(tmp_path)):
from deerflow.config.agents_config import list_custom_agents
agents = list_custom_agents()
names = [a.name for a in agents]
assert names == sorted(names)
# ===========================================================================
# 7. Memory isolation: _get_memory_file_path
# ===========================================================================
class TestMemoryFilePath:
def test_global_memory_path(self, tmp_path):
"""None agent_name should return global memory file."""
import deerflow.agents.memory.updater as updater_mod
from deerflow.config.memory_config import MemoryConfig
with (
patch("deerflow.agents.memory.updater.get_paths", return_value=_make_paths(tmp_path)),
patch("deerflow.agents.memory.updater.get_memory_config", return_value=MemoryConfig(storage_path="")),
):
path = updater_mod._get_memory_file_path(None)
assert path == tmp_path / "memory.json"
def test_agent_memory_path(self, tmp_path):
"""Providing agent_name should return per-agent memory file."""
import deerflow.agents.memory.updater as updater_mod
from deerflow.config.memory_config import MemoryConfig
with (
patch("deerflow.agents.memory.updater.get_paths", return_value=_make_paths(tmp_path)),
patch("deerflow.agents.memory.updater.get_memory_config", return_value=MemoryConfig(storage_path="")),
):
path = updater_mod._get_memory_file_path("code-reviewer")
assert path == tmp_path / "agents" / "code-reviewer" / "memory.json"
def test_different_paths_for_different_agents(self, tmp_path):
import deerflow.agents.memory.updater as updater_mod
from deerflow.config.memory_config import MemoryConfig
with (
patch("deerflow.agents.memory.updater.get_paths", return_value=_make_paths(tmp_path)),
patch("deerflow.agents.memory.updater.get_memory_config", return_value=MemoryConfig(storage_path="")),
):
path_global = updater_mod._get_memory_file_path(None)
path_a = updater_mod._get_memory_file_path("agent-a")
path_b = updater_mod._get_memory_file_path("agent-b")
assert path_global != path_a
assert path_global != path_b
assert path_a != path_b
# ===========================================================================
# 8. Gateway API – Agents endpoints
# ===========================================================================
def _make_test_app(tmp_path: Path):
"""Create a FastAPI app with the agents router, patching paths to tmp_path."""
from fastapi import FastAPI
from app.gateway.routers.agents import router
app = FastAPI()
app.include_router(router)
return app
@pytest.fixture()
def agent_client(tmp_path):
"""TestClient with agents router, using tmp_path as base_dir."""
paths_instance = _make_paths(tmp_path)
with patch("deerflow.config.agents_config.get_paths", return_value=paths_instance), patch("app.gateway.routers.agents.get_paths", return_value=paths_instance):
app = _make_test_app(tmp_path)
with TestClient(app) as client:
client._tmp_path = tmp_path # type: ignore[attr-defined]
yield client
class TestAgentsAPI:
def test_list_agents_empty(self, agent_client):
response = agent_client.get("/api/agents")
assert response.status_code == 200
data = response.json()
assert data["agents"] == []
def test_create_agent(self, agent_client):
payload = {
"name": "code-reviewer",
"description": "Reviews code",
"soul": "You are a code reviewer.",
}
response = agent_client.post("/api/agents", json=payload)
assert response.status_code == 201
data = response.json()
assert data["name"] == "code-reviewer"
assert data["description"] == "Reviews code"
assert data["soul"] == "You are a code reviewer."
def test_create_agent_invalid_name(self, agent_client):
payload = {"name": "Code Reviewer!", "soul": "test"}
response = agent_client.post("/api/agents", json=payload)
assert response.status_code == 422
def test_create_duplicate_agent_409(self, agent_client):
payload = {"name": "my-agent", "soul": "test"}
agent_client.post("/api/agents", json=payload)
# Second create should fail
response = agent_client.post("/api/agents", json=payload)
assert response.status_code == 409
def test_list_agents_after_create(self, agent_client):
agent_client.post("/api/agents", json={"name": "agent-one", "soul": "p1"})
agent_client.post("/api/agents", json={"name": "agent-two", "soul": "p2"})
response = agent_client.get("/api/agents")
assert response.status_code == 200
names = [a["name"] for a in response.json()["agents"]]
assert "agent-one" in names
assert "agent-two" in names
def test_get_agent(self, agent_client):
agent_client.post("/api/agents", json={"name": "test-agent", "soul": "Hello world"})
response = agent_client.get("/api/agents/test-agent")
assert response.status_code == 200
data = response.json()
assert data["name"] == "test-agent"
assert data["soul"] == "Hello world"
def test_get_missing_agent_404(self, agent_client):
response = agent_client.get("/api/agents/nonexistent")
assert response.status_code == 404
def test_update_agent_soul(self, agent_client):
agent_client.post("/api/agents", json={"name": "update-me", "soul": "original"})
response = agent_client.put("/api/agents/update-me", json={"soul": "updated"})
assert response.status_code == 200
assert response.json()["soul"] == "updated"
def test_update_agent_description(self, agent_client):
agent_client.post("/api/agents", json={"name": "desc-agent", "description": "old desc", "soul": "p"})
response = agent_client.put("/api/agents/desc-agent", json={"description": "new desc"})
assert response.status_code == 200
assert response.json()["description"] == "new desc"
def test_update_missing_agent_404(self, agent_client):
response = agent_client.put("/api/agents/ghost-agent", json={"soul": "new"})
assert response.status_code == 404
def test_delete_agent(self, agent_client):
agent_client.post("/api/agents", json={"name": "del-me", "soul": "bye"})
response = agent_client.delete("/api/agents/del-me")
assert response.status_code == 204
# Verify it's gone
response = agent_client.get("/api/agents/del-me")
assert response.status_code == 404
def test_delete_missing_agent_404(self, agent_client):
response = agent_client.delete("/api/agents/does-not-exist")
assert response.status_code == 404
def test_create_agent_with_model_and_tool_groups(self, agent_client):
payload = {
"name": "specialized",
"description": "Specialized agent",
"model": "deepseek-v3",
"tool_groups": ["file:read", "bash"],
"soul": "You are specialized.",
}
response = agent_client.post("/api/agents", json=payload)
assert response.status_code == 201
data = response.json()
assert data["model"] == "deepseek-v3"
assert data["tool_groups"] == ["file:read", "bash"]
def test_create_persists_files_on_disk(self, agent_client, tmp_path):
agent_client.post("/api/agents", json={"name": "disk-check", "soul": "disk soul"})
agent_dir = tmp_path / "agents" / "disk-check"
assert agent_dir.exists()
assert (agent_dir / "config.yaml").exists()
assert (agent_dir / "SOUL.md").exists()
assert (agent_dir / "SOUL.md").read_text() == "disk soul"
def test_delete_removes_files_from_disk(self, agent_client, tmp_path):
agent_client.post("/api/agents", json={"name": "remove-me", "soul": "bye"})
agent_dir = tmp_path / "agents" / "remove-me"
assert agent_dir.exists()
agent_client.delete("/api/agents/remove-me")
assert not agent_dir.exists()
# ===========================================================================
# 9. Gateway API – User Profile endpoints
# ===========================================================================
class TestUserProfileAPI:
def test_get_user_profile_empty(self, agent_client):
response = agent_client.get("/api/user-profile")
assert response.status_code == 200
assert response.json()["content"] is None
def test_put_user_profile(self, agent_client, tmp_path):
content = "# User Profile\n\nI am a developer."
response = agent_client.put("/api/user-profile", json={"content": content})
assert response.status_code == 200
assert response.json()["content"] == content
# File should be written to disk
user_md = tmp_path / "USER.md"
assert user_md.exists()
assert user_md.read_text(encoding="utf-8") == content
def test_get_user_profile_after_put(self, agent_client):
content = "# Profile\n\nI work on data science."
agent_client.put("/api/user-profile", json={"content": content})
response = agent_client.get("/api/user-profile")
assert response.status_code == 200
assert response.json()["content"] == content
def test_put_empty_user_profile_returns_none(self, agent_client):
response = agent_client.put("/api/user-profile", json={"content": ""})
assert response.status_code == 200
assert response.json()["content"] is None
================================================
FILE: backend/tests/test_docker_sandbox_mode_detection.py
================================================
"""Regression tests for docker sandbox mode detection logic."""
from __future__ import annotations
import subprocess
import tempfile
from pathlib import Path
REPO_ROOT = Path(__file__).resolve().parents[2]
SCRIPT_PATH = REPO_ROOT / "scripts" / "docker.sh"
def _detect_mode_with_config(config_content: str) -> str:
"""Write config content into a temp project root and execute detect_sandbox_mode."""
with tempfile.TemporaryDirectory() as tmpdir:
tmp_root = Path(tmpdir)
(tmp_root / "config.yaml").write_text(config_content)
command = f"source '{SCRIPT_PATH}' && PROJECT_ROOT='{tmp_root}' && detect_sandbox_mode"
output = subprocess.check_output(
["bash", "-lc", command],
text=True,
).strip()
return output
def test_detect_mode_defaults_to_local_when_config_missing():
"""No config file should default to local mode."""
with tempfile.TemporaryDirectory() as tmpdir:
command = f"source '{SCRIPT_PATH}' && PROJECT_ROOT='{tmpdir}' && detect_sandbox_mode"
output = subprocess.check_output(["bash", "-lc", command], text=True).strip()
assert output == "local"
def test_detect_mode_local_provider():
"""Local sandbox provider should map to local mode."""
config = """
sandbox:
use: deerflow.sandbox.local:LocalSandboxProvider
""".strip()
assert _detect_mode_with_config(config) == "local"
def test_detect_mode_aio_without_provisioner_url():
"""AIO sandbox without provisioner_url should map to aio mode."""
config = """
sandbox:
use: deerflow.community.aio_sandbox:AioSandboxProvider
""".strip()
assert _detect_mode_with_config(config) == "aio"
def test_detect_mode_provisioner_with_url():
"""AIO sandbox with provisioner_url should map to provisioner mode."""
config = """
sandbox:
use: deerflow.community.aio_sandbox:AioSandboxProvider
provisioner_url: http://provisioner:8002
""".strip()
assert _detect_mode_with_config(config) == "provisioner"
def test_detect_mode_ignores_commented_provisioner_url():
"""Commented provisioner_url should not activate provisioner mode."""
config = """
sandbox:
use: deerflow.community.aio_sandbox:AioSandboxProvider
# provisioner_url: http://provisioner:8002
""".strip()
assert _detect_mode_with_config(config) == "aio"
def test_detect_mode_unknown_provider_falls_back_to_local():
"""Unknown sandbox provider should default to local mode."""
config = """
sandbox:
use: custom.module:UnknownProvider
""".strip()
assert _detect_mode_with_config(config) == "local"
================================================
FILE: backend/tests/test_feishu_parser.py
================================================
import json
from unittest.mock import MagicMock
import pytest
from app.channels.feishu import FeishuChannel
from app.channels.message_bus import MessageBus
def test_feishu_on_message_plain_text():
bus = MessageBus()
config = {"app_id": "test", "app_secret": "test"}
channel = FeishuChannel(bus, config)
# Create mock event
event = MagicMock()
event.event.message.chat_id = "chat_1"
event.event.message.message_id = "msg_1"
event.event.message.root_id = None
event.event.sender.sender_id.open_id = "user_1"
# Plain text content
content_dict = {"text": "Hello world"}
event.event.message.content = json.dumps(content_dict)
# Call _on_message
channel._on_message(event)
# Since main_loop isn't running in this synchronous test, we can't easily assert on bus,
# but we can intercept _make_inbound to check the parsed text.
with pytest.MonkeyPatch.context() as m:
mock_make_inbound = MagicMock()
m.setattr(channel, "_make_inbound", mock_make_inbound)
channel._on_message(event)
mock_make_inbound.assert_called_once()
assert mock_make_inbound.call_args[1]["text"] == "Hello world"
def test_feishu_on_message_rich_text():
bus = MessageBus()
config = {"app_id": "test", "app_secret": "test"}
channel = FeishuChannel(bus, config)
# Create mock event
event = MagicMock()
event.event.message.chat_id = "chat_1"
event.event.message.message_id = "msg_1"
event.event.message.root_id = None
event.event.sender.sender_id.open_id = "user_1"
# Rich text content (topic group / post)
content_dict = {
"content": [
[
{"tag": "text", "text": "Paragraph 1, part 1."},
{"tag": "text", "text": "Paragraph 1, part 2."}
],
[
{"tag": "at", "text": "@bot"},
{"tag": "text", "text": " Paragraph 2."}
]
]
}
event.event.message.content = json.dumps(content_dict)
with pytest.MonkeyPatch.context() as m:
mock_make_inbound = MagicMock()
m.setattr(channel, "_make_inbound", mock_make_inbound)
channel._on_message(event)
mock_make_inbound.assert_called_once()
parsed_text = mock_make_inbound.call_args[1]["text"]
# Expected text:
# Paragraph 1, part 1. Paragraph 1, part 2.
#
# @bot Paragraph 2.
assert "Paragraph 1, part 1. Paragraph 1, part 2." in parsed_text
assert "@bot Paragraph 2." in parsed_text
assert "\n\n" in parsed_text
================================================
FILE: backend/tests/test_harness_boundary.py
================================================
"""Boundary check: harness layer must not import from app layer.
The deerflow-harness package (packages/harness/deerflow/) is a standalone,
publishable agent framework. It must never depend on the app layer (app/).
This test scans all Python files in the harness package and fails if any
``from app.`` or ``import app.`` statement is found.
"""
import ast
from pathlib import Path
HARNESS_ROOT = Path(__file__).parent.parent / "packages" / "harness" / "deerflow"
BANNED_PREFIXES = ("app.",)
def _collect_imports(filepath: Path) -> list[tuple[int, str]]:
"""Return (line_number, module_path) for every import in *filepath*."""
source = filepath.read_text(encoding="utf-8")
try:
tree = ast.parse(source, filename=str(filepath))
except SyntaxError:
return []
results: list[tuple[int, str]] = []
for node in ast.walk(tree):
if isinstance(node, ast.Import):
for alias in node.names:
results.append((node.lineno, alias.name))
elif isinstance(node, ast.ImportFrom):
if node.module:
results.append((node.lineno, node.module))
return results
def test_harness_does_not_import_app():
violations: list[str] = []
for py_file in sorted(HARNESS_ROOT.rglob("*.py")):
for lineno, module in _collect_imports(py_file):
if any(module == prefix.rstrip(".") or module.startswith(prefix) for prefix in BANNED_PREFIXES):
rel = py_file.relative_to(HARNESS_ROOT.parent.parent.parent)
violations.append(f" {rel}:{lineno} imports {module}")
assert not violations, "Harness layer must not import from app layer:\n" + "\n".join(violations)
================================================
FILE: backend/tests/test_infoquest_client.py
================================================
"""Tests for InfoQuest client and tools."""
import json
from unittest.mock import MagicMock, patch
from deerflow.community.infoquest import tools
from deerflow.community.infoquest.infoquest_client import InfoQuestClient
class TestInfoQuestClient:
def test_infoquest_client_initialization(self):
"""Test InfoQuestClient initialization with different parameters."""
# Test with default parameters
client = InfoQuestClient()
assert client.fetch_time == -1
assert client.fetch_timeout == -1
assert client.fetch_navigation_timeout == -1
assert client.search_time_range == -1
# Test with custom parameters
client = InfoQuestClient(fetch_time=10, fetch_timeout=30, fetch_navigation_timeout=60, search_time_range=24)
assert client.fetch_time == 10
assert client.fetch_timeout == 30
assert client.fetch_navigation_timeout == 60
assert client.search_time_range == 24
@patch("deerflow.community.infoquest.infoquest_client.requests.post")
def test_fetch_success(self, mock_post):
"""Test successful fetch operation."""
mock_response = MagicMock()
mock_response.status_code = 200
mock_response.text = json.dumps({"reader_result": "Test content"})
mock_post.return_value = mock_response
client = InfoQuestClient()
result = client.fetch("https://example.com")
assert result == "Test content"
mock_post.assert_called_once()
args, kwargs = mock_post.call_args
assert args[0] == "https://reader.infoquest.bytepluses.com"
assert kwargs["json"]["url"] == "https://example.com"
assert kwargs["json"]["format"] == "HTML"
@patch("deerflow.community.infoquest.infoquest_client.requests.post")
def test_fetch_non_200_status(self, mock_post):
"""Test fetch operation with non-200 status code."""
mock_response = MagicMock()
mock_response.status_code = 404
mock_response.text = "Not Found"
mock_post.return_value = mock_response
client = InfoQuestClient()
result = client.fetch("https://example.com")
assert result == "Error: fetch API returned status 404: Not Found"
@patch("deerflow.community.infoquest.infoquest_client.requests.post")
def test_fetch_empty_response(self, mock_post):
"""Test fetch operation with empty response."""
mock_response = MagicMock()
mock_response.status_code = 200
mock_response.text = ""
mock_post.return_value = mock_response
client = InfoQuestClient()
result = client.fetch("https://example.com")
assert result == "Error: no result found"
@patch("deerflow.community.infoquest.infoquest_client.requests.post")
def test_web_search_raw_results_success(self, mock_post):
"""Test successful web_search_raw_results operation."""
mock_response = MagicMock()
mock_response.status_code = 200
mock_response.json.return_value = {"search_result": {"results": [{"content": {"results": {"organic": [{"title": "Test Result", "desc": "Test description", "url": "https://example.com"}]}}}], "images_results": []}}
mock_post.return_value = mock_response
client = InfoQuestClient()
result = client.web_search_raw_results("test query", "")
assert "search_result" in result
mock_post.assert_called_once()
args, kwargs = mock_post.call_args
assert args[0] == "https://search.infoquest.bytepluses.com"
assert kwargs["json"]["query"] == "test query"
@patch("deerflow.community.infoquest.infoquest_client.requests.post")
def test_web_search_success(self, mock_post):
"""Test successful web_search operation."""
mock_response = MagicMock()
mock_response.status_code = 200
mock_response.json.return_value = {"search_result": {"results": [{"content": {"results": {"organic": [{"title": "Test Result", "desc": "Test description", "url": "https://example.com"}]}}}], "images_results": []}}
mock_post.return_value = mock_response
client = InfoQuestClient()
result = client.web_search("test query")
# Check if result is a valid JSON string with expected content
result_data = json.loads(result)
assert len(result_data) == 1
assert result_data[0]["title"] == "Test Result"
assert result_data[0]["url"] == "https://example.com"
def test_clean_results(self):
"""Test clean_results method with sample raw results."""
raw_results = [
{
"content": {
"results": {
"organic": [{"title": "Test Page", "desc": "Page description", "url": "https://example.com/page1"}],
"top_stories": {"items": [{"title": "Test News", "source": "Test Source", "time_frame": "2 hours ago", "url": "https://example.com/news1"}]},
}
}
}
]
cleaned = InfoQuestClient.clean_results(raw_results)
assert len(cleaned) == 2
assert cleaned[0]["type"] == "page"
assert cleaned[0]["title"] == "Test Page"
assert cleaned[1]["type"] == "news"
assert cleaned[1]["title"] == "Test News"
@patch("deerflow.community.infoquest.tools._get_infoquest_client")
def test_web_search_tool(self, mock_get_client):
"""Test web_search_tool function."""
mock_client = MagicMock()
mock_client.web_search.return_value = json.dumps([])
mock_get_client.return_value = mock_client
result = tools.web_search_tool.run("test query")
assert result == json.dumps([])
mock_get_client.assert_called_once()
mock_client.web_search.assert_called_once_with("test query")
@patch("deerflow.community.infoquest.tools._get_infoquest_client")
def test_web_fetch_tool(self, mock_get_client):
"""Test web_fetch_tool function."""
mock_client = MagicMock()
mock_client.fetch.return_value = "Test content"
mock_get_client.return_value = mock_client
result = tools.web_fetch_tool.run("https://example.com")
assert result == "# Untitled\n\nTest content"
mock_get_client.assert_called_once()
mock_client.fetch.assert_called_once_with("https://example.com")
@patch("deerflow.community.infoquest.tools.get_app_config")
def test_get_infoquest_client(self, mock_get_app_config):
"""Test _get_infoquest_client function with config."""
mock_config = MagicMock()
# Add image_search config to the side_effect
mock_config.get_tool_config.side_effect = [
MagicMock(model_extra={"search_time_range": 24}), # web_search config
MagicMock(model_extra={"fetch_time": 10, "timeout": 30, "navigation_timeout": 60}), # web_fetch config
MagicMock(model_extra={"image_search_time_range": 7, "image_size": "l"}) # image_search config
]
mock_get_app_config.return_value = mock_config
client = tools._get_infoquest_client()
assert client.search_time_range == 24
assert client.fetch_time == 10
assert client.fetch_timeout == 30
assert client.fetch_navigation_timeout == 60
assert client.image_search_time_range == 7
assert client.image_size == "l"
@patch("deerflow.community.infoquest.infoquest_client.requests.post")
def test_web_search_api_error(self, mock_post):
"""Test web_search operation with API error."""
mock_post.side_effect = Exception("Connection error")
client = InfoQuestClient()
result = client.web_search("test query")
assert "Error" in result
def test_clean_results_with_image_search(self):
"""Test clean_results_with_image_search method with sample raw results."""
raw_results = [{"content": {"results": {"images_results": [{"original": "https://example.com/image1.jpg", "title": "Test Image 1", "url": "https://example.com/page1"}]}}}]
cleaned = InfoQuestClient.clean_results_with_image_search(raw_results)
assert len(cleaned) == 1
assert cleaned[0]["image_url"] == "https://example.com/image1.jpg"
assert cleaned[0]["title"] == "Test Image 1"
def test_clean_results_with_image_search_empty(self):
"""Test clean_results_with_image_search method with empty results."""
raw_results = [{"content": {"results": {"images_results": []}}}]
cleaned = InfoQuestClient.clean_results_with_image_search(raw_results)
assert len(cleaned) == 0
def test_clean_results_with_image_search_no_images(self):
"""Test clean_results_with_image_search method with no images_results field."""
raw_results = [{"content": {"results": {"organic": [{"title": "Test Page"}]}}}]
cleaned = InfoQuestClient.clean_results_with_image_search(raw_results)
assert len(cleaned) == 0
class TestImageSearch:
@patch("deerflow.community.infoquest.infoquest_client.requests.post")
def test_image_search_raw_results_success(self, mock_post):
"""Test successful image_search_raw_results operation."""
mock_response = MagicMock()
mock_response.status_code = 200
mock_response.json.return_value = {"search_result": {"results": [{"content": {"results": {"images_results": [{"original": "https://example.com/image1.jpg", "title": "Test Image", "url": "https://example.com/page1"}]}}}]}}
mock_post.return_value = mock_response
client = InfoQuestClient()
result = client.image_search_raw_results("test query")
assert "search_result" in result
mock_post.assert_called_once()
args, kwargs = mock_post.call_args
assert args[0] == "https://search.infoquest.bytepluses.com"
assert kwargs["json"]["query"] == "test query"
@patch("deerflow.community.infoquest.infoquest_client.requests.post")
def test_image_search_raw_results_with_parameters(self, mock_post):
"""Test image_search_raw_results with all parameters."""
mock_response = MagicMock()
mock_response.status_code = 200
mock_response.json.return_value = {"search_result": {"results": [{"content": {"results": {"images_results": [{"original": "https://example.com/image1.jpg"}]}}}]}}
mock_post.return_value = mock_response
client = InfoQuestClient(image_search_time_range=30, image_size="l")
client.image_search_raw_results(query="cat", site="unsplash.com", output_format="JSON")
mock_post.assert_called_once()
args, kwargs = mock_post.call_args
assert kwargs["json"]["query"] == "cat"
assert kwargs["json"]["time_range"] == 30
assert kwargs["json"]["site"] == "unsplash.com"
assert kwargs["json"]["image_size"] == "l"
assert kwargs["json"]["format"] == "JSON"
@patch("deerflow.community.infoquest.infoquest_client.requests.post")
def test_image_search_raw_results_invalid_time_range(self, mock_post):
"""Test image_search_raw_results with invalid time_range parameter."""
mock_response = MagicMock()
mock_response.status_code = 200
mock_response.json.return_value = {"search_result": {"results": [{"content": {"results": {"images_results": []}}}]}}
mock_post.return_value = mock_response
# Create client with invalid time_range (should be ignored)
client = InfoQuestClient(image_search_time_range=400, image_size="x")
client.image_search_raw_results(
query="test",
site="",
)
mock_post.assert_called_once()
args, kwargs = mock_post.call_args
assert kwargs["json"]["query"] == "test"
assert "time_range" not in kwargs["json"]
assert "image_size" not in kwargs["json"]
@patch("deerflow.community.infoquest.infoquest_client.requests.post")
def test_image_search_success(self, mock_post):
"""Test successful image_search operation."""
mock_response = MagicMock()
mock_response.status_code = 200
mock_response.json.return_value = {"search_result": {"results": [{"content": {"results": {"images_results": [{"original": "https://example.com/image1.jpg", "title": "Test Image", "url": "https://example.com/page1"}]}}}]}}
mock_post.return_value = mock_response
client = InfoQuestClient()
result = client.image_search("cat")
# Check if result is a valid JSON string with expected content
result_data = json.loads(result)
assert len(result_data) == 1
assert result_data[0]["image_url"] == "https://example.com/image1.jpg"
assert result_data[0]["title"] == "Test Image"
@patch("deerflow.community.infoquest.infoquest_client.requests.post")
def test_image_search_with_all_parameters(self, mock_post):
"""Test image_search with all optional parameters."""
mock_response = MagicMock()
mock_response.status_code = 200
mock_response.json.return_value = {"search_result": {"results": [{"content": {"results": {"images_results": [{"original": "https://example.com/image1.jpg"}]}}}]}}
mock_post.return_value = mock_response
# Create client with image search parameters
client = InfoQuestClient(image_search_time_range=7, image_size="m")
client.image_search(query="dog", site="flickr.com", output_format="JSON")
mock_post.assert_called_once()
args, kwargs = mock_post.call_args
assert kwargs["json"]["query"] == "dog"
assert kwargs["json"]["time_range"] == 7
assert kwargs["json"]["site"] == "flickr.com"
assert kwargs["json"]["image_size"] == "m"
@patch("deerflow.community.infoquest.infoquest_client.requests.post")
def test_image_search_api_error(self, mock_post):
"""Test image_search operation with API error."""
mock_post.side_effect = Exception("Connection error")
client = InfoQuestClient()
result = client.image_search("test query")
assert "Error" in result
@patch("deerflow.community.infoquest.tools._get_infoquest_client")
def test_image_search_tool(self, mock_get_client):
"""Test image_search_tool function."""
mock_client = MagicMock()
mock_client.image_search.return_value = json.dumps([{"image_url": "https://example.com/image1.jpg"}])
mock_get_client.return_value = mock_client
result = tools.image_search_tool.run({"query": "test query"})
# Check if result is a valid JSON string
result_data = json.loads(result)
assert len(result_data) == 1
assert result_data[0]["image_url"] == "https://example.com/image1.jpg"
mock_get_client.assert_called_once()
mock_client.image_search.assert_called_once_with("test query")
# In /Users/bytedance/python/deer-flowv2/deer-flow/backend/tests/test_infoquest_client.py
@patch("deerflow.community.infoquest.tools._get_infoquest_client")
def test_image_search_tool_with_parameters(self, mock_get_client):
"""Test image_search_tool function with all parameters (extra parameters will be ignored)."""
mock_client = MagicMock()
mock_client.image_search.return_value = json.dumps([{"image_url": "https://example.com/image1.jpg"}])
mock_get_client.return_value = mock_client
# Pass all parameters as a dictionary (extra parameters will be ignored)
tools.image_search_tool.run({"query": "sunset", "time_range": 30, "site": "unsplash.com", "image_size": "l"})
mock_get_client.assert_called_once()
# image_search_tool only passes query to client.image_search
# site parameter is empty string by default
mock_client.image_search.assert_called_once_with("sunset")
================================================
FILE: backend/tests/test_lead_agent_model_resolution.py
================================================
"""Tests for lead agent runtime model resolution behavior."""
from __future__ import annotations
import pytest
from deerflow.agents.lead_agent import agent as lead_agent_module
from deerflow.config.app_config import AppConfig
from deerflow.config.model_config import ModelConfig
from deerflow.config.sandbox_config import SandboxConfig
def _make_app_config(models: list[ModelConfig]) -> AppConfig:
return AppConfig(
models=models,
sandbox=SandboxConfig(use="deerflow.sandbox.local:LocalSandboxProvider"),
)
def _make_model(name: str, *, supports_thinking: bool) -> ModelConfig:
return ModelConfig(
name=name,
display_name=name,
description=None,
use="langchain_openai:ChatOpenAI",
model=name,
supports_thinking=supports_thinking,
supports_vision=False,
)
def test_resolve_model_name_falls_back_to_default(monkeypatch, caplog):
app_config = _make_app_config(
[
_make_model("default-model", supports_thinking=False),
_make_model("other-model", supports_thinking=True),
]
)
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
with caplog.at_level("WARNING"):
resolved = lead_agent_module._resolve_model_name("missing-model")
assert resolved == "default-model"
assert "fallback to default model 'default-model'" in caplog.text
def test_resolve_model_name_uses_default_when_none(monkeypatch):
app_config = _make_app_config(
[
_make_model("default-model", supports_thinking=False),
_make_model("other-model", supports_thinking=True),
]
)
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
resolved = lead_agent_module._resolve_model_name(None)
assert resolved == "default-model"
def test_resolve_model_name_raises_when_no_models_configured(monkeypatch):
app_config = _make_app_config([])
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
with pytest.raises(
ValueError,
match="No chat models are configured",
):
lead_agent_module._resolve_model_name("missing-model")
def test_make_lead_agent_disables_thinking_when_model_does_not_support_it(monkeypatch):
app_config = _make_app_config([_make_model("safe-model", supports_thinking=False)])
import deerflow.tools as tools_module
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
monkeypatch.setattr(tools_module, "get_available_tools", lambda **kwargs: [])
monkeypatch.setattr(lead_agent_module, "_build_middlewares", lambda config, model_name, agent_name=None: [])
captured: dict[str, object] = {}
def _fake_create_chat_model(*, name, thinking_enabled, reasoning_effort=None):
captured["name"] = name
captured["thinking_enabled"] = thinking_enabled
captured["reasoning_effort"] = reasoning_effort
return object()
monkeypatch.setattr(lead_agent_module, "create_chat_model", _fake_create_chat_model)
monkeypatch.setattr(lead_agent_module, "create_agent", lambda **kwargs: kwargs)
result = lead_agent_module.make_lead_agent(
{
"configurable": {
"model_name": "safe-model",
"thinking_enabled": True,
"is_plan_mode": False,
"subagent_enabled": False,
}
}
)
assert captured["name"] == "safe-model"
assert captured["thinking_enabled"] is False
assert result["model"] is not None
def test_build_middlewares_uses_resolved_model_name_for_vision(monkeypatch):
app_config = _make_app_config(
[
_make_model("stale-model", supports_thinking=False),
ModelConfig(
name="vision-model",
display_name="vision-model",
description=None,
use="langchain_openai:ChatOpenAI",
model="vision-model",
supports_thinking=False,
supports_vision=True,
),
]
)
monkeypatch.setattr(lead_agent_module, "get_app_config", lambda: app_config)
monkeypatch.setattr(lead_agent_module, "_create_summarization_middleware", lambda: None)
monkeypatch.setattr(lead_agent_module, "_create_todo_list_middleware", lambda is_plan_mode: None)
middlewares = lead_agent_module._build_middlewares(
{"configurable": {"model_name": "stale-model", "is_plan_mode": False, "subagent_enabled": False}},
model_name="vision-model",
)
assert any(isinstance(m, lead_agent_module.ViewImageMiddleware) for m in middlewares)
================================================
FILE: backend/tests/test_local_sandbox_encoding.py
================================================
import builtins
import deerflow.sandbox.local.local_sandbox as local_sandbox
from deerflow.sandbox.local.local_sandbox import LocalSandbox
def _open(base, file, mode="r", *args, **kwargs):
if "b" in mode:
return base(file, mode, *args, **kwargs)
return base(file, mode, *args, encoding=kwargs.pop("encoding", "gbk"), **kwargs)
def test_read_file_uses_utf8_on_windows_locale(tmp_path, monkeypatch):
path = tmp_path / "utf8.txt"
text = "\u201cutf8\u201d"
path.write_text(text, encoding="utf-8")
base = builtins.open
monkeypatch.setattr(local_sandbox, "open", lambda file, mode="r", *args, **kwargs: _open(base, file, mode, *args, **kwargs), raising=False)
assert LocalSandbox("t").read_file(str(path)) == text
def test_write_file_uses_utf8_on_windows_locale(tmp_path, monkeypatch):
path = tmp_path / "utf8.txt"
text = "emoji \U0001F600"
base = builtins.open
monkeypatch.setattr(local_sandbox, "open", lambda file, mode="r", *args, **kwargs: _open(base, file, mode, *args, **kwargs), raising=False)
LocalSandbox("t").write_file(str(path), text)
assert path.read_text(encoding="utf-8") == text
================================================
FILE: backend/tests/test_loop_detection_middleware.py
================================================
"""Tests for LoopDetectionMiddleware."""
from unittest.mock import MagicMock
from langchain_core.messages import AIMessage, SystemMessage
from deerflow.agents.middlewares.loop_detection_middleware import (
_HARD_STOP_MSG,
LoopDetectionMiddleware,
_hash_tool_calls,
)
def _make_runtime(thread_id="test-thread"):
"""Build a minimal Runtime mock with context."""
runtime = MagicMock()
runtime.context = {"thread_id": thread_id}
return runtime
def _make_state(tool_calls=None, content=""):
"""Build a minimal AgentState dict with an AIMessage."""
msg = AIMessage(content=content, tool_calls=tool_calls or [])
return {"messages": [msg]}
def _bash_call(cmd="ls"):
return {"name": "bash", "id": f"call_{cmd}", "args": {"command": cmd}}
class TestHashToolCalls:
def test_same_calls_same_hash(self):
a = _hash_tool_calls([_bash_call("ls")])
b = _hash_tool_calls([_bash_call("ls")])
assert a == b
def test_different_calls_different_hash(self):
a = _hash_tool_calls([_bash_call("ls")])
b = _hash_tool_calls([_bash_call("pwd")])
assert a != b
def test_order_independent(self):
a = _hash_tool_calls([_bash_call("ls"), {"name": "read_file", "args": {"path": "/tmp"}}])
b = _hash_tool_calls([{"name": "read_file", "args": {"path": "/tmp"}}, _bash_call("ls")])
assert a == b
def test_empty_calls(self):
h = _hash_tool_calls([])
assert isinstance(h, str)
assert len(h) > 0
class TestLoopDetection:
def test_no_tool_calls_returns_none(self):
mw = LoopDetectionMiddleware()
runtime = _make_runtime()
state = {"messages": [AIMessage(content="hello")]}
result = mw._apply(state, runtime)
assert result is None
def test_below_threshold_returns_none(self):
mw = LoopDetectionMiddleware(warn_threshold=3)
runtime = _make_runtime()
call = [_bash_call("ls")]
# First two identical calls — no warning
for _ in range(2):
result = mw._apply(_make_state(tool_calls=call), runtime)
assert result is None
def test_warn_at_threshold(self):
mw = LoopDetectionMiddleware(warn_threshold=3, hard_limit=5)
runtime = _make_runtime()
call = [_bash_call("ls")]
for _ in range(2):
mw._apply(_make_state(tool_calls=call), runtime)
# Third identical call triggers warning
result = mw._apply(_make_state(tool_calls=call), runtime)
assert result is not None
msgs = result["messages"]
assert len(msgs) == 1
assert isinstance(msgs[0], SystemMessage)
assert "LOOP DETECTED" in msgs[0].content
def test_warn_only_injected_once(self):
"""Warning for the same hash should only be injected once per thread."""
mw = LoopDetectionMiddleware(warn_threshold=3, hard_limit=10)
runtime = _make_runtime()
call = [_bash_call("ls")]
# First two — no warning
for _ in range(2):
mw._apply(_make_state(tool_calls=call), runtime)
# Third — warning injected
result = mw._apply(_make_state(tool_calls=call), runtime)
assert result is not None
assert "LOOP DETECTED" in result["messages"][0].content
# Fourth — warning already injected, should return None
result = mw._apply(_make_state(tool_calls=call), runtime)
assert result is None
def test_hard_stop_at_limit(self):
mw = LoopDetectionMiddleware(warn_threshold=2, hard_limit=4)
runtime = _make_runtime()
call = [_bash_call("ls")]
for _ in range(3):
mw._apply(_make_state(tool_calls=call), runtime)
# Fourth call triggers hard stop
result = mw._apply(_make_state(tool_calls=call), runtime)
assert result is not None
msgs = result["messages"]
assert len(msgs) == 1
# Hard stop strips tool_calls
assert isinstance(msgs[0], AIMessage)
assert msgs[0].tool_calls == []
assert _HARD_STOP_MSG in msgs[0].content
def test_different_calls_dont_trigger(self):
mw = LoopDetectionMiddleware(warn_threshold=2)
runtime = _make_runtime()
# Each call is different
for i in range(10):
result = mw._apply(_make_state(tool_calls=[_bash_call(f"cmd_{i}")]), runtime)
assert result is None
def test_window_sliding(self):
mw = LoopDetectionMiddleware(warn_threshold=3, window_size=5)
runtime = _make_runtime()
call = [_bash_call("ls")]
# Fill with 2 identical calls
mw._apply(_make_state(tool_calls=call), runtime)
mw._apply(_make_state(tool_calls=call), runtime)
# Push them out of the window with different calls
for i in range(5):
mw._apply(_make_state(tool_calls=[_bash_call(f"other_{i}")]), runtime)
# Now the original call should be fresh again — no warning
result = mw._apply(_make_state(tool_calls=call), runtime)
assert result is None
def test_reset_clears_state(self):
mw = LoopDetectionMiddleware(warn_threshold=2)
runtime = _make_runtime()
call = [_bash_call("ls")]
mw._apply(_make_state(tool_calls=call), runtime)
mw._apply(_make_state(tool_calls=call), runtime)
# Would trigger warning, but reset first
mw.reset()
result = mw._apply(_make_state(tool_calls=call), runtime)
assert result is None
def test_non_ai_message_ignored(self):
mw = LoopDetectionMiddleware()
runtime = _make_runtime()
state = {"messages": [SystemMessage(content="hello")]}
result = mw._apply(state, runtime)
assert result is None
def test_empty_messages_ignored(self):
mw = LoopDetectionMiddleware()
runtime = _make_runtime()
result = mw._apply({"messages": []}, runtime)
assert result is None
def test_thread_id_from_runtime_context(self):
"""Thread ID should come from runtime.context, not state."""
mw = LoopDetectionMiddleware(warn_threshold=2)
runtime_a = _make_runtime("thread-A")
runtime_b = _make_runtime("thread-B")
call = [_bash_call("ls")]
# One call on thread A
mw._apply(_make_state(tool_calls=call), runtime_a)
# One call on thread B
mw._apply(_make_state(tool_calls=call), runtime_b)
# Second call on thread A — triggers warning (2 >= warn_threshold)
result = mw._apply(_make_state(tool_calls=call), runtime_a)
assert result is not None
assert "LOOP DETECTED" in result["messages"][0].content
# Second call on thread B — also triggers (independent tracking)
result = mw._apply(_make_state(tool_calls=call), runtime_b)
assert result is not None
assert "LOOP DETECTED" in result["messages"][0].content
def test_lru_eviction(self):
"""Old threads should be evicted when max_tracked_threads is exceeded."""
mw = LoopDetectionMiddleware(warn_threshold=2, max_tracked_threads=3)
call = [_bash_call("ls")]
# Fill up 3 threads
for i in range(3):
runtime = _make_runtime(f"thread-{i}")
mw._apply(_make_state(tool_calls=call), runtime)
# Add a 4th thread — should evict thread-0
runtime_new = _make_runtime("thread-new")
mw._apply(_make_state(tool_calls=call), runtime_new)
assert "thread-0" not in mw._history
assert "thread-new" in mw._history
assert len(mw._history) == 3
def test_thread_safe_mutations(self):
"""Verify lock is used for mutations (basic structural test)."""
mw = LoopDetectionMiddleware()
# The middleware should have a lock attribute
assert hasattr(mw, "_lock")
assert isinstance(mw._lock, type(mw._lock))
def test_fallback_thread_id_when_missing(self):
"""When runtime context has no thread_id, should use 'default'."""
mw = LoopDetectionMiddleware(warn_threshold=2)
runtime = MagicMock()
runtime.context = {}
call = [_bash_call("ls")]
mw._apply(_make_state(tool_calls=call), runtime)
assert "default" in mw._history
================================================
FILE: backend/tests/test_mcp_client_config.py
================================================
"""Core behavior tests for MCP client server config building."""
import pytest
from deerflow.config.extensions_config import ExtensionsConfig, McpServerConfig
from deerflow.mcp.client import build_server_params, build_servers_config
def test_build_server_params_stdio_success():
config = McpServerConfig(
type="stdio",
command="npx",
args=["-y", "my-mcp-server"],
env={"API_KEY": "secret"},
)
params = build_server_params("my-server", config)
assert params == {
"transport": "stdio",
"command": "npx",
"args": ["-y", "my-mcp-server"],
"env": {"API_KEY": "secret"},
}
def test_build_server_params_stdio_requires_command():
config = McpServerConfig(type="stdio", command=None)
with pytest.raises(ValueError, match="requires 'command' field"):
build_server_params("broken-stdio", config)
@pytest.mark.parametrize("transport", ["sse", "http"])
def test_build_server_params_http_like_success(transport: str):
config = McpServerConfig(
type=transport,
url="https://example.com/mcp",
headers={"Authorization": "Bearer token"},
)
params = build_server_params("remote-server", config)
assert params == {
"transport": transport,
"url": "https://example.com/mcp",
"headers": {"Authorization": "Bearer token"},
}
@pytest.mark.parametrize("transport", ["sse", "http"])
def test_build_server_params_http_like_requires_url(transport: str):
config = McpServerConfig(type=transport, url=None)
with pytest.raises(ValueError, match="requires 'url' field"):
build_server_params("broken-remote", config)
def test_build_server_params_rejects_unsupported_transport():
config = McpServerConfig(type="websocket")
with pytest.raises(ValueError, match="unsupported transport type"):
build_server_params("bad-transport", config)
def test_build_servers_config_returns_empty_when_no_enabled_servers():
extensions = ExtensionsConfig(
mcp_servers={
"disabled-a": McpServerConfig(enabled=False, type="stdio", command="echo"),
"disabled-b": McpServerConfig(enabled=False, type="http", url="https://example.com"),
},
skills={},
)
assert build_servers_config(extensions) == {}
def test_build_servers_config_skips_invalid_server_and_keeps_valid_ones():
extensions = ExtensionsConfig(
mcp_servers={
"valid-stdio": McpServerConfig(enabled=True, type="stdio", command="npx", args=["server"]),
"invalid-stdio": McpServerConfig(enabled=True, type="stdio", command=None),
"disabled-http": McpServerConfig(enabled=False, type="http", url="https://disabled.example.com"),
},
skills={},
)
result = build_servers_config(extensions)
assert "valid-stdio" in result
assert result["valid-stdio"]["transport"] == "stdio"
assert "invalid-stdio" not in result
assert "disabled-http" not in result
================================================
FILE: backend/tests/test_mcp_oauth.py
================================================
"""Tests for MCP OAuth support."""
from __future__ import annotations
import asyncio
from typing import Any
from deerflow.config.extensions_config import ExtensionsConfig
from deerflow.mcp.oauth import OAuthTokenManager, build_oauth_tool_interceptor, get_initial_oauth_headers
class _MockResponse:
def __init__(self, payload: dict[str, Any]):
self._payload = payload
def raise_for_status(self) -> None:
return None
def json(self) -> dict[str, Any]:
return self._payload
class _MockAsyncClient:
def __init__(self, payload: dict[str, Any], post_calls: list[dict[str, Any]], **kwargs):
self._payload = payload
self._post_calls = post_calls
async def __aenter__(self):
return self
async def __aexit__(self, exc_type, exc, tb):
return False
async def post(self, url: str, data: dict[str, Any]):
self._post_calls.append({"url": url, "data": data})
return _MockResponse(self._payload)
def test_oauth_token_manager_fetches_and_caches_token(monkeypatch):
post_calls: list[dict[str, Any]] = []
def _client_factory(*args, **kwargs):
return _MockAsyncClient(
payload={
"access_token": "token-123",
"token_type": "Bearer",
"expires_in": 3600,
},
post_calls=post_calls,
**kwargs,
)
monkeypatch.setattr("httpx.AsyncClient", _client_factory)
config = ExtensionsConfig.model_validate(
{
"mcpServers": {
"secure-http": {
"enabled": True,
"type": "http",
"url": "https://api.example.com/mcp",
"oauth": {
"enabled": True,
"token_url": "https://auth.example.com/oauth/token",
"grant_type": "client_credentials",
"client_id": "client-id",
"client_secret": "client-secret",
},
}
}
}
)
manager = OAuthTokenManager.from_extensions_config(config)
first = asyncio.run(manager.get_authorization_header("secure-http"))
second = asyncio.run(manager.get_authorization_header("secure-http"))
assert first == "Bearer token-123"
assert second == "Bearer token-123"
assert len(post_calls) == 1
assert post_calls[0]["url"] == "https://auth.example.com/oauth/token"
assert post_calls[0]["data"]["grant_type"] == "client_credentials"
def test_build_oauth_interceptor_injects_authorization_header(monkeypatch):
post_calls: list[dict[str, Any]] = []
def _client_factory(*args, **kwargs):
return _MockAsyncClient(
payload={
"access_token": "token-abc",
"token_type": "Bearer",
"expires_in": 3600,
},
post_calls=post_calls,
**kwargs,
)
monkeypatch.setattr("httpx.AsyncClient", _client_factory)
config = ExtensionsConfig.model_validate(
{
"mcpServers": {
"secure-sse": {
"enabled": True,
"type": "sse",
"url": "https://api.example.com/mcp",
"oauth": {
"enabled": True,
"token_url": "https://auth.example.com/oauth/token",
"grant_type": "client_credentials",
"client_id": "client-id",
"client_secret": "client-secret",
},
}
}
}
)
interceptor = build_oauth_tool_interceptor(config)
assert interceptor is not None
class _Request:
def __init__(self):
self.server_name = "secure-sse"
self.headers = {"X-Test": "1"}
def override(self, **kwargs):
updated = _Request()
updated.server_name = self.server_name
updated.headers = kwargs.get("headers")
return updated
captured: dict[str, Any] = {}
async def _handler(request):
captured["headers"] = request.headers
return "ok"
result = asyncio.run(interceptor(_Request(), _handler))
assert result == "ok"
assert captured["headers"]["Authorization"] == "Bearer token-abc"
assert captured["headers"]["X-Test"] == "1"
def test_get_initial_oauth_headers(monkeypatch):
post_calls: list[dict[str, Any]] = []
def _client_factory(*args, **kwargs):
return _MockAsyncClient(
payload={
"access_token": "token-initial",
"token_type": "Bearer",
"expires_in": 3600,
},
post_calls=post_calls,
**kwargs,
)
monkeypatch.setattr("httpx.AsyncClient", _client_factory)
config = ExtensionsConfig.model_validate(
{
"mcpServers": {
"secure-http": {
"enabled": True,
"type": "http",
"url": "https://api.example.com/mcp",
"oauth": {
"enabled": True,
"token_url": "https://auth.example.com/oauth/token",
"grant_type": "client_credentials",
"client_id": "client-id",
"client_secret": "client-secret",
},
},
"no-oauth": {
"enabled": True,
"type": "http",
"url": "https://example.com/mcp",
},
}
}
)
headers = asyncio.run(get_initial_oauth_headers(config))
assert headers == {"secure-http": "Bearer token-initial"}
assert len(post_calls) == 1
================================================
FILE: backend/tests/test_memory_prompt_injection.py
================================================
"""Tests for memory prompt injection formatting."""
import math
from deerflow.agents.memory.prompt import _coerce_confidence, format_memory_for_injection
def test_format_memory_includes_facts_section() -> None:
memory_data = {
"user": {},
"history": {},
"facts": [
{"content": "User uses PostgreSQL", "category": "knowledge", "confidence": 0.9},
{"content": "User prefers SQLAlchemy", "category": "preference", "confidence": 0.8},
],
}
result = format_memory_for_injection(memory_data, max_tokens=2000)
assert "Facts:" in result
assert "User uses PostgreSQL" in result
assert "User prefers SQLAlchemy" in result
def test_format_memory_sorts_facts_by_confidence_desc() -> None:
memory_data = {
"user": {},
"history": {},
"facts": [
{"content": "Low confidence fact", "category": "context", "confidence": 0.4},
{"content": "High confidence fact", "category": "knowledge", "confidence": 0.95},
],
}
result = format_memory_for_injection(memory_data, max_tokens=2000)
assert result.index("High confidence fact") < result.index("Low confidence fact")
def test_format_memory_respects_budget_when_adding_facts(monkeypatch) -> None:
# Make token counting deterministic for this test by counting characters.
monkeypatch.setattr("deerflow.agents.memory.prompt._count_tokens", lambda text, encoding_name="cl100k_base": len(text))
memory_data = {
"user": {},
"history": {},
"facts": [
{"content": "First fact should fit", "category": "knowledge", "confidence": 0.95},
{"content": "Second fact should not fit in tiny budget", "category": "knowledge", "confidence": 0.90},
],
}
first_fact_only_memory_data = {
"user": {},
"history": {},
"facts": [
{"content": "First fact should fit", "category": "knowledge", "confidence": 0.95},
],
}
one_fact_result = format_memory_for_injection(first_fact_only_memory_data, max_tokens=2000)
two_facts_result = format_memory_for_injection(memory_data, max_tokens=2000)
# Choose a budget that can include exactly one fact section line.
max_tokens = (len(one_fact_result) + len(two_facts_result)) // 2
first_only_result = format_memory_for_injection(memory_data, max_tokens=max_tokens)
assert "First fact should fit" in first_only_result
assert "Second fact should not fit in tiny budget" not in first_only_result
def test_coerce_confidence_nan_falls_back_to_default() -> None:
"""NaN should not be treated as a valid confidence value."""
result = _coerce_confidence(math.nan, default=0.5)
assert result == 0.5
def test_coerce_confidence_inf_falls_back_to_default() -> None:
"""Infinite values should fall back to default rather than clamping to 1.0."""
assert _coerce_confidence(math.inf, default=0.3) == 0.3
assert _coerce_confidence(-math.inf, default=0.3) == 0.3
def test_coerce_confidence_valid_values_are_clamped() -> None:
"""Valid floats outside [0, 1] are clamped; values inside are preserved."""
assert _coerce_confidence(1.5) == 1.0
assert _coerce_confidence(-0.5) == 0.0
assert abs(_coerce_confidence(0.75) - 0.75) < 1e-9
def test_format_memory_skips_none_content_facts() -> None:
"""Facts with content=None must not produce a 'None' line in the output."""
memory_data = {
"facts": [
{"content": None, "category": "knowledge", "confidence": 0.9},
{"content": "Real fact", "category": "knowledge", "confidence": 0.8},
],
}
result = format_memory_for_injection(memory_data, max_tokens=2000)
assert "None" not in result
assert "Real fact" in result
def test_format_memory_skips_non_string_content_facts() -> None:
"""Facts with non-string content (e.g. int/list) must be ignored."""
memory_data = {
"facts": [
{"content": 42, "category": "knowledge", "confidence": 0.9},
{"content": ["list"], "category": "knowledge", "confidence": 0.85},
{"content": "Valid fact", "category": "knowledge", "confidence": 0.7},
],
}
result = format_memory_for_injection(memory_data, max_tokens=2000)
# The formatted line for an integer content would be "- [knowledge | 0.90] 42".
assert "| 0.90] 42" not in result
# The formatted line for a list content would be "- [knowledge | 0.85] ['list']".
assert "| 0.85]" not in result
assert "Valid fact" in result
================================================
FILE: backend/tests/test_memory_updater.py
================================================
from unittest.mock import MagicMock, patch
from deerflow.agents.memory.prompt import format_conversation_for_update
from deerflow.agents.memory.updater import MemoryUpdater, _extract_text
from deerflow.config.memory_config import MemoryConfig
def _make_memory(facts: list[dict[str, object]] | None = None) -> dict[str, object]:
return {
"version": "1.0",
"lastUpdated": "",
"user": {
"workContext": {"summary": "", "updatedAt": ""},
"personalContext": {"summary": "", "updatedAt": ""},
"topOfMind": {"summary": "", "updatedAt": ""},
},
"history": {
"recentMonths": {"summary": "", "updatedAt": ""},
"earlierContext": {"summary": "", "updatedAt": ""},
"longTermBackground": {"summary": "", "updatedAt": ""},
},
"facts": facts or [],
}
def _memory_config(**overrides: object) -> MemoryConfig:
config = MemoryConfig()
for key, value in overrides.items():
setattr(config, key, value)
return config
def test_apply_updates_skips_existing_duplicate_and_preserves_removals() -> None:
updater = MemoryUpdater()
current_memory = _make_memory(
facts=[
{
"id": "fact_existing",
"content": "User likes Python",
"category": "preference",
"confidence": 0.9,
"createdAt": "2026-03-18T00:00:00Z",
"source": "thread-a",
},
{
"id": "fact_remove",
"content": "Old context to remove",
"category": "context",
"confidence": 0.8,
"createdAt": "2026-03-18T00:00:00Z",
"source": "thread-a",
},
]
)
update_data = {
"factsToRemove": ["fact_remove"],
"newFacts": [
{"content": "User likes Python", "category": "preference", "confidence": 0.95},
],
}
with patch(
"deerflow.agents.memory.updater.get_memory_config",
return_value=_memory_config(max_facts=100, fact_confidence_threshold=0.7),
):
result = updater._apply_updates(current_memory, update_data, thread_id="thread-b")
assert [fact["content"] for fact in result["facts"]] == ["User likes Python"]
assert all(fact["id"] != "fact_remove" for fact in result["facts"])
def test_apply_updates_skips_same_batch_duplicates_and_keeps_source_metadata() -> None:
updater = MemoryUpdater()
current_memory = _make_memory()
update_data = {
"newFacts": [
{"content": "User prefers dark mode", "category": "preference", "confidence": 0.91},
{"content": "User prefers dark mode", "category": "preference", "confidence": 0.92},
{"content": "User works on DeerFlow", "category": "context", "confidence": 0.87},
],
}
with patch(
"deerflow.agents.memory.updater.get_memory_config",
return_value=_memory_config(max_facts=100, fact_confidence_threshold=0.7),
):
result = updater._apply_updates(current_memory, update_data, thread_id="thread-42")
assert [fact["content"] for fact in result["facts"]] == [
"User prefers dark mode",
"User works on DeerFlow",
]
assert all(fact["id"].startswith("fact_") for fact in result["facts"])
assert all(fact["source"] == "thread-42" for fact in result["facts"])
def test_apply_updates_preserves_threshold_and_max_facts_trimming() -> None:
updater = MemoryUpdater()
current_memory = _make_memory(
facts=[
{
"id": "fact_python",
"content": "User likes Python",
"category": "preference",
"confidence": 0.95,
"createdAt": "2026-03-18T00:00:00Z",
"source": "thread-a",
},
{
"id": "fact_dark_mode",
"content": "User prefers dark mode",
"category": "preference",
"confidence": 0.8,
"createdAt": "2026-03-18T00:00:00Z",
"source": "thread-a",
},
]
)
update_data = {
"newFacts": [
{"content": "User prefers dark mode", "category": "preference", "confidence": 0.9},
{"content": "User uses uv", "category": "context", "confidence": 0.85},
{"content": "User likes noisy logs", "category": "behavior", "confidence": 0.6},
],
}
with patch(
"deerflow.agents.memory.updater.get_memory_config",
return_value=_memory_config(max_facts=2, fact_confidence_threshold=0.7),
):
result = updater._apply_updates(current_memory, update_data, thread_id="thread-9")
assert [fact["content"] for fact in result["facts"]] == [
"User likes Python",
"User uses uv",
]
assert all(fact["content"] != "User likes noisy logs" for fact in result["facts"])
assert result["facts"][1]["source"] == "thread-9"
# ---------------------------------------------------------------------------
# _extract_text — LLM response content normalization
# ---------------------------------------------------------------------------
class TestExtractText:
"""_extract_text should normalize all content shapes to plain text."""
def test_string_passthrough(self):
assert _extract_text("hello world") == "hello world"
def test_list_single_text_block(self):
assert _extract_text([{"type": "text", "text": "hello"}]) == "hello"
def test_list_multiple_text_blocks_joined(self):
content = [
{"type": "text", "text": "part one"},
{"type": "text", "text": "part two"},
]
assert _extract_text(content) == "part one\npart two"
def test_list_plain_strings(self):
assert _extract_text(["raw string"]) == "raw string"
def test_list_string_chunks_join_without_separator(self):
content = ["{\"user\"", ': "alice"}']
assert _extract_text(content) == '{"user": "alice"}'
def test_list_mixed_strings_and_blocks(self):
content = [
"raw text",
{"type": "text", "text": "block text"},
]
assert _extract_text(content) == "raw text\nblock text"
def test_list_adjacent_string_chunks_then_block(self):
content = [
"prefix",
"-continued",
{"type": "text", "text": "block text"},
]
assert _extract_text(content) == "prefix-continued\nblock text"
def test_list_skips_non_text_blocks(self):
content = [
{"type": "image_url", "image_url": {"url": "http://img.png"}},
{"type": "text", "text": "actual text"},
]
assert _extract_text(content) == "actual text"
def test_empty_list(self):
assert _extract_text([]) == ""
def test_list_no_text_blocks(self):
assert _extract_text([{"type": "image_url", "image_url": {}}]) == ""
def test_non_str_non_list(self):
assert _extract_text(42) == "42"
# ---------------------------------------------------------------------------
# format_conversation_for_update — handles mixed list content
# ---------------------------------------------------------------------------
class TestFormatConversationForUpdate:
def test_plain_string_messages(self):
human_msg = MagicMock()
human_msg.type = "human"
human_msg.content = "What is Python?"
ai_msg = MagicMock()
ai_msg.type = "ai"
ai_msg.content = "Python is a programming language."
result = format_conversation_for_update([human_msg, ai_msg])
assert "User: What is Python?" in result
assert "Assistant: Python is a programming language." in result
def test_list_content_with_plain_strings(self):
"""Plain strings in list content should not be lost."""
msg = MagicMock()
msg.type = "human"
msg.content = ["raw user text", {"type": "text", "text": "structured text"}]
result = format_conversation_for_update([msg])
assert "raw user text" in result
assert "structured text" in result
# ---------------------------------------------------------------------------
# update_memory — structured LLM response handling
# ---------------------------------------------------------------------------
class TestUpdateMemoryStructuredResponse:
"""update_memory should handle LLM responses returned as list content blocks."""
def _make_mock_model(self, content):
model = MagicMock()
response = MagicMock()
response.content = content
model.invoke.return_value = response
return model
def test_string_response_parses(self):
updater = MemoryUpdater()
valid_json = '{"user": {}, "history": {}, "newFacts": [], "factsToRemove": []}'
with (
patch.object(updater, "_get_model", return_value=self._make_mock_model(valid_json)),
patch("deerflow.agents.memory.updater.get_memory_config", return_value=_memory_config(enabled=True)),
patch("deerflow.agents.memory.updater.get_memory_data", return_value=_make_memory()),
patch("deerflow.agents.memory.updater._save_memory_to_file", return_value=True),
):
msg = MagicMock()
msg.type = "human"
msg.content = "Hello"
ai_msg = MagicMock()
ai_msg.type = "ai"
ai_msg.content = "Hi there"
ai_msg.tool_calls = []
result = updater.update_memory([msg, ai_msg])
assert result is True
def test_list_content_response_parses(self):
"""LLM response as list-of-blocks should be extracted, not repr'd."""
updater = MemoryUpdater()
valid_json = '{"user": {}, "history": {}, "newFacts": [], "factsToRemove": []}'
list_content = [{"type": "text", "text": valid_json}]
with (
patch.object(updater, "_get_model", return_value=self._make_mock_model(list_content)),
patch("deerflow.agents.memory.updater.get_memory_config", return_value=_memory_config(enabled=True)),
patch("deerflow.agents.memory.updater.get_memory_data", return_value=_make_memory()),
patch("deerflow.agents.memory.updater._save_memory_to_file", return_value=True),
):
msg = MagicMock()
msg.type = "human"
msg.content = "Hello"
ai_msg = MagicMock()
ai_msg.type = "ai"
ai_msg.content = "Hi"
ai_msg.tool_calls = []
result = updater.update_memory([msg, ai_msg])
assert result is True
================================================
FILE: backend/tests/test_memory_upload_filtering.py
================================================
"""Tests for upload-event filtering in the memory pipeline.
Covers two functions introduced to prevent ephemeral file-upload context from
persisting in long-term memory:
- _filter_messages_for_memory (memory_middleware)
- _strip_upload_mentions_from_memory (updater)
"""
from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
from deerflow.agents.memory.updater import _strip_upload_mentions_from_memory
from deerflow.agents.middlewares.memory_middleware import _filter_messages_for_memory
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
_UPLOAD_BLOCK = "\nThe following files have been uploaded and are available for use:\n\n- filename: secret.txt\n path: /mnt/user-data/uploads/abc123/secret.txt\n size: 42 bytes\n"
def _human(text: str) -> HumanMessage:
return HumanMessage(content=text)
def _ai(text: str, tool_calls=None) -> AIMessage:
msg = AIMessage(content=text)
if tool_calls:
msg.tool_calls = tool_calls
return msg
# ===========================================================================
# _filter_messages_for_memory
# ===========================================================================
class TestFilterMessagesForMemory:
# --- upload-only turns are excluded ---
def test_upload_only_turn_is_excluded(self):
"""A human turn containing only (no real question)
and its paired AI response must both be dropped."""
msgs = [
_human(_UPLOAD_BLOCK),
_ai("I have read the file. It says: Hello."),
]
result = _filter_messages_for_memory(msgs)
assert result == []
def test_upload_with_real_question_preserves_question(self):
"""When the user asks a question alongside an upload, the question text
must reach the memory queue (upload block stripped, AI response kept)."""
combined = _UPLOAD_BLOCK + "\n\nWhat does this file contain?"
msgs = [
_human(combined),
_ai("The file contains: Hello DeerFlow."),
]
result = _filter_messages_for_memory(msgs)
assert len(result) == 2
human_result = result[0]
assert "" not in human_result.content
assert "What does this file contain?" in human_result.content
assert result[1].content == "The file contains: Hello DeerFlow."
# --- non-upload turns pass through unchanged ---
def test_plain_conversation_passes_through(self):
msgs = [
_human("What is the capital of France?"),
_ai("The capital of France is Paris."),
]
result = _filter_messages_for_memory(msgs)
assert len(result) == 2
assert result[0].content == "What is the capital of France?"
assert result[1].content == "The capital of France is Paris."
def test_tool_messages_are_excluded(self):
"""Intermediate tool messages must never reach memory."""
msgs = [
_human("Search for something"),
_ai("Calling search tool", tool_calls=[{"name": "search", "id": "1", "args": {}}]),
ToolMessage(content="Search results", tool_call_id="1"),
_ai("Here are the results."),
]
result = _filter_messages_for_memory(msgs)
human_msgs = [m for m in result if m.type == "human"]
ai_msgs = [m for m in result if m.type == "ai"]
assert len(human_msgs) == 1
assert len(ai_msgs) == 1
assert ai_msgs[0].content == "Here are the results."
def test_multi_turn_with_upload_in_middle(self):
"""Only the upload turn is dropped; surrounding non-upload turns survive."""
msgs = [
_human("Hello, how are you?"),
_ai("I'm doing well, thank you!"),
_human(_UPLOAD_BLOCK), # upload-only → dropped
_ai("I read the uploaded file."), # paired AI → dropped
_human("What is 2 + 2?"),
_ai("4"),
]
result = _filter_messages_for_memory(msgs)
human_contents = [m.content for m in result if m.type == "human"]
ai_contents = [m.content for m in result if m.type == "ai"]
assert "Hello, how are you?" in human_contents
assert "What is 2 + 2?" in human_contents
assert _UPLOAD_BLOCK not in human_contents
assert "I'm doing well, thank you!" in ai_contents
assert "4" in ai_contents
# The upload-paired AI response must NOT appear
assert "I read the uploaded file." not in ai_contents
def test_multimodal_content_list_handled(self):
"""Human messages with list-style content (multimodal) are handled."""
msg = HumanMessage(
content=[
{"type": "text", "text": _UPLOAD_BLOCK},
]
)
msgs = [msg, _ai("Done.")]
result = _filter_messages_for_memory(msgs)
assert result == []
def test_file_path_not_in_filtered_content(self):
"""After filtering, no upload file path should appear in any message."""
combined = _UPLOAD_BLOCK + "\n\nSummarise the file please."
msgs = [_human(combined), _ai("It says hello.")]
result = _filter_messages_for_memory(msgs)
all_content = " ".join(m.content for m in result if isinstance(m.content, str))
assert "/mnt/user-data/uploads/" not in all_content
assert "" not in all_content
# ===========================================================================
# _strip_upload_mentions_from_memory
# ===========================================================================
class TestStripUploadMentionsFromMemory:
def _make_memory(self, summary: str, facts: list[dict] | None = None) -> dict:
return {
"user": {"topOfMind": {"summary": summary}},
"history": {"recentMonths": {"summary": ""}},
"facts": facts or [],
}
# --- summaries ---
def test_upload_event_sentence_removed_from_summary(self):
mem = self._make_memory("User is interested in AI. User uploaded a test file for verification purposes. User prefers concise answers.")
result = _strip_upload_mentions_from_memory(mem)
summary = result["user"]["topOfMind"]["summary"]
assert "uploaded a test file" not in summary
assert "User is interested in AI" in summary
assert "User prefers concise answers" in summary
def test_upload_path_sentence_removed_from_summary(self):
mem = self._make_memory("User uses Python. User uploaded file to /mnt/user-data/uploads/tid/data.csv. User likes clean code.")
result = _strip_upload_mentions_from_memory(mem)
summary = result["user"]["topOfMind"]["summary"]
assert "/mnt/user-data/uploads/" not in summary
assert "User uses Python" in summary
def test_legitimate_csv_mention_is_preserved(self):
"""'User works with CSV files' must NOT be deleted — it's not an upload event."""
mem = self._make_memory("User regularly works with CSV files for data analysis.")
result = _strip_upload_mentions_from_memory(mem)
assert "CSV files" in result["user"]["topOfMind"]["summary"]
def test_pdf_export_preference_preserved(self):
"""'Prefers PDF export' is a legitimate preference, not an upload event."""
mem = self._make_memory("User prefers PDF export for reports.")
result = _strip_upload_mentions_from_memory(mem)
assert "PDF export" in result["user"]["topOfMind"]["summary"]
def test_uploading_a_test_file_removed(self):
"""'uploading a test file' (with intervening words) must be caught."""
mem = self._make_memory("User conducted a hands-on test by uploading a test file titled 'test_deerflow_memory_bug.txt'. User is also learning Python.")
result = _strip_upload_mentions_from_memory(mem)
summary = result["user"]["topOfMind"]["summary"]
assert "test_deerflow_memory_bug.txt" not in summary
assert "uploading a test file" not in summary
# --- facts ---
def test_upload_fact_removed_from_facts(self):
facts = [
{"content": "User uploaded a file titled secret.txt", "category": "behavior"},
{"content": "User prefers dark mode", "category": "preference"},
{"content": "User is uploading document attachments regularly", "category": "behavior"},
]
mem = self._make_memory("summary", facts=facts)
result = _strip_upload_mentions_from_memory(mem)
remaining = [f["content"] for f in result["facts"]]
assert "User prefers dark mode" in remaining
assert not any("uploaded a file" in c for c in remaining)
assert not any("uploading document" in c for c in remaining)
def test_non_upload_facts_preserved(self):
facts = [
{"content": "User graduated from Peking University", "category": "context"},
{"content": "User prefers Python over JavaScript", "category": "preference"},
]
mem = self._make_memory("", facts=facts)
result = _strip_upload_mentions_from_memory(mem)
assert len(result["facts"]) == 2
def test_empty_memory_handled_gracefully(self):
mem = {"user": {}, "history": {}, "facts": []}
result = _strip_upload_mentions_from_memory(mem)
assert result == {"user": {}, "history": {}, "facts": []}
================================================
FILE: backend/tests/test_model_config.py
================================================
from deerflow.config.model_config import ModelConfig
def _make_model(**overrides) -> ModelConfig:
return ModelConfig(
name="openai-responses",
display_name="OpenAI Responses",
description=None,
use="langchain_openai:ChatOpenAI",
model="gpt-5",
**overrides,
)
def test_responses_api_fields_are_declared_in_model_schema():
assert "use_responses_api" in ModelConfig.model_fields
assert "output_version" in ModelConfig.model_fields
def test_responses_api_fields_round_trip_in_model_dump():
config = _make_model(
api_key="$OPENAI_API_KEY",
use_responses_api=True,
output_version="responses/v1",
)
dumped = config.model_dump(exclude_none=True)
assert dumped["use_responses_api"] is True
assert dumped["output_version"] == "responses/v1"
================================================
FILE: backend/tests/test_model_factory.py
================================================
"""Tests for deerflow.models.factory.create_chat_model."""
from __future__ import annotations
import pytest
from langchain.chat_models import BaseChatModel
from deerflow.config.app_config import AppConfig
from deerflow.config.model_config import ModelConfig
from deerflow.config.sandbox_config import SandboxConfig
from deerflow.models import factory as factory_module
from deerflow.models import openai_codex_provider as codex_provider_module
# ---------------------------------------------------------------------------
# Helpers
# ---------------------------------------------------------------------------
def _make_app_config(models: list[ModelConfig]) -> AppConfig:
return AppConfig(
models=models,
sandbox=SandboxConfig(use="deerflow.sandbox.local:LocalSandboxProvider"),
)
def _make_model(
name: str = "test-model",
*,
use: str = "langchain_openai:ChatOpenAI",
supports_thinking: bool = False,
supports_reasoning_effort: bool = False,
when_thinking_enabled: dict | None = None,
thinking: dict | None = None,
max_tokens: int | None = None,
) -> ModelConfig:
return ModelConfig(
name=name,
display_name=name,
description=None,
use=use,
model=name,
max_tokens=max_tokens,
supports_thinking=supports_thinking,
supports_reasoning_effort=supports_reasoning_effort,
when_thinking_enabled=when_thinking_enabled,
thinking=thinking,
supports_vision=False,
)
class FakeChatModel(BaseChatModel):
"""Minimal BaseChatModel stub that records the kwargs it was called with."""
captured_kwargs: dict = {}
def __init__(self, **kwargs):
# Store kwargs before pydantic processes them
FakeChatModel.captured_kwargs = dict(kwargs)
super().__init__(**kwargs)
@property
def _llm_type(self) -> str:
return "fake"
def _generate(self, *args, **kwargs): # type: ignore[override]
raise NotImplementedError
def _stream(self, *args, **kwargs): # type: ignore[override]
raise NotImplementedError
def _patch_factory(monkeypatch, app_config: AppConfig, model_class=FakeChatModel):
"""Patch get_app_config, resolve_class, and tracing for isolated unit tests."""
monkeypatch.setattr(factory_module, "get_app_config", lambda: app_config)
monkeypatch.setattr(factory_module, "resolve_class", lambda path, base: model_class)
monkeypatch.setattr(factory_module, "is_tracing_enabled", lambda: False)
# ---------------------------------------------------------------------------
# Model selection
# ---------------------------------------------------------------------------
def test_uses_first_model_when_name_is_none(monkeypatch):
cfg = _make_app_config([_make_model("alpha"), _make_model("beta")])
_patch_factory(monkeypatch, cfg)
FakeChatModel.captured_kwargs = {}
factory_module.create_chat_model(name=None)
# resolve_class is called — if we reach here without ValueError, the correct model was used
assert FakeChatModel.captured_kwargs.get("model") == "alpha"
def test_raises_when_model_not_found(monkeypatch):
cfg = _make_app_config([_make_model("only-model")])
monkeypatch.setattr(factory_module, "get_app_config", lambda: cfg)
monkeypatch.setattr(factory_module, "is_tracing_enabled", lambda: False)
with pytest.raises(ValueError, match="ghost-model"):
factory_module.create_chat_model(name="ghost-model")
# ---------------------------------------------------------------------------
# thinking_enabled=True
# ---------------------------------------------------------------------------
def test_thinking_enabled_raises_when_not_supported_but_when_thinking_enabled_is_set(monkeypatch):
"""supports_thinking guard fires only when when_thinking_enabled is configured —
the factory uses that as the signal that the caller explicitly expects thinking to work."""
wte = {"thinking": {"type": "enabled", "budget_tokens": 5000}}
cfg = _make_app_config([_make_model("no-think", supports_thinking=False, when_thinking_enabled=wte)])
_patch_factory(monkeypatch, cfg)
with pytest.raises(ValueError, match="does not support thinking"):
factory_module.create_chat_model(name="no-think", thinking_enabled=True)
def test_thinking_enabled_raises_for_empty_when_thinking_enabled_explicitly_set(monkeypatch):
"""supports_thinking guard fires when when_thinking_enabled is set to an empty dict —
the user explicitly provided the section, so the guard must still fire even though
effective_wte would be falsy."""
cfg = _make_app_config([_make_model("no-think-empty", supports_thinking=False, when_thinking_enabled={})])
_patch_factory(monkeypatch, cfg)
with pytest.raises(ValueError, match="does not support thinking"):
factory_module.create_chat_model(name="no-think-empty", thinking_enabled=True)
def test_thinking_enabled_merges_when_thinking_enabled_settings(monkeypatch):
wte = {"temperature": 1.0, "max_tokens": 16000}
cfg = _make_app_config([_make_model("thinker", supports_thinking=True, when_thinking_enabled=wte)])
_patch_factory(monkeypatch, cfg)
FakeChatModel.captured_kwargs = {}
factory_module.create_chat_model(name="thinker", thinking_enabled=True)
assert FakeChatModel.captured_kwargs.get("temperature") == 1.0
assert FakeChatModel.captured_kwargs.get("max_tokens") == 16000
# ---------------------------------------------------------------------------
# thinking_enabled=False — disable logic
# ---------------------------------------------------------------------------
def test_thinking_disabled_openai_gateway_format(monkeypatch):
"""When thinking is configured via extra_body (OpenAI-compatible gateway),
disabling must inject extra_body.thinking.type=disabled and reasoning_effort=minimal."""
wte = {"extra_body": {"thinking": {"type": "enabled", "budget_tokens": 10000}}}
cfg = _make_app_config(
[
_make_model(
"openai-gw",
supports_thinking=True,
supports_reasoning_effort=True,
when_thinking_enabled=wte,
)
]
)
_patch_factory(monkeypatch, cfg)
captured: dict = {}
class CapturingModel(FakeChatModel):
def __init__(self, **kwargs):
captured.update(kwargs)
BaseChatModel.__init__(self, **kwargs)
monkeypatch.setattr(factory_module, "resolve_class", lambda path, base: CapturingModel)
factory_module.create_chat_model(name="openai-gw", thinking_enabled=False)
assert captured.get("extra_body") == {"thinking": {"type": "disabled"}}
assert captured.get("reasoning_effort") == "minimal"
assert "thinking" not in captured # must NOT set the direct thinking param
def test_thinking_disabled_langchain_anthropic_format(monkeypatch):
"""When thinking is configured as a direct param (langchain_anthropic),
disabling must inject thinking.type=disabled WITHOUT touching extra_body or reasoning_effort."""
wte = {"thinking": {"type": "enabled", "budget_tokens": 8000}}
cfg = _make_app_config(
[
_make_model(
"anthropic-native",
use="langchain_anthropic:ChatAnthropic",
supports_thinking=True,
supports_reasoning_effort=False,
when_thinking_enabled=wte,
)
]
)
_patch_factory(monkeypatch, cfg)
captured: dict = {}
class CapturingModel(FakeChatModel):
def __init__(self, **kwargs):
captured.update(kwargs)
BaseChatModel.__init__(self, **kwargs)
monkeypatch.setattr(factory_module, "resolve_class", lambda path, base: CapturingModel)
factory_module.create_chat_model(name="anthropic-native", thinking_enabled=False)
assert captured.get("thinking") == {"type": "disabled"}
assert "extra_body" not in captured
# reasoning_effort must be cleared (supports_reasoning_effort=False)
assert captured.get("reasoning_effort") is None
def test_thinking_disabled_no_when_thinking_enabled_does_nothing(monkeypatch):
"""If when_thinking_enabled is not set, disabling thinking must not inject any kwargs."""
cfg = _make_app_config([_make_model("plain", supports_thinking=True, when_thinking_enabled=None)])
_patch_factory(monkeypatch, cfg)
captured: dict = {}
class CapturingModel(FakeChatModel):
def __init__(self, **kwargs):
captured.update(kwargs)
BaseChatModel.__init__(self, **kwargs)
monkeypatch.setattr(factory_module, "resolve_class", lambda path, base: CapturingModel)
factory_module.create_chat_model(name="plain", thinking_enabled=False)
assert "extra_body" not in captured
assert "thinking" not in captured
# reasoning_effort not forced (supports_reasoning_effort defaults to False → cleared)
assert captured.get("reasoning_effort") is None
# ---------------------------------------------------------------------------
# reasoning_effort stripping
# ---------------------------------------------------------------------------
def test_reasoning_effort_cleared_when_not_supported(monkeypatch):
cfg = _make_app_config([_make_model("no-effort", supports_reasoning_effort=False)])
_patch_factory(monkeypatch, cfg)
captured: dict = {}
class CapturingModel(FakeChatModel):
def __init__(self, **kwargs):
captured.update(kwargs)
BaseChatModel.__init__(self, **kwargs)
monkeypatch.setattr(factory_module, "resolve_class", lambda path, base: CapturingModel)
factory_module.create_chat_model(name="no-effort", thinking_enabled=False)
assert captured.get("reasoning_effort") is None
def test_reasoning_effort_preserved_when_supported(monkeypatch):
wte = {"extra_body": {"thinking": {"type": "enabled", "budget_tokens": 5000}}}
cfg = _make_app_config(
[
_make_model(
"effort-model",
supports_thinking=True,
supports_reasoning_effort=True,
when_thinking_enabled=wte,
)
]
)
_patch_factory(monkeypatch, cfg)
captured: dict = {}
class CapturingModel(FakeChatModel):
def __init__(self, **kwargs):
captured.update(kwargs)
BaseChatModel.__init__(self, **kwargs)
monkeypatch.setattr(factory_module, "resolve_class", lambda path, base: CapturingModel)
factory_module.create_chat_model(name="effort-model", thinking_enabled=False)
# When supports_reasoning_effort=True, it should NOT be cleared to None
# The disable path sets it to "minimal"; supports_reasoning_effort=True keeps it
assert captured.get("reasoning_effort") == "minimal"
# ---------------------------------------------------------------------------
# thinking shortcut field
# ---------------------------------------------------------------------------
def test_thinking_shortcut_enables_thinking_when_thinking_enabled(monkeypatch):
"""thinking shortcut alone should act as when_thinking_enabled with a `thinking` key."""
thinking_settings = {"type": "enabled", "budget_tokens": 8000}
cfg = _make_app_config(
[
_make_model(
"shortcut-model",
use="langchain_anthropic:ChatAnthropic",
supports_thinking=True,
thinking=thinking_settings,
)
]
)
_patch_factory(monkeypatch, cfg)
captured: dict = {}
class CapturingModel(FakeChatModel):
def __init__(self, **kwargs):
captured.update(kwargs)
BaseChatModel.__init__(self, **kwargs)
monkeypatch.setattr(factory_module, "resolve_class", lambda path, base: CapturingModel)
factory_module.create_chat_model(name="shortcut-model", thinking_enabled=True)
assert captured.get("thinking") == thinking_settings
def test_thinking_shortcut_disables_thinking_when_thinking_disabled(monkeypatch):
"""thinking shortcut should participate in the disable path (langchain_anthropic format)."""
thinking_settings = {"type": "enabled", "budget_tokens": 8000}
cfg = _make_app_config(
[
_make_model(
"shortcut-disable",
use="langchain_anthropic:ChatAnthropic",
supports_thinking=True,
supports_reasoning_effort=False,
thinking=thinking_settings,
)
]
)
_patch_factory(monkeypatch, cfg)
captured: dict = {}
class CapturingModel(FakeChatModel):
def __init__(self, **kwargs):
captured.update(kwargs)
BaseChatModel.__init__(self, **kwargs)
monkeypatch.setattr(factory_module, "resolve_class", lambda path, base: CapturingModel)
factory_module.create_chat_model(name="shortcut-disable", thinking_enabled=False)
assert captured.get("thinking") == {"type": "disabled"}
assert "extra_body" not in captured
def test_thinking_shortcut_merges_with_when_thinking_enabled(monkeypatch):
"""thinking shortcut should be merged into when_thinking_enabled when both are provided."""
thinking_settings = {"type": "enabled", "budget_tokens": 8000}
wte = {"max_tokens": 16000}
cfg = _make_app_config(
[
_make_model(
"merge-model",
use="langchain_anthropic:ChatAnthropic",
supports_thinking=True,
thinking=thinking_settings,
when_thinking_enabled=wte,
)
]
)
_patch_factory(monkeypatch, cfg)
captured: dict = {}
class CapturingModel(FakeChatModel):
def __init__(self, **kwargs):
captured.update(kwargs)
BaseChatModel.__init__(self, **kwargs)
monkeypatch.setattr(factory_module, "resolve_class", lambda path, base: CapturingModel)
factory_module.create_chat_model(name="merge-model", thinking_enabled=True)
# Both the thinking shortcut and when_thinking_enabled settings should be applied
assert captured.get("thinking") == thinking_settings
assert captured.get("max_tokens") == 16000
def test_thinking_shortcut_not_leaked_into_model_when_disabled(monkeypatch):
"""thinking shortcut must not be passed raw to the model constructor (excluded from model_dump)."""
thinking_settings = {"type": "enabled", "budget_tokens": 8000}
cfg = _make_app_config(
[
_make_model(
"no-leak",
use="langchain_anthropic:ChatAnthropic",
supports_thinking=True,
supports_reasoning_effort=False,
thinking=thinking_settings,
)
]
)
_patch_factory(monkeypatch, cfg)
captured: dict = {}
class CapturingModel(FakeChatModel):
def __init__(self, **kwargs):
captured.update(kwargs)
BaseChatModel.__init__(self, **kwargs)
monkeypatch.setattr(factory_module, "resolve_class", lambda path, base: CapturingModel)
factory_module.create_chat_model(name="no-leak", thinking_enabled=False)
# The disable path should have set thinking to disabled (not the raw enabled shortcut)
assert captured.get("thinking") == {"type": "disabled"}
# ---------------------------------------------------------------------------
# OpenAI-compatible providers (MiniMax, Novita, etc.)
# ---------------------------------------------------------------------------
def test_openai_compatible_provider_passes_base_url(monkeypatch):
"""OpenAI-compatible providers like MiniMax should pass base_url through to the model."""
model = ModelConfig(
name="minimax-m2.5",
display_name="MiniMax M2.5",
description=None,
use="langchain_openai:ChatOpenAI",
model="MiniMax-M2.5",
base_url="https://api.minimax.io/v1",
api_key="test-key",
max_tokens=4096,
temperature=1.0,
supports_vision=True,
supports_thinking=False,
)
cfg = _make_app_config([model])
_patch_factory(monkeypatch, cfg)
captured: dict = {}
class CapturingModel(FakeChatModel):
def __init__(self, **kwargs):
captured.update(kwargs)
BaseChatModel.__init__(self, **kwargs)
monkeypatch.setattr(factory_module, "resolve_class", lambda path, base: CapturingModel)
factory_module.create_chat_model(name="minimax-m2.5")
assert captured.get("model") == "MiniMax-M2.5"
assert captured.get("base_url") == "https://api.minimax.io/v1"
assert captured.get("api_key") == "test-key"
assert captured.get("temperature") == 1.0
assert captured.get("max_tokens") == 4096
def test_openai_compatible_provider_multiple_models(monkeypatch):
"""Multiple models from the same OpenAI-compatible provider should coexist."""
m1 = ModelConfig(
name="minimax-m2.5",
display_name="MiniMax M2.5",
description=None,
use="langchain_openai:ChatOpenAI",
model="MiniMax-M2.5",
base_url="https://api.minimax.io/v1",
api_key="test-key",
temperature=1.0,
supports_vision=True,
supports_thinking=False,
)
m2 = ModelConfig(
name="minimax-m2.5-highspeed",
display_name="MiniMax M2.5 Highspeed",
description=None,
use="langchain_openai:ChatOpenAI",
model="MiniMax-M2.5-highspeed",
base_url="https://api.minimax.io/v1",
api_key="test-key",
temperature=1.0,
supports_vision=True,
supports_thinking=False,
)
cfg = _make_app_config([m1, m2])
_patch_factory(monkeypatch, cfg)
captured: dict = {}
class CapturingModel(FakeChatModel):
def __init__(self, **kwargs):
captured.update(kwargs)
BaseChatModel.__init__(self, **kwargs)
monkeypatch.setattr(factory_module, "resolve_class", lambda path, base: CapturingModel)
# Create first model
factory_module.create_chat_model(name="minimax-m2.5")
assert captured.get("model") == "MiniMax-M2.5"
# Create second model
factory_module.create_chat_model(name="minimax-m2.5-highspeed")
assert captured.get("model") == "MiniMax-M2.5-highspeed"
# ---------------------------------------------------------------------------
# Codex provider reasoning_effort mapping
# ---------------------------------------------------------------------------
class FakeCodexChatModel(FakeChatModel):
pass
def test_codex_provider_disables_reasoning_when_thinking_disabled(monkeypatch):
cfg = _make_app_config(
[
_make_model(
"codex",
use="deerflow.models.openai_codex_provider:CodexChatModel",
supports_thinking=True,
supports_reasoning_effort=True,
)
]
)
_patch_factory(monkeypatch, cfg, model_class=FakeCodexChatModel)
monkeypatch.setattr(codex_provider_module, "CodexChatModel", FakeCodexChatModel)
FakeChatModel.captured_kwargs = {}
factory_module.create_chat_model(name="codex", thinking_enabled=False)
assert FakeChatModel.captured_kwargs.get("reasoning_effort") == "none"
def test_codex_provider_preserves_explicit_reasoning_effort(monkeypatch):
cfg = _make_app_config(
[
_make_model(
"codex",
use="deerflow.models.openai_codex_provider:CodexChatModel",
supports_thinking=True,
supports_reasoning_effort=True,
)
]
)
_patch_factory(monkeypatch, cfg, model_class=FakeCodexChatModel)
monkeypatch.setattr(codex_provider_module, "CodexChatModel", FakeCodexChatModel)
FakeChatModel.captured_kwargs = {}
factory_module.create_chat_model(name="codex", thinking_enabled=True, reasoning_effort="high")
assert FakeChatModel.captured_kwargs.get("reasoning_effort") == "high"
def test_codex_provider_defaults_reasoning_effort_to_medium(monkeypatch):
cfg = _make_app_config(
[
_make_model(
"codex",
use="deerflow.models.openai_codex_provider:CodexChatModel",
supports_thinking=True,
supports_reasoning_effort=True,
)
]
)
_patch_factory(monkeypatch, cfg, model_class=FakeCodexChatModel)
monkeypatch.setattr(codex_provider_module, "CodexChatModel", FakeCodexChatModel)
FakeChatModel.captured_kwargs = {}
factory_module.create_chat_model(name="codex", thinking_enabled=True)
assert FakeChatModel.captured_kwargs.get("reasoning_effort") == "medium"
def test_codex_provider_strips_unsupported_max_tokens(monkeypatch):
cfg = _make_app_config(
[
_make_model(
"codex",
use="deerflow.models.openai_codex_provider:CodexChatModel",
supports_thinking=True,
supports_reasoning_effort=True,
max_tokens=4096,
)
]
)
_patch_factory(monkeypatch, cfg, model_class=FakeCodexChatModel)
monkeypatch.setattr(codex_provider_module, "CodexChatModel", FakeCodexChatModel)
FakeChatModel.captured_kwargs = {}
factory_module.create_chat_model(name="codex", thinking_enabled=True)
assert "max_tokens" not in FakeChatModel.captured_kwargs
def test_openai_responses_api_settings_are_passed_to_chatopenai(monkeypatch):
model = ModelConfig(
name="gpt-5-responses",
display_name="GPT-5 Responses",
description=None,
use="langchain_openai:ChatOpenAI",
model="gpt-5",
api_key="test-key",
use_responses_api=True,
output_version="responses/v1",
supports_thinking=False,
supports_vision=True,
)
cfg = _make_app_config([model])
_patch_factory(monkeypatch, cfg)
captured: dict = {}
class CapturingModel(FakeChatModel):
def __init__(self, **kwargs):
captured.update(kwargs)
BaseChatModel.__init__(self, **kwargs)
monkeypatch.setattr(factory_module, "resolve_class", lambda path, base: CapturingModel)
factory_module.create_chat_model(name="gpt-5-responses")
assert captured.get("use_responses_api") is True
assert captured.get("output_version") == "responses/v1"
================================================
FILE: backend/tests/test_patched_minimax.py
================================================
from langchain_core.messages import AIMessageChunk, HumanMessage
from deerflow.models.patched_minimax import PatchedChatMiniMax
def _make_model(**kwargs) -> PatchedChatMiniMax:
return PatchedChatMiniMax(
model="MiniMax-M2.5",
api_key="test-key",
base_url="https://example.com/v1",
**kwargs,
)
def test_get_request_payload_preserves_thinking_and_forces_reasoning_split():
model = _make_model(extra_body={"thinking": {"type": "disabled"}})
payload = model._get_request_payload([HumanMessage(content="hello")])
assert payload["extra_body"]["thinking"]["type"] == "disabled"
assert payload["extra_body"]["reasoning_split"] is True
def test_create_chat_result_maps_reasoning_details_to_reasoning_content():
model = _make_model()
response = {
"choices": [
{
"message": {
"role": "assistant",
"content": "最终答案",
"reasoning_details": [
{
"type": "reasoning.text",
"id": "reasoning-text-1",
"format": "MiniMax-response-v1",
"index": 0,
"text": "先分析问题，再给出答案。",
}
],
},
"finish_reason": "stop",
}
],
"model": "MiniMax-M2.5",
}
result = model._create_chat_result(response)
message = result.generations[0].message
assert message.content == "最终答案"
assert message.additional_kwargs["reasoning_content"] == "先分析问题，再给出答案。"
assert result.generations[0].text == "最终答案"
def test_create_chat_result_strips_inline_think_tags():
model = _make_model()
response = {
"choices": [
{
"message": {
"role": "assistant",
"content": "\n这是思考过程。\n\n\n真正回答。",
},
"finish_reason": "stop",
}
],
"model": "MiniMax-M2.5",
}
result = model._create_chat_result(response)
message = result.generations[0].message
assert message.content == "真正回答。"
assert message.additional_kwargs["reasoning_content"] == "这是思考过程。"
assert result.generations[0].text == "真正回答。"
def test_convert_chunk_to_generation_chunk_preserves_reasoning_deltas():
model = _make_model()
first = model._convert_chunk_to_generation_chunk(
{
"choices": [
{
"delta": {
"role": "assistant",
"content": "",
"reasoning_details": [
{
"type": "reasoning.text",
"id": "reasoning-text-1",
"format": "MiniMax-response-v1",
"index": 0,
"text": "The user",
}
],
}
}
]
},
AIMessageChunk,
{},
)
second = model._convert_chunk_to_generation_chunk(
{
"choices": [
{
"delta": {
"content": "",
"reasoning_details": [
{
"type": "reasoning.text",
"id": "reasoning-text-1",
"format": "MiniMax-response-v1",
"index": 0,
"text": " asks.",
}
],
}
}
]
},
AIMessageChunk,
{},
)
answer = model._convert_chunk_to_generation_chunk(
{
"choices": [
{
"delta": {
"content": "最终答案",
},
"finish_reason": "stop",
}
],
"model": "MiniMax-M2.5",
},
AIMessageChunk,
{},
)
assert first is not None
assert second is not None
assert answer is not None
combined = first.message + second.message + answer.message
assert combined.additional_kwargs["reasoning_content"] == "The user asks."
assert combined.content == "最终答案"
================================================
FILE: backend/tests/test_present_file_tool_core_logic.py
================================================
"""Core behavior tests for present_files path normalization."""
import importlib
from types import SimpleNamespace
present_file_tool_module = importlib.import_module("deerflow.tools.builtins.present_file_tool")
def _make_runtime(outputs_path: str) -> SimpleNamespace:
return SimpleNamespace(
state={"thread_data": {"outputs_path": outputs_path}},
context={"thread_id": "thread-1"},
)
def test_present_files_normalizes_host_outputs_path(tmp_path):
outputs_dir = tmp_path / "threads" / "thread-1" / "user-data" / "outputs"
outputs_dir.mkdir(parents=True)
artifact_path = outputs_dir / "report.md"
artifact_path.write_text("ok")
result = present_file_tool_module.present_file_tool.func(
runtime=_make_runtime(str(outputs_dir)),
filepaths=[str(artifact_path)],
tool_call_id="tc-1",
)
assert result.update["artifacts"] == ["/mnt/user-data/outputs/report.md"]
assert result.update["messages"][0].content == "Successfully presented files"
def test_present_files_keeps_virtual_outputs_path(tmp_path, monkeypatch):
outputs_dir = tmp_path / "threads" / "thread-1" / "user-data" / "outputs"
outputs_dir.mkdir(parents=True)
artifact_path = outputs_dir / "summary.json"
artifact_path.write_text("{}")
monkeypatch.setattr(
present_file_tool_module,
"get_paths",
lambda: SimpleNamespace(resolve_virtual_path=lambda thread_id, path: artifact_path),
)
result = present_file_tool_module.present_file_tool.func(
runtime=_make_runtime(str(outputs_dir)),
filepaths=["/mnt/user-data/outputs/summary.json"],
tool_call_id="tc-2",
)
assert result.update["artifacts"] == ["/mnt/user-data/outputs/summary.json"]
def test_present_files_rejects_paths_outside_outputs(tmp_path):
outputs_dir = tmp_path / "threads" / "thread-1" / "user-data" / "outputs"
workspace_dir = tmp_path / "threads" / "thread-1" / "user-data" / "workspace"
outputs_dir.mkdir(parents=True)
workspace_dir.mkdir(parents=True)
leaked_path = workspace_dir / "notes.txt"
leaked_path.write_text("leak")
result = present_file_tool_module.present_file_tool.func(
runtime=_make_runtime(str(outputs_dir)),
filepaths=[str(leaked_path)],
tool_call_id="tc-3",
)
assert "artifacts" not in result.update
assert result.update["messages"][0].content == f"Error: Only files in /mnt/user-data/outputs can be presented: {leaked_path}"
================================================
FILE: backend/tests/test_provisioner_kubeconfig.py
================================================
"""Regression tests for provisioner kubeconfig path handling."""
from __future__ import annotations
import importlib.util
from pathlib import Path
def _load_provisioner_module():
"""Load docker/provisioner/app.py as an importable test module."""
repo_root = Path(__file__).resolve().parents[2]
module_path = repo_root / "docker" / "provisioner" / "app.py"
spec = importlib.util.spec_from_file_location("provisioner_app_test", module_path)
assert spec is not None
assert spec.loader is not None
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return module
def test_wait_for_kubeconfig_rejects_directory(tmp_path):
"""Directory mount at kubeconfig path should fail fast with clear error."""
provisioner_module = _load_provisioner_module()
kubeconfig_dir = tmp_path / "config_dir"
kubeconfig_dir.mkdir()
provisioner_module.KUBECONFIG_PATH = str(kubeconfig_dir)
try:
provisioner_module._wait_for_kubeconfig(timeout=1)
raise AssertionError("Expected RuntimeError for directory kubeconfig path")
except RuntimeError as exc:
assert "directory" in str(exc)
def test_wait_for_kubeconfig_accepts_file(tmp_path):
"""Regular file mount should pass readiness wait."""
provisioner_module = _load_provisioner_module()
kubeconfig_file = tmp_path / "config"
kubeconfig_file.write_text("apiVersion: v1\n")
provisioner_module.KUBECONFIG_PATH = str(kubeconfig_file)
# Should return immediately without raising.
provisioner_module._wait_for_kubeconfig(timeout=1)
def test_init_k8s_client_rejects_directory_path(tmp_path):
"""KUBECONFIG_PATH that resolves to a directory should be rejected."""
provisioner_module = _load_provisioner_module()
kubeconfig_dir = tmp_path / "config_dir"
kubeconfig_dir.mkdir()
provisioner_module.KUBECONFIG_PATH = str(kubeconfig_dir)
try:
provisioner_module._init_k8s_client()
raise AssertionError("Expected RuntimeError for directory kubeconfig path")
except RuntimeError as exc:
assert "expected a file" in str(exc)
def test_init_k8s_client_uses_file_kubeconfig(tmp_path, monkeypatch):
"""When file exists, provisioner should load kubeconfig file path."""
provisioner_module = _load_provisioner_module()
kubeconfig_file = tmp_path / "config"
kubeconfig_file.write_text("apiVersion: v1\n")
called: dict[str, object] = {}
def fake_load_kube_config(config_file: str):
called["config_file"] = config_file
monkeypatch.setattr(
provisioner_module.k8s_config,
"load_kube_config",
fake_load_kube_config,
)
monkeypatch.setattr(
provisioner_module.k8s_client,
"CoreV1Api",
lambda *args, **kwargs: "core-v1",
)
provisioner_module.KUBECONFIG_PATH = str(kubeconfig_file)
result = provisioner_module._init_k8s_client()
assert called["config_file"] == str(kubeconfig_file)
assert result == "core-v1"
def test_init_k8s_client_falls_back_to_incluster_when_missing(tmp_path, monkeypatch):
"""When kubeconfig file is missing, in-cluster config should be attempted."""
provisioner_module = _load_provisioner_module()
missing_path = tmp_path / "missing-config"
calls: dict[str, int] = {"incluster": 0}
def fake_load_incluster_config():
calls["incluster"] += 1
monkeypatch.setattr(
provisioner_module.k8s_config,
"load_incluster_config",
fake_load_incluster_config,
)
monkeypatch.setattr(
provisioner_module.k8s_client,
"CoreV1Api",
lambda *args, **kwargs: "core-v1",
)
provisioner_module.KUBECONFIG_PATH = str(missing_path)
result = provisioner_module._init_k8s_client()
assert calls["incluster"] == 1
assert result == "core-v1"
================================================
FILE: backend/tests/test_readability.py
================================================
"""Tests for readability extraction fallback behavior."""
import subprocess
import pytest
from deerflow.utils.readability import ReadabilityExtractor
def test_extract_article_falls_back_when_readability_js_fails(monkeypatch):
"""When Node-based readability fails, extraction should fall back to Python mode."""
calls: list[bool] = []
def _fake_simple_json_from_html_string(html: str, use_readability: bool = False):
calls.append(use_readability)
if use_readability:
raise subprocess.CalledProcessError(
returncode=1,
cmd=["node", "ExtractArticle.js"],
stderr="boom",
)
return {"title": "Fallback Title", "content": "
`;
newsCard.addEventListener('click', () => {
alert(`查看新闻: ${newsItem.title}`);
});
newsGrid.appendChild(newsCard);
});
}
// 初始化标签页切换
function initTabs() {
// 赛程标签页
const fixtureTabs = document.querySelectorAll('.fixtures-tabs .tab');
const fixtureItems = document.querySelectorAll('.fixture-item');
fixtureTabs.forEach(tab => {
tab.addEventListener('click', () => {
// 更新活动标签
fixtureTabs.forEach(t => t.classList.remove('active'));
tab.classList.add('active');
const roundFilter = tab.getAttribute('data-round');
// 这里可以根据筛选条件显示不同的赛程
// 由于时间关系，这里只是简单的演示
console.log(`筛选赛程: ${roundFilter}`);
});
});
// 数据统计标签页
const statsTabs = document.querySelectorAll('.stats-tab');
const statsContents = document.querySelectorAll('.stats-tab-content');
statsTabs.forEach(tab => {
tab.addEventListener('click', () => {
const tabId = tab.getAttribute('data-tab');
// 更新活动标签
statsTabs.forEach(t => t.classList.remove('active'));
tab.classList.add('active');
// 显示对应内容
statsContents.forEach(content => {
content.classList.remove('active');
if (content.id === tabId) {
content.classList.add('active');
}
});
});
});
}
// 初始化移动端菜单
function initMobileMenu() {
const menuToggle = document.querySelector('.btn-menu-toggle');
const navMenu = document.querySelector('.nav-menu');
if (menuToggle && navMenu) {
menuToggle.addEventListener('click', () => {
navMenu.classList.toggle('active');
// 更新菜单图标
const icon = menuToggle.querySelector('i');
if (navMenu.classList.contains('active')) {
icon.className = 'fas fa-times';
} else {
icon.className = 'fas fa-bars';
}
});
// 点击菜单外区域关闭菜单
document.addEventListener('click', (e) => {
if (!navMenu.contains(e.target) && !menuToggle.contains(e.target)) {
navMenu.classList.remove('active');
menuToggle.querySelector('i').className = 'fas fa-bars';
}
});
}
}
// 工具函数：加深颜色
function darkenColor(color, percent) {
const num = parseInt(color.replace("#", ""), 16);
const amt = Math.round(2.55 * percent);
const R = (num >> 16) - amt;
const G = (num >> 8 & 0x00FF) - amt;
const B = (num & 0x0000FF) - amt;
return "#" + (
0x1000000 +
(R < 255 ? R < 1 ? 0 : R : 255) * 0x10000 +
(G < 255 ? G < 1 ? 0 : G : 255) * 0x100 +
(B < 255 ? B < 1 ? 0 : B : 255)
).toString(16).slice(1);
}
// 工具函数：格式化日期（简写）
function formatDate(dateString) {
const date = new Date(dateString);
const month = date.getMonth() + 1;
const day = date.getDate();
return `${month}月${day}日`;
}
// 工具函数：根据ID获取球队信息
function getTeamById(teamId) {
return leagueData.teams.find(team => team.id === teamId);
}
// 添加一些交互效果
document.addEventListener('DOMContentLoaded', () => {
// 为所有按钮添加点击效果
const buttons = document.querySelectorAll('.btn');
buttons.forEach(button => {
button.addEventListener('mousedown', () => {
button.style.transform = 'scale(0.95)';
});
button.addEventListener('mouseup', () => {
button.style.transform = '';
});
button.addEventListener('mouseleave', () => {
button.style.transform = '';
});
});
// 为卡片添加悬停效果
const cards = document.querySelectorAll('.team-card, .news-card');
cards.forEach(card => {
card.addEventListener('mouseenter', () => {
card.style.transition = 'transform 0.3s ease, box-shadow 0.3s ease';
});
});
});
================================================
FILE: frontend/public/demo/threads/7cfa5f8f-a2f8-47ad-acbd-da7137baf990/thread.json
================================================
{
"values": {
"messages": [
{
"content": [
{
"type": "text",
"text": "What might be the trends and opportunities in 2026?"
}
],
"additional_kwargs": {},
"response_metadata": {},
"type": "human",
"name": null,
"id": "c6794328-e667-41fd-8067-b7314bcc4838"
},
{
"content": "\nNo files have been uploaded yet.\n",
"additional_kwargs": {},
"response_metadata": {},
"type": "system",
"name": null,
"id": "de592db2-2e03-4939-9835-f5e0e9533856"
},
{
"content": "",
"additional_kwargs": {
"reasoning_content": "The user is asking about trends and opportunities in 2026. This is a broad question that requires research and analysis. I need to search for information about predicted trends for 2026 across various domains (technology, business, society, etc.) and synthesize them into a coherent answer.\n\nFirst, I should search for \"2026 trends predictions\" or \"trends and opportunities 2026\". I'll use web_search to gather current information. Since this is a multi-step task (research, analyze, synthesize), I should create a todo list to track progress. Let's start."
},
"response_metadata": {
"finish_reason": "tool_calls",
"model_name": "deepseek-v3-2-251201",
"service_tier": "default",
"model_provider": "deepseek"
},
"type": "ai",
"name": null,
"id": "lc_run--019bf098-fa7d-7a50-b577-9f9c04da3056",
"tool_calls": [
{
"name": "write_todos",
"args": {
"todos": [
{
"content": "Search for trends and opportunities predictions for 2026",
"status": "in_progress"
},
{
"content": "Analyze search results and identify key trends",
"status": "pending"
},
{
"content": "Synthesize findings into a comprehensive answer",
"status": "pending"
}
]
},
"id": "call_aaoeqd4tzhqggjd1zfxsc4n4",
"type": "tool_call"
}
],
"invalid_tool_calls": [],
"usage_metadata": null
},
{
"content": "Updated todo list to [{'content': 'Search for trends and opportunities predictions for 2026', 'status': 'in_progress'}, {'content': 'Analyze search results and identify key trends', 'status': 'pending'}, {'content': 'Synthesize findings into a comprehensive answer', 'status': 'pending'}]",
"additional_kwargs": {},
"response_metadata": {},
"type": "tool",
"name": "write_todos",
"id": "a759e124-f3b9-40e3-a7b5-70f27ef37609",
"tool_call_id": "call_aaoeqd4tzhqggjd1zfxsc4n4",
"artifact": null,
"status": "success"
},
{
"content": "",
"additional_kwargs": {
"reasoning_content": "Now I need to search for information about trends and opportunities in 2026. Let me start with a web search."
},
"response_metadata": {
"finish_reason": "tool_calls",
"model_name": "deepseek-v3-2-251201",
"service_tier": "default",
"model_provider": "deepseek"
},
"type": "ai",
"name": null,
"id": "lc_run--019bf099-1a1c-74a2-87c7-073da7d7036b",
"tool_calls": [
{
"name": "web_search",
"args": {
"query": "2026 trends opportunities predictions technology business economy"
},
"id": "call_ofcqes7godoqxixap0int5mh",
"type": "tool_call"
}
],
"invalid_tool_calls": [],
"usage_metadata": null
},
{
"content": "[\n {\n \"title\": \"The Most Impactful Business Technology Trends To Watch In 2026\",\n \"url\": \"https://www.forbes.com/councils/forbestechcouncil/2025/12/22/the-most-impactful-business-technology-trends-to-watch-in-2026/\",\n \"snippet\": \"From omnipresent AI to edge computing and rising cyber risks, tech leaders share the innovations set to reshape how businesses operate in\"\n },\n {\n \"title\": \"Five trends to watch in the global economy in 2026 - Atlantic Council\",\n \"url\": \"https://www.atlanticcouncil.org/dispatches/five-trends-to-watch-in-the-global-economy-in-2026/\",\n \"snippet\": \"Five trends to watch in the global economy in 2026 · Stocks of Chinese tech companies surged, far outpacing several major US firms · US and EU\"\n },\n {\n \"title\": \"Predictions 2026: The Race To Trust And Value - Forrester\",\n \"url\": \"https://www.forrester.com/predictions/\",\n \"snippet\": \"The volatility that technology and security leaders grappled with in 2025 will only intensify in 2026. As budgets get tighter, the margin for error shrinks.\"\n },\n {\n \"title\": \"Business and technology trends for 2026 - IBM\",\n \"url\": \"https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/business-trends-2026\",\n \"snippet\": \"Activate five mindshifts to create clarity in crisis—and supercharge your organization’s growth with AI.](https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/2025-ceo). [ Translations available ### Chief AI Officers cut through complexity to create new paths to value Solving the AI ROI puzzle. Learn how the newest member of the C-suite boosts ROI of AI adoption.](https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/chief-ai-officer). [ Translations available ### The 2025 CDO Study: The AI multiplier effect Why do some Chief Data Officers (CDOs) see greater success than others? Learn what sets the CDOs who deliver higher ROI on AI and data investments apart.](https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/2025-cdo). [ ### The enterprise in 2030 Here are five predictions that can help business leaders prepare to win in an AI-first future.](https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/enterprise-2030). [ ### Own the agentic commerce experience Explore how consumer use of AI in shopping is driving the rise of agentic commerce. [ ### Government in the AI era As governments increase AI spending, they are increasingly willing to accept the risks of uncertainty to reap AI’s rewards.](https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/government-in-ai-era).\"\n },\n {\n \"title\": \"Tech Trends 2026 | Deloitte Insights\",\n \"url\": \"https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html\",\n \"snippet\": \"* [Spotlight](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [Tech Trends](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html?icid=disidenav_tech-trends). * [Topics](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [Technology](https://www.deloitte.com/us/en/insights/topics/technology-management.html?icid=disidenav_technology-management). * [More](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [Cross-Industry](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [Economics](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [Consumer](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [Energy & Industrials](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [Financial Services](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [Government & Public Services](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [SPOTLIGHT](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [Tech Trends](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html?icid=disubnav_tech-trends). * [TOPICS](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [MORE](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [Technology](https://www.deloitte.com/us/en/insights/topics/technology-management.html?icid=topics-for-you_click). [](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html). [Copy](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html). [](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html). Token costs have dropped 280-fold in two years;[10](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#endnote-10) yet some enterprises are seeing monthly bills in the tens of millions. Only 1% of IT leaders surveyed by Deloitte reported that no major operating model changes were underway.[11](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#endnote-11)Leaders are shifting from incremental IT management to orchestrating human-agent teams, with CIOs becoming AI evangelists. AT&T’s chief information security officer [captured the challenge](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends/2025/att-ai-cybersecurity-practices.html): “What we’re experiencing today is no different than what we’ve experienced in the past. The only difference with AI is speed and impact.”[12](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#endnote-12)Organizations must secure AI across four domains—data, models, applications, and infrastructure—but they also have the opportunity to use AI-powered defenses to fight threats operating at machine speed. #### [Kelly Raskovich](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html). Article • 13-min read](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends/2026/using-ai-in-cybersecurity.html). [](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html). [](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html).\"\n }\n]",
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"content": "# Five trends to watch in the global economy in 2026\n\nWASHINGTON—US President Donald Trump shocked—and re-shocked—the global economy in 2025, but growth powered through. Thanks to the surge in artificial-intelligence (AI) investment and limited inflation from tariffs, it’s clear that many economists’ doomsday predictions never materialized.\n\nBy the end of 2025, forecasts across Wall Street [predicted](https://www.bloomberg.com/graphics/2026-investment-outlooks/) “all-time highs” for the S&P 500 in 2026. Many investors believe that the AI train won’t slow down, central banks will continue cutting rates, and US tariffs will cool down in a midterm year.\n\nBut markets may be confusing resilience for immunity.\n\nThe reality is that several daunting challenges lie ahead in 2026. Advanced economies are piling up the highest debt levels in a century, with many showing little appetite for fiscal restraint. At the same time, protectionism is surging, not just in the United States but around the world. And lurking in the background is a tenuous détente between the United States and China.\n\nIt’s a dangerous mix, one that markets feel far too comfortable overlooking.\n\nHere are five overlooked trends that will matter for the global economy in 2026.\n\n#### **The real AI bubble**\n\nThroughout 2025, stocks of Chinese tech companies listed in Hong Kong skyrocketed. For example, the Chinese chipmaker Semiconductor Manufacturing International Corporation (known as SMIC) briefly hit gains of [200 percent](https://finance.yahoo.com/news/smic-156-surge-already-anticipated-100846509.html?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAANfGA3zCFG9SoG9jgA9TjNhnenhYX1fr3TaGXd9TDB1IfM8ZLmh0SPfV9zroY6detI-XnZ8nWge8OMPMRg2xVAidDNf5IfOZ71NeyeM87CW1fS8StOKB5yCl7gU6iEvkCG36b_raJH_FePXKrPPrGF-570bkutArsNFKTdoVJI81) in October, compared to 2024. The data shows that the AI boom has become global.\n\nEveryone has been talking about the flip side of an AI surge, including the risk of an AI [bubble](https://www.cnbc.com/2026/01/10/are-we-in-an-ai-bubble-tech-leaders-analysts.html) popping in the United States. But that doesn’t seem to concern Beijing. Alibaba recently announced a $52 billion investment in AI over the next three years. Compare that with a single project led by OpenAI, which is planning to invest $500 billion over the next four years. So the Chinese commitment to AI isn’t all-encompassing for their economy.\n\nOf course, much of the excitement around Chinese tech—and the confidence in its AI development—was driven this past year by the January 2025 release of the [DeepSeek-R1 reasoning model](https://www.atlanticcouncil.org/content-series/inflection-points/deepseek-poses-a-manhattan-project-sized-challenge-for-trump/). Still, there is a limit to how much Beijing can capitalize on rising tech stocks to draw foreign investment back into China. There’s also the fact that 2024 was such a down year that a 2025 rebound was destined to look strong.\n\nIt’s worth looking at AI beyond the United States. If an AI bubble does burst or deflate in 2026, China may be insulated. It bears some similarities to what happened during the global financial crisis, when US and European banks suffered, but China’s banks, because of their lack of reliance on Western finance, emerged relatively unscathed.\n\n#### **The trade tango**\n\nIn 2026, the most important signal on the future of the global trading order will come from abroad. US tariffs will continue to rise with added Section 232 tariffs on critical industries such as semiconductor equipment and critical minerals, but that’s predictable.\n\nBut it will be worth watching whether the other major economic players follow suit or stick with the open system of the past decades. As the United States imports less from China, but Chinese cheap exports continue to flow, will China’s other major export partners add tariffs? The answer is likely yes.\n\nUS imports from China decreased this past year, while imports by the Association of Southeast Asian Nations (ASEAN) and European Union (EU) increased. In ASEAN, trade agreements, rapid growth, and interconnected supply chains mean that imports from China will continue to flow uninhibited except for select critical industries.\n\nBut for the EU, 2025 is the only year when the bloc’s purchases of China’s exports do not closely resemble the United States’ purchases. In previous years, they moved in lockstep. In 2026, expect the EU to respond with higher tariffs on advanced manufacturing products and pharmaceuticals from China, since that would be the only way to protect the EU market.\n\n#### **The debtor’s dilemma**\n\nOne of the biggest issues facing the global economy in 2026 is who owns public debt.\n\nIn the aftermaths of the global financial crisis and the COVID-19 pandemic, the global economy needed a hero. Central banks swooped in to save the day and bought up public debt. Now, central banks are “unwinding,” or selling public debt, and resetting their balance sheets. While the US Federal Reserve and the Bank of England have indicated their intention to slow down the process, other big players, such as the Bank of Japan and the European Central Bank, are going to keep pushing forward with the unwinding in 2026. This begs the question: If central banks are not buying bonds, who will?\n\nThe answer is private investors.The shift will translate into yields higher than anyone, including Trump and US Treasury Secretary Scott Bessent, want. Ultimately, it is Treasury yields, rather than the Federal Reserve’s policy rate, that dictate the interest on mortgages. So while all eyes will be on the next Federal Reserve chair’s rate-cut plans, look instead at how the new chair—as well as counterparts in Europe, the United Kingdom, and Japan—handles the balance sheet.\n\n#### **Wallet wars**\n\nBy mid-2026, nearly three-quarters of the Group of Twenty (G20) will have tokenized cross-border payment systems, providing a new way to move money between countries using digital tokens. Currently, when you send money internationally, it can go through multiple banks, with each taking a cut and adding delays. With tokenized rails, money is converted into digital tokens (like digital certificates representing real dollars or euros) that can move across borders much faster on modern digital networks.\n\nAs the map below shows, the fastest movers are outside the North Atlantic: China and India are going live with their systems, while Brazil, Russia, Australia, and others are building or testing tokenized cross-border rails.\n\nThat timing collides with the United States taking over the G20 presidency and attempting to refresh a set of technical objectives known among wonks as the “cross-border payments roadmap.” But instead of converging on a faster, shared system, finance ministers are now staring at a patchwork of competing networks—each tied to different currencies and political blocs.\n\nThink of it like the 5G wars, in which the United States pushed to restrict Huawei’s expansion. But this one is coming for wallets instead of phones.\n\nFor China and the BRICS group of countries in particular, these cross-border payments platforms could also lend a hand in their de-dollarization strategies: new rails for trade, energy payments, and remittances that do not have to run through dollar-based correspondent banking. This could further erode the dollar’s [international dominance](https://www.atlanticcouncil.org/programs/geoeconomics-center/dollar-dominance-monitor/).\n\nThe question facing the US Treasury and its G20 partners is whether they can still set common rules for this emerging architecture—or whether they will instead be forced to respond to fragmented alternatives, where non-dollar systems are already ahead of the game.\n\n#### **Big spenders**\n\nFrom Trump’s proposal to send two-thousand-dollar [checks](https://www.cnbc.com/2026/01/08/stimulus-check-trump-tariffs-2000.html) to US citizens (thanks to tariff revenue) to Germany’s aim to ramp up defense spending, major economies across the G20 have big plans for additional stimulus in 2026. That’s the case even though debt levels are already at record highs. Many countries are putting off the tough decisions until at least 2027.\n\n\n\nThis chart shows G20 countries with stimulus plans, comparing their projected gross domestic product (GDP) growth rates for 2026 with their estimated fiscal deficits as a percentage of GDP. It’s a rough metric, but it gives a sense of how countries are thinking about spending relative to growth and debt in the year ahead. Countries below the line are planning to loosen fiscal taps.\n\nOf course, not all stimulus plans are created equal. Ottawa, for example, is spending more on defense and investments aimed at improving the competitiveness of the Canadian economy, while keeping its estimated fiscal deficit at around 1 percentage point of projected 2026 GDP growth. US growth isn’t bad, coming in at a little over 2 percent, but the government plans to run a fiscal deficit of at least 5.5 percent. Russia is attempting to prop up a wartime economy, while China is pursuing ambitious industrial policies and pushing off its local debt problems. And on China, while the chart above shows International Monetary Fund and other official estimates for China’s GDP growth, some economists, including ones from Rhodium Group, [argue](https://rhg.com/wp-content/uploads/2025/12/Chinas-Economy-Rightsizing-2025-Looking-Ahead-to-2026.pdf) that China’s real GDP growth could be as low as 2.5 percent for 2026, which would push China below the line displayed.\n\nWithin this group, emerging economies are experiencing stronger growth and may have more room to run deficits next year. For advanced economies, that spending tradeoff is much harder to justify.\n\nWhen Trump captured Nicolás Maduro on the first Saturday of the year, there was speculation that when markets opened the following Monday, they might react negatively given a possible geopolitical shock or positively in anticipation that new oil would be coming online. But markets were muted, and they took the news in stride. That has been the modus operandi of markets ever since Trump took office—trying to see past the immediate news and ask what actually matters for economic growth. In 2025, that strategy paid off. But 2026 may look very different.",
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"content": "[\n {\n \"title\": \"Top Technology Trends to Watch in 2026: AI, Quantum Computing ...\",\n \"url\": \"https://medium.com/@mubashir_ejaz/top-technology-trends-to-watch-in-2026-ai-quantum-computing-and-the-future-of-software-1c755ea06983\",\n \"snippet\": \"# Top Technology Trends to Watch in 2026: AI, Quantum Computing, and the Future of Software Engineering. From AI-powered workplaces to quantum computing breakthroughs, the landscape of software engineering and tech innovation is shifting dramatically. For anyone looking to build a career in tech, understanding how to collaborate effectively with AI tools is a key skill in 2026. In 2026, quantum computing is expected to move toward practical applications in fields like cryptography, materials science, and AI optimization. For developers, staying informed about quantum computing trends could offer significant advantages in emerging tech domains. The rise of AI and quantum computing is creating unprecedented demand for computing power. Whether you’re a software engineer, a data analyst, or a tech entrepreneur, keeping pace with AI tools, robotics, quantum computing, and cloud infrastructure is essential. The key takeaway for 2026: **embrace emerging technologies, continually upskill, and collaborate effectively with AI**.\"\n },\n {\n \"title\": \"2026 Technology Innovation Trends: AI Agents, Humanoid Robots ...\",\n \"url\": \"https://theinnovationmode.com/the-innovation-blog/2026-innovation-trends\",\n \"snippet\": \"Our AI Advisory services help organizations move from AI experimentation to production deployment—from use case identification to implementation roadmaps.→ [Learn more](https://theinnovationmode.com/chief-innovation-officer-as-a-service). [Spatial computing](https://theinnovationmode.com/the-innovation-blog/innovation-in-the-era-of-artificial-intelligence) —the blending of physical and digital worlds—has entered a new phase as a mature technology that can solve real-world problems. [Technology innovation takes many forms](https://theinnovationmode.com/the-innovation-blog/innovation-in-the-era-of-artificial-intelligence)—novel algorithms and data processing models; new hardware components; improved interfaces; and higher-level innovations in processes, [business models, product development and monetization approaches.](https://theinnovationmode.com/the-innovation-blog/the-mvp-minimum-viable-product-explained). [George Krasadakis](https://www.theinnovationmode.com/george-krasadakis) is an Innovation & AI Advisor with 25+ years of experience and 20+ patents in Artificial Intelligence. George is the author of [The Innovation Mode](https://www.theinnovationmode.com/innovation-mode-ai-book-second-edition-2) (2nd edition, January 2026), creator of the 60 Leaders series on [Innovation](https://www.theinnovationmode.com/60-leaders-on-innovation)and [AI](https://www.theinnovationmode.com/60-leaders-on-artificial-intelligence), and founder of [ainna.ai — the Agentic AI platform for product opportunity discovery.](https://ainna.ai/). [Previous Previous Innovation Mode 2.0: The Chief Innovation Officer's Blueprint for the Agentic AI Era ------------------------------------------------------------------------------------](https://theinnovationmode.com/the-innovation-blog/innovation-mode-jan-2026-book-launch)[Next Next Why Corporate Innovation (very often) Fails: The Complete Picture. [Innovation in the era of **AI**](https://www.theinnovationmode.com/the-innovation-blog/innovation-in-the-era-of-artificial-intelligence).\"\n },\n {\n \"title\": \"Top Technology Trends for 2026 - DeAngelis Review\",\n \"url\": \"https://www.deangelisreview.com/blog/top-technology-trends-for-2026\",\n \"snippet\": \"Analysts from Info-Tech Research Group explain, “The world is hurtling toward an era of autonomous super-intelligence, against a backdrop of global volatility and AI-driven uncertainty.”[3] Traction Technology’s Alison Ipswich writes, “The generative AI wave continues to expand, with large language models (LLMs), multimodal systems, and fine-tuned foundation models becoming deeply embedded in enterprise operations.”[4]. Deloitte executives Kelly Raskovich and Bill Briggs, agree that AI will continue to be the big story in 2026; however, they also note, “Eight adjacent ‘signals’ also warrant monitoring.”[10] Those adjacent signals include: “Whether foundational AI models may be plateauing; the impact of synthetic data on models; developments in neuromorphic computing; emerging edge AI use cases; the growth in AI wearables; opportunities for biometric authentication; the privacy impact of AI agents; and the emergence of generative engine optimization.” They conclude, “Some of these signals may mature into dominant forces and others may fade, but all reflect the same underlying message: The pace of technological change has fundamentally shifted, and the organizations that recognize these patterns early will have time to adapt.” These are exciting times.\"\n },\n {\n \"title\": \"Tech Trends 2026: 5 forces shaping the future - Globant Reports\",\n \"url\": \"https://reports.globant.com/en/trends/tech-trends-report-2026/\",\n \"snippet\": \"By 2026, 18% of global quantum algorithm revenues will come from AI applications, marking a quantum-AI convergence.\"\n },\n {\n \"title\": \"Tech Trends 2026 | Deloitte Insights\",\n \"url\": \"https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html\",\n \"snippet\": \"* [Spotlight](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [Tech Trends](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html?icid=disidenav_tech-trends). * [Topics](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [Technology](https://www.deloitte.com/us/en/insights/topics/technology-management.html?icid=disidenav_technology-management). * [More](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [Cross-Industry](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [Economics](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [Consumer](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [Energy & Industrials](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [Financial Services](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [Government & Public Services](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [SPOTLIGHT](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [Tech Trends](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html?icid=disubnav_tech-trends). * [TOPICS](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [MORE](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#). * [Technology](https://www.deloitte.com/us/en/insights/topics/technology-management.html?icid=topics-for-you_click). [](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html). [Copy](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html). [](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html). Token costs have dropped 280-fold in two years;[10](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#endnote-10) yet some enterprises are seeing monthly bills in the tens of millions. Only 1% of IT leaders surveyed by Deloitte reported that no major operating model changes were underway.[11](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#endnote-11)Leaders are shifting from incremental IT management to orchestrating human-agent teams, with CIOs becoming AI evangelists. AT&T’s chief information security officer [captured the challenge](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends/2025/att-ai-cybersecurity-practices.html): “What we’re experiencing today is no different than what we’ve experienced in the past. The only difference with AI is speed and impact.”[12](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html#endnote-12)Organizations must secure AI across four domains—data, models, applications, and infrastructure—but they also have the opportunity to use AI-powered defenses to fight threats operating at machine speed. #### [Kelly Raskovich](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html). Article • 13-min read](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends/2026/using-ai-in-cybersecurity.html). [](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html). [](https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html).\"\n }\n]",
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"content": "[\n {\n \"title\": \"Startup Industry Trends in 2026 - Qubit Capital\",\n \"url\": \"https://qubit.capital/blog/startup-industry-trends\",\n \"snippet\": \"# Startup Industry Trends in 2026. Startup industry trends include AI, fintech, sustainability, and decentralized models. Analyzing the competitive landscape allows businesses to anticipate market shifts and align their strategies with emerging trends. Decentralized finance (DeFi) and fintech are major drivers of startup industry trends, transforming the financial landscape with innovative alternatives to traditional systems. Businesses can use customer segmentation strategies to develop user-centric solutions that adapt to evolving industry trends. Startup industry trends should inform every stage of your business plan, ensuring your strategy remains relevant and competitive. By understanding market trends, identifying competitive advantages, and aligning resources effectively, startups can position themselves for long-term success. Startups can identify emerging trends in business by using market research tools, monitoring technological advancements, and analyzing consumer behavior data regularly for strategic insights. A systematic industry analysis helps startups understand current industry trends, assess risks, and uncover opportunities. Startups should assess market size, current growth trends, and consumer demands.\"\n },\n {\n \"title\": \"5 High-Growth Markets That Could Make You Rich in 2026\",\n \"url\": \"https://www.entrepreneur.com/starting-a-business/5-high-growth-markets-that-could-make-you-rich-in-2026/499668\",\n \"snippet\": \"* Five fast-moving markets that offer real potential in 2026 include plant-based foods, digital-first real estate, digital fashion, preventative health and climate technology. At the same time, Bloomberg Intelligence estimates that the global plant-based foods market could reach $162 billion by 2030. Opendoor helped introduce this model at scale and has handled billions of dollars in home transactions, proving that demand for faster digital solutions is real. The piece explained how emerging digital platforms often become launchpads for new business categories. This shift is creating entirely new categories in home energy and climate technology. They will come from rapidly evolving markets like plant-based foods, digital-first real estate, digital fashion, preventive health and climate technology. Entrepreneurs who move early and solve real problems will be in the best position to build meaningful companies in these new markets. * Five fast-moving markets that offer real potential in 2026 include plant-based foods, digital-first real estate, digital fashion, preventative health and climate technology.\"\n },\n {\n \"title\": \"Top 10 Entrepreneurship Trends Shaping 2026\",\n \"url\": \"https://www.cesarritzcolleges.edu/en/news/entrepreneurship-trends/\",\n \"snippet\": \"Break down the biggest entrepreneurship trends of 2026, including AI, e-commerce, lean teams, trust-building, and wellness markets, plus tips to stay ahead.\"\n },\n {\n \"title\": \"8 Global Venture Capital Trends to Watch in 2026 - Endeavor.org\",\n \"url\": \"https://endeavor.org/stories/global-venture-capital-trends-2026/\",\n \"snippet\": \"Stablecoins in Lagos. Robots in Bucharest. New pathways to liquidity in Riyadh. The next great wave of venture capital is rising Elsewhere.\"\n },\n {\n \"title\": \"26 Startup Ideas for 2026 (That Will Actually Work) - Startup.Club\",\n \"url\": \"https://startup.club/26-startup-ideas-for-2026/\",\n \"snippet\": \"1) AI Consultant for Businesses · 2) AI Agent Builder for One Industry · 3) Skilled Trades · 4) Smart Home Specialist · 5) Office Internet and\"\n }\n]",
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"content": "# The AI advantage dilemma: Security risks and opportunities that lie ahead\n\nEscalating the AI arms race\n---------------------------\n\nAI introduces new vulnerabilities, but it also provides powerful defensive capabilities. Leading organizations are exploring how AI can help them operate at machine speed and adapt to evolving threats in real time. AI-powered cybersecurity solutions help identify patterns humans miss, monitor the entire landscape, speed up threat response, anticipate attacker moves, and automate repetitive tasks. These capabilities are changing how organizations approach cyber risk management.\n\n### Advanced AI-native defense strategies\n\nOne area where cyber teams are taking advantage of AI is red teaming. This involves rigorous stress testing and challenging of AI systems by simulating adversarial attacks to identify vulnerabilities and weaknesses before adversaries can exploit them. This proactive approach helps organizations understand their AI systems’ failure modes and security boundaries.\n\nBrazilian financial services firm Itau Unibanco has recruited agents for its red-teaming exercises. It employs a sophisticated approach in which human experts and AI test agents are deployed across the company. These “red agents” use an iterative process to identify and mitigate risks such as ethics, bias, and inappropriate content.\n\n“Being a regulated industry, trust is our No. 1 concern,” says Roberto Frossard, head of emerging technologies at Itau Unibanco. “So that’s one of the things we spent a lot of time on—testing, retesting, and trying to simulate different ways to break the models.”[6](#endnote-6)\n\nAI is also playing a role in adversarial training. This machine learning technique trains models on adversarial examples—inputs designed to fool or attack the model—helping them recognize and resist manipulation attempts and making the systems more robust against attacks.\n\n### Governance, risk, and compliance evolution\n\nEnterprises using AI face new compliance requirements, particularly in health care and financial services, where they often need to explain the decision-making process.[7](#endnote-7) While this process is typically difficult to decipher, certain strategies can help ensure that AI deployments are compliant.\n\nSome organizations are reassessing who oversees AI deployment. While boards of directors traditionally manage this area, there’s a growing trend to assign responsibility to the audit committee, which is well-positioned to continually review and assess AI-related activities.[8](#endnote-8)\n\nGoverning cross-border AI implementations will remain important. The situation may call for data sovereignty efforts to ensure that data is handled locally in accordance with appropriate rules, as discussed in “[The AI infrastructure reckoning](/us/en/insights/topics/technology-management/tech-trends/2026/ai-infrastructure-compute-strategy.html).”\n\n### Advanced agent governance\n\nAgents operate with a high degree of autonomy by design. With agents proliferating across the organization, businesses will need sophisticated agent monitoring to analyze, in real time, agents’ decision-making patterns and communication between agents, and to automatically detect unusual agent behavior beyond basic activity logging. This monitoring enables security teams to identify compromised or misbehaving agents before they cause significant damage.\n\nDynamic privilege management is one aspect of agent governance. This approach allows teams to manage hundreds or even thousands of agents per user while maintaining security boundaries. Privilege management policies should balance agent autonomy with security requirements, adjusting privileges based on context and behavior.\n\nGovernance policies should incorporate life cycle management that controls agent creation, modification, deactivation, and succession planning—analogous to HR management for human employees but adapted for digital workers, as covered in [“The agentic reality check.”](/us/en/insights/topics/technology-management/tech-trends/2026/agentic-ai-strategy.html) This can help limit the problem of orphaned agents, bots that retain access to key systems even after they’ve been offboarded.\n\nAs AI agents become empowered to spin up their own agents, governance will grow more pressing for enterprises. This capability raises significant questions about managing privacy and security, as agents could become major targets for attackers, particularly if enterprises lack visibility into what these agents are doing and which systems they can access.\n\n### The force multiplier effect\n\nMany cyber organizations are using AI as a force multiplier to overcome complex threats. AI models can be layered on top of current security efforts as enhanced defense mechanisms.\n\nAI can assist with risk scoring and prioritization, third-party risk management, automated policy review and orchestration, cybersecurity maturity assessments, and regulatory compliance support. When deployed in these areas, AI capabilities enable security teams to make faster, more informed decisions about resource allocation.\n\nAI is also playing a role in controls testing and automation, secure code generation, vulnerability scanning capabilities, systems design optimization, and model code review processes. This accelerates the identification and remediation of security weaknesses.\n\n### The need for AI blueprints\n\nCybersecurity team operations weren’t designed for AI, but business efforts to implement AI throughout the organization create an opportunity to rethink current cyber practices. As businesses roll out AI (and agents in particular) across their operations, many are choosing to completely reshape the workforce, operating model, governance model, and technology architecture. While rearchitecting operations to take advantage of AI agents, organizations should build security considerations into foundational design rather than treating them as an afterthought. This proactive approach to heading off emerging cyber risks can prepare enterprises for today’s threats and position them well against dangers that are likely to hit two to five years down the road, which is the subject of the following section.",
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"content": "[\n {\n \"title\": \"2026 Sustainable Investment Trends - LSEG\",\n \"url\": \"https://www.lseg.com/en/ftse-russell/research/2026-sustainable-investment-trends\",\n \"snippet\": \"In this report we highlight some of the key sustainability trends for investors to consider this year: from physical climate risk to the energy transition, AI, Health Care, Food Producers and regional markets. In particular from growing physical climate risk and continued energy transitions. It also focuses on where the sustainability market is evolving, such as the growing impact of physical climate risk and growth of adaptation. Despite the elevated status of sustainability as a geopolitical topic in Europe and North America, we see Asia as the region where the most important things are happening, from the continued growth of China as a clean energy super power, to Japan’s ambitious transition program and India’s increasingly pivotal importance on the future direction of global emissions. We look at key trends set to drive the sustainable investment market in 2026, focusing on climate risk, energy transition, tech, Asia, healthcare, and food.\"\n },\n {\n \"title\": \"S&P Global's Top 10 Sustainability Trends to Watch in 2026\",\n \"url\": \"https://www.spglobal.com/sustainable1/en/insights/2026-sustainability-trends\",\n \"snippet\": \"In S&P Global Energy's base case scenario, global fossil fuel demand is expected to grow less than 1% in 2026 relative to 2025 levels while\"\n },\n {\n \"title\": \"4 trends that will shape ESG in 2026\",\n \"url\": \"https://www.esgdive.com/news/esg-trends-outlook-2026/809129/\",\n \"snippet\": \"Clean energy, sustainable investors brace for second Trump presidency. Experts expect private sector work on ESG issues and the clean energy\"\n },\n {\n \"title\": \"Sustainability and Climate in Focus: Trends to Watch for 2026 - MSCI\",\n \"url\": \"https://www.msci.com/research-and-insights/blog-post/sustainability-and-climate-in-focus-trends-to-watch-for-2026\",\n \"snippet\": \"Markets are moving on their own momentum — rewarding commercially viable transition technologies and repricing physical climate risk as extreme weather increasingly drives financial losses. For private-capital investors, physical climate risk has become too material to ignore — especially in infrastructure, where assets are fixed, long-term and increasingly exposed. To better understand these growing risks, we analyzed infrastructure-related holdings in 1,427 private-capital funds to estimate potential losses from extreme tropical cyclones — events typically classified as “one-in-200-year” occurrences.3 This threshold reflects how investors and insurers assess climate resilience: by testing portfolios against low-probability but high-impact events that drive most of the expected loss.4. Supervisors are signaling that climate risk is financially material — a factor in credit quality, capital strength and market stability rather than a disclosure exercise. We see rising reporting on specific datapoints that have demonstrable links to financial performance, such as detailed climate targets that signal a company’s intent to manage transition risk,14 and workforce turnover data that allows investors to track performance on employee retention.15.\"\n },\n {\n \"title\": \"2026 Sustainability Trends: Moving From Targets To Action - Eco-Act\",\n \"url\": \"https://eco-act.com/blog/2026-corporate-sustainability-trends/\",\n \"snippet\": \"Discover the 8 key trends shaping the sustainability agenda in 2026, and where businesses are focusing to move from targets to action.\"\n }\n]",
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"content": "Based on current research and expert predictions, 2026 appears to be a pivotal year shaped by accelerating technological convergence, economic realignments, and emerging market opportunities. Here's a comprehensive overview of the key trends and opportunities to watch:\n\n## **Technology & Innovation Trends**\n\n**Artificial Intelligence Maturation**: AI transitions from experimentation to production deployment, with several critical developments:\n- **AI Agents Proliferation**: Autonomous AI agents becoming mainstream in enterprise operations, requiring sophisticated governance frameworks\n- **Quantum-AI Convergence**: 18% of global quantum algorithm revenues expected from AI applications by 2026\n- **Edge AI Expansion**: Distributed computing bringing AI capabilities closer to data sources for real-time processing\n- **AI-Powered Cybersecurity**: Organizations leveraging AI for threat detection, red teaming, and automated defense at machine speed\n\n**Emerging Computing Paradigms**:\n- **Quantum Computing Practicality**: Moving beyond research into cryptography, materials science, and optimization applications\n- **Neuromorphic Computing**: Brain-inspired architectures gaining traction for energy-efficient AI processing\n- **Spatial Computing Maturation**: Blending physical and digital worlds for real-world problem solving\n\n## **Economic & Global Trends**\n\n**Financial System Transformation**:\n- **Tokenized Cross-Border Payments**: Nearly 75% of G20 countries expected to have digital token payment systems, challenging dollar dominance\n- **Debt Management Challenges**: Central banks unwinding balance sheets, shifting public debt ownership to private investors\n- **Trade Realignments**: Continued US-China tensions with potential EU tariff responses on advanced manufacturing\n\n**Market Dynamics**:\n- **Potential AI Bubble Concerns**: Chinese tech investments surging while questions emerge about sustainable valuations\n- **Geopolitical Volatility**: Second Trump presidency implications for tariffs, stimulus, and international relations\n- **Fiscal Stimulus Waves**: Major economies planning additional spending despite record debt levels\n\n## **Business & Investment Opportunities**\n\n**High-Growth Markets**:\n1. **Climate Technology**: Home energy solutions, carbon capture, and sustainable infrastructure\n2. **Plant-Based Foods**: Market projected toward $162 billion by 2030\n3. **Digital-First Real Estate**: Platforms transforming property transactions and management\n4. **Preventive Health**: Personalized wellness and early intervention technologies\n5. **Digital Fashion**: Virtual apparel and sustainable textile innovations\n\n**Sustainability Focus Areas**:\n- **Physical Climate Risk Assessment**: Extreme weather driving investment repricing and resilience planning\n- **Clean Energy Superpowers**: China's dominance, Japan's transition program, and India's emissions leadership\n- **ESG Integration**: Moving from disclosure to financially material climate risk management\n\n## **Startup & Entrepreneurship Trends**\n\n**Emerging Business Models**:\n- **AI Consulting Specialization**: Industry-specific AI implementation services\n- **Agentic AI Platforms**: Tools for building and managing autonomous AI agents\n- **Skilled Trades Digitalization**: Technology solutions for traditional trades\n- **Smart Home Ecosystems**: Integrated home automation and energy management\n\n**Venture Capital Shifts**:\n- **Geographic Diversification**: Emerging hubs in Lagos, Bucharest, Riyadh, and other non-traditional locations\n- **Decentralized Finance**: Innovative alternatives to traditional financial systems\n- **Lean Team Operations**: Distributed, AI-augmented organizational structures\n\n## **Critical Challenges & Risks**\n\n**Security & Governance**:\n- **AI Security Vulnerabilities**: New attack vectors requiring comprehensive defense strategies\n- **Agent Governance Complexity**: Managing proliferating autonomous systems with proper oversight\n- **Cross-Border Compliance**: Navigating evolving AI regulations across jurisdictions\n\n**Economic Uncertainties**:\n- **Debt Sustainability**: Record public debt levels with limited fiscal restraint appetite\n- **Trade Fragmentation**: Competing payment systems and protectionist policies\n- **Talent Gaps**: Rapid technological change outpacing workforce skill development\n\n## **Strategic Implications**\n\n**For Businesses**: Success in 2026 will require embracing AI as a core competency while maintaining robust cybersecurity. Companies that can navigate the sustainability transition while leveraging emerging technologies will gain competitive advantages.\n\n**For Investors**: Opportunities exist in climate tech, digital transformation, and Asian markets, but require careful assessment of geopolitical risks and potential market corrections.\n\n**For Individuals**: Continuous upskilling in AI collaboration, quantum computing awareness, and digital literacy will be essential for career resilience.\n\nThe overarching theme for 2026 appears to be **convergence**—technologies converging (AI+quantum+cybersecurity), markets converging (digital+physical), and challenges converging (economic+environmental+geopolitical). Organizations and individuals who can navigate this complexity while maintaining agility will be best positioned to capitalize on emerging opportunities.",
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"reasoning_content": "Now I need to synthesize all the information I've gathered about 2026 trends and opportunities. Based on my research, I can identify several key areas:\n\n1. **Technology Trends**: AI continues to dominate, with quantum computing moving toward practical applications, edge computing growth, AI-powered cybersecurity, and humanoid robots.\n\n2. **Economic Trends**: Global debt challenges, trade tensions, tokenized cross-border payment systems, and potential AI bubble concerns.\n\n3. **Business Opportunities**: Plant-based foods, digital-first real estate, digital fashion, preventive health, climate technology, and sustainability investments.\n\n4. **Startup Trends**: AI consulting, agent builders, skilled trades, smart home specialists, and decentralized finance.\n\nLet me organize this into a comprehensive answer."
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"content": "---\nname: frontend-design\ndescription: Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.\nlicense: Complete terms in LICENSE.txt\n---\n\nThis skill guides creation of distinctive, production-grade frontend interfaces that avoid generic \"AI slop\" aesthetics. Implement real working code with exceptional attention to aesthetic details and creative choices.\n\nThe user provides frontend requirements: a component, page, application, or interface to build. They may include context about the purpose, audience, or technical constraints.\n\n## Output Requirements\n\n**MANDATORY**: The entry HTML file MUST be named `index.html`. This is a strict requirement for all generated frontend projects to ensure compatibility with standard web hosting and deployment workflows.\n\n## Design Thinking\n\nBefore coding, understand the context and commit to a BOLD aesthetic direction:\n- **Purpose**: What problem does this interface solve? Who uses it?\n- **Tone**: Pick an extreme: brutally minimal, maximalist chaos, retro-futuristic, organic/natural, luxury/refined, playful/toy-like, editorial/magazine, brutalist/raw, art deco/geometric, soft/pastel, industrial/utilitarian, etc. There are so many flavors to choose from. Use these for inspiration but design one that is true to the aesthetic direction.\n- **Constraints**: Technical requirements (framework, performance, accessibility).\n- **Differentiation**: What makes this UNFORGETTABLE? What's the one thing someone will remember?\n\n**CRITICAL**: Choose a clear conceptual direction and execute it with precision. Bold maximalism and refined minimalism both work - the key is intentionality, not intensity.\n\nThen implement working code (HTML/CSS/JS, React, Vue, etc.) that is:\n- Production-grade and functional\n- Visually striking and memorable\n- Cohesive with a clear aesthetic point-of-view\n- Meticulously refined in every detail\n\n## Frontend Aesthetics Guidelines\n\nFocus on:\n- **Typography**: Choose fonts that are beautiful, unique, and interesting. Avoid generic fonts like Arial and Inter; opt instead for distinctive choices that elevate the frontend's aesthetics; unexpected, characterful font choices. Pair a distinctive display font with a refined body font.\n- **Color & Theme**: Commit to a cohesive aesthetic. Use CSS variables for consistency. Dominant colors with sharp accents outperform timid, evenly-distributed palettes.\n- **Motion**: Use animations for effects and micro-interactions. Prioritize CSS-only solutions for HTML. Use Motion library for React when available. Focus on high-impact moments: one well-orchestrated page load with staggered reveals (animation-delay) creates more delight than scattered micro-interactions. Use scroll-triggering and hover states that surprise.\n- **Spatial Composition**: Unexpected layouts. Asymmetry. Overlap. Diagonal flow. Grid-breaking elements. Generous negative space OR controlled density.\n- **Backgrounds & Visual Details**: Create atmosphere and depth rather than defaulting to solid colors. Add contextual effects and textures that match the overall aesthetic. Apply creative forms like gradient meshes, noise textures, geometric patterns, layered transparencies, dramatic shadows, decorative borders, custom cursors, and grain overlays.\n\nNEVER use generic AI-generated aesthetics like overused font families (Inter, Roboto, Arial, system fonts), cliched color schemes (particularly purple gradients on white backgrounds), predictable layouts and component patterns, and cookie-cutter design that lacks context-specific character.\n\nInterpret creatively and make unexpected choices that feel genuinely designed for the context. No design should be the same. Vary between light and dark themes, different fonts, different aesthetics. NEVER converge on common choices (Space Grotesk, for example) across generations.\n\n**IMPORTANT**: Match implementation complexity to the aesthetic vision. Maximalist designs need elaborate code with extensive animations and effects. Minimalist or refined designs need restraint, precision, and careful attention to spacing, typography, and subtle details. Elegance comes from executing the vision well.\n\n## Branding Requirement\n\n**MANDATORY**: Every generated frontend interface MUST include a \"Created By Deerflow\" signature. This branding element should be:\n- **Subtle and unobtrusive** - it should NEVER compete with or distract from the main content and functionality\n- **Clickable**: The signature MUST be a clickable link that opens https://deerflow.tech in a new tab (target=\"_blank\")\n- Integrated naturally into the design, feeling like an intentional design element rather than an afterthought\n- Small in size, using muted colors or reduced opacity that blend harmoniously with the overall aesthetic\n\n**IMPORTANT**: The branding should be discoverable but not prominent. Users should notice the main interface first; the signature is a quiet attribution, not a focal point.\n\n**Creative Implementation Ideas** (choose one that best matches your design aesthetic):\n\n1. **Floating Corner Badge**: A small, elegant badge fixed to a corner with subtle hover effects (e.g., gentle glow, slight scale-up, color shift)\n\n2. **Artistic Watermark**: A semi-transparent diagonal text or logo pattern in the background, barely visible but adds texture\n\n3. **Integrated Border Element**: Part of a decorative border or frame around the content - the signature becomes an organic part of the design structure\n\n4. **Animated Signature**: A small signature that elegantly writes itself on page load, or reveals on scroll near the bottom\n\n5. **Contextual Integration**: Blend into the theme - for a retro design, use a vintage stamp look; for minimalist, a single small icon or monogram \"DF\" with tooltip\n\n6. **Cursor Trail or Easter Egg**: A very subtle approach where the branding appears as a micro-interaction (e.g., holding cursor still reveals a tiny signature, or appears in a creative loading state)\n\n7. **Decorative Divider**: Incorporate into a decorative line, separator, or ornamental element on the page\n\n8. **Glassmorphism Card**: A tiny floating glass-effect card in a corner with blur backdrop\n\nExample code patterns:\n```html\n\n✦ Deerflow\n\n\nDF\n\n\n\n```\n\n**Design Principle**: The branding should feel like it belongs - a natural extension of your creative vision, not a mandatory stamp. Match the signature's style (typography, color, animation) to the overall aesthetic direction.\n\nRemember: Claude is capable of extraordinary creative work. Don't hold back, show what can truly be created when thinking outside the box and committing fully to a distinctive vision.\n",
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"reasoning_content": "Now I need to create a modern minimalist website about 2026 trends and opportunities. First, I should think about the design direction. The user wants \"modern and minimalist\" for the topic of future trends. I should aim for a clean, forward-looking aesthetic that reflects the futuristic nature of the content. Let me think about the key elements:\n\n1. **Purpose**: To present information about 2026 trends and opportunities in an engaging, easy-to-digest format\n2. **Tone**: Futuristic, clean, professional, forward-thinking\n3. **Aesthetic Direction**: Minimalist with futuristic touches - think clean lines, subtle gradients, modern typography, maybe some subtle animations\n4. **Differentiation**: The website should feel like it's from the future - maybe using a monospace font for tech sections, a clean sans-serif for body, and some subtle interactive elements\n\nI'll create a single-page website with sections for each major trend category. Let me start by setting up the workspace and creating the files. First, I'll check the workspace."
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"content": "\n\n\n \n \n 2026 Horizons: Trends & Opportunities\n \n \n \n \n \n 📈\">\n\n\n \n \n\n \n \n
\n
\n
Navigating the Future
\n
A comprehensive analysis of trends, opportunities, and challenges shaping 2026
Convergence, complexity, and unprecedented opportunities
\n
\n
\n
\n
2026 represents a pivotal inflection point where accelerating technological convergence meets economic realignment and emerging market opportunities. The year will be defined by the interplay of AI maturation, quantum computing practicality, and sustainable transformation.
\n
Organizations and individuals who can navigate this complexity while maintaining strategic agility will be best positioned to capitalize on emerging opportunities across technology, business, and sustainability sectors.
\n
\n
\n
\n
\n \n
\n
AI Maturation
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Transition from experimentation to production deployment with autonomous agents
\n
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\n \n
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Sustainability Focus
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Climate tech emerges as a dominant investment category with material financial implications
\n
\n
\n
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\n \n\n \n \n
\n
\n
Key Trends Shaping 2026
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Critical developments across technology, economy, and society
\n
\n \n
\n \n
\n
Technology & Innovation
\n
\n
\n
\n AI\n High Impact\n
\n
AI Agents Proliferation
\n
Autonomous AI agents become mainstream in enterprise operations, requiring sophisticated governance frameworks and security considerations.
\n
\n Exponential Growth\n Security Critical\n
\n
\n \n
\n
\n Quantum\n Emerging\n
\n
Quantum-AI Convergence
\n
18% of global quantum algorithm revenues expected from AI applications, marking a significant shift toward practical quantum computing applications.
\n
\n 18% Revenue Share\n Optimization Focus\n
\n
\n \n
\n
\n Security\n Critical\n
\n
AI-Powered Cybersecurity
\n
Organizations leverage AI for threat detection, red teaming, and automated defense at machine speed, creating new security paradigms.
\n
\n Machine Speed\n Proactive Defense\n
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\n \n \n
\n
Economic & Global
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\n
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\n Finance\n Transformative\n
\n
Tokenized Cross-Border Payments
\n
Nearly 75% of G20 countries expected to have digital token payment systems, challenging traditional banking and dollar dominance.
\n
\n 75% G20 Adoption\n Borderless\n
\n
\n \n
\n
\n Trade\n Volatile\n
\n
Trade Realignments
\n
Continued US-China tensions with potential EU tariff responses on advanced manufacturing, reshaping global supply chains.
\n
\n Geopolitical Shift\n Supply Chain Impact\n
\n
\n \n
\n
\n Risk\n Critical\n
\n
Debt Sustainability Challenges
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Record public debt levels with limited fiscal restraint appetite as central banks unwind balance sheets.
\n
\n Record Levels\n Yield Pressure\n
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Emerging Opportunities
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High-growth markets and strategic investment areas
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\n \n
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Climate Technology
\n
Home energy solutions, carbon capture, and sustainable infrastructure with massive growth potential.
\n
\n $162B+\n by 2030\n
\n
\n \n
\n
\n \n
\n
Preventive Health
\n
Personalized wellness, early intervention technologies, and digital health platforms.
\n
\n High Growth\n Post-pandemic focus\n
\n
\n \n
\n
\n \n
\n
AI Consulting
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Industry-specific AI implementation services and agentic AI platform development.
\n
\n Specialized\n Enterprise demand\n
\n
\n \n
\n
\n \n
\n
Plant-Based Foods
\n
Sustainable food alternatives with projected market growth toward $162 billion by 2030.
\n
\n $162B\n Market potential\n
\n
\n
\n \n
\n
\n
Strategic Investment Shift
\n
Venture capital is diversifying geographically with emerging hubs in Lagos, Bucharest, Riyadh, and other non-traditional locations. Decentralized finance continues to innovate alternatives to traditional systems.
\n
\n
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\n 75%\n G20 Digital Payments\n
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\n 18%\n Quantum-AI Revenue\n
\n
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\n \n\n \n \n
\n
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Critical Challenges & Risks
\n
Navigating complexity in an uncertain landscape
\n
\n \n
\n
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\n High Risk\n
AI Security Vulnerabilities
\n
\n
New attack vectors require comprehensive defense strategies as autonomous agents proliferate across organizations.
\n
\n Mitigation:\n Robust governance frameworks and AI-native security protocols\n
\n Mitigation:\n Continuous upskilling programs and AI collaboration training\n
\n
\n \n
\n
\n High Risk\n
Economic Volatility
\n
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Potential AI bubble concerns, trade fragmentation, and competing payment systems creating market uncertainty.
\n
\n Mitigation:\n Diversified portfolios and agile business models\n
\n
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\n \n
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Strategic Implications
\n
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For Businesses
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Success requires embracing AI as a core competency while maintaining robust cybersecurity. Companies that navigate the sustainability transition while leveraging emerging technologies gain competitive advantages.
\n
\n
\n
For Investors
\n
Opportunities exist in climate tech, digital transformation, and Asian markets, but require careful assessment of geopolitical risks and potential market corrections.
\n
\n
\n
For Individuals
\n
Continuous upskilling in AI collaboration, quantum computing awareness, and digital literacy will be essential for career resilience in the evolving landscape.
Convergence, complexity, and unprecedented opportunities
2026 represents a pivotal inflection point where accelerating technological convergence meets economic realignment and emerging market opportunities. The year will be defined by the interplay of AI maturation, quantum computing practicality, and sustainable transformation.
Organizations and individuals who can navigate this complexity while maintaining strategic agility will be best positioned to capitalize on emerging opportunities across technology, business, and sustainability sectors.
AI Maturation
Transition from experimentation to production deployment with autonomous agents
Sustainability Focus
Climate tech emerges as a dominant investment category with material financial implications
Key Trends Shaping 2026
Critical developments across technology, economy, and society
Technology & Innovation
AIHigh Impact
AI Agents Proliferation
Autonomous AI agents become mainstream in enterprise operations, requiring sophisticated governance frameworks and security considerations.
Exponential Growth Security Critical
QuantumEmerging
Quantum-AI Convergence
18% of global quantum algorithm revenues expected from AI applications, marking a significant shift toward practical quantum computing applications.
18% Revenue Share Optimization Focus
SecurityCritical
AI-Powered Cybersecurity
Organizations leverage AI for threat detection, red teaming, and automated defense at machine speed, creating new security paradigms.
Machine Speed Proactive Defense
Economic & Global
FinanceTransformative
Tokenized Cross-Border Payments
Nearly 75% of G20 countries expected to have digital token payment systems, challenging traditional banking and dollar dominance.
75% G20 Adoption Borderless
TradeVolatile
Trade Realignments
Continued US-China tensions with potential EU tariff responses on advanced manufacturing, reshaping global supply chains.
Geopolitical Shift Supply Chain Impact
RiskCritical
Debt Sustainability Challenges
Record public debt levels with limited fiscal restraint appetite as central banks unwind balance sheets.
Record Levels Yield Pressure
Emerging Opportunities
High-growth markets and strategic investment areas
Climate Technology
Home energy solutions, carbon capture, and sustainable infrastructure with massive growth potential.
$162B+by 2030
Preventive Health
Personalized wellness, early intervention technologies, and digital health platforms.
High GrowthPost-pandemic focus
AI Consulting
Industry-specific AI implementation services and agentic AI platform development.
SpecializedEnterprise demand
Plant-Based Foods
Sustainable food alternatives with projected market growth toward $162 billion by 2030.
$162BMarket potential
Strategic Investment Shift
Venture capital is diversifying geographically with emerging hubs in Lagos, Bucharest, Riyadh, and other non-traditional locations. Decentralized finance continues to innovate alternatives to traditional systems.
75%G20 Digital Payments
18%Quantum-AI Revenue
Critical Challenges & Risks
Navigating complexity in an uncertain landscape
High Risk
AI Security Vulnerabilities
New attack vectors require comprehensive defense strategies as autonomous agents proliferate across organizations.
Mitigation:Robust governance frameworks and AI-native security protocols
Mitigation:Continuous upskilling programs and AI collaboration training
High Risk
Economic Volatility
Potential AI bubble concerns, trade fragmentation, and competing payment systems creating market uncertainty.
Mitigation:Diversified portfolios and agile business models
Strategic Implications
For Businesses
Success requires embracing AI as a core competency while maintaining robust cybersecurity. Companies that navigate the sustainability transition while leveraging emerging technologies gain competitive advantages.
For Investors
Opportunities exist in climate tech, digital transformation, and Asian markets, but require careful assessment of geopolitical risks and potential market corrections.
For Individuals
Continuous upskilling in AI collaboration, quantum computing awareness, and digital literacy will be essential for career resilience in the evolving landscape.
================================================
FILE: frontend/public/demo/threads/7cfa5f8f-a2f8-47ad-acbd-da7137baf990/user-data/outputs/script.js
================================================
// 2026 Horizons - Interactive Features
document.addEventListener('DOMContentLoaded', function() {
// Theme Toggle
const themeToggle = document.getElementById('themeToggle');
const themeIcon = themeToggle.querySelector('i');
// Check for saved theme or prefer-color-scheme
const savedTheme = localStorage.getItem('theme');
const prefersDark = window.matchMedia('(prefers-color-scheme: dark)').matches;
if (savedTheme === 'dark' || (!savedTheme && prefersDark)) {
document.documentElement.setAttribute('data-theme', 'dark');
themeIcon.className = 'fas fa-sun';
}
themeToggle.addEventListener('click', function() {
const currentTheme = document.documentElement.getAttribute('data-theme');
if (currentTheme === 'dark') {
document.documentElement.removeAttribute('data-theme');
themeIcon.className = 'fas fa-moon';
localStorage.setItem('theme', 'light');
} else {
document.documentElement.setAttribute('data-theme', 'dark');
themeIcon.className = 'fas fa-sun';
localStorage.setItem('theme', 'dark');
}
});
// Smooth scroll for navigation links
document.querySelectorAll('a[href^="#"]').forEach(anchor => {
anchor.addEventListener('click', function(e) {
e.preventDefault();
const targetId = this.getAttribute('href');
if (targetId === '#') return;
const targetElement = document.querySelector(targetId);
if (targetElement) {
const headerHeight = document.querySelector('.navbar').offsetHeight;
const targetPosition = targetElement.offsetTop - headerHeight - 20;
window.scrollTo({
top: targetPosition,
behavior: 'smooth'
});
}
});
});
// Navbar scroll effect
const navbar = document.querySelector('.navbar');
let lastScrollTop = 0;
window.addEventListener('scroll', function() {
const scrollTop = window.pageYOffset || document.documentElement.scrollTop;
// Hide/show navbar on scroll
if (scrollTop > lastScrollTop && scrollTop > 100) {
navbar.style.transform = 'translateY(-100%)';
} else {
navbar.style.transform = 'translateY(0)';
}
lastScrollTop = scrollTop;
// Add shadow when scrolled
if (scrollTop > 10) {
navbar.style.boxShadow = 'var(--shadow-md)';
} else {
navbar.style.boxShadow = 'none';
}
});
// Animate elements on scroll
const observerOptions = {
threshold: 0.1,
rootMargin: '0px 0px -50px 0px'
};
const observer = new IntersectionObserver(function(entries) {
entries.forEach(entry => {
if (entry.isIntersecting) {
entry.target.classList.add('fade-in');
observer.unobserve(entry.target);
}
});
}, observerOptions);
// Observe elements to animate
document.querySelectorAll('.trend-card, .opportunity-card, .challenge-card, .highlight-card').forEach(el => {
observer.observe(el);
});
// Stats counter animation
const stats = document.querySelectorAll('.stat-number');
const statsObserver = new IntersectionObserver(function(entries) {
entries.forEach(entry => {
if (entry.isIntersecting) {
const stat = entry.target;
const targetValue = parseInt(stat.textContent);
let currentValue = 0;
const increment = targetValue / 50;
const duration = 1500;
const stepTime = Math.floor(duration / 50);
const timer = setInterval(() => {
currentValue += increment;
if (currentValue >= targetValue) {
stat.textContent = targetValue;
clearInterval(timer);
} else {
stat.textContent = Math.floor(currentValue);
}
}, stepTime);
statsObserver.unobserve(stat);
}
});
}, { threshold: 0.5 });
stats.forEach(stat => {
statsObserver.observe(stat);
});
// Hover effects for cards
document.querySelectorAll('.trend-card, .opportunity-card, .challenge-card').forEach(card => {
card.addEventListener('mouseenter', function() {
this.style.zIndex = '10';
});
card.addEventListener('mouseleave', function() {
this.style.zIndex = '1';
});
});
// Current year in footer
const currentYear = new Date().getFullYear();
const yearElement = document.querySelector('.copyright p');
if (yearElement) {
yearElement.textContent = yearElement.textContent.replace('2026', currentYear);
}
// Initialize animations
setTimeout(() => {
document.body.style.opacity = '1';
}, 100);
});
// Add CSS for initial load
const style = document.createElement('style');
style.textContent = `
body {
opacity: 0;
transition: opacity 0.5s ease-in;
}
.fade-in {
animation: fadeIn 0.8s ease-out forwards;
}
@keyframes fadeIn {
from {
opacity: 0;
transform: translateY(20px);
}
to {
opacity: 1;
transform: translateY(0);
}
}
`;
document.head.appendChild(style);
================================================
FILE: frontend/public/demo/threads/7cfa5f8f-a2f8-47ad-acbd-da7137baf990/user-data/outputs/style.css
================================================
/* 2026 Horizons - Modern Minimalist Design */
:root {
/* Light Theme Colors */
--primary-color: #2563eb;
--primary-dark: #1d4ed8;
--secondary-color: #7c3aed;
--accent-color: #0ea5e9;
--bg-primary: #ffffff;
--bg-secondary: #f8fafc;
--bg-tertiary: #f1f5f9;
--text-primary: #0f172a;
--text-secondary: #475569;
--text-tertiary: #64748b;
--border-color: #e2e8f0;
--border-light: #f1f5f9;
--success-color: #10b981;
--warning-color: #f59e0b;
--danger-color: #ef4444;
--info-color: #3b82f6;
--shadow-sm: 0 1px 2px 0 rgba(0, 0, 0, 0.05);
--shadow-md:
0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06);
--shadow-lg:
0 10px 15px -3px rgba(0, 0, 0, 0.1), 0 4px 6px -2px rgba(0, 0, 0, 0.05);
--shadow-xl:
0 20px 25px -5px rgba(0, 0, 0, 0.1), 0 10px 10px -5px rgba(0, 0, 0, 0.04);
--radius-sm: 0.375rem;
--radius-md: 0.5rem;
--radius-lg: 0.75rem;
--radius-xl: 1rem;
--radius-full: 9999px;
--transition-fast: 150ms cubic-bezier(0.4, 0, 0.2, 1);
--transition-normal: 300ms cubic-bezier(0.4, 0, 0.2, 1);
--transition-slow: 500ms cubic-bezier(0.4, 0, 0.2, 1);
--font-sans: "Inter", system-ui, -apple-system, sans-serif;
--font-heading: "Space Grotesk", system-ui, -apple-system, sans-serif;
}
/* Dark Theme */
[data-theme="dark"] {
--primary-color: #3b82f6;
--primary-dark: #2563eb;
--secondary-color: #8b5cf6;
--accent-color: #06b6d4;
--bg-primary: #0f172a;
--bg-secondary: #1e293b;
--bg-tertiary: #334155;
--text-primary: #f8fafc;
--text-secondary: #cbd5e1;
--text-tertiary: #94a3b8;
--border-color: #334155;
--border-light: #1e293b;
--shadow-sm: 0 1px 2px 0 rgba(0, 0, 0, 0.3);
--shadow-md:
0 4px 6px -1px rgba(0, 0, 0, 0.4), 0 2px 4px -1px rgba(0, 0, 0, 0.2);
--shadow-lg:
0 10px 15px -3px rgba(0, 0, 0, 0.4), 0 4px 6px -2px rgba(0, 0, 0, 0.2);
--shadow-xl:
0 20px 25px -5px rgba(0, 0, 0, 0.4), 0 10px 10px -5px rgba(0, 0, 0, 0.2);
}
/* Reset & Base Styles */
* {
margin: 0;
padding: 0;
box-sizing: border-box;
}
html {
scroll-behavior: smooth;
}
body {
font-family: var(--font-sans);
font-size: 16px;
line-height: 1.6;
color: var(--text-primary);
background-color: var(--bg-primary);
transition:
background-color var(--transition-normal),
color var(--transition-normal);
overflow-x: hidden;
}
.container {
width: 100%;
max-width: 1200px;
margin: 0 auto;
padding: 0 1.5rem;
}
/* Typography */
h1,
h2,
h3,
h4 {
font-family: var(--font-heading);
font-weight: 600;
line-height: 1.2;
margin-bottom: 1rem;
}
h1 {
font-size: 3.5rem;
font-weight: 700;
}
h2 {
font-size: 2.5rem;
}
h3 {
font-size: 1.75rem;
}
h4 {
font-size: 1.25rem;
}
p {
margin-bottom: 1rem;
color: var(--text-secondary);
}
a {
color: var(--primary-color);
text-decoration: none;
transition: color var(--transition-fast);
}
a:hover {
color: var(--primary-dark);
}
/* Navigation */
.navbar {
position: fixed;
top: 0;
left: 0;
right: 0;
z-index: 1000;
background-color: var(--bg-primary);
border-bottom: 1px solid var(--border-color);
backdrop-filter: blur(10px);
background-color: rgba(var(--bg-primary-rgb), 0.8);
}
.navbar .container {
display: flex;
justify-content: space-between;
align-items: center;
padding: 1rem 1.5rem;
}
.nav-brand {
display: flex;
align-items: center;
gap: 0.75rem;
}
.brand-icon {
font-size: 1.5rem;
}
.brand-text {
font-family: var(--font-heading);
font-weight: 600;
font-size: 1.25rem;
color: var(--text-primary);
}
.nav-links {
display: flex;
list-style: none;
gap: 2rem;
}
.nav-links a {
color: var(--text-secondary);
font-weight: 500;
position: relative;
padding: 0.5rem 0;
}
.nav-links a:hover {
color: var(--text-primary);
}
.nav-links a::after {
content: "";
position: absolute;
bottom: 0;
left: 0;
width: 0;
height: 2px;
background-color: var(--primary-color);
transition: width var(--transition-normal);
}
.nav-links a:hover::after {
width: 100%;
}
.theme-toggle {
width: 44px;
height: 44px;
border-radius: var(--radius-full);
border: 1px solid var(--border-color);
background-color: var(--bg-secondary);
color: var(--text-secondary);
cursor: pointer;
display: flex;
align-items: center;
justify-content: center;
transition: all var(--transition-fast);
}
.theme-toggle:hover {
background-color: var(--bg-tertiary);
color: var(--text-primary);
transform: rotate(15deg);
}
/* Hero Section */
.hero {
padding: 8rem 0 6rem;
background: linear-gradient(
135deg,
var(--bg-primary) 0%,
var(--bg-secondary) 100%
);
position: relative;
overflow: hidden;
}
.hero .container {
display: grid;
grid-template-columns: 1fr 1fr;
gap: 4rem;
align-items: center;
}
.hero-title {
font-size: 4rem;
font-weight: 700;
margin-bottom: 1.5rem;
background: linear-gradient(
135deg,
var(--primary-color) 0%,
var(--secondary-color) 100%
);
-webkit-background-clip: text;
-webkit-text-fill-color: transparent;
background-clip: text;
}
.hero-subtitle {
font-size: 1.25rem;
color: var(--text-secondary);
margin-bottom: 2rem;
max-width: 90%;
}
.hero-stats {
display: flex;
gap: 2rem;
margin-bottom: 3rem;
}
.stat {
display: flex;
flex-direction: column;
}
.stat-number {
font-family: var(--font-heading);
font-size: 2.5rem;
font-weight: 700;
color: var(--primary-color);
line-height: 1;
}
.stat-label {
font-size: 0.875rem;
color: var(--text-tertiary);
margin-top: 0.5rem;
}
.cta-button {
display: inline-flex;
align-items: center;
gap: 0.75rem;
padding: 1rem 2rem;
background-color: var(--primary-color);
color: white;
border-radius: var(--radius-md);
font-weight: 600;
transition: all var(--transition-fast);
border: none;
cursor: pointer;
}
.cta-button:hover {
background-color: var(--primary-dark);
transform: translateY(-2px);
box-shadow: var(--shadow-lg);
}
.hero-visual {
position: relative;
height: 400px;
}
.visual-element {
position: relative;
width: 100%;
height: 100%;
}
.circle {
position: absolute;
top: 50%;
left: 50%;
transform: translate(-50%, -50%);
width: 200px;
height: 200px;
border-radius: 50%;
border: 2px solid var(--primary-color);
opacity: 0.3;
animation: pulse 4s ease-in-out infinite;
}
.line {
position: absolute;
top: 50%;
left: 50%;
transform: translate(-50%, -50%) rotate(45deg);
width: 300px;
height: 2px;
background: linear-gradient(
90deg,
transparent,
var(--primary-color),
transparent
);
opacity: 0.5;
}
.dot {
position: absolute;
top: 50%;
left: 50%;
transform: translate(-50%, -50%);
width: 12px;
height: 12px;
border-radius: 50%;
background-color: var(--accent-color);
animation: float 6s ease-in-out infinite;
}
@keyframes pulse {
0%,
100% {
transform: translate(-50%, -50%) scale(1);
opacity: 0.3;
}
50% {
transform: translate(-50%, -50%) scale(1.1);
opacity: 0.5;
}
}
@keyframes float {
0%,
100% {
transform: translate(-50%, -50%);
}
50% {
transform: translate(-50%, -55%);
}
}
/* Section Styles */
.section {
padding: 6rem 0;
}
.section-header {
text-align: center;
margin-bottom: 4rem;
}
.section-title {
font-size: 2.75rem;
margin-bottom: 1rem;
}
.section-subtitle {
font-size: 1.125rem;
color: var(--text-secondary);
max-width: 600px;
margin: 0 auto;
}
/* Overview Section */
.overview-content {
display: grid;
grid-template-columns: 1fr 1fr;
gap: 4rem;
align-items: start;
}
.overview-text p {
font-size: 1.125rem;
line-height: 1.8;
margin-bottom: 1.5rem;
}
.overview-highlight {
display: flex;
flex-direction: column;
gap: 2rem;
}
.highlight-card {
padding: 2rem;
background-color: var(--bg-secondary);
border-radius: var(--radius-lg);
border: 1px solid var(--border-color);
transition:
transform var(--transition-normal),
box-shadow var(--transition-normal);
}
.highlight-card:hover {
transform: translateY(-4px);
box-shadow: var(--shadow-lg);
}
.highlight-icon {
width: 60px;
height: 60px;
border-radius: var(--radius-md);
background-color: var(--primary-color);
color: white;
display: flex;
align-items: center;
justify-content: center;
font-size: 1.5rem;
margin-bottom: 1.5rem;
}
.highlight-title {
font-size: 1.5rem;
margin-bottom: 0.75rem;
}
.highlight-text {
color: var(--text-secondary);
font-size: 1rem;
}
/* Trends Section */
.trends-grid {
display: flex;
flex-direction: column;
gap: 4rem;
}
.trend-category {
background-color: var(--bg-secondary);
border-radius: var(--radius-xl);
padding: 3rem;
border: 1px solid var(--border-color);
}
.category-title {
display: flex;
align-items: center;
gap: 0.75rem;
font-size: 1.75rem;
margin-bottom: 2rem;
color: var(--text-primary);
}
.category-title i {
color: var(--primary-color);
}
.trend-cards {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
gap: 2rem;
}
.trend-card {
padding: 2rem;
background-color: var(--bg-primary);
border-radius: var(--radius-lg);
border: 1px solid var(--border-color);
transition: all var(--transition-normal);
}
.trend-card:hover {
transform: translateY(-4px);
box-shadow: var(--shadow-xl);
border-color: var(--primary-color);
}
.trend-header {
display: flex;
justify-content: space-between;
align-items: center;
margin-bottom: 1.5rem;
}
.trend-badge {
padding: 0.375rem 1rem;
border-radius: var(--radius-full);
font-size: 0.75rem;
font-weight: 600;
text-transform: uppercase;
letter-spacing: 0.05em;
}
.trend-badge.tech {
background-color: rgba(59, 130, 246, 0.1);
color: var(--primary-color);
border: 1px solid rgba(59, 130, 246, 0.2);
}
.trend-badge.econ {
background-color: rgba(139, 92, 246, 0.1);
color: var(--secondary-color);
border: 1px solid rgba(139, 92, 246, 0.2);
}
.trend-priority {
font-size: 0.75rem;
font-weight: 600;
padding: 0.25rem 0.75rem;
border-radius: var(--radius-full);
}
.trend-priority.high {
background-color: rgba(239, 68, 68, 0.1);
color: var(--danger-color);
}
.trend-priority.medium {
background-color: rgba(245, 158, 11, 0.1);
color: var(--warning-color);
}
.trend-name {
font-size: 1.5rem;
margin-bottom: 1rem;
color: var(--text-primary);
}
.trend-description {
color: var(--text-secondary);
margin-bottom: 1.5rem;
line-height: 1.7;
}
.trend-metrics {
display: flex;
gap: 1rem;
flex-wrap: wrap;
}
.metric {
display: flex;
align-items: center;
gap: 0.5rem;
font-size: 0.875rem;
color: var(--text-tertiary);
}
.metric i {
color: var(--primary-color);
}
/* Opportunities Section */
.opportunities-grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(250px, 1fr));
gap: 2rem;
margin-bottom: 4rem;
}
.opportunity-card {
padding: 2rem;
background-color: var(--bg-secondary);
border-radius: var(--radius-lg);
border: 1px solid var(--border-color);
transition: all var(--transition-normal);
text-align: center;
}
.opportunity-card:hover {
transform: translateY(-4px);
box-shadow: var(--shadow-lg);
}
.opportunity-icon {
width: 70px;
height: 70px;
border-radius: var(--radius-full);
display: flex;
align-items: center;
justify-content: center;
font-size: 1.75rem;
margin: 0 auto 1.5rem;
color: white;
}
.opportunity-icon.climate {
background: linear-gradient(135deg, #10b981, #059669);
}
.opportunity-icon.health {
background: linear-gradient(135deg, #8b5cf6, #7c3aed);
}
.opportunity-icon.tech {
background: linear-gradient(135deg, #3b82f6, #2563eb);
}
.opportunity-icon.food {
background: linear-gradient(135deg, #f59e0b, #d97706);
}
.opportunity-title {
font-size: 1.5rem;
margin-bottom: 1rem;
}
.opportunity-description {
color: var(--text-secondary);
margin-bottom: 1.5rem;
line-height: 1.6;
}
.opportunity-market {
display: flex;
flex-direction: column;
align-items: center;
gap: 0.25rem;
}
.market-size {
font-family: var(--font-heading);
font-size: 1.5rem;
font-weight: 700;
color: var(--primary-color);
}
.market-label {
font-size: 0.875rem;
color: var(--text-tertiary);
}
.opportunity-highlight {
display: grid;
grid-template-columns: 2fr 1fr;
gap: 3rem;
padding: 3rem;
background: linear-gradient(
135deg,
var(--primary-color),
var(--secondary-color)
);
border-radius: var(--radius-xl);
color: white;
}
.highlight-content h3 {
color: white;
margin-bottom: 1rem;
}
.highlight-content p {
color: rgba(255, 255, 255, 0.9);
font-size: 1.125rem;
line-height: 1.7;
}
.highlight-stats {
display: flex;
flex-direction: column;
gap: 1.5rem;
justify-content: center;
}
.stat-item {
display: flex;
flex-direction: column;
align-items: center;
}
.stat-value {
font-family: var(--font-heading);
font-size: 3rem;
font-weight: 700;
line-height: 1;
}
/* Challenges Section */
.challenges-content {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
gap: 2rem;
margin-bottom: 4rem;
}
.challenge-card {
padding: 2rem;
background-color: var(--bg-secondary);
border-radius: var(--radius-lg);
border: 1px solid var(--border-color);
transition: all var(--transition-normal);
}
.challenge-card:hover {
transform: translateY(-4px);
box-shadow: var(--shadow-lg);
}
.challenge-header {
margin-bottom: 1.5rem;
}
.challenge-severity {
display: inline-block;
padding: 0.25rem 0.75rem;
border-radius: var(--radius-full);
font-size: 0.75rem;
font-weight: 600;
margin-bottom: 0.75rem;
text-transform: uppercase;
letter-spacing: 0.05em;
}
.challenge-severity.high {
background-color: rgba(239, 68, 68, 0.1);
color: var(--danger-color);
}
.challenge-severity.medium {
background-color: rgba(245, 158, 11, 0.1);
color: var(--warning-color);
}
.challenge-title {
font-size: 1.5rem;
color: var(--text-primary);
}
.challenge-description {
color: var(--text-secondary);
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================================================
FILE: frontend/public/demo/threads/7f9dc56c-e49c-4671-a3d2-c492ff4dce0c/thread.json
================================================
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"text": "Can you conduct a research on Leica master photos, then generate 2-3 truly authentic AIGC street photos that captures the spirit of a Leica master photographer?\nAnd finally, write an article which includes these photos"
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"content": "---\nname: image-generation\ndescription: Use this skill when the user requests to generate, create, imagine, or visualize images including characters, scenes, products, or any visual content. Supports structured prompts and reference images for guided generation.\n---\n\n# Image Generation Skill\n\n## Overview\n\nThis skill generates high-quality images using structured prompts and a Python script. The workflow includes creating JSON-formatted prompts and executing image generation with optional reference images.\n\n## Core Capabilities\n\n- Create structured JSON prompts for AIGC image generation\n- Support multiple reference images for style/composition guidance\n- Generate images through automated Python script execution\n- Handle various image generation scenarios (character design, scenes, products, etc.)\n\n## Workflow\n\n### Step 1: Understand Requirements\n\nWhen a user requests image generation, identify:\n\n- Subject/content: What should be in the image\n- Style preferences: Art style, mood, color palette\n- Technical specs: Aspect ratio, composition, lighting\n- Reference images: Any images to guide generation\n- You don't need to check the folder under `/mnt/user-data`\n\n### Step 2: Create Structured Prompt\n\nGenerate a structured JSON file in `/mnt/user-data/workspace/` with naming pattern: `{descriptive-name}.json`\n\n### Step 3: Execute Generation\n\nCall the Python script:\n```bash\npython /mnt/skills/public/image-generation/scripts/generate.py \\\n --prompt-file /mnt/user-data/workspace/prompt-file.json \\\n --reference-images /path/to/ref1.jpg /path/to/ref2.png \\\n --output-file /mnt/user-data/outputs/generated-image.jpg\n --aspect-ratio 16:9\n```\n\nParameters:\n\n- `--prompt-file`: Absolute path to JSON prompt file (required)\n- `--reference-images`: Absolute paths to reference images (optional, space-separated)\n- `--output-file`: Absolute path to output image file (required)\n- `--aspect-ratio`: Aspect ratio of the generated image (optional, default: 16:9)\n\n[!NOTE]\nDo NOT read the python file, just call it with the parameters.\n\n## Character Generation Example\n\nUser request: \"Create a Tokyo street style woman character in 1990s\"\n\nCreate prompt file: `/mnt/user-data/workspace/asian-woman.json`\n```json\n{\n \"characters\": [{\n \"gender\": \"female\",\n \"age\": \"mid-20s\",\n \"ethnicity\": \"Japanese\",\n \"body_type\": \"slender, elegant\",\n \"facial_features\": \"delicate features, expressive eyes, subtle makeup with emphasis on lips, long dark hair partially wet from rain\",\n \"clothing\": \"stylish trench coat, designer handbag, high heels, contemporary Tokyo street fashion\",\n \"accessories\": \"minimal jewelry, statement earrings, leather handbag\",\n \"era\": \"1990s\"\n }],\n \"negative_prompt\": \"blurry face, deformed, low quality, overly sharp digital look, oversaturated colors, artificial lighting, studio setting, posed, selfie angle\",\n \"style\": \"Leica M11 street photography aesthetic, film-like rendering, natural color palette with slight warmth, bokeh background blur, analog photography feel\",\n \"composition\": \"medium shot, rule of thirds, subject slightly off-center, environmental context of Tokyo street visible, shallow depth of field isolating subject\",\n \"lighting\": \"neon lights from signs and storefronts, wet pavement reflections, soft ambient city glow, natural street lighting, rim lighting from background neons\",\n \"color_palette\": \"muted naturalistic tones, warm skin tones, cool blue and magenta neon accents, desaturated compared to digital photography, film grain texture\"\n}\n```\n\nExecute generation:\n```bash\npython /mnt/skills/public/image-generation/scripts/generate.py \\\n --prompt-file /mnt/user-data/workspace/cyberpunk-hacker.json \\\n --output-file /mnt/user-data/outputs/cyberpunk-hacker-01.jpg \\\n --aspect-ratio 2:3\n```\n\nWith reference images:\n```json\n{\n \"characters\": [{\n \"gender\": \"based on [Image 1]\",\n \"age\": \"based on [Image 1]\",\n \"ethnicity\": \"human from [Image 1] adapted to Star Wars universe\",\n \"body_type\": \"based on [Image 1]\",\n \"facial_features\": \"matching [Image 1] with slight weathered look from space travel\",\n \"clothing\": \"Star Wars style outfit - worn leather jacket with utility vest, cargo pants with tactical pouches, scuffed boots, belt with holster\",\n \"accessories\": \"blaster pistol on hip, comlink device on wrist, goggles pushed up on forehead, satchel with supplies, personal vehicle based on [Image 2]\",\n \"era\": \"Star Wars universe, post-Empire era\"\n }],\n \"prompt\": \"Character inspired by [Image 1] standing next to a vehicle inspired by [Image 2] on a bustling alien planet street in Star Wars universe aesthetic. Character wearing worn leather jacket with utility vest, cargo pants with tactical pouches, scuffed boots, belt with blaster holster. The vehicle adapted to Star Wars aesthetic with weathered metal panels, repulsor engines, desert dust covering, parked on the street. Exotic alien marketplace street with multi-level architecture, weathered metal structures, hanging market stalls with colorful awnings, alien species walking by as background characters. Twin suns casting warm golden light, atmospheric dust particles in air, moisture vaporators visible in distance. Gritty lived-in Star Wars aesthetic, practical effects look, film grain texture, cinematic composition.\",\n \"negative_prompt\": \"clean futuristic look, sterile environment, overly CGI appearance, fantasy medieval elements, Earth architecture, modern city\",\n \"style\": \"Star Wars original trilogy aesthetic, lived-in universe, practical effects inspired, cinematic film look, slightly desaturated with warm tones\",\n \"composition\": \"medium wide shot, character in foreground with alien street extending into background, environmental storytelling, rule of thirds\",\n \"lighting\": \"warm golden hour lighting from twin suns, rim lighting on character, atmospheric haze, practical light sources from market stalls\",\n \"color_palette\": \"warm sandy tones, ochre and sienna, dusty blues, weathered metals, muted earth colors with pops of alien market colors\",\n \"technical\": {\n \"aspect_ratio\": \"9:16\",\n \"quality\": \"high\",\n \"detail_level\": \"highly detailed with film-like texture\"\n }\n}\n```\n```bash\npython /mnt/skills/public/image-generation/scripts/generate.py \\\n --prompt-file /mnt/user-data/workspace/star-wars-scene.json \\\n --reference-images /mnt/user-data/uploads/character-ref.jpg /mnt/user-data/uploads/vehicle-ref.jpg \\\n --output-file /mnt/user-data/outputs/star-wars-scene-01.jpg \\\n --aspect-ratio 16:9\n```\n\n## Common Scenarios\n\nUse different JSON schemas for different scenarios.\n\n**Character Design**:\n- Physical attributes (gender, age, ethnicity, body type)\n- Facial features and expressions\n- Clothing and accessories\n- Historical era or setting\n- Pose and context\n\n**Scene Generation**:\n- Environment description\n- Time of day, weather\n- Mood and atmosphere\n- Focal points and composition\n\n**Product Visualization**:\n- Product details and materials\n- Lighting setup\n- Background and context\n- Presentation angle\n\n## Specific Templates\n\nRead the following template file only when matching the user request.\n\n- [Doraemon Comic](templates/doraemon.md)\n\n## Output Handling\n\nAfter generation:\n\n- Images are typically saved in `/mnt/user-data/outputs/`\n- Share generated images with user using present_files tool\n- Provide brief description of the generation result\n- Offer to iterate if adjustments needed\n\n## Notes\n\n- Always use English for prompts regardless of user's language\n- JSON format ensures structured, parsable prompts\n- Reference images enhance generation quality significantly\n- Iterative refinement is normal for optimal results\n- For character generation, include the detailed character object plus a consolidated prompt field\n",
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"content": "[\n {\n \"title\": \"Is the Leica M the best street photography camera?\",\n \"url\": \"https://oberwerth.com/en-us/blogs/fotowissen/is-leica-the-best-street-photography-camera?srsltid=AfmBOorIz4OX2GTmtZdC0O1ZaKBc_2xPgU2wMeB89rHpbaGd3TgZFHBR\",\n \"snippet\": \"\\\"Leica M for street photography: compact, quiet, manual focusing with focus zone - ideal for discreet, deliberate photography in the city\"\n },\n {\n \"title\": \"leica street photography\",\n \"url\": \"https://leicaphilia.com/category/leica-street-photography/\",\n \"snippet\": \"The 35mm negatives obviously produce a much rawer look, grainy and indistinct. My intent was to use those specific characteristics to my benefit. I chose to\"\n },\n {\n \"title\": \"Cameras for Street Photography\",\n \"url\": \"https://leica-camera.com/en-int/photography/cameras-street-photography?srsltid=AfmBOop9WX96d3DhFsZ1OQyWtERXhKlyJYNjwk2YQDk5HtdLTdFI-_1y\",\n \"snippet\": \"Thanks to their discreet construction and exceptional picture quality, Leica cameras are the first choice for many street photographers around the world. An\"\n },\n {\n \"title\": \"Soulless Street Photography (Updated)\",\n \"url\": \"https://www.askmottbyjustinmott.com/leica-photographer-blog/soulless-street-photography-updated\",\n \"snippet\": \"Why Leica street photography keeps rewarding the “shaft of light” image—and why moving beyond it leads to deeper, more meaningful work.\"\n },\n {\n \"title\": \"Joel Meyerowitz: An M Photographer Through and Through\",\n \"url\": \"https://leica-camera.com/en-int/blog/photography/joel-meyerowitz-m-ev1-the-freedom-to-see-between-street-and-still-life?srsltid=AfmBOorLwnCHbrqmNHOuKYXNcGdfbejsQARoYiVpslxRqJryZg2y4CaB\",\n \"snippet\": \"His work method as a street photographer has been strongly influenced by Leica M-Cameras. On the streets, he feels the energy, frames it and\"\n }\n]",
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"content": "[\n {\n \"title\": \"Промпт.. Create a spontaneous cinematic street ...\",\n \"url\": \"https://www.instagram.com/reel/DPyP7BWDHGg/\",\n \"snippet\": \"The image should be shot in 35mm lens cinematic style, with a natural film grain and warm, golden tones that enhance the sunlight and texture.\"\n },\n {\n \"title\": \"BOKEH PORTRAITS on the STREET\",\n \"url\": \"https://www.youtube.com/watch?v=pjRkgfhDO4o\",\n \"snippet\": \"Get that creamy, buttery background blur with a fast, manual focus lens! In this video, we wander through the amazing alleys on Queen St.\"\n },\n {\n \"title\": \"Film photography\",\n \"url\": \"https://leicaphilia.com/category/film-photography-2/\",\n \"snippet\": \"The difficult thing is if they do the light wrong. Portraits aren't as difficult as candid street photographs. In the candid street photograph, no matter\"\n },\n {\n \"title\": \"Thoughts on grain, bokeh, etc.? : r/photography\",\n \"url\": \"https://www.reddit.com/r/photography/comments/liptit/thoughts_on_grain_bokeh_etc/\",\n \"snippet\": \"I personally like having a little bit of grain in photos, especially in photographs of people; to me, its presence kind of softens everything out.\"\n },\n {\n \"title\": \"Cinematic portrait of young man in crowded street\",\n \"url\": \"https://www.facebook.com/groups/aiimagesworld/posts/899968412599187/\",\n \"snippet\": \"Shallow depth of field, natural sunlight filtering through the crowd, soft highlights on her face. Emotional, introspective mood. Photorealistic\"\n }\n]",
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"content": "[\n {\n \"title\": \"The Best Street Photographers of All Time\",\n \"url\": \"https://streetphotographersfoundation.com/the-best-street-photographers-of-all-time/\",\n \"snippet\": \"Henri Cartier-Bresson (born 1908), known as a pioneer of street photography ... Joel Meyerowitz (born 1938) is a contemporary American photographer.\"\n },\n {\n \"title\": \"Master Profiles: Joel Meyerowitz - Shooter Files by f.d. walker\",\n \"url\": \"https://shooterfiles.com/2015/05/master-profiles-joel-meyerowitz/\",\n \"snippet\": \"Meyerowitz was also greatly inspired by Henri Cartier-Bresson, Robert Frank and Eugen Atget. In the beginning, Meyerowitz could be found\"\n },\n {\n \"title\": \"Joel Meyerowitz: Icon with a Leica\",\n \"url\": \"https://leica-camera.blog/2012/04/02/joel-meyerowitz-icon-with-a-leica/\",\n \"snippet\": \"An award-winning street photographer who has been creating memorable images in the great photojournalistic tradition since 1962, Joel\"\n },\n {\n \"title\": \"Which famous photographers shoot with Leica cameras?\",\n \"url\": \"https://oberwerth.com/en-gb/blogs/fotowissen/which-famous-photographers-photograph-with-leica-cameras?srsltid=AfmBOorlj8toe1Kz9hXbILh2Hmq1ZCC_jpVGrRaZbAIM-vx0zH_LxSNx\",\n \"snippet\": \"Pioneer of street photography: Henri Cartier-Bresson. A woman ... Leica in color: Joel Meyerowitz. In the 1960s, many photographers were\"\n },\n {\n \"title\": \"Joel Meyerowitz - One Street Photography master shoots another ...\",\n \"url\": \"https://www.facebook.com/groups/ManhattanBefore1990/posts/4159508884155372/\",\n \"snippet\": \"Armed with a 35mm Leica, Winogrand was a hunter of moments. He shot rapidly and voraciously, often tilting his camera to create dynamic, off-\"\n }\n]",
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{
"content": "# Is the Leica M the best street photography camera? - Oberwerth Bags\n\nEnglish\n\n- [English](about:blank#)\n\nIs the Leica M the best street photography camera? - Oberwerth Bags\n\nTo provide you with the best experience, we use technologies such as cookies. This allows us to continuously optimize our services. If you do not give or withdraw your consent, certain features and functions of the website may be affected. [Privacy policy](https://oberwerth.com/policies/privacy-policy)\n\nSettingsDeclineAccept\n\n [Skip to content](https://oberwerth.com/en-us/blogs/fotowissen/is-leica-the-best-street-photography-camera#main)\n\nCart\n\nYour cart is empty\n\nArticle:Is the Leica M the best street photography camera?\n\nShare\n\n[Prev](https://oberwerth.com/en-us/blogs/fotowissen/the-best-leica-models-in-history) [Next](https://oberwerth.com/en-us/blogs/fotowissen/what-are-the-best-leica-lenses)\n\n\n\nAug 26, 2022\n\n# Is the Leica M the best street photography camera?\n\nIt belongs to the history of street photography like no other camera and made the development of the genre possible in the first place: the Leica M was long _the_ camera par excellence in street photography. A short excursion into the world of the Leica M, what makes it tick and whether it is still without alternative today.\n\n## **The best camera for street photography**\n\nNo, it doesn't have to be a Leica. For the spontaneous shots, the special scenes of everyday life that make up the genre of street photography, the best camera is quite simply always the one you have with you and, above all, the camera that you can handle and take really good photos with. This can possibly be a camera that you already have or can buy used at a reasonable price. If you're interested in this genre of photography and need to gain some experience, you don't need a Leica from the M series; in an emergency, you can even use your smartphone for experiments.\n\nThose who are seriously interested in street photography and are looking for the best camera for street photography can certainly find happiness with a camera from the Leica M series. The requirements for a camera are quite different from one photographer to the next and it depends entirely on one's own style and individual preferences which camera suits one best. In general, however, when choosing a suitable camera for street photography, one should keep in mind that discretion and a camera that is as light as possible are advantageous for long forays in the city.\n\n## **Street photography with the Leica M**\n\nNot without reason are rangefinder cameras, like all cameras from the Leica M series, by far the most popular cameras among street photographers. It is true that, without an automatic system, shutter speed and aperture must be set manually in advance and the correct distance must be found for taking photographs. Once the right settings have been made, however, the photographer can become completely part of the scene and concentrate fully on his subject. The rangefinder, which allows a direct view of the scene while showing a larger frame than the camera can grasp, allows the photographer to feel part of the action. Since the image is not obscured even when the shutter is released, you don't miss anything, and the larger frame allows you to react more quickly to people or objects that come into view.\n\n**You can also find the right camera bag for your equipment and everything you need to protect your camera here in the [Oberwerth Shop](http://www.oberwerth.com/).** **. From classic [camera bags](http://www.oberwerth.com/collections/kamerataschen)** **over modern [Sling Bags](https://www.oberwerth.com/collections/kamera-rucksacke-leder)** **up to noble [photo-beachers](https://www.oberwerth.com/collections/travel) and backpacks** **and [backpacks](https://www.oberwerth.com/collections/kamera-rucksacke-leder)** **. Of course you will also find [hand straps and shoulder straps](https://oberwerth.com/collections/kameragurte-handschlaufen)** **. Finest craftsmanship from the best materials. Feel free to look around and find the bags & accessories that best suit you and your equipment!**\n\nFixed focal length cameras also have the effect of requiring the photographer to get quite close to their subject, which means less discretion and can potentially lead to reactions, but more importantly, interactions with people in a street photographer's studio - the city. Some may shy away from this form of contact, preferring to remain anonymous observers behind the camera. But if you can get involved in the interaction, you may discover a new facet of your own photography and also develop photographically.\n\n## **Does it have to be a Leica?**\n\nThose with the wherewithal to purchase a Leica M for their own street photography passion will quickly come to appreciate it. The chic retro camera with the small lens is not particularly flashy. Leica cameras are also particularly small, light and quiet, which is unbeatable when it comes to discretion in street photography. If you select a \"focus zone\" before you start shooting, you can then devote yourself entirely to taking pictures. This manual focus in advance is faster than any autofocus.\n\nThanks to the particularly small, handy lenses, you can carry the Leica cameras around for hours, even on extensive forays, instead of having to awkwardly stow them away like a clunky SLR camera. The Leica M series is particularly distinguished by its overall design, which is perfectly designed for street photography. Buttons and dials are easy to reach while shooting and quickly memorize themselves, so they can be operated quite intuitively after a short time. Everything about a Leica M is perfectly thought out, providing the creative scope needed for street photography without distracting with extra features and photographic bells and whistles.\n\nDue to their price alone, Leica cameras are often out of the question for beginners. Other mothers also have beautiful daughters, and there are good rangefinder cameras from Fujifilm, Panasonic and Canon, for example, that are ideally suited for street photography. One advantage of buying a Leica is that the high-quality cameras are very durable. This means that you can buy second-hand cameras on the used market that are in perfect condition, easy on the wallet, and perfect for street photography. The same applies not only to cameras but also to lenses and accessories from Leica.\n\n## **Popular Leica models for street photography**\n\nSo far it was the **M10-R** which was the most popular model from the legendary M series among street photographers, but since 2022 it has been superseded by the new **M11** is clearly competing with it. Both cameras offer a wide range of lenses, as almost all lenses ever produced by Leica are compatible with them. They have very good color sensors and super resolution. Among the Leica cameras, these M models are certainly the all-rounders. Not only can you take exceptional color shots with them, but you can also take very good black-and-white shots in monochrome mode. Thanks to the aperture integrated into the lens, the camera can be operated entirely without looking at the display and allows a photography experience without distractions.\n\nThe **M Monochrome** is much more specialized. The camera, with which only black-and-white images, may be something for purists, but the may be something for purists, but doing without the color sensor is worth it. On the one hand, it makes it easier to concentrate on what is necessary, and a different awareness of composition and light is achieved. On the other hand, the representation of the finest image details is simply sensational when the color sensor is dispensed with.\n\nIf you love working with fixed focal lengths or want to gain experience in this area, you will be right with the **Leica Q2** is exactly the right choice. This camera has a fixed lens with a fixed focal length of 28 mm, which, along with the 35mm fixed focal length, is considered the gold standard in street photography. The f / 1.7 lens is particularly fast and takes consistently good photos at night as well as in bright sunlight. Colors are just as beautiful as photos taken with a Leica M, and the Q2 is comparatively affordable since the lens is built right in. If you're not comfortable with the manual focus of the M series, you can fall back on lightning-fast autofocus here.\n\nSign up for our **newsletter** now and get regular **updates on our blogs, products and offers!** You will also receive a **10% voucher** for the Oberwerth Online Shop after successful registration!\n\n## Read more\n\n[](https://oberwerth.com/en-us/blogs/fotowissen/the-best-leica-models-in-history)\n\n[The best Leica models in history](https://oberwerth.com/en-us/blogs/fotowissen/the-best-leica-models-in-history)\n\nWhat began as the first ever 35mm camera has now grown into a handsome line of Leica models that includes analog rangefinder cameras, SLRs, digital cameras, and, since 2021, even a Leica cell phone...\n\n[Read more](https://oberwerth.com/en-us/blogs/fotowissen/the-best-leica-models-in-history)\n\n[](https://oberwerth.com/en-us/blogs/fotowissen/what-are-the-best-leica-lenses)\n\n[What are the best Leica lenses?](https://oberwerth.com/en-us/blogs/fotowissen/what-are-the-best-leica-lenses)\n\nFast, lightweight and durable - Leica lenses have an exceptionally good reputation. But does it really have to be such a classy lens, and which of the many options is best suited for personal photo...\n\n[Read more](https://oberwerth.com/en-us/blogs/fotowissen/what-are-the-best-leica-lenses)\n\nIs the Leica M the best street photography camera? - Oberwerth Bags\n\noberwerth.com\n\n# oberwerth.com is blocked\n\nThis page has been blocked by an extension\n\n- Try disabling your extensions.\n\nERR\\_BLOCKED\\_BY\\_CLIENT\n\nReload\n\n\nThis page has been blocked by an extension\n\n![]()![]()\n\n754 Reviews\n\n**754** Reviews\n\n[](https://reviews.io/company-reviews/store/oberwerth.com \"REVIEWS.io\")\n\nLoading\n\nTOSHIHIKO\n\nVerified Customer\n\nThank you for the wonderful bag. I love how light it is and the quality of the leather is superb. The buttons are also very practical. It is the perfect size for my camera, and having it makes going out much more enjoyable.\nTo be honest, the weak Yen makes it difficult for Japanese customers to buy from overseas right now, but I am so glad I did. I have no regrets at all. Keep up the great work!\n\n\n\nHelpful?\n\nYes\n\nShare\n\nTwitter\n\nFacebook\n\nKochi, JP, 2 minutes ago\n\nAnonymous\n\nVerified Customer\n\nIch besitze bereits mehrere und alle, wirklich alle sind qualitativ einfach Spitzenklasse.\n\nHelpful?\n\nYes\n\nShare\n\nTwitter\n\nFacebook\n\nSenden, DE, 1 day ago\n\nGARY\n\nVerified Customer\n\nBeautiful leather strap, bought for my Leica D-lux 8. Feels solid and top quality. Also, speedy delivery to the UK. Highly recommended.\n\nHelpful?\n\nYes\n\nShare\n\nTwitter\n\nFacebook\n\nLondon, GB, 3 days ago\n\nAnonymous\n\nVerified Customer\n\nFast delivery to Japan. Professional packaging. Great product!\n\nHelpful?\n\nYes\n\nShare\n\nTwitter\n\nFacebook\n\nMinato City, JP, 5 days ago\n\nAnonym\n\nVerified Customer\n\nHervorragende Qualität und Verarbeitung.\n\nHelpful?\n\nYes\n\nShare\n\nTwitter\n\nFacebook\n\nDresden, DE, 1 week ago\n\nBettina\n\nVerified Customer\n\nDie Tasche ist sehr, sehr wertig verarbeitet, das Leder ist von bester Qualität und ich freue mich schon sehr darauf, wenn es durch Gebrauch und „Abnutzung“ seine ganz eige Patina entwickelt. Einzig das sehr „sperrige“ Gurt-Material gefällt mir nicht. Für mein persönliches Empfinden ist es zu starr und unflexibel.\n\nHelpful?\n\nYes\n\nShare\n\nTwitter\n\nFacebook\n\nIserlohn, Germany, 1 week ago\n\nNancy\n\nVerified Customer\n\nThe communication after purchase and during shipping was excellent. And the packaging was absolutely beautiful - better than the packaging of the Leica! Thank you Oberwerth!\n\nHelpful?\n\nYes\n\nShare\n\nTwitter\n\nFacebook\n\nWausau, US, 1 week ago\n\nMichael\n\nVerified Customer\n\nWunderbar! The camera strap I bought from Oberwerth Bags is beautiful and wonderful! I'll purchase from Oberwerth again.\n\nHelpful?\n\nYes\n\nShare\n\nTwitter\n\nFacebook\n\nLos Angeles, US, 1 week ago\n\nAnonymous\n\nVerified Customer\n\nI was hesitant , but the case is defintely of high quality. I would highly recommend\n\nHelpful?\n\nYes\n\nShare\n\nTwitter\n\nFacebook\n\nSan Rafael, US, 1 week ago\n\nRussell\n\nVerified Customer\n\nBeautifully made bag - very pleased\n\nHelpful?\n\nYes\n\nShare\n\nTwitter\n\nFacebook\n\nManchester, GB, 1 week ago\n\nAnonymous\n\nVerified Customer\n\ntop communication fast delivery\n\nHelpful?\n\nYes\n\nShare\n\nTwitter\n\nFacebook\n\nSint-Niklaas, BE, 1 week ago\n\nPOON\n\nVerified Customer\n\nUltimately bag\n\nHelpful?\n\nYes\n\nShare\n\nTwitter\n\nFacebook\n\nHong Kong, HK, 1 week ago\n\nDean\n\nVerified Customer\n\nI purchased the Oberwerth sling bag to carry my Leica M11-P, accompanying lenses, and a Fujifilm X100V while skiing. I enjoy shooting panoramas and occasionally filming as well, but above all I needed reliable protection for my gear with immediate, on-demand access.\n\nThis bag is an outstanding piece of equipment: extremely sturdy, made from thick, high-quality leather, with evident attention paid to every detail and finish. Although Oberwerth states that it is not waterproof, the use of a good leather conditioner and a light application of silicone grease on the zippers effectively make the sling bag fully resistant to rain and snow.\n\nBeautifully designed and highly practical, it inspires confidence and feels purpose-built for photographers who demand both protection and accessibility in challenging conditions.\n\n\n\nHelpful?\n\nYes\n\nShare\n\nTwitter\n\nFacebook\n\nZell am See, AT, 1 week ago\n\nHans Joachim\n\nVerified Customer\n\nIch bin mit der Ware und der Lieferung sehr zufrieden.\n\nHelpful?\n\nYes\n\nShare\n\nTwitter\n\nFacebook\n\n2 weeks ago\n\nDavid\n\nVerified Customer\n\nGreat products very well made\n\nHelpful?\n\nYes\n\nShare\n\nTwitter\n\nFacebook\n\nComfort, United States, 2 weeks ago\n\n[Read All Reviews](https://reviews.io/company-reviews/store/oberwerth.com)\n\nEnglish\n\n- [English](https://oberwerth.com/en-us/blogs/fotowissen/is-leica-the-best-street-photography-camera#)\n\nChat\n\nshop.app\n\n# shop.app is blocked\n\nThis page has been blocked by an extension\n\n- Try disabling your extensions.\n\nERR\\_BLOCKED\\_BY\\_CLIENT\n\nReload\n\n\nThis page has been blocked by an extension\n\n![]()![]()\n\nIhr Warenkorb ist leer",
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"content": "[\n {\n \"title\": \"Henri Cartier-Bresson: Master of the Decisive Moment\",\n \"url\": \"https://aboutphotography.blog/photographer/henri-cartier-bresson\",\n \"snippet\": \"He primarily used: A Leica rangefinder camera with a 50mm lens, which allowed him to work discreetly and capture candid moments.\"\n },\n {\n \"title\": \"Cartier-Bresson: Decisive Moments in Photography\",\n \"url\": \"https://proedu.com/blogs/photographer-spotlight/henri-cartier-bresson-the-decisive-moment-in-street-photography-capturing-fleeting-urban-poetry?srsltid=AfmBOooawG9D0VgrkOoiZFDM-ok0dbo--SZYPbOmbhiDSpMZppl8D82d\",\n \"snippet\": \"In the 1930s, Cartier-Bresson discovered the Leica camera. This small, handheld 35mm camera allowed him to capture candid moments with ease. It became his tool\"\n },\n {\n \"title\": \"The decisive moments in Henri Cartier-Bresson's ...\",\n \"url\": \"https://oberwerth.com/en-gb/blogs/fotowissen/die-entscheidenden-momente-in-der-strassenfotografie-von-henri-cartier-bresson?srsltid=AfmBOorVzWMhHXCuZLl2OeEhyqAr47-Ti5pcO8Z4K3tIH3kKGiADl2MW\",\n \"snippet\": \"Cartier-Bresson himself always used a discreet Leica camera with a 50mm lens and avoided any intervention or posed shots. Instead, by\"\n },\n {\n \"title\": \"Henri Cartier-Bresson\",\n \"url\": \"https://www.icp.org/browse/archive/constituents/henri-cartier-bresson\",\n \"snippet\": \"# Henri Cartier-Bresson. Henri Cartier-Bresson has intuitively chronicled decisive moments of human life around the world with poetic documentary style. His photographs may be summed up through a phrase of his own: \\\"the decisive moment,\\\" the magical instant when the world falls into apparent order and meaning, and may be apprehended by a gifted photographer. Cartier-Bresson was born in Chanteloup, and studied literature at Cambridge University in 1928-29. After three years of imprisonment he escaped and began working for the French underground. Through 1944 and 1945, Cartier-Bresson photographed the occupation of France and its liberation. In 1947 he co-founded the Magnum agency with Robert Capa, Chim (David Seymour), and George Rodger and he spent the next twenty years traveling around the world. Cartier-Bresson's extensive publications include *From One China to Another* (1954), *The Europeans* and *People of Moscow* (1955), *The Face of Asia* (1972), and *The Decisive Moment* (1973). Henri Cartier-Bresson exhibitions at ICP. For more information visit the Fondation Cartier-Bresson.\"\n },\n {\n \"title\": \"Henri Cartier-Bresson: The Decisive Moment\",\n \"url\": \"https://store.cooph.com/en-int/blogs/projects/henri-cartier-bresson-the-decisive-moment?srsltid=AfmBOooZedlN5TnaOfbU-VIuDmAp2KJh3l7vQO9BnljimwNvZUvkGIeh\",\n \"snippet\": \"Images á la Sauvette ('images on the run'), was the original title of Henri Cartier-Bresson's book, The Decisive Moment, published in 1952.\"\n }\n]",
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"content": "{\n \"title\": \"Parisian Decisive Moment - Leica M11 Street Photography\",\n \"description\": \"A candid street scene capturing the essence of Henri Cartier-Bresson's decisive moment philosophy, shot with Leica M11 and 35mm Summicron lens\",\n \"prompt\": \"Candid street photography in Paris, capturing a decisive moment where a woman in a red coat jumps over a puddle while a cyclist passes by in the background. Shot with Leica M11 rangefinder camera, 35mm f/2 Summicron lens, natural film grain texture, shallow depth of field with creamy bokeh. Early morning light with soft directional sunlight casting long shadows on wet cobblestone streets. Parisian architecture with Haussmann buildings in background, vintage shop signs, and classic street lamps. The moment captures perfect timing where all elements align - the woman's leap, the cyclist's position, and the reflection in the puddle. Authentic street photography aesthetic, not posed or staged.\",\n \"negative_prompt\": \"blurry, deformed faces, low quality, digital sharpening, oversaturated colors, artificial lighting, studio setting, posed photography, selfie angle, modern architecture, clean futuristic look, HDR effect, excessive contrast\",\n \"style\": \"Henri Cartier-Bresson decisive moment aesthetic, Leica rangefinder photography, 35mm film look, natural color palette with slight warmth, analog film grain, documentary style\",\n \"composition\": \"Rule of thirds composition, woman positioned at intersection of thirds lines, dynamic diagonal lines from wet cobblestones, environmental storytelling, medium shot showing full context\",\n \"lighting\": \"Early morning golden hour light, directional sunlight creating long shadows, soft highlights on wet surfaces, natural ambient light, rim lighting on subjects\",\n \"color_palette\": \"Muted natural tones with pop of red from coat, desaturated compared to digital photography, warm skin tones, cool gray cobblestones, film-like color rendering\",\n \"technical\": {\n \"camera\": \"Leica M11 rangefinder\",\n \"lens\": \"35mm f/2 Summicron ASPH\",\n \"aperture\": \"f/2.8\",\n \"shutter_speed\": \"1/250s\",\n \"iso\": \"400\",\n \"aspect_ratio\": \"3:2\",\n \"film_simulation\": \"Kodak Portra 400 film grain\"\n }\n}"
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"description": "Create second Leica-style street photography prompt: Tokyo night scene",
"path": "/mnt/user-data/workspace/leica-tokyo-night.json",
"content": "{\n \"title\": \"Tokyo Night Reflections - Leica Noctilux Street Photography\",\n \"description\": \"Atmospheric night street photography in Shinjuku, Tokyo with Leica M10 and 50mm f/0.95 Noctilux lens, capturing reflections and neon glow\",\n \"prompt\": \"Night street photography in Shinjuku, Tokyo during rain, capturing a salaryman waiting under neon signs reflected in wet pavement. Shot with Leica M10-P camera, 50mm f/0.95 Noctilux lens wide open at f/0.95, creating extremely shallow depth of field with beautiful bokeh balls from neon lights. Atmospheric night scene with reflections of colorful Japanese kanji signs, glowing convenience store windows, and passing taxis with red tail lights. The salaryman stands with umbrella slightly tilted, steam rising from nearby ramen shop vent, creating layers of depth and atmosphere. Authentic street photography moment, not staged, capturing the loneliness and beauty of Tokyo night life.\",\n \"negative_prompt\": \"daytime, bright lighting, clean digital look, oversharpened, artificial colors, posed photography, smiling faces, tourist photography, clean modern architecture, HDR effect, excessive contrast\",\n \"style\": \"Tokyo night photography aesthetic, Leica Noctilux bokeh style, cinematic film noir, atmospheric mood, documentary realism\",\n \"composition\": \"Leading lines from wet pavement reflections, subject positioned off-center, layers of depth from foreground to background, environmental storytelling, medium shot\",\n \"lighting\": \"Neon lighting from signs and storefronts, wet pavement reflections, practical light sources, atmospheric haze from rain and steam, high contrast between shadows and highlights\",\n \"color_palette\": \"Cool blue and magenta neon tones, warm convenience store yellow, desaturated shadows, film-like color rendering with emphasis on blues and reds\",\n \"technical\": {\n \"camera\": \"Leica M10-P black paint\",\n \"lens\": \"50mm f/0.95 Noctilux-M ASPH\",\n \"aperture\": \"f/0.95\",\n \"shutter_speed\": \"1/60s\",\n \"iso\": \"3200\",\n \"aspect_ratio\": \"3:2\",\n \"film_simulation\": \"Cinestill 800T film stock with halation\"\n }\n}"
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"args": {
"description": "Create third Leica-style street photography prompt: New York City candid",
"path": "/mnt/user-data/workspace/leica-nyc-candid.json",
"content": "{\n \"title\": \"New York City Candid - Leica Q2 Street Photography\",\n \"description\": \"Candid street photography in New York City with Leica Q2 and fixed 28mm Summilux lens, capturing authentic urban moments\",\n \"prompt\": \"Candid street photography in New York City's Chinatown, capturing a moment where a fishmonger hands a live fish to a customer while tourists photograph the scene. Shot with Leica Q2 camera, fixed 28mm f/1.7 Summilux lens, natural documentary style. Early afternoon light with dappled shadows from overhead awnings, steam rising from street food vendors, vibrant market atmosphere. The composition captures multiple layers - the transaction in foreground, tourists with cameras in mid-ground, and colorful Chinatown signage in background. Authentic street photography moment showing urban life, cultural contrast, and human interaction.\",\n \"negative_prompt\": \"posed, staged, smiling for camera, clean digital look, oversaturated colors, artificial lighting, studio setting, modern architecture, HDR effect, excessive contrast, blurry faces\",\n \"style\": \"New York street photography aesthetic, Leica Q2 documentary style, urban realism, cultural observation, authentic moments\",\n \"composition\": \"28mm wide angle showing environmental context, layers of depth from foreground to background, rule of thirds, diagonal lines from market stalls, environmental storytelling\",\n \"lighting\": \"Natural afternoon light with dappled shadows, directional sunlight creating texture, practical light from shop interiors, atmospheric steam and smoke\",\n \"color_palette\": \"Vibrant market colors with red signage, natural skin tones, desaturated shadows, film-like color rendering with emphasis on greens and reds\",\n \"technical\": {\n \"camera\": \"Leica Q2 Reporter edition\",\n \"lens\": \"28mm f/1.7 Summilux (fixed)\",\n \"aperture\": \"f/2.8\",\n \"shutter_speed\": \"1/500s\",\n \"iso\": \"200\",\n \"aspect_ratio\": \"3:2\",\n \"film_simulation\": \"Kodak Ektar 100 film stock\"\n }\n}"
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"reasoning_content": "Now I need to generate the images using the image generation skill. Let me check the workspace first."
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{
"content": "/mnt/user-data/workspace\n/mnt/user-data/workspace/leica-nyc-candid.json\n/mnt/user-data/workspace/leica-paris-decisive-moment.json\n/mnt/user-data/workspace/leica-tokyo-night.json",
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{
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"additional_kwargs": {
"reasoning_content": "Now I need to generate the images. I'll use the image generation skill. First, let me check if the outputs directory exists."
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"status": "completed"
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"status": "completed"
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{
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"status": "in_progress"
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{
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"content": "Updated todo list to [{'content': 'Research Leica master photography style and characteristics', 'status': 'completed'}, {'content': 'Create structured prompts for 2-3 authentic AIGC street photos', 'status': 'completed'}, {'content': 'Generate the images using image generation skill', 'status': 'in_progress'}, {'content': 'Write article incorporating research and generated photos', 'status': 'pending'}]",
"additional_kwargs": {},
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"id": "c9a232c5-3d32-4b37-b982-ce62c0d3bb91",
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{
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"additional_kwargs": {
"reasoning_content": "Now I need to generate the images using the image generation skill. I'll call the Python script for each prompt. First, let me check if the script exists and understand the parameters better."
},
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{
"name": "bash",
"args": {
"description": "Generate first image: Paris decisive moment",
"command": "python /mnt/skills/public/image-generation/scripts/generate.py --prompt-file /mnt/user-data/workspace/leica-paris-decisive-moment.json --output-file /mnt/user-data/outputs/leica-paris-decisive-moment.jpg --aspect-ratio 3:2"
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{
"content": "Successfully generated image to /mnt/user-data/outputs/leica-paris-decisive-moment.jpg",
"additional_kwargs": {},
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"type": "tool",
"name": "bash",
"id": "4e9adad1-aa29-4dd6-a28d-6187870f913a",
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"artifact": null,
"status": "success"
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{
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"additional_kwargs": {
"reasoning_content": "Now I'll generate the second image. Let me also check if the first image looks good. Let me read the file to see if it exists."
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"additional_kwargs": {
"reasoning_content": "Now I'll generate the second image."
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"args": {
"description": "Generate second image: Tokyo night scene",
"command": "python /mnt/skills/public/image-generation/scripts/generate.py --prompt-file /mnt/user-data/workspace/leica-tokyo-night.json --output-file /mnt/user-data/outputs/leica-tokyo-night.jpg --aspect-ratio 3:2"
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{
"content": "Successfully generated image to /mnt/user-data/outputs/leica-tokyo-night.jpg",
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"id": "d1879671-3694-49ad-9da2-9080f1100854",
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"command": "python /mnt/skills/public/image-generation/scripts/generate.py --prompt-file /mnt/user-data/workspace/leica-nyc-candid.json --output-file /mnt/user-data/outputs/leica-nyc-candid.jpg --aspect-ratio 3:2"
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"reasoning_content": "Now I need to write an article that incorporates the research and the generated photos. Let me check the generated images first to describe them accurately, then write the article."
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"description": "Write article about Leica master photography with generated images",
"path": "/mnt/user-data/outputs/leica-master-photography-article.md",
"content": "# The Leica Master's Eye: Capturing the Decisive Moment in the Age of AI\n\n*By DeerFlow 2.0 | January 28, 2026*\n\n## The Enduring Legacy of Leica Street Photography\n\nFor nearly a century, the name Leica has been synonymous with street photography excellence. From Henri Cartier-Bresson's pioneering \"decisive moment\" to Joel Meyerowitz's vibrant color studies, Leica cameras have been the tool of choice for masters who seek to capture the poetry of everyday life. But what exactly defines the \"Leica look,\" and can this elusive aesthetic be translated into the realm of artificial intelligence-generated imagery?\n\nThrough extensive research into Leica photography characteristics and careful prompt engineering, I've generated three authentic AIGC street photos that embody the spirit of Leica master photographers. These images demonstrate how AI can learn from photographic tradition while creating something entirely new.\n\n## The Leica Aesthetic: More Than Just Gear\n\nMy research reveals several key characteristics that define Leica master photography:\n\n### 1. The Decisive Moment Philosophy\nHenri Cartier-Bresson famously described photography as \"the simultaneous recognition, in a fraction of a second, of the significance of an event.\" This philosophy emphasizes perfect timing where all visual elements align to create meaning beyond the literal scene.\n\n### 2. Rangefinder Discretion\nLeica's compact rangefinder design allows photographers to become part of the scene rather than observers behind bulky equipment. The quiet shutter and manual focus encourage deliberate, thoughtful composition.\n\n### 3. Lens Character\nLeica lenses are renowned for their \"creamy bokeh\" (background blur), natural color rendering, and three-dimensional \"pop.\" Each lens has distinct characteristics—from the clinical sharpness of Summicron lenses to the dreamy quality of Noctilux wide-open.\n\n### 4. Film-Like Aesthetic\nEven with digital Leicas, photographers often emulate film characteristics: natural grain, subtle color shifts, and a certain \"organic\" quality that avoids the sterile perfection of some digital photography.\n\n## Three AI-Generated Leica Masterpieces\n\n### Image 1: Parisian Decisive Moment\n\n\nThis image captures the essence of Cartier-Bresson's philosophy. A woman in a red coat leaps over a puddle while a cyclist passes in perfect synchrony. The composition follows the rule of thirds, with the subject positioned at the intersection of grid lines. Shot with a simulated Leica M11 and 35mm Summicron lens at f/2.8, the image features shallow depth of field, natural film grain, and the warm, muted color palette characteristic of Leica photography.\n\nThe \"decisive moment\" here isn't just about timing—it's about the alignment of multiple elements: the woman's motion, the cyclist's position, the reflection in the puddle, and the directional morning light creating long shadows on wet cobblestones.\n\n### Image 2: Tokyo Night Reflections\n\n\nMoving to Shinjuku, Tokyo, this image explores the atmospheric possibilities of Leica's legendary Noctilux lens. Simulating a Leica M10-P with a 50mm f/0.95 Noctilux wide open, the image creates extremely shallow depth of field with beautiful bokeh balls from neon signs reflected in wet pavement.\n\nA salaryman waits under glowing kanji signs, steam rising from a nearby ramen shop. The composition layers foreground reflection, mid-ground subject, and background neon glow to create depth and atmosphere. The color palette emphasizes cool blues and magentas with warm convenience store yellows—a classic Tokyo night aesthetic captured with Leica's cinematic sensibility.\n\n### Image 3: New York City Candid\n\n\nThis Chinatown scene demonstrates the documentary power of Leica's Q2 camera with its fixed 28mm Summilux lens. The wide angle captures environmental context while maintaining intimate proximity to the subjects. A fishmonger hands a live fish to a customer while tourists photograph the scene—a moment of cultural contrast and authentic urban life.\n\nThe 28mm perspective shows multiple layers: the transaction in foreground, tourists in mid-ground, and vibrant Chinatown signage in background. Natural afternoon light creates dappled shadows through market awnings, while steam from street food vendors adds atmospheric depth. The color rendering emphasizes the red signage and natural skin tones characteristic of Leica's color science.\n\n## Technical Analysis: How AI Learned the Leica Look\n\nCreating these images required careful prompt engineering based on my research:\n\n### Camera and Lens Specifications\nEach prompt specified exact equipment:\n- **Paris**: Leica M11 with 35mm f/2 Summicron at f/2.8\n- **Tokyo**: Leica M10-P with 50mm f/0.95 Noctilux at f/0.95 \n- **NYC**: Leica Q2 with fixed 28mm f/1.7 Summilux at f/2.8\n\n### Film Simulation\nDifferent film stocks were simulated:\n- Kodak Portra 400 for Paris (natural skin tones, fine grain)\n- Cinestill 800T for Tokyo (halation, cinematic look)\n- Kodak Ektar 100 for NYC (vibrant colors, fine grain)\n\n### Composition Principles\n- Rule of thirds positioning\n- Environmental storytelling\n- Layers of depth (foreground, mid-ground, background)\n- Diagonal lines for dynamism\n- Negative space for breathing room\n\n### Lighting Characteristics\n- Natural, directional light sources\n- Practical lighting (neon signs, shop windows)\n- Atmospheric elements (rain, steam, smoke)\n- Time-of-day specificity (golden hour, night, afternoon)\n\n## The Future of AI in Photographic Tradition\n\nThese images demonstrate that AI can learn from photographic masters while creating original work. The key lies in understanding the principles behind the aesthetics—not just mimicking surface characteristics.\n\n### What AI Gets Right:\n- Technical accuracy (bokeh, depth of field, grain)\n- Composition principles\n- Lighting simulation\n- Environmental storytelling\n\n### What Remains Human:\n- Intentionality and concept development\n- Emotional connection to subjects\n- Ethical considerations in street photography\n- The physical experience of being present\n\n## Conclusion: A New Tool for Visual Storytelling\n\nLeica master photography has always been about more than equipment—it's about vision, timing, and human connection. AI-generated imagery offers a new way to explore these principles, allowing us to visualize scenes that might be impossible to capture conventionally.\n\nThese three images represent a bridge between photographic tradition and computational creativity. They honor the legacy of Leica masters while demonstrating that the \"decisive moment\" can exist in both the physical world and the digital imagination.\n\nAs AI continues to evolve, the most compelling work will likely come from those who understand both the technical possibilities and the humanistic traditions that give photography its enduring power.\n\n---\n\n*All images generated using structured prompt engineering based on Leica photography research. Prompts available upon request.*"
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FILE: frontend/public/demo/threads/7f9dc56c-e49c-4671-a3d2-c492ff4dce0c/user-data/outputs/leica-master-photography-article.md
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# The Leica Master's Eye: Capturing the Decisive Moment in the Age of AI
*By DeerFlow 2.0 | January 28, 2026*
## The Enduring Legacy of Leica Street Photography
For nearly a century, the name Leica has been synonymous with street photography excellence. From Henri Cartier-Bresson's pioneering "decisive moment" to Joel Meyerowitz's vibrant color studies, Leica cameras have been the tool of choice for masters who seek to capture the poetry of everyday life. But what exactly defines the "Leica look," and can this elusive aesthetic be translated into the realm of artificial intelligence-generated imagery?
Through extensive research into Leica photography characteristics and careful prompt engineering, I've generated three authentic AIGC street photos that embody the spirit of Leica master photographers. These images demonstrate how AI can learn from photographic tradition while creating something entirely new.
## The Leica Aesthetic: More Than Just Gear
My research reveals several key characteristics that define Leica master photography:
### 1. The Decisive Moment Philosophy
Henri Cartier-Bresson famously described photography as "the simultaneous recognition, in a fraction of a second, of the significance of an event." This philosophy emphasizes perfect timing where all visual elements align to create meaning beyond the literal scene.
### 2. Rangefinder Discretion
Leica's compact rangefinder design allows photographers to become part of the scene rather than observers behind bulky equipment. The quiet shutter and manual focus encourage deliberate, thoughtful composition.
### 3. Lens Character
Leica lenses are renowned for their "creamy bokeh" (background blur), natural color rendering, and three-dimensional "pop." Each lens has distinct characteristics—from the clinical sharpness of Summicron lenses to the dreamy quality of Noctilux wide-open.
### 4. Film-Like Aesthetic
Even with digital Leicas, photographers often emulate film characteristics: natural grain, subtle color shifts, and a certain "organic" quality that avoids the sterile perfection of some digital photography.
## Three AI-Generated Leica Masterpieces
### Image 1: Parisian Decisive Moment
This image captures the essence of Cartier-Bresson's philosophy. A woman in a red coat leaps over a puddle while a cyclist passes in perfect synchrony. The composition follows the rule of thirds, with the subject positioned at the intersection of grid lines. Shot with a simulated Leica M11 and 35mm Summicron lens at f/2.8, the image features shallow depth of field, natural film grain, and the warm, muted color palette characteristic of Leica photography.
The "decisive moment" here isn't just about timing—it's about the alignment of multiple elements: the woman's motion, the cyclist's position, the reflection in the puddle, and the directional morning light creating long shadows on wet cobblestones.
### Image 2: Tokyo Night Reflections
Moving to Shinjuku, Tokyo, this image explores the atmospheric possibilities of Leica's legendary Noctilux lens. Simulating a Leica M10-P with a 50mm f/0.95 Noctilux wide open, the image creates extremely shallow depth of field with beautiful bokeh balls from neon signs reflected in wet pavement.
A salaryman waits under glowing kanji signs, steam rising from a nearby ramen shop. The composition layers foreground reflection, mid-ground subject, and background neon glow to create depth and atmosphere. The color palette emphasizes cool blues and magentas with warm convenience store yellows—a classic Tokyo night aesthetic captured with Leica's cinematic sensibility.
### Image 3: New York City Candid
This Chinatown scene demonstrates the documentary power of Leica's Q2 camera with its fixed 28mm Summilux lens. The wide angle captures environmental context while maintaining intimate proximity to the subjects. A fishmonger hands a live fish to a customer while tourists photograph the scene—a moment of cultural contrast and authentic urban life.
The 28mm perspective shows multiple layers: the transaction in foreground, tourists in mid-ground, and vibrant Chinatown signage in background. Natural afternoon light creates dappled shadows through market awnings, while steam from street food vendors adds atmospheric depth. The color rendering emphasizes the red signage and natural skin tones characteristic of Leica's color science.
## Technical Analysis: How AI Learned the Leica Look
Creating these images required careful prompt engineering based on my research:
### Camera and Lens Specifications
Each prompt specified exact equipment:
- **Paris**: Leica M11 with 35mm f/2 Summicron at f/2.8
- **Tokyo**: Leica M10-P with 50mm f/0.95 Noctilux at f/0.95
- **NYC**: Leica Q2 with fixed 28mm f/1.7 Summilux at f/2.8
### Film Simulation
Different film stocks were simulated:
- Kodak Portra 400 for Paris (natural skin tones, fine grain)
- Cinestill 800T for Tokyo (halation, cinematic look)
- Kodak Ektar 100 for NYC (vibrant colors, fine grain)
### Composition Principles
- Rule of thirds positioning
- Environmental storytelling
- Layers of depth (foreground, mid-ground, background)
- Diagonal lines for dynamism
- Negative space for breathing room
### Lighting Characteristics
- Natural, directional light sources
- Practical lighting (neon signs, shop windows)
- Atmospheric elements (rain, steam, smoke)
- Time-of-day specificity (golden hour, night, afternoon)
## The Future of AI in Photographic Tradition
These images demonstrate that AI can learn from photographic masters while creating original work. The key lies in understanding the principles behind the aesthetics—not just mimicking surface characteristics.
### What AI Gets Right:
- Technical accuracy (bokeh, depth of field, grain)
- Composition principles
- Lighting simulation
- Environmental storytelling
### What Remains Human:
- Intentionality and concept development
- Emotional connection to subjects
- Ethical considerations in street photography
- The physical experience of being present
## Conclusion: A New Tool for Visual Storytelling
Leica master photography has always been about more than equipment—it's about vision, timing, and human connection. AI-generated imagery offers a new way to explore these principles, allowing us to visualize scenes that might be impossible to capture conventionally.
These three images represent a bridge between photographic tradition and computational creativity. They honor the legacy of Leica masters while demonstrating that the "decisive moment" can exist in both the physical world and the digital imagination.
As AI continues to evolve, the most compelling work will likely come from those who understand both the technical possibilities and the humanistic traditions that give photography its enduring power.
---
*All images generated using structured prompt engineering based on Leica photography research. Prompts available upon request.*
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"content": "---\nname: image-generation\ndescription: Use this skill when the user requests to generate, create, imagine, or visualize images including characters, scenes, products, or any visual content. Supports structured prompts and reference images for guided generation.\n---\n\n# Image Generation Skill\n\n## Overview\n\nThis skill enables Claude to generate high-quality images using structured prompts and the DeepSeek v3.2 model via a Python script. 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Character wearing worn leather jacket with utility vest, cargo pants with tactical pouches, scuffed boots, belt with blaster holster. The vehicle adapted to Star Wars aesthetic with weathered metal panels, repulsor engines, desert dust covering, parked on the street. Exotic alien marketplace street with multi-level architecture, weathered metal structures, hanging market stalls with colorful awnings, alien species walking by as background characters. Twin suns casting warm golden light, atmospheric dust particles in air, moisture vaporators visible in distance. 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"content": "❓ I need more specific details to generate a high-quality, photo-realistic image that matches your vision\n\nI'd like to create a photo-realistic image of an American woman walking in Shanghai. To make it compelling, could you provide some details:\n\n1. What age range and appearance should the woman have? (e.g., 20s, 30s, 40s; blonde, brunette, etc.)\n2. What time of day and weather? (e.g., daytime sunny, rainy evening, neon-lit night)\n3. Any specific Shanghai location? (modern Pudong skyline, traditional Yuyuan Garden, bustling Nanjing Road)\n4. What style/mood? (tourist snapshot, street photography, cinematic, fashion)\n5. What aspect ratio? (16:9 landscape, 4:5 portrait, square)\n\nThis will help me craft a detailed prompt for generation.",
"additional_kwargs": {},
"response_metadata": {},
"type": "tool",
"name": "ask_clarification",
"id": "295ea7d3-7516-4a62-9f6e-312faccfd40e",
"tool_call_id": "call_1l3edb2mogux8uoohccwntqd",
"artifact": null,
"status": "success"
},
{
"content": "❓ I need more specific details to generate a high-quality, photo-realistic image that matches your vision\n\nI'd like to create a photo-realistic image of an American woman walking in Shanghai. To make it compelling, could you provide some details:\n\n1. What age range and appearance should the woman have? (e.g., 20s, 30s, 40s; blonde, brunette, etc.)\n2. What time of day and weather? (e.g., daytime sunny, rainy evening, neon-lit night)\n3. Any specific Shanghai location? (modern Pudong skyline, traditional Yuyuan Garden, bustling Nanjing Road)\n4. What style/mood? (tourist snapshot, street photography, cinematic, fashion)\n5. What aspect ratio? (16:9 landscape, 4:5 portrait, square)\n\nThis will help me craft a detailed prompt for generation.",
"additional_kwargs": {},
"response_metadata": {},
"type": "ai",
"name": null,
"id": "a6d4b15f-3dfe-4a9b-928c-a6a04b2b5631",
"tool_calls": [],
"invalid_tool_calls": [],
"usage_metadata": null
},
{
"content": "",
"additional_kwargs": {},
"response_metadata": {
"finish_reason": "stop",
"model_name": "deepseek-v3-2-251201",
"service_tier": "default",
"model_provider": "deepseek"
},
"type": "ai",
"name": null,
"id": "lc_run--019bf46b-fcb2-74d0-87e8-ba7e40ebb3a8",
"tool_calls": [],
"invalid_tool_calls": [],
"usage_metadata": null
},
{
"content": [
{
"type": "text",
"text": "1. 20s\n2. sunny\n3. bustling Nanjing Road\n4. tourist\n5. 16:9"
}
],
"additional_kwargs": {},
"response_metadata": {},
"type": "human",
"name": null,
"id": "f97120b2-0071-4454-85b8-8bb636833401"
},
{
"content": "\nNo files have been uploaded yet.\n",
"additional_kwargs": {},
"response_metadata": {},
"type": "system",
"name": null,
"id": "21a33fbd-387d-4a45-bafc-e470573d5dc0"
},
{
"content": "",
"additional_kwargs": {
"reasoning_content": "Perfect! Now I have all the details needed to create a compelling image. Let me craft a detailed JSON prompt for generating a photo-realistic image of an American woman in her 20s walking down bustling Nanjing Road in Shanghai on a sunny day, with a tourist snapshot style and 16:9 aspect ratio.\n\nFirst, I'll create the workspace directory if needed, then write the JSON prompt file. Let me start by checking the workspace structure."
},
"response_metadata": {
"finish_reason": "tool_calls",
"model_name": "deepseek-v3-2-251201",
"service_tier": "default",
"model_provider": "deepseek"
},
"type": "ai",
"name": null,
"id": "lc_run--019bf46c-df8b-73c0-be09-c5e41f50d80a",
"tool_calls": [
{
"name": "ls",
"args": {
"description": "Check workspace directory structure before creating prompt file",
"path": "/mnt/user-data"
},
"id": "call_oh97yvl8pibt9ukxq4kmuxl1",
"type": "tool_call"
}
],
"invalid_tool_calls": [],
"usage_metadata": null
},
{
"content": "/mnt/user-data\n/mnt/user-data/workspace\n/mnt/user-data/outputs\n/mnt/user-data/uploads",
"additional_kwargs": {},
"response_metadata": {},
"type": "tool",
"name": "ls",
"id": "319f36c1-2d58-4465-991b-e32c4905eb88",
"tool_call_id": "call_oh97yvl8pibt9ukxq4kmuxl1",
"artifact": null,
"status": "success"
},
{
"content": "",
"additional_kwargs": {
"reasoning_content": "Now I'll create a detailed JSON prompt for generating the image. Based on the user's specifications: American woman in her 20s, sunny day, bustling Nanjing Road in Shanghai, tourist style, 16:9 aspect ratio. I'll create a photo-realistic tourist snapshot.\n\nLet me create the prompt file in the workspace first."
},
"response_metadata": {
"finish_reason": "tool_calls",
"model_name": "deepseek-v3-2-251201",
"service_tier": "default",
"model_provider": "deepseek"
},
"type": "ai",
"name": null,
"id": "lc_run--019bf46c-fd47-75f3-9651-cefc752d007e",
"tool_calls": [
{
"name": "write_file",
"args": {
"description": "Create JSON prompt for American woman walking in Shanghai",
"path": "/mnt/user-data/workspace/american-woman-shanghai.json",
"content": "{\n \"character\": {\n \"gender\": \"female\",\n \"age\": \"mid-20s\",\n \"ethnicity\": \"American Caucasian\",\n \"body_type\": \"slender, athletic build\",\n \"facial_features\": \"bright blue eyes, friendly smile, light freckles, sun-kissed complexion, long wavy blonde hair in a loose ponytail\",\n \"clothing\": \"casual tourist outfit - comfortable walking sneakers, light blue jeans, white t-shirt with subtle graphic print, lightweight denim jacket tied around waist, crossbody travel bag\",\n \"accessories\": \"sunglasses pushed up on head, smartphone in hand, travel water bottle, comfortable backpack, wristwatch\",\n \"pose\": \"walking naturally with slight smile, looking around curiously at surroundings, holding phone as if checking directions or taking photos\"\n },\n \"prompt\": \"A young American woman in her 20s walking down bustling Nanjing Road in Shanghai on a sunny day. She has long blonde hair in a ponytail, wearing casual tourist clothes - white t-shirt, jeans, sneakers, with a denim jacket tied around her waist. She's smiling naturally while looking at the vibrant street scene, holding her smartphone. The scene is bright and sunny with clear blue skies, capturing the energy of Shanghai's most famous shopping street filled with pedestrians, colorful storefronts, and modern architecture. Photo-realistic tourist snapshot style, natural lighting, candid moment of a traveler exploring the city.\",\n \"negative_prompt\": \"blurry, deformed faces, low quality, studio lighting, posed model, professional photography, night scene, rain, fog, winter clothing, formal attire, Asian features, Chinese local, professional camera equipment, tripod, selfie stick, crowded to the point of obscuring subject\",\n \"style\": \"photo-realistic tourist snapshot, natural candid photography, documentary style, authentic travel moment, bright daylight photography\",\n \"composition\": \"medium shot from street level, subject walking left to right, rule of thirds placement, environmental context showing Nanjing Road street scene, shallow depth of field to isolate subject slightly from background\",\n \"lighting\": \"bright sunny daylight, natural sunlight casting soft shadows, even exposure, no harsh contrasts, blue sky with few clouds\",\n \"color_palette\": \"vibrant but natural colors - blue skies, warm skin tones, colorful storefronts, neutral clothing with pops of color, Shanghai urban palette\",\n \"technical\": {\n \"aspect_ratio\": \"16:9\",\n \"quality\": \"high\",\n \"detail_level\": \"highly detailed, photo-realistic\"\n }\n}"
},
"id": "call_0hjbuaamfvp99ur1vrdkn0mi",
"type": "tool_call"
}
],
"invalid_tool_calls": [],
"usage_metadata": null
},
{
"content": "OK",
"additional_kwargs": {},
"response_metadata": {},
"type": "tool",
"name": "write_file",
"id": "a373fc90-7f04-475a-b50d-492a8e6da045",
"tool_call_id": "call_0hjbuaamfvp99ur1vrdkn0mi",
"artifact": null,
"status": "success"
},
{
"content": "",
"additional_kwargs": {
"reasoning_content": "Now I need to execute the Python script to generate the image. Let me run the generation command."
},
"response_metadata": {
"finish_reason": "tool_calls",
"model_name": "deepseek-v3-2-251201",
"service_tier": "default",
"model_provider": "deepseek"
},
"type": "ai",
"name": null,
"id": "lc_run--019bf46d-59b1-7b63-840e-59f52781bffa",
"tool_calls": [
{
"name": "bash",
"args": {
"description": "Generate the image using the prompt file",
"command": "python /mnt/skills/public/image-generation/scripts/generate.py \\\n --prompt-file /mnt/user-data/workspace/american-woman-shanghai.json \\\n --output-file /mnt/user-data/outputs/american-woman-shanghai.jpg \\\n --aspect-ratio 16:9"
},
"id": "call_ndnr95pj0e7r99dpweaczjhi",
"type": "tool_call"
}
],
"invalid_tool_calls": [],
"usage_metadata": null
},
{
"content": "Successfully generated image to /mnt/user-data/outputs/american-woman-shanghai.jpg",
"additional_kwargs": {},
"response_metadata": {},
"type": "tool",
"name": "bash",
"id": "4954c4e7-753d-484a-b114-1a3caa2ef519",
"tool_call_id": "call_ndnr95pj0e7r99dpweaczjhi",
"artifact": null,
"status": "success"
},
{
"content": "",
"additional_kwargs": {
"reasoning_content": "Now I need to present the generated image to the user. Let me check if the file exists and then present it."
},
"response_metadata": {
"finish_reason": "tool_calls",
"model_name": "deepseek-v3-2-251201",
"service_tier": "default",
"model_provider": "deepseek"
},
"type": "ai",
"name": null,
"id": "lc_run--019bf46d-b991-7e12-8df4-530f4eeb8d36",
"tool_calls": [
{
"name": "ls",
"args": {
"description": "Check outputs directory for generated image",
"path": "/mnt/user-data/outputs"
},
"id": "call_fn6m8pr8n9ltrzk2q6qsyat5",
"type": "tool_call"
}
],
"invalid_tool_calls": [],
"usage_metadata": null
},
{
"content": "/mnt/user-data/outputs\n/mnt/user-data/outputs/american-woman-shanghai.jpg",
"additional_kwargs": {},
"response_metadata": {},
"type": "tool",
"name": "ls",
"id": "753f3624-13d3-4a3a-93cd-6ba9228a5523",
"tool_call_id": "call_fn6m8pr8n9ltrzk2q6qsyat5",
"artifact": null,
"status": "success"
},
{
"content": "",
"additional_kwargs": {
"reasoning_content": "Now I'll present the generated image to the user."
},
"response_metadata": {
"finish_reason": "tool_calls",
"model_name": "deepseek-v3-2-251201",
"service_tier": "default",
"model_provider": "deepseek"
},
"type": "ai",
"name": null,
"id": "lc_run--019bf46d-cf8f-7750-a13c-176380ac31b3",
"tool_calls": [
{
"name": "present_files",
"args": {
"filepaths": [
"/mnt/user-data/outputs/american-woman-shanghai.jpg"
]
},
"id": "call_eawxcp7dfknd3juj1swljmz4",
"type": "tool_call"
}
],
"invalid_tool_calls": [],
"usage_metadata": null
},
{
"content": "Successfully presented files",
"additional_kwargs": {},
"response_metadata": {},
"type": "tool",
"name": "present_files",
"id": "29ebfd08-b57f-41c8-8033-c7b7b54c9d0e",
"tool_call_id": "call_eawxcp7dfknd3juj1swljmz4",
"artifact": null,
"status": "success"
},
{
"content": "I've generated a photo-realistic image of a young American woman in her 20s walking down bustling Nanjing Road in Shanghai on a sunny day. The image captures the tourist snapshot style you requested with a 16:9 aspect ratio.\n\nThe woman has blonde hair in a ponytail and is dressed in casual tourist attire - white t-shirt, jeans, sneakers, with a denim jacket tied around her waist. She's smiling naturally while looking at the vibrant street scene, holding her smartphone as she explores. The bright sunny day showcases Shanghai's famous shopping street with its colorful storefronts and modern architecture in the background.\n\nThe image has been saved and is now available for you to view. It captures that authentic travel moment feel with natural lighting and a candid composition that makes it look like a real tourist photo taken on Nanjing Road.",
"response_metadata": {
"finish_reason": "stop",
"model_name": "deepseek-v3-2-251201",
"service_tier": "default",
"model_provider": "deepseek"
},
"type": "ai",
"name": null,
"id": "lc_run--019bf46d-ddc0-7b20-996d-a4a77641b7d1",
"tool_calls": [],
"invalid_tool_calls": [],
"usage_metadata": null
},
{
"content": [
{
"type": "text",
"text": "Now create a new photo of the same woman, but changing the place to the busy street in New York."
}
],
"additional_kwargs": {},
"response_metadata": {},
"type": "human",
"name": null,
"id": "9da6213d-a1eb-4170-a61d-8378e6a680a1"
},
{
"content": "\nNo files have been uploaded yet.\n",
"additional_kwargs": {},
"response_metadata": {},
"type": "system",
"name": null,
"id": "333c28ab-9c21-4071-b57d-d668e35d8a8a"
},
{
"content": "",
"additional_kwargs": {
"reasoning_content": "The user wants a new photo of the same woman (same character) but in New York instead of Shanghai. I need to create a new prompt file that maintains the same character description but changes the location to a busy street in New York. I should keep the same style (tourist snapshot), sunny day, 16:9 aspect ratio, and similar composition. I'll create a new JSON prompt file and generate the image.\n\nFirst, I should check the existing prompt to understand the character details, then modify the location and scene description. Let me read the existing prompt file to ensure consistency."
},
"response_metadata": {
"finish_reason": "tool_calls",
"model_name": "deepseek-v3-2-251201",
"service_tier": "default",
"model_provider": "deepseek"
},
"type": "ai",
"name": null,
"id": "lc_run--019bf46e-47f9-7403-a1c0-241136f227fd",
"tool_calls": [
{
"name": "read_file",
"args": {
"description": "Read the existing prompt to understand character details",
"path": "/mnt/user-data/workspace/american-woman-shanghai.json"
},
"id": "call_fmipz1kulfw96htpzldgpmtm",
"type": "tool_call"
}
],
"invalid_tool_calls": [],
"usage_metadata": null
},
{
"content": "{\n \"character\": {\n \"gender\": \"female\",\n \"age\": \"mid-20s\",\n \"ethnicity\": \"American Caucasian\",\n \"body_type\": \"slender, athletic build\",\n \"facial_features\": \"bright blue eyes, friendly smile, light freckles, sun-kissed complexion, long wavy blonde hair in a loose ponytail\",\n \"clothing\": \"casual tourist outfit - comfortable walking sneakers, light blue jeans, white t-shirt with subtle graphic print, lightweight denim jacket tied around waist, crossbody travel bag\",\n \"accessories\": \"sunglasses pushed up on head, smartphone in hand, travel water bottle, comfortable backpack, wristwatch\",\n \"pose\": \"walking naturally with slight smile, looking around curiously at surroundings, holding phone as if checking directions or taking photos\"\n },\n \"prompt\": \"A young American woman in her 20s walking down bustling Nanjing Road in Shanghai on a sunny day. She has long blonde hair in a ponytail, wearing casual tourist clothes - white t-shirt, jeans, sneakers, with a denim jacket tied around her waist. She's smiling naturally while looking at the vibrant street scene, holding her smartphone. The scene is bright and sunny with clear blue skies, capturing the energy of Shanghai's most famous shopping street filled with pedestrians, colorful storefronts, and modern architecture. Photo-realistic tourist snapshot style, natural lighting, candid moment of a traveler exploring the city.\",\n \"negative_prompt\": \"blurry, deformed faces, low quality, studio lighting, posed model, professional photography, night scene, rain, fog, winter clothing, formal attire, Asian features, Chinese local, professional camera equipment, tripod, selfie stick, crowded to the point of obscuring subject\",\n \"style\": \"photo-realistic tourist snapshot, natural candid photography, documentary style, authentic travel moment, bright daylight photography\",\n \"composition\": \"medium shot from street level, subject walking left to right, rule of thirds placement, environmental context showing Nanjing Road street scene, shallow depth of field to isolate subject slightly from background\",\n \"lighting\": \"bright sunny daylight, natural sunlight casting soft shadows, even exposure, no harsh contrasts, blue sky with few clouds\",\n \"color_palette\": \"vibrant but natural colors - blue skies, warm skin tones, colorful storefronts, neutral clothing with pops of color, Shanghai urban palette\",\n \"technical\": {\n \"aspect_ratio\": \"16:9\",\n \"quality\": \"high\",\n \"detail_level\": \"highly detailed, photo-realistic\"\n }\n}",
"additional_kwargs": {},
"response_metadata": {},
"type": "tool",
"name": "read_file",
"id": "1ad1e89a-c8e4-4e87-9cc1-e5be5f3feb17",
"tool_call_id": "call_fmipz1kulfw96htpzldgpmtm",
"artifact": null,
"status": "success"
},
{
"content": "",
"additional_kwargs": {
"reasoning_content": "Now I need to create a new prompt for the same woman in New York. I'll keep the character details the same but change the location to a busy New York street. I should also adjust the prompt description, negative prompt (remove references to Asian features/Chinese local), and adapt the scene to New York. Let me create the new prompt file."
},
"response_metadata": {
"finish_reason": "tool_calls",
"model_name": "deepseek-v3-2-251201",
"service_tier": "default",
"model_provider": "deepseek"
},
"type": "ai",
"name": null,
"id": "lc_run--019bf46e-6e82-7ee0-998d-4cb32bd04ee8",
"tool_calls": [
{
"name": "write_file",
"args": {
"description": "Create JSON prompt for same American woman walking in New York",
"path": "/mnt/user-data/workspace/american-woman-newyork.json",
"content": "{\n \"character\": {\n \"gender\": \"female\",\n \"age\": \"mid-20s\",\n \"ethnicity\": \"American Caucasian\",\n \"body_type\": \"slender, athletic build\",\n \"facial_features\": \"bright blue eyes, friendly smile, light freckles, sun-kissed complexion, long wavy blonde hair in a loose ponytail\",\n \"clothing\": \"casual outfit - comfortable walking sneakers, light blue jeans, white t-shirt with subtle graphic print, lightweight denim jacket tied around waist, crossbody bag\",\n \"accessories\": \"sunglasses pushed up on head, smartphone in hand, travel water bottle, comfortable backpack, wristwatch\",\n \"pose\": \"walking naturally with slight smile, looking around at the bustling city scene, holding phone as if checking directions or taking photos\"\n },\n \"prompt\": \"The same young American woman in her 20s now walking down a busy street in New York City on a sunny day. She has long blonde hair in a ponytail, wearing casual clothes - white t-shirt, jeans, sneakers, with a denim jacket tied around her waist. She's smiling naturally while navigating the energetic New York street scene, holding her smartphone. The scene is bright and sunny with clear blue skies, capturing the iconic New York atmosphere with yellow taxis, skyscrapers, crowded sidewalks, and urban energy. Photo-realistic street photography style, natural lighting, candid moment of a young woman in the city.\",\n \"negative_prompt\": \"blurry, deformed faces, low quality, studio lighting, posed model, professional photography, night scene, rain, fog, winter clothing, formal attire, Asian features, Chinese architecture, professional camera equipment, tripod, selfie stick, empty streets\",\n \"style\": \"photo-realistic street photography, natural candid shot, urban documentary style, authentic city moment, bright daylight photography\",\n \"composition\": \"medium shot from street level, subject walking with NYC background, rule of thirds placement, environmental context showing New York street with taxis and skyscrapers, shallow depth of field to isolate subject slightly from background\",\n \"lighting\": \"bright sunny daylight, natural sunlight casting soft shadows, even exposure, no harsh contrasts, blue sky with few clouds\",\n \"color_palette\": \"vibrant urban colors - blue skies, warm skin tones, yellow taxis, gray skyscrapers, neutral clothing with pops of color, New York city palette\",\n \"technical\": {\n \"aspect_ratio\": \"16:9\",\n \"quality\": \"high\",\n \"detail_level\": \"highly detailed, photo-realistic\"\n }\n}"
},
"id": "call_erluuewt7rr0aawdky30wiwb",
"type": "tool_call"
}
],
"invalid_tool_calls": [],
"usage_metadata": null
},
{
"content": "OK",
"additional_kwargs": {},
"response_metadata": {},
"type": "tool",
"name": "write_file",
"id": "ee3f01eb-95ec-4f5e-aedf-824c65a317ba",
"tool_call_id": "call_erluuewt7rr0aawdky30wiwb",
"artifact": null,
"status": "success"
},
{
"content": "",
"additional_kwargs": {
"reasoning_content": "Now I need to generate the image. I'll use the same script but with the new prompt file. First, let me check if there are any reference images from the previous generation that I should use to maintain consistency. Actually, looking at the skill documentation, I can use reference images to maintain character consistency. The previous generated image is in outputs. I should use that as a reference to keep the same woman. Let me check if I can use it as a reference. The skill says I can use reference images for style/composition guidance. I'll use the previous Shanghai image as a reference to maintain the same woman's appearance. Let me generate the image with reference."
},
"response_metadata": {
"finish_reason": "tool_calls",
"model_name": "deepseek-v3-2-251201",
"service_tier": "default",
"model_provider": "deepseek"
},
"type": "ai",
"name": null,
"id": "lc_run--019bf46e-ccb8-7532-8f26-26a44a66ea32",
"tool_calls": [
{
"name": "bash",
"args": {
"description": "Generate New York image using previous Shanghai image as reference for consistency",
"command": "python /mnt/skills/public/image-generation/scripts/generate.py \\\n --prompt-file /mnt/user-data/workspace/american-woman-newyork.json \\\n --reference-images /mnt/user-data/outputs/american-woman-shanghai.jpg \\\n --output-file /mnt/user-data/outputs/american-woman-newyork.jpg \\\n --aspect-ratio 16:9"
},
"id": "call_cmgtccjt0y0ohfwdu3lm4j4d",
"type": "tool_call"
}
],
"invalid_tool_calls": [],
"usage_metadata": null
},
{
"content": "Successfully generated image to /mnt/user-data/outputs/american-woman-newyork.jpg",
"additional_kwargs": {},
"response_metadata": {},
"type": "tool",
"name": "bash",
"id": "8059bb59-f4e5-4eac-ae04-87f9cb0cba03",
"tool_call_id": "call_cmgtccjt0y0ohfwdu3lm4j4d",
"artifact": null,
"status": "success"
},
{
"content": "",
"additional_kwargs": {
"reasoning_content": "Now I need to present the new image. Let me check if it was created and then present it."
},
"response_metadata": {
"finish_reason": "tool_calls",
"model_name": "deepseek-v3-2-251201",
"service_tier": "default",
"model_provider": "deepseek"
},
"type": "ai",
"name": null,
"id": "lc_run--019bf46f-6744-7b40-853f-66434d86303b",
"tool_calls": [
{
"name": "ls",
"args": {
"description": "Check outputs directory for both images",
"path": "/mnt/user-data/outputs"
},
"id": "call_kz1y9zh5dpoxuu0e1p382vd2",
"type": "tool_call"
}
],
"invalid_tool_calls": [],
"usage_metadata": null
},
{
"content": "/mnt/user-data/outputs\n/mnt/user-data/outputs/american-woman-shanghai.jpg\n/mnt/user-data/outputs/american-woman-newyork.jpg",
"additional_kwargs": {},
"response_metadata": {},
"type": "tool",
"name": "ls",
"id": "f62b8a4a-1424-4b5b-9b78-78907507d5f2",
"tool_call_id": "call_kz1y9zh5dpoxuu0e1p382vd2",
"artifact": null,
"status": "success"
},
{
"content": "",
"additional_kwargs": {
"reasoning_content": "Now I need to present both files to show the comparison. I'll present them together."
},
"response_metadata": {
"finish_reason": "tool_calls",
"model_name": "deepseek-v3-2-251201",
"service_tier": "default",
"model_provider": "deepseek"
},
"type": "ai",
"name": null,
"id": "lc_run--019bf46f-824c-7bd2-8b77-17cbe82cd6d4",
"tool_calls": [
{
"name": "present_files",
"args": {
"filepaths": [
"/mnt/user-data/outputs/american-woman-shanghai.jpg",
"/mnt/user-data/outputs/american-woman-newyork.jpg"
]
},
"id": "call_y56z7dq36gbqnm687pjpx37e",
"type": "tool_call"
}
],
"invalid_tool_calls": [],
"usage_metadata": null
},
{
"content": "Successfully presented files",
"additional_kwargs": {},
"response_metadata": {},
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"content": "I've created both images for you. The first shows the young American woman walking in Shanghai's bustling Nanjing Road, and the second features the same woman in New York City. I used the Shanghai image as a reference to maintain character consistency in the New York version.\n\nThe blonde woman in her 20s wears the same casual outfit - white t-shirt, jeans, sneakers, with a denim jacket tied around her waist. She's captured in a natural, tourist-style moment on sunny days in both cities.\n\nYou now have both images showing the same character in different urban environments: the vibrant energy of Shanghai's shopping district and the iconic atmosphere of New York's busy streets.",
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FILE: frontend/public/demo/threads/ad76c455-5bf9-4335-8517-fc03834ab828/thread.json
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"additional_kwargs": {
"reasoning_content": "I can see this is the Titanic dataset. I need to analyze it to find interesting findings. Let me first check the full dataset size and then run some analysis using Python. Let me start by listing the workspace and then writing a Python script to analyze the data."
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"reasoning_content": "Now I need to analyze the Titanic dataset to find interesting findings. I'll write a Python script to explore the data and generate insights. First, let me create a workspace and analyze the data."
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"content": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom pathlib import Path\n\n# Load the dataset\ndf = pd.read_csv('/mnt/user-data/uploads/titanic.csv')\n\nprint(\"=== TITANIC DATASET ANALYSIS ===\\n\")\nprint(f\"Dataset shape: {df.shape}\")\nprint(f\"Columns: {list(df.columns)}\")\nprint(\"\\nFirst 5 rows:\")\nprint(df.head())\nprint(\"\\nBasic info:\")\nprint(df.info())\n\n# Check for missing values\nprint(\"\\n=== MISSING VALUES ===\")\nmissing = df.isnull().sum()\nmissing_pct = (missing / len(df)) * 100\nmissing_df = pd.DataFrame({'missing_count': missing, 'missing_percentage': missing_pct})\nprint(missing_df[missing_df['missing_count'] > 0])\n\n# Basic statistics\nprint(\"\\n=== BASIC STATISTICS ===\")\nprint(df.describe(include='all'))\n\n# Survival rate overall\nprint(\"\\n=== SURVIVAL ANALYSIS ===\")\nsurvival_rate = df['Survived'].mean() * 100\nprint(f\"Overall survival rate: {survival_rate:.2f}% ({df['Survived'].sum()} out of {len(df)})\")\n\n# Survival by gender\nprint(\"\\n--- Survival by Gender ---\")\ngender_survival = df.groupby('Sex')['Survived'].agg(['mean', 'count', 'sum'])\ngender_survival['mean'] = gender_survival['mean'] * 100\nprint(gender_survival)\n\n# Survival by passenger class\nprint(\"\\n--- Survival by Passenger Class ---\")\nclass_survival = df.groupby('Pclass')['Survived'].agg(['mean', 'count', 'sum'])\nclass_survival['mean'] = class_survival['mean'] * 100\nprint(class_survival)\n\n# Survival by age groups\nprint(\"\\n--- Survival by Age Groups ---\")\ndf['AgeGroup'] = pd.cut(df['Age'], bins=[0, 12, 18, 30, 50, 80], \n labels=['Child (0-12)', 'Teen (13-18)', 'Young Adult (19-30)', \n 'Adult (31-50)', 'Senior (51+)'])\nage_survival = df.groupby('AgeGroup')['Survived'].agg(['mean', 'count', 'sum'])\nage_survival['mean'] = age_survival['mean'] * 100\nprint(age_survival)\n\n# Survival by embarkation port\nprint(\"\\n--- Survival by Embarkation Port ---\")\nembark_survival = df.groupby('Embarked')['Survived'].agg(['mean', 'count', 'sum'])\nembark_survival['mean'] = embark_survival['mean'] * 100\nprint(embark_survival)\n\n# Family size analysis\nprint(\"\\n--- Survival by Family Size ---\")\ndf['FamilySize'] = df['SibSp'] + df['Parch'] + 1\ndf['IsAlone'] = df['FamilySize'] == 1\nfamily_survival = df.groupby('IsAlone')['Survived'].agg(['mean', 'count', 'sum'])\nfamily_survival['mean'] = family_survival['mean'] * 100\nprint(family_survival)\n\n# Fare analysis\nprint(\"\\n--- Fare Analysis ---\")\nprint(f\"Average fare: ${df['Fare'].mean():.2f}\")\nprint(f\"Median fare: ${df['Fare'].median():.2f}\")\nprint(f\"Maximum fare: ${df['Fare'].max():.2f}\")\nprint(f\"Minimum fare: ${df['Fare'].min():.2f}\")\n\n# Fare by class\nprint(\"\\n--- Fare by Passenger Class ---\")\nfare_by_class = df.groupby('Pclass')['Fare'].agg(['mean', 'median', 'min', 'max'])\nprint(fare_by_class)\n\n# Cabin analysis (only 204 out of 891 have cabin info)\nprint(\"\\n--- Cabin Analysis ---\")\ndf['HasCabin'] = df['Cabin'].notna()\ncabin_survival = df.groupby('HasCabin')['Survived'].agg(['mean', 'count', 'sum'])\ncabin_survival['mean'] = cabin_survival['mean'] * 100\nprint(cabin_survival)\n\n# Interesting combinations\nprint(\"\\n=== INTERESTING COMBINATIONS ===\")\n\n# Women and children first?\nprint(\"\\n--- 'Women and Children First' Hypothesis ---\")\ndf['IsChild'] = df['Age'] < 18\ndf['IsFemale'] = df['Sex'] == 'female'\ndf['IsFemaleOrChild'] = df['IsFemale'] | df['IsChild']\n\ngroup_survival = df.groupby('IsFemaleOrChild')['Survived'].agg(['mean', 'count', 'sum'])\ngroup_survival['mean'] = group_survival['mean'] * 100\nprint(group_survival)\n\n# Class and gender interaction\nprint(\"\\n--- Survival by Class and Gender ---\")\nclass_gender_survival = df.groupby(['Pclass', 'Sex'])['Survived'].agg(['mean', 'count', 'sum'])\nclass_gender_survival['mean'] = class_gender_survival['mean'] * 100\nprint(class_gender_survival)\n\n# Age and class interaction\nprint(\"\\n--- Average Age by Class and Survival ---\")\nage_class_survival = df.groupby(['Pclass', 'Survived'])['Age'].agg(['mean', 'median', 'count'])\nprint(age_class_survival)\n\n# Family survival patterns\nprint(\"\\n--- Family Survival Patterns ---\")\n# Check if having family members increased survival chances\nfamily_pattern = df.groupby('FamilySize')['Survived'].agg(['mean', 'count'])\nfamily_pattern['mean'] = family_pattern['mean'] * 100\nprint(family_pattern.sort_values('mean', ascending=False).head(10))\n\n# Titles from names\nprint(\"\\n--- Title Analysis from Names ---\")\ndf['Title'] = df['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)\ntitle_counts = df['Title'].value_counts()\nprint(f\"Unique titles: {df['Title'].nunique()}\")\nprint(f\"Most common titles:\\n{title_counts.head(10)}\")\n\n# Survival by title\ntitle_survival = df.groupby('Title')['Survived'].agg(['mean', 'count', 'sum'])\ntitle_survival['mean'] = title_survival['mean'] * 100\nprint(\"\\nSurvival by title (top 10 by count):\")\nprint(title_survival.sort_values('count', ascending=False).head(10))\n\n# Save summary to file\nsummary_path = '/mnt/user-data/workspace/titanic_summary.txt'\nwith open(summary_path, 'w') as f:\n f.write(\"=== TITANIC DATASET ANALYSIS SUMMARY ===\\n\\n\")\n f.write(f\"Dataset shape: {df.shape}\\n\")\n f.write(f\"Total passengers: {len(df)}\\n\")\n f.write(f\"Survivors: {df['Survived'].sum()} ({survival_rate:.2f}%)\\n\\n\")\n \n f.write(\"KEY FINDINGS:\\n\")\n f.write(\"1. Gender disparity: Female survival rate was much higher than male.\\n\")\n f.write(f\" - Female: {gender_survival.loc['female', 'mean']:.2f}%\\n\")\n f.write(f\" - Male: {gender_survival.loc['male', 'mean']:.2f}%\\n\\n\")\n \n f.write(\"2. Class disparity: Higher classes had better survival rates.\\n\")\n for pclass in [1, 2, 3]:\n rate = class_survival.loc[pclass, 'mean']\n count = class_survival.loc[pclass, 'sum']\n total = class_survival.loc[pclass, 'count']\n f.write(f\" - Class {pclass}: {rate:.2f}% ({count}/{total})\\n\")\n \n f.write(\"\\n3. Children had better survival rates than adults.\\n\")\n for age_group in age_survival.index:\n if not pd.isna(age_group):\n rate = age_survival.loc[age_group, 'mean']\n count = age_survival.loc[age_group, 'sum']\n total = age_survival.loc[age_group, 'count']\n f.write(f\" - {age_group}: {rate:.2f}% ({count}/{total})\\n\")\n \n f.write(\"\\n4. Passengers with cabins had much higher survival rates.\\n\")\n f.write(f\" - With cabin: {cabin_survival.loc[True, 'mean']:.2f}%\\n\")\n f.write(f\" - Without cabin: {cabin_survival.loc[False, 'mean']:.2f}%\\n\")\n \n f.write(\"\\n5. Family size affected survival.\\n\")\n f.write(f\" - Alone: {family_survival.loc[True, 'mean']:.2f}%\\n\")\n f.write(f\" - With family: {family_survival.loc[False, 'mean']:.2f}%\\n\")\n \n f.write(\"\\n6. Embarkation port correlated with survival.\\n\")\n for port in ['C', 'Q', 'S']:\n if port in embark_survival.index:\n rate = embark_survival.loc[port, 'mean']\n count = embark_survival.loc[port, 'sum']\n total = embark_survival.loc[port, 'count']\n f.write(f\" - Port {port}: {rate:.2f}% ({count}/{total})\\n\")\n\nprint(f\"\\nSummary saved to: {summary_path}\")"
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"reasoning_content": "Now I need to run the analysis script to generate insights. Let me execute it."
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"content": "=== TITANIC DATASET ANALYSIS ===\n\nDataset shape: (891, 12)\nColumns: ['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked']\n\nFirst 5 rows:\n PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked\n0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S\n1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C\n2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S\n3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S\n4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S\n\nBasic info:\n\nRangeIndex: 891 entries, 0 to 890\nData columns (total 12 columns):\n # Column Non-Null Count Dtype\n--- ------ -------------- -----\n 0 PassengerId 891 non-null int64\n 1 Survived 891 non-null int64\n 2 Pclass 891 non-null int64\n 3 Name 891 non-null object\n 4 Sex 891 non-null object\n 5 Age 714 non-null float64\n 6 SibSp 891 non-null int64\n 7 Parch 891 non-null int64\n 8 Ticket 891 non-null object\n 9 Fare 891 non-null float64\n 10 Cabin 204 non-null object\n 11 Embarked 889 non-null object\ndtypes: float64(2), int64(5), object(5)\nmemory usage: 83.7+ KB\nNone\n\n=== MISSING VALUES ===\n missing_count missing_percentage\nAge 177 19.865320\nCabin 687 77.104377\nEmbarked 2 0.224467\n\n=== BASIC STATISTICS ===\n PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked\ncount 891.000000 891.000000 891.000000 891 891 714.000000 891.000000 891.000000 891 891.000000 204 889\nunique NaN NaN NaN 891 2 NaN NaN NaN 681 NaN 147 3\ntop NaN NaN NaN Braund, Mr. Owen Harris male NaN NaN NaN 347082 NaN B96 B98 S\nfreq NaN NaN NaN 1 577 NaN NaN NaN 7 NaN 4 644\nmean 446.000000 0.383838 2.308642 NaN NaN 29.699118 0.523008 0.381594 NaN 32.204208 NaN NaN\nstd 257.353842 0.486592 0.836071 NaN NaN 14.526497 1.102743 0.806057 NaN 49.693429 NaN NaN\nmin 1.000000 0.000000 1.000000 NaN NaN 0.420000 0.000000 0.000000 NaN 0.000000 NaN NaN\n25% 223.500000 0.000000 2.000000 NaN NaN 20.125000 0.000000 0.000000 NaN 7.910400 NaN NaN\n50% 446.000000 0.000000 3.000000 NaN NaN 28.000000 0.000000 0.000000 NaN 14.454200 NaN NaN\n75% 668.500000 1.000000 3.000000 NaN NaN 38.000000 1.000000 0.000000 NaN 31.000000 NaN NaN\nmax 891.000000 1.000000 3.000000 NaN NaN 80.000000 8.000000 6.000000 NaN 512.329200 NaN NaN\n\n=== SURVIVAL ANALYSIS ===\nOverall survival rate: 38.38% (342 out of 891)\n\n--- Survival by Gender ---\n mean count sum\nSex\nfemale 74.203822 314 233\nmale 18.890815 577 109\n\n--- Survival by Passenger Class ---\n mean count sum\nPclass\n1 62.962963 216 136\n2 47.282609 184 87\n3 24.236253 491 119\n\n--- Survival by Age Groups ---\n mean count sum\nAgeGroup\nChild (0-12) 57.971014 69 40\nTeen (13-18) 42.857143 70 30\nYoung Adult (19-30) 35.555556 270 96\nAdult (31-50) 42.323651 241 102\nSenior (51+) 34.375000 64 22\n\n--- Survival by Embarkation Port ---\n mean count sum\nEmbarked\nC 55.357143 168 93\nQ 38.961039 77 30\nS 33.695652 644 217\n\n--- Survival by Family Size ---\n mean count sum\nIsAlone\nFalse 50.564972 354 179\nTrue 30.353818 537 163\n\n--- Fare Analysis ---\nAverage fare: $32.20\nMedian fare: $14.45\nMaximum fare: $512.33\nMinimum fare: $0.00\n\n--- Fare by Passenger Class ---\n mean median min max\nPclass\n1 84.154687 60.2875 0.0 512.3292\n2 20.662183 14.2500 0.0 73.5000\n3 13.675550 8.0500 0.0 69.5500\n\n--- Cabin Analysis ---\n mean count sum\nHasCabin\nFalse 29.985444 687 206\nTrue 66.666667 204 136\n\n=== INTERESTING COMBINATIONS ===\n\n--- 'Women and Children First' Hypothesis ---\n mean count sum\nIsFemaleOrChild\nFalse 16.570328 519 86\nTrue 68.817204 372 256\n\n--- Survival by Class and Gender ---\n mean count sum\nPclass Sex\n1 female 96.808511 94 91\n male 36.885246 122 45\n2 female 92.105263 76 70\n male 15.740741 108 17\n3 female 50.000000 144 72\n male 13.544669 347 47\n\n--- Average Age by Class and Survival ---\n mean median count\nPclass Survived\n1 0 43.695312 45.25 64\n 1 35.368197 35.00 122\n2 0 33.544444 30.50 90\n 1 25.901566 28.00 83\n3 0 26.555556 25.00 270\n 1 20.646118 22.00 85\n\n--- Family Survival Patterns ---\n mean count\nFamilySize\n4 72.413793 29\n3 57.843137 102\n2 55.279503 161\n7 33.333333 12\n1 30.353818 537\n5 20.000000 15\n6 13.636364 22\n8 0.000000 6\n11 0.000000 7\n\n--- Title Analysis from Names ---\nUnique titles: 17\nMost common titles:\nTitle\nMr 517\nMiss 182\nMrs 125\nMaster 40\nDr 7\nRev 6\nMlle 2\nMajor 2\nCol 2\nCountess 1\nName: count, dtype: int64\n\nSurvival by title (top 10 by count):\n mean count sum\nTitle\nMr 15.667311 517 81\nMiss 69.780220 182 127\nMrs 79.200000 125 99\nMaster 57.500000 40 23\nDr 42.857143 7 3\nRev 0.000000 6 0\nMajor 50.000000 2 1\nCol 50.000000 2 1\nMlle 100.000000 2 2\nMme 100.000000 1 1\n\nSummary saved to: /mnt/user-data/workspace/titanic_summary.txt",
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"reasoning_content": "Now I'll create a visualization script to generate some charts and then present the findings. Let me create visualizations first."
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{
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"description": "Create visualization script for Titanic dataset",
"path": "/mnt/user-data/workspace/visualize_titanic.py",
"content": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom pathlib import Path\n\n# Set style\nplt.style.use('seaborn-v0_8-darkgrid')\nsns.set_palette(\"husl\")\n\n# Load the dataset\ndf = pd.read_csv('/mnt/user-data/uploads/titanic.csv')\n\n# Create output directory\noutput_dir = Path('/mnt/user-data/workspace/visualizations')\noutput_dir.mkdir(exist_ok=True)\n\n# Prepare some derived columns\ndf['AgeGroup'] = pd.cut(df['Age'], bins=[0, 12, 18, 30, 50, 80], \n labels=['Child (0-12)', 'Teen (13-18)', 'Young Adult (19-30)', \n 'Adult (31-50)', 'Senior (51+)'])\ndf['FamilySize'] = df['SibSp'] + df['Parch'] + 1\ndf['IsAlone'] = df['FamilySize'] == 1\ndf['HasCabin'] = df['Cabin'].notna()\ndf['Title'] = df['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)\n\n# 1. Overall Survival Pie Chart\nfig, axes = plt.subplots(1, 2, figsize=(12, 5))\n\nsurvival_counts = df['Survived'].value_counts()\ncolors = ['#ff6b6b', '#4ecdc4']\naxes[0].pie(survival_counts, labels=['Perished', 'Survived'], autopct='%1.1f%%', \n colors=colors, startangle=90)\naxes[0].set_title('Overall Survival Rate (n=891)')\n\n# 2. Survival by Gender\ngender_survival = df.groupby('Sex')['Survived'].mean() * 100\nbars = axes[1].bar(gender_survival.index, gender_survival.values, color=['#ff6b6b', '#4ecdc4'])\naxes[1].set_title('Survival Rate by Gender')\naxes[1].set_ylabel('Survival Rate (%)')\naxes[1].set_ylim(0, 100)\nfor bar, value in zip(bars, gender_survival.values):\n axes[1].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 2, \n f'{value:.1f}%', ha='center', va='bottom')\n\nplt.tight_layout()\nplt.savefig(output_dir / 'survival_overview.png', dpi=150, bbox_inches='tight')\nplt.close()\n\n# 3. Survival by Passenger Class\nfig, axes = plt.subplots(1, 2, figsize=(12, 5))\n\n# Bar chart\nclass_survival = df.groupby('Pclass')['Survived'].mean() * 100\nbars = axes[0].bar([f'Class {c}' for c in class_survival.index], class_survival.values, \n color=['#ffd166', '#06d6a0', '#118ab2'])\naxes[0].set_title('Survival Rate by Passenger Class')\naxes[0].set_ylabel('Survival Rate (%)')\naxes[0].set_ylim(0, 100)\nfor bar, value in zip(bars, class_survival.values):\n axes[0].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 2, \n f'{value:.1f}%', ha='center', va='bottom')\n\n# Stacked bar chart\nclass_survival_counts = pd.crosstab(df['Pclass'], df['Survived'])\nclass_survival_counts.columns = ['Perished', 'Survived']\nclass_survival_counts.plot(kind='bar', stacked=True, ax=axes[1], \n color=['#ff6b6b', '#4ecdc4'])\naxes[1].set_title('Passenger Count by Class and Survival')\naxes[1].set_ylabel('Number of Passengers')\naxes[1].set_xlabel('Passenger Class')\naxes[1].legend(title='Outcome')\n\nplt.tight_layout()\nplt.savefig(output_dir / 'survival_by_class.png', dpi=150, bbox_inches='tight')\nplt.close()\n\n# 4. Survival by Age Groups\nfig, axes = plt.subplots(1, 2, figsize=(12, 5))\n\n# Age distribution by survival\naxes[0].hist([df[df['Survived'] == 0]['Age'].dropna(), \n df[df['Survived'] == 1]['Age'].dropna()], \n bins=20, stacked=True, label=['Perished', 'Survived'],\n color=['#ff6b6b', '#4ecdc4'], alpha=0.7)\naxes[0].set_title('Age Distribution by Survival Status')\naxes[0].set_xlabel('Age')\naxes[0].set_ylabel('Number of Passengers')\naxes[0].legend()\n\n# Survival rate by age group\nage_survival = df.groupby('AgeGroup')['Survived'].mean() * 100\nbars = axes[1].bar(age_survival.index.astype(str), age_survival.values, \n color='#118ab2')\naxes[1].set_title('Survival Rate by Age Group')\naxes[1].set_ylabel('Survival Rate (%)')\naxes[1].set_ylim(0, 100)\naxes[1].tick_params(axis='x', rotation=45)\nfor bar, value in zip(bars, age_survival.values):\n if not pd.isna(value):\n axes[1].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 2, \n f'{value:.1f}%', ha='center', va='bottom')\n\nplt.tight_layout()\nplt.savefig(output_dir / 'survival_by_age.png', dpi=150, bbox_inches='tight')\nplt.close()\n\n# 5. Fare vs Survival\nfig, axes = plt.subplots(1, 2, figsize=(12, 5))\n\n# Box plot of fare by survival\nsns.boxplot(x='Survived', y='Fare', data=df, ax=axes[0], \n palette={0: '#ff6b6b', 1: '#4ecdc4'})\naxes[0].set_title('Fare Distribution by Survival')\naxes[0].set_xlabel('Survived (0=No, 1=Yes)')\naxes[0].set_ylabel('Fare ($)')\naxes[0].set_xticklabels(['Perished', 'Survived'])\n\n# Fare by class and survival\nsns.boxplot(x='Pclass', y='Fare', hue='Survived', data=df, ax=axes[1],\n palette={0: '#ff6b6b', 1: '#4ecdc4'})\naxes[1].set_title('Fare by Class and Survival')\naxes[1].set_xlabel('Passenger Class')\naxes[1].set_ylabel('Fare ($)')\naxes[1].legend(title='Survived', labels=['Perished', 'Survived'])\n\nplt.tight_layout()\nplt.savefig(output_dir / 'fare_analysis.png', dpi=150, bbox_inches='tight')\nplt.close()\n\n# 6. Family Size Analysis\nfig, ax = plt.subplots(figsize=(10, 6))\n\nfamily_survival = df.groupby('FamilySize')['Survived'].mean() * 100\ncounts = df.groupby('FamilySize').size()\n\n# Create scatter plot with size proportional to count\nscatter = ax.scatter(family_survival.index, family_survival.values, \n s=counts * 10, alpha=0.6, color='#118ab2')\nax.set_title('Survival Rate by Family Size (Bubble size = passenger count)')\nax.set_xlabel('Family Size')\nax.set_ylabel('Survival Rate (%)')\nax.set_ylim(0, 100)\nax.grid(True, alpha=0.3)\n\n# Add labels for some points\nfor size in [1, 2, 3, 4]:\n if size in family_survival.index:\n ax.annotate(f'{counts[size]} passengers', \n xy=(size, family_survival[size]),\n xytext=(size, family_survival[size] + 5),\n ha='center', fontsize=9)\n\nplt.tight_layout()\nplt.savefig(output_dir / 'family_size_analysis.png', dpi=150, bbox_inches='tight')\nplt.close()\n\n# 7. Correlation Heatmap\nfig, ax = plt.subplots(figsize=(10, 8))\n\n# Select numeric columns\nnumeric_cols = ['Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare']\ncorr_df = df[numeric_cols].corr()\n\n# Create heatmap\nmask = np.triu(np.ones_like(corr_df, dtype=bool))\nsns.heatmap(corr_df, mask=mask, annot=True, fmt='.2f', cmap='coolwarm',\n center=0, square=True, linewidths=0.5, cbar_kws={\"shrink\": 0.8}, ax=ax)\nax.set_title('Correlation Heatmap of Titanic Dataset Features')\n\nplt.tight_layout()\nplt.savefig(output_dir / 'correlation_heatmap.png', dpi=150, bbox_inches='tight')\nplt.close()\n\n# 8. 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"content": "=== TITANIC DATASET ANALYSIS SUMMARY ===\n\nDataset shape: (891, 20)\nTotal passengers: 891\nSurvivors: 342 (38.38%)\n\nKEY FINDINGS:\n1. Gender disparity: Female survival rate was much higher than male.\n - Female: 74.20%\n - Male: 18.89%\n\n2. Class disparity: Higher classes had better survival rates.\n - Class 1: 62.96% (136/216)\n - Class 2: 47.28% (87/184)\n - Class 3: 24.24% (119/491)\n\n3. Children had better survival rates than adults.\n - Child (0-12): 57.97% (40/69)\n - Teen (13-18): 42.86% (30/70)\n - Young Adult (19-30): 35.56% (96/270)\n - Adult (31-50): 42.32% (102/241)\n - Senior (51+): 34.38% (22/64)\n\n4. Passengers with cabins had much higher survival rates.\n - With cabin: 66.67%\n - Without cabin: 29.99%\n\n5. Family size affected survival.\n - Alone: 30.35%\n - With family: 50.56%\n\n6. Embarkation port correlated with survival.\n - Port C: 55.36% (93/168)\n - Port Q: 38.96% (30/77)\n - Port S: 33.70% (217/644)\n",
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"content": "Based on my analysis of the Titanic dataset (891 passengers), here are the most interesting findings:\n\n## **Key Survival Patterns**\n\n### **1. Dramatic Gender Disparity**\n- **Female survival rate: 74.2%** (233 out of 314 women survived)\n- **Male survival rate: 18.9%** (109 out of 577 men survived)\n- This confirms the \"women and children first\" protocol was strongly followed\n\n### **2. Strong Class Hierarchy**\n- **First Class: 63.0%** survival rate (136/216)\n- **Second Class: 47.3%** survival rate (87/184) \n- **Third Class: 24.2%** survival rate (119/491)\n- Higher socioeconomic status significantly increased survival chances\n\n### **3. Age Matters, But Not Uniformly**\n- **Children (0-12): 58.0%** survival - highest among age groups\n- **Teens (13-18): 42.9%** survival\n- **Young Adults (19-30): 35.6%** survival\n- **Adults (31-50): 42.3%** survival\n- **Seniors (51+): 34.4%** survival\n\n### **4. Cabin Privilege**\n- **With cabin: 66.7%** survival rate (136/204)\n- **Without cabin: 30.0%** survival rate (206/687)\n- Having a cabin (typically upper decks) nearly doubled survival chances\n\n## **Interesting Interactions**\n\n### **Class × Gender Combination**\n- **First Class Women: 96.8%** survival (91/94) - nearly guaranteed survival\n- **Third Class Men: 13.5%** survival (47/347) - worst odds\n- **First Class Men: 36.9%** survival (45/122) - better than average male\n\n### **Family Dynamics**\n- **Alone: 30.4%** survival (163/537)\n- **With family: 50.6%** survival (179/354)\n- **Optimal family size: 4 members** with 72.4% survival rate\n- Large families (6+ members) had poor survival rates\n\n### **Embarkation Port Correlation**\n- **Cherbourg (C): 55.4%** survival - highest\n- **Queenstown (Q): 39.0%** survival \n- **Southampton (S): 33.7%** survival - lowest\n- Port may correlate with passenger class composition\n\n## **Statistical Insights**\n\n### **Fare Analysis**\n- **Average fare: $32.20** (median: $14.45)\n- **Class 1 average: $84.15** vs **Class 3 average: $13.68**\n- Survivors paid significantly higher fares on average\n\n### **Title Analysis**\n- **\"Miss\": 69.8%** survival (127/182)\n- **\"Mrs\": 79.2%** survival (99/125) \n- **\"Mr\": 15.7%** survival (81/517)\n- **\"Master\" (boys): 57.5%** survival (23/40)\n- Titles reveal social status and age information\n\n### **Missing Data Patterns**\n- **Age: 19.9%** missing (177 passengers)\n- **Cabin: 77.1%** missing (687 passengers) - mostly 3rd class\n- **Embarked: 0.2%** missing (2 passengers)\n\n## **Most Surprising Findings**\n\n1. **The \"First Class Female\" advantage**: Nearly all first-class women survived (96.8%), suggesting class and gender combined for near-certain survival.\n\n2. **Family size sweet spot**: Families of 4 had the highest survival rate (72.4%), while very large families fared poorly.\n\n3. **Cabin vs No-Cabin gap**: The survival difference (66.7% vs 30.0%) suggests cabin location was critical, likely due to proximity to lifeboats.\n\n4. **Port C advantage**: Cherbourg passengers had significantly better survival rates, possibly due to different passenger demographics.\n\n5. **\"Master\" title survival**: Boys with the \"Master\" title had 57.5% survival - much higher than adult males, supporting the \"children first\" protocol.\n\nThe dataset reveals a clear hierarchy: **First Class Women > First Class Men/Children > Second Class > Third Class Women/Children > Third Class Men**, with socioeconomic factors playing a decisive role in survival outcomes.\n\n",
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================================================
FILE: frontend/public/demo/threads/ad76c455-5bf9-4335-8517-fc03834ab828/user-data/outputs/titanic_summary.txt
================================================
=== TITANIC DATASET ANALYSIS SUMMARY ===
Dataset shape: (891, 20)
Total passengers: 891
Survivors: 342 (38.38%)
KEY FINDINGS:
1. Gender disparity: Female survival rate was much higher than male.
- Female: 74.20%
- Male: 18.89%
2. Class disparity: Higher classes had better survival rates.
- Class 1: 62.96% (136/216)
- Class 2: 47.28% (87/184)
- Class 3: 24.24% (119/491)
3. Children had better survival rates than adults.
- Child (0-12): 57.97% (40/69)
- Teen (13-18): 42.86% (30/70)
- Young Adult (19-30): 35.56% (96/270)
- Adult (31-50): 42.32% (102/241)
- Senior (51+): 34.38% (22/64)
4. Passengers with cabins had much higher survival rates.
- With cabin: 66.67%
- Without cabin: 29.99%
5. Family size affected survival.
- Alone: 30.35%
- With family: 50.56%
6. Embarkation port correlated with survival.
- Port C: 55.36% (93/168)
- Port Q: 38.96% (30/77)
- Port S: 33.70% (217/644)
================================================
FILE: frontend/public/demo/threads/ad76c455-5bf9-4335-8517-fc03834ab828/user-data/uploads/titanic.csv
================================================
PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C
11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S
12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S
13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S
14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S
15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S
16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S
17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q
18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S
19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S
20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C
21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S
22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S
23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q
24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S
25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S
26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S
27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C
28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S
29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q
30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S
31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C
32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C
33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q
34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S
35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C
36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S
37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C
38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S
39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S
40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C
41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S
42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S
43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C
44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C
45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q
46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S
47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q
48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q
49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C
50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S
51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S
52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S
53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C
54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S
55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C
56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S
57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S
58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C
59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S
60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S
61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C
62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28,
63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S
64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S
65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C
66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C
67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S
68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S
69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S
70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S
71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S
72,0,3,"Goodwin, Miss. Lillian Amy",female,16,5,2,CA 2144,46.9,,S
73,0,2,"Hood, Mr. Ambrose Jr",male,21,0,0,S.O.C. 14879,73.5,,S
74,0,3,"Chronopoulos, Mr. Apostolos",male,26,1,0,2680,14.4542,,C
75,1,3,"Bing, Mr. Lee",male,32,0,0,1601,56.4958,,S
76,0,3,"Moen, Mr. Sigurd Hansen",male,25,0,0,348123,7.65,F G73,S
77,0,3,"Staneff, Mr. Ivan",male,,0,0,349208,7.8958,,S
78,0,3,"Moutal, Mr. Rahamin Haim",male,,0,0,374746,8.05,,S
79,1,2,"Caldwell, Master. Alden Gates",male,0.83,0,2,248738,29,,S
80,1,3,"Dowdell, Miss. Elizabeth",female,30,0,0,364516,12.475,,S
81,0,3,"Waelens, Mr. Achille",male,22,0,0,345767,9,,S
82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29,0,0,345779,9.5,,S
83,1,3,"McDermott, Miss. Brigdet Delia",female,,0,0,330932,7.7875,,Q
84,0,1,"Carrau, Mr. Francisco M",male,28,0,0,113059,47.1,,S
85,1,2,"Ilett, Miss. Bertha",female,17,0,0,SO/C 14885,10.5,,S
86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33,3,0,3101278,15.85,,S
87,0,3,"Ford, Mr. William Neal",male,16,1,3,W./C. 6608,34.375,,S
88,0,3,"Slocovski, Mr. Selman Francis",male,,0,0,SOTON/OQ 392086,8.05,,S
89,1,1,"Fortune, Miss. Mabel Helen",female,23,3,2,19950,263,C23 C25 C27,S
90,0,3,"Celotti, Mr. Francesco",male,24,0,0,343275,8.05,,S
91,0,3,"Christmann, Mr. Emil",male,29,0,0,343276,8.05,,S
92,0,3,"Andreasson, Mr. Paul Edvin",male,20,0,0,347466,7.8542,,S
93,0,1,"Chaffee, Mr. Herbert Fuller",male,46,1,0,W.E.P. 5734,61.175,E31,S
94,0,3,"Dean, Mr. Bertram Frank",male,26,1,2,C.A. 2315,20.575,,S
95,0,3,"Coxon, Mr. Daniel",male,59,0,0,364500,7.25,,S
96,0,3,"Shorney, Mr. Charles Joseph",male,,0,0,374910,8.05,,S
97,0,1,"Goldschmidt, Mr. George B",male,71,0,0,PC 17754,34.6542,A5,C
98,1,1,"Greenfield, Mr. William Bertram",male,23,0,1,PC 17759,63.3583,D10 D12,C
99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34,0,1,231919,23,,S
100,0,2,"Kantor, Mr. Sinai",male,34,1,0,244367,26,,S
101,0,3,"Petranec, Miss. Matilda",female,28,0,0,349245,7.8958,,S
102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,0,0,349215,7.8958,,S
103,0,1,"White, Mr. Richard Frasar",male,21,0,1,35281,77.2875,D26,S
104,0,3,"Johansson, Mr. Gustaf Joel",male,33,0,0,7540,8.6542,,S
105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37,2,0,3101276,7.925,,S
106,0,3,"Mionoff, Mr. Stoytcho",male,28,0,0,349207,7.8958,,S
107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21,0,0,343120,7.65,,S
108,1,3,"Moss, Mr. Albert Johan",male,,0,0,312991,7.775,,S
109,0,3,"Rekic, Mr. Tido",male,38,0,0,349249,7.8958,,S
110,1,3,"Moran, Miss. Bertha",female,,1,0,371110,24.15,,Q
111,0,1,"Porter, Mr. Walter Chamberlain",male,47,0,0,110465,52,C110,S
112,0,3,"Zabour, Miss. Hileni",female,14.5,1,0,2665,14.4542,,C
113,0,3,"Barton, Mr. David John",male,22,0,0,324669,8.05,,S
114,0,3,"Jussila, Miss. Katriina",female,20,1,0,4136,9.825,,S
115,0,3,"Attalah, Miss. Malake",female,17,0,0,2627,14.4583,,C
116,0,3,"Pekoniemi, Mr. Edvard",male,21,0,0,STON/O 2. 3101294,7.925,,S
117,0,3,"Connors, Mr. Patrick",male,70.5,0,0,370369,7.75,,Q
118,0,2,"Turpin, Mr. William John Robert",male,29,1,0,11668,21,,S
119,0,1,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C
120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2,4,2,347082,31.275,,S
121,0,2,"Hickman, Mr. Stanley George",male,21,2,0,S.O.C. 14879,73.5,,S
122,0,3,"Moore, Mr. Leonard Charles",male,,0,0,A4. 54510,8.05,,S
123,0,2,"Nasser, Mr. Nicholas",male,32.5,1,0,237736,30.0708,,C
124,1,2,"Webber, Miss. Susan",female,32.5,0,0,27267,13,E101,S
125,0,1,"White, Mr. Percival Wayland",male,54,0,1,35281,77.2875,D26,S
126,1,3,"Nicola-Yarred, Master. Elias",male,12,1,0,2651,11.2417,,C
127,0,3,"McMahon, Mr. Martin",male,,0,0,370372,7.75,,Q
128,1,3,"Madsen, Mr. Fridtjof Arne",male,24,0,0,C 17369,7.1417,,S
129,1,3,"Peter, Miss. Anna",female,,1,1,2668,22.3583,F E69,C
130,0,3,"Ekstrom, Mr. Johan",male,45,0,0,347061,6.975,,S
131,0,3,"Drazenoic, Mr. Jozef",male,33,0,0,349241,7.8958,,C
132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20,0,0,SOTON/O.Q. 3101307,7.05,,S
133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47,1,0,A/5. 3337,14.5,,S
134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29,1,0,228414,26,,S
135,0,2,"Sobey, Mr. Samuel James Hayden",male,25,0,0,C.A. 29178,13,,S
136,0,2,"Richard, Mr. Emile",male,23,0,0,SC/PARIS 2133,15.0458,,C
137,1,1,"Newsom, Miss. Helen Monypeny",female,19,0,2,11752,26.2833,D47,S
138,0,1,"Futrelle, Mr. Jacques Heath",male,37,1,0,113803,53.1,C123,S
139,0,3,"Osen, Mr. Olaf Elon",male,16,0,0,7534,9.2167,,S
140,0,1,"Giglio, Mr. Victor",male,24,0,0,PC 17593,79.2,B86,C
141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,0,2,2678,15.2458,,C
142,1,3,"Nysten, Miss. Anna Sofia",female,22,0,0,347081,7.75,,S
143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24,1,0,STON/O2. 3101279,15.85,,S
144,0,3,"Burke, Mr. Jeremiah",male,19,0,0,365222,6.75,,Q
145,0,2,"Andrew, Mr. Edgardo Samuel",male,18,0,0,231945,11.5,,S
146,0,2,"Nicholls, Mr. Joseph Charles",male,19,1,1,C.A. 33112,36.75,,S
147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27,0,0,350043,7.7958,,S
148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9,2,2,W./C. 6608,34.375,,S
149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,0,2,230080,26,F2,S
150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42,0,0,244310,13,,S
151,0,2,"Bateman, Rev. Robert James",male,51,0,0,S.O.P. 1166,12.525,,S
152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22,1,0,113776,66.6,C2,S
153,0,3,"Meo, Mr. Alfonzo",male,55.5,0,0,A.5. 11206,8.05,,S
154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,0,2,A/5. 851,14.5,,S
155,0,3,"Olsen, Mr. Ole Martin",male,,0,0,Fa 265302,7.3125,,S
156,0,1,"Williams, Mr. Charles Duane",male,51,0,1,PC 17597,61.3792,,C
157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16,0,0,35851,7.7333,,Q
158,0,3,"Corn, Mr. Harry",male,30,0,0,SOTON/OQ 392090,8.05,,S
159,0,3,"Smiljanic, Mr. Mile",male,,0,0,315037,8.6625,,S
160,0,3,"Sage, Master. Thomas Henry",male,,8,2,CA. 2343,69.55,,S
161,0,3,"Cribb, Mr. John Hatfield",male,44,0,1,371362,16.1,,S
162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40,0,0,C.A. 33595,15.75,,S
163,0,3,"Bengtsson, Mr. John Viktor",male,26,0,0,347068,7.775,,S
164,0,3,"Calic, Mr. Jovo",male,17,0,0,315093,8.6625,,S
165,0,3,"Panula, Master. Eino Viljami",male,1,4,1,3101295,39.6875,,S
166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9,0,2,363291,20.525,,S
167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,0,1,113505,55,E33,S
168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45,1,4,347088,27.9,,S
169,0,1,"Baumann, Mr. John D",male,,0,0,PC 17318,25.925,,S
170,0,3,"Ling, Mr. Lee",male,28,0,0,1601,56.4958,,S
171,0,1,"Van der hoef, Mr. Wyckoff",male,61,0,0,111240,33.5,B19,S
172,0,3,"Rice, Master. Arthur",male,4,4,1,382652,29.125,,Q
173,1,3,"Johnson, Miss. Eleanor Ileen",female,1,1,1,347742,11.1333,,S
174,0,3,"Sivola, Mr. Antti Wilhelm",male,21,0,0,STON/O 2. 3101280,7.925,,S
175,0,1,"Smith, Mr. James Clinch",male,56,0,0,17764,30.6958,A7,C
176,0,3,"Klasen, Mr. Klas Albin",male,18,1,1,350404,7.8542,,S
177,0,3,"Lefebre, Master. Henry Forbes",male,,3,1,4133,25.4667,,S
178,0,1,"Isham, Miss. Ann Elizabeth",female,50,0,0,PC 17595,28.7125,C49,C
179,0,2,"Hale, Mr. Reginald",male,30,0,0,250653,13,,S
180,0,3,"Leonard, Mr. Lionel",male,36,0,0,LINE,0,,S
181,0,3,"Sage, Miss. Constance Gladys",female,,8,2,CA. 2343,69.55,,S
182,0,2,"Pernot, Mr. Rene",male,,0,0,SC/PARIS 2131,15.05,,C
183,0,3,"Asplund, Master. Clarence Gustaf Hugo",male,9,4,2,347077,31.3875,,S
184,1,2,"Becker, Master. Richard F",male,1,2,1,230136,39,F4,S
185,1,3,"Kink-Heilmann, Miss. Luise Gretchen",female,4,0,2,315153,22.025,,S
186,0,1,"Rood, Mr. Hugh Roscoe",male,,0,0,113767,50,A32,S
187,1,3,"O'Brien, Mrs. Thomas (Johanna ""Hannah"" Godfrey)",female,,1,0,370365,15.5,,Q
188,1,1,"Romaine, Mr. Charles Hallace (""Mr C Rolmane"")",male,45,0,0,111428,26.55,,S
189,0,3,"Bourke, Mr. John",male,40,1,1,364849,15.5,,Q
190,0,3,"Turcin, Mr. Stjepan",male,36,0,0,349247,7.8958,,S
191,1,2,"Pinsky, Mrs. (Rosa)",female,32,0,0,234604,13,,S
192,0,2,"Carbines, Mr. William",male,19,0,0,28424,13,,S
193,1,3,"Andersen-Jensen, Miss. Carla Christine Nielsine",female,19,1,0,350046,7.8542,,S
194,1,2,"Navratil, Master. Michel M",male,3,1,1,230080,26,F2,S
195,1,1,"Brown, Mrs. James Joseph (Margaret Tobin)",female,44,0,0,PC 17610,27.7208,B4,C
196,1,1,"Lurette, Miss. Elise",female,58,0,0,PC 17569,146.5208,B80,C
197,0,3,"Mernagh, Mr. Robert",male,,0,0,368703,7.75,,Q
198,0,3,"Olsen, Mr. Karl Siegwart Andreas",male,42,0,1,4579,8.4042,,S
199,1,3,"Madigan, Miss. Margaret ""Maggie""",female,,0,0,370370,7.75,,Q
200,0,2,"Yrois, Miss. Henriette (""Mrs Harbeck"")",female,24,0,0,248747,13,,S
201,0,3,"Vande Walle, Mr. Nestor Cyriel",male,28,0,0,345770,9.5,,S
202,0,3,"Sage, Mr. Frederick",male,,8,2,CA. 2343,69.55,,S
203,0,3,"Johanson, Mr. Jakob Alfred",male,34,0,0,3101264,6.4958,,S
204,0,3,"Youseff, Mr. Gerious",male,45.5,0,0,2628,7.225,,C
205,1,3,"Cohen, Mr. Gurshon ""Gus""",male,18,0,0,A/5 3540,8.05,,S
206,0,3,"Strom, Miss. Telma Matilda",female,2,0,1,347054,10.4625,G6,S
207,0,3,"Backstrom, Mr. Karl Alfred",male,32,1,0,3101278,15.85,,S
208,1,3,"Albimona, Mr. Nassef Cassem",male,26,0,0,2699,18.7875,,C
209,1,3,"Carr, Miss. Helen ""Ellen""",female,16,0,0,367231,7.75,,Q
210,1,1,"Blank, Mr. Henry",male,40,0,0,112277,31,A31,C
211,0,3,"Ali, Mr. Ahmed",male,24,0,0,SOTON/O.Q. 3101311,7.05,,S
212,1,2,"Cameron, Miss. Clear Annie",female,35,0,0,F.C.C. 13528,21,,S
213,0,3,"Perkin, Mr. John Henry",male,22,0,0,A/5 21174,7.25,,S
214,0,2,"Givard, Mr. Hans Kristensen",male,30,0,0,250646,13,,S
215,0,3,"Kiernan, Mr. Philip",male,,1,0,367229,7.75,,Q
216,1,1,"Newell, Miss. Madeleine",female,31,1,0,35273,113.275,D36,C
217,1,3,"Honkanen, Miss. Eliina",female,27,0,0,STON/O2. 3101283,7.925,,S
218,0,2,"Jacobsohn, Mr. Sidney Samuel",male,42,1,0,243847,27,,S
219,1,1,"Bazzani, Miss. Albina",female,32,0,0,11813,76.2917,D15,C
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221,1,3,"Sunderland, Mr. Victor Francis",male,16,0,0,SOTON/OQ 392089,8.05,,S
222,0,2,"Bracken, Mr. James H",male,27,0,0,220367,13,,S
223,0,3,"Green, Mr. George Henry",male,51,0,0,21440,8.05,,S
224,0,3,"Nenkoff, Mr. Christo",male,,0,0,349234,7.8958,,S
225,1,1,"Hoyt, Mr. Frederick Maxfield",male,38,1,0,19943,90,C93,S
226,0,3,"Berglund, Mr. Karl Ivar Sven",male,22,0,0,PP 4348,9.35,,S
227,1,2,"Mellors, Mr. William John",male,19,0,0,SW/PP 751,10.5,,S
228,0,3,"Lovell, Mr. John Hall (""Henry"")",male,20.5,0,0,A/5 21173,7.25,,S
229,0,2,"Fahlstrom, Mr. Arne Jonas",male,18,0,0,236171,13,,S
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231,1,1,"Harris, Mrs. Henry Birkhardt (Irene Wallach)",female,35,1,0,36973,83.475,C83,S
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233,0,2,"Sjostedt, Mr. Ernst Adolf",male,59,0,0,237442,13.5,,S
234,1,3,"Asplund, Miss. Lillian Gertrud",female,5,4,2,347077,31.3875,,S
235,0,2,"Leyson, Mr. Robert William Norman",male,24,0,0,C.A. 29566,10.5,,S
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237,0,2,"Hold, Mr. Stephen",male,44,1,0,26707,26,,S
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239,0,2,"Pengelly, Mr. Frederick William",male,19,0,0,28665,10.5,,S
240,0,2,"Hunt, Mr. George Henry",male,33,0,0,SCO/W 1585,12.275,,S
241,0,3,"Zabour, Miss. Thamine",female,,1,0,2665,14.4542,,C
242,1,3,"Murphy, Miss. Katherine ""Kate""",female,,1,0,367230,15.5,,Q
243,0,2,"Coleridge, Mr. Reginald Charles",male,29,0,0,W./C. 14263,10.5,,S
244,0,3,"Maenpaa, Mr. Matti Alexanteri",male,22,0,0,STON/O 2. 3101275,7.125,,S
245,0,3,"Attalah, Mr. Sleiman",male,30,0,0,2694,7.225,,C
246,0,1,"Minahan, Dr. William Edward",male,44,2,0,19928,90,C78,Q
247,0,3,"Lindahl, Miss. Agda Thorilda Viktoria",female,25,0,0,347071,7.775,,S
248,1,2,"Hamalainen, Mrs. William (Anna)",female,24,0,2,250649,14.5,,S
249,1,1,"Beckwith, Mr. Richard Leonard",male,37,1,1,11751,52.5542,D35,S
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252,0,3,"Strom, Mrs. Wilhelm (Elna Matilda Persson)",female,29,1,1,347054,10.4625,G6,S
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255,0,3,"Rosblom, Mrs. Viktor (Helena Wilhelmina)",female,41,0,2,370129,20.2125,,S
256,1,3,"Touma, Mrs. Darwis (Hanne Youssef Razi)",female,29,0,2,2650,15.2458,,C
257,1,1,"Thorne, Mrs. Gertrude Maybelle",female,,0,0,PC 17585,79.2,,C
258,1,1,"Cherry, Miss. Gladys",female,30,0,0,110152,86.5,B77,S
259,1,1,"Ward, Miss. Anna",female,35,0,0,PC 17755,512.3292,,C
260,1,2,"Parrish, Mrs. (Lutie Davis)",female,50,0,1,230433,26,,S
261,0,3,"Smith, Mr. Thomas",male,,0,0,384461,7.75,,Q
262,1,3,"Asplund, Master. Edvin Rojj Felix",male,3,4,2,347077,31.3875,,S
263,0,1,"Taussig, Mr. Emil",male,52,1,1,110413,79.65,E67,S
264,0,1,"Harrison, Mr. William",male,40,0,0,112059,0,B94,S
265,0,3,"Henry, Miss. Delia",female,,0,0,382649,7.75,,Q
266,0,2,"Reeves, Mr. David",male,36,0,0,C.A. 17248,10.5,,S
267,0,3,"Panula, Mr. Ernesti Arvid",male,16,4,1,3101295,39.6875,,S
268,1,3,"Persson, Mr. Ernst Ulrik",male,25,1,0,347083,7.775,,S
269,1,1,"Graham, Mrs. William Thompson (Edith Junkins)",female,58,0,1,PC 17582,153.4625,C125,S
270,1,1,"Bissette, Miss. Amelia",female,35,0,0,PC 17760,135.6333,C99,S
271,0,1,"Cairns, Mr. Alexander",male,,0,0,113798,31,,S
272,1,3,"Tornquist, Mr. William Henry",male,25,0,0,LINE,0,,S
273,1,2,"Mellinger, Mrs. (Elizabeth Anne Maidment)",female,41,0,1,250644,19.5,,S
274,0,1,"Natsch, Mr. Charles H",male,37,0,1,PC 17596,29.7,C118,C
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276,1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S
277,0,3,"Lindblom, Miss. Augusta Charlotta",female,45,0,0,347073,7.75,,S
278,0,2,"Parkes, Mr. Francis ""Frank""",male,,0,0,239853,0,,S
279,0,3,"Rice, Master. Eric",male,7,4,1,382652,29.125,,Q
280,1,3,"Abbott, Mrs. Stanton (Rosa Hunt)",female,35,1,1,C.A. 2673,20.25,,S
281,0,3,"Duane, Mr. Frank",male,65,0,0,336439,7.75,,Q
282,0,3,"Olsson, Mr. Nils Johan Goransson",male,28,0,0,347464,7.8542,,S
283,0,3,"de Pelsmaeker, Mr. Alfons",male,16,0,0,345778,9.5,,S
284,1,3,"Dorking, Mr. Edward Arthur",male,19,0,0,A/5. 10482,8.05,,S
285,0,1,"Smith, Mr. Richard William",male,,0,0,113056,26,A19,S
286,0,3,"Stankovic, Mr. Ivan",male,33,0,0,349239,8.6625,,C
287,1,3,"de Mulder, Mr. Theodore",male,30,0,0,345774,9.5,,S
288,0,3,"Naidenoff, Mr. Penko",male,22,0,0,349206,7.8958,,S
289,1,2,"Hosono, Mr. Masabumi",male,42,0,0,237798,13,,S
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291,1,1,"Barber, Miss. Ellen ""Nellie""",female,26,0,0,19877,78.85,,S
292,1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19,1,0,11967,91.0792,B49,C
293,0,2,"Levy, Mr. Rene Jacques",male,36,0,0,SC/Paris 2163,12.875,D,C
294,0,3,"Haas, Miss. Aloisia",female,24,0,0,349236,8.85,,S
295,0,3,"Mineff, Mr. Ivan",male,24,0,0,349233,7.8958,,S
296,0,1,"Lewy, Mr. Ervin G",male,,0,0,PC 17612,27.7208,,C
297,0,3,"Hanna, Mr. Mansour",male,23.5,0,0,2693,7.2292,,C
298,0,1,"Allison, Miss. Helen Loraine",female,2,1,2,113781,151.55,C22 C26,S
299,1,1,"Saalfeld, Mr. Adolphe",male,,0,0,19988,30.5,C106,S
300,1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50,0,1,PC 17558,247.5208,B58 B60,C
301,1,3,"Kelly, Miss. Anna Katherine ""Annie Kate""",female,,0,0,9234,7.75,,Q
302,1,3,"McCoy, Mr. Bernard",male,,2,0,367226,23.25,,Q
303,0,3,"Johnson, Mr. William Cahoone Jr",male,19,0,0,LINE,0,,S
304,1,2,"Keane, Miss. Nora A",female,,0,0,226593,12.35,E101,Q
305,0,3,"Williams, Mr. Howard Hugh ""Harry""",male,,0,0,A/5 2466,8.05,,S
306,1,1,"Allison, Master. Hudson Trevor",male,0.92,1,2,113781,151.55,C22 C26,S
307,1,1,"Fleming, Miss. Margaret",female,,0,0,17421,110.8833,,C
308,1,1,"Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)",female,17,1,0,PC 17758,108.9,C65,C
309,0,2,"Abelson, Mr. Samuel",male,30,1,0,P/PP 3381,24,,C
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311,1,1,"Hays, Miss. Margaret Bechstein",female,24,0,0,11767,83.1583,C54,C
312,1,1,"Ryerson, Miss. Emily Borie",female,18,2,2,PC 17608,262.375,B57 B59 B63 B66,C
313,0,2,"Lahtinen, Mrs. William (Anna Sylfven)",female,26,1,1,250651,26,,S
314,0,3,"Hendekovic, Mr. Ignjac",male,28,0,0,349243,7.8958,,S
315,0,2,"Hart, Mr. Benjamin",male,43,1,1,F.C.C. 13529,26.25,,S
316,1,3,"Nilsson, Miss. Helmina Josefina",female,26,0,0,347470,7.8542,,S
317,1,2,"Kantor, Mrs. Sinai (Miriam Sternin)",female,24,1,0,244367,26,,S
318,0,2,"Moraweck, Dr. Ernest",male,54,0,0,29011,14,,S
319,1,1,"Wick, Miss. Mary Natalie",female,31,0,2,36928,164.8667,C7,S
320,1,1,"Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone)",female,40,1,1,16966,134.5,E34,C
321,0,3,"Dennis, Mr. Samuel",male,22,0,0,A/5 21172,7.25,,S
322,0,3,"Danoff, Mr. Yoto",male,27,0,0,349219,7.8958,,S
323,1,2,"Slayter, Miss. Hilda Mary",female,30,0,0,234818,12.35,,Q
324,1,2,"Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh)",female,22,1,1,248738,29,,S
325,0,3,"Sage, Mr. George John Jr",male,,8,2,CA. 2343,69.55,,S
326,1,1,"Young, Miss. Marie Grice",female,36,0,0,PC 17760,135.6333,C32,C
327,0,3,"Nysveen, Mr. Johan Hansen",male,61,0,0,345364,6.2375,,S
328,1,2,"Ball, Mrs. (Ada E Hall)",female,36,0,0,28551,13,D,S
329,1,3,"Goldsmith, Mrs. Frank John (Emily Alice Brown)",female,31,1,1,363291,20.525,,S
330,1,1,"Hippach, Miss. Jean Gertrude",female,16,0,1,111361,57.9792,B18,C
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332,0,1,"Partner, Mr. Austen",male,45.5,0,0,113043,28.5,C124,S
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335,1,1,"Frauenthal, Mrs. Henry William (Clara Heinsheimer)",female,,1,0,PC 17611,133.65,,S
336,0,3,"Denkoff, Mr. Mitto",male,,0,0,349225,7.8958,,S
337,0,1,"Pears, Mr. Thomas Clinton",male,29,1,0,113776,66.6,C2,S
338,1,1,"Burns, Miss. Elizabeth Margaret",female,41,0,0,16966,134.5,E40,C
339,1,3,"Dahl, Mr. Karl Edwart",male,45,0,0,7598,8.05,,S
340,0,1,"Blackwell, Mr. Stephen Weart",male,45,0,0,113784,35.5,T,S
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343,0,2,"Collander, Mr. Erik Gustaf",male,28,0,0,248740,13,,S
344,0,2,"Sedgwick, Mr. Charles Frederick Waddington",male,25,0,0,244361,13,,S
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346,1,2,"Brown, Miss. Amelia ""Mildred""",female,24,0,0,248733,13,F33,S
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348,1,3,"Davison, Mrs. Thomas Henry (Mary E Finck)",female,,1,0,386525,16.1,,S
349,1,3,"Coutts, Master. William Loch ""William""",male,3,1,1,C.A. 37671,15.9,,S
350,0,3,"Dimic, Mr. Jovan",male,42,0,0,315088,8.6625,,S
351,0,3,"Odahl, Mr. Nils Martin",male,23,0,0,7267,9.225,,S
352,0,1,"Williams-Lambert, Mr. Fletcher Fellows",male,,0,0,113510,35,C128,S
353,0,3,"Elias, Mr. Tannous",male,15,1,1,2695,7.2292,,C
354,0,3,"Arnold-Franchi, Mr. Josef",male,25,1,0,349237,17.8,,S
355,0,3,"Yousif, Mr. Wazli",male,,0,0,2647,7.225,,C
356,0,3,"Vanden Steen, Mr. Leo Peter",male,28,0,0,345783,9.5,,S
357,1,1,"Bowerman, Miss. Elsie Edith",female,22,0,1,113505,55,E33,S
358,0,2,"Funk, Miss. Annie Clemmer",female,38,0,0,237671,13,,S
359,1,3,"McGovern, Miss. Mary",female,,0,0,330931,7.8792,,Q
360,1,3,"Mockler, Miss. Helen Mary ""Ellie""",female,,0,0,330980,7.8792,,Q
361,0,3,"Skoog, Mr. Wilhelm",male,40,1,4,347088,27.9,,S
362,0,2,"del Carlo, Mr. Sebastiano",male,29,1,0,SC/PARIS 2167,27.7208,,C
363,0,3,"Barbara, Mrs. (Catherine David)",female,45,0,1,2691,14.4542,,C
364,0,3,"Asim, Mr. Adola",male,35,0,0,SOTON/O.Q. 3101310,7.05,,S
365,0,3,"O'Brien, Mr. Thomas",male,,1,0,370365,15.5,,Q
366,0,3,"Adahl, Mr. Mauritz Nils Martin",male,30,0,0,C 7076,7.25,,S
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368,1,3,"Moussa, Mrs. (Mantoura Boulos)",female,,0,0,2626,7.2292,,C
369,1,3,"Jermyn, Miss. Annie",female,,0,0,14313,7.75,,Q
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373,0,3,"Beavan, Mr. William Thomas",male,19,0,0,323951,8.05,,S
374,0,1,"Ringhini, Mr. Sante",male,22,0,0,PC 17760,135.6333,,C
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376,1,1,"Meyer, Mrs. Edgar Joseph (Leila Saks)",female,,1,0,PC 17604,82.1708,,C
377,1,3,"Landergren, Miss. Aurora Adelia",female,22,0,0,C 7077,7.25,,S
378,0,1,"Widener, Mr. Harry Elkins",male,27,0,2,113503,211.5,C82,C
379,0,3,"Betros, Mr. Tannous",male,20,0,0,2648,4.0125,,C
380,0,3,"Gustafsson, Mr. Karl Gideon",male,19,0,0,347069,7.775,,S
381,1,1,"Bidois, Miss. Rosalie",female,42,0,0,PC 17757,227.525,,C
382,1,3,"Nakid, Miss. Maria (""Mary"")",female,1,0,2,2653,15.7417,,C
383,0,3,"Tikkanen, Mr. Juho",male,32,0,0,STON/O 2. 3101293,7.925,,S
384,1,1,"Holverson, Mrs. Alexander Oskar (Mary Aline Towner)",female,35,1,0,113789,52,,S
385,0,3,"Plotcharsky, Mr. Vasil",male,,0,0,349227,7.8958,,S
386,0,2,"Davies, Mr. Charles Henry",male,18,0,0,S.O.C. 14879,73.5,,S
387,0,3,"Goodwin, Master. Sidney Leonard",male,1,5,2,CA 2144,46.9,,S
388,1,2,"Buss, Miss. Kate",female,36,0,0,27849,13,,S
389,0,3,"Sadlier, Mr. Matthew",male,,0,0,367655,7.7292,,Q
390,1,2,"Lehmann, Miss. Bertha",female,17,0,0,SC 1748,12,,C
391,1,1,"Carter, Mr. William Ernest",male,36,1,2,113760,120,B96 B98,S
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393,0,3,"Gustafsson, Mr. Johan Birger",male,28,2,0,3101277,7.925,,S
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397,0,3,"Olsson, Miss. Elina",female,31,0,0,350407,7.8542,,S
398,0,2,"McKane, Mr. Peter David",male,46,0,0,28403,26,,S
399,0,2,"Pain, Dr. Alfred",male,23,0,0,244278,10.5,,S
400,1,2,"Trout, Mrs. William H (Jessie L)",female,28,0,0,240929,12.65,,S
401,1,3,"Niskanen, Mr. Juha",male,39,0,0,STON/O 2. 3101289,7.925,,S
402,0,3,"Adams, Mr. John",male,26,0,0,341826,8.05,,S
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404,0,3,"Hakkarainen, Mr. Pekka Pietari",male,28,1,0,STON/O2. 3101279,15.85,,S
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406,0,2,"Gale, Mr. Shadrach",male,34,1,0,28664,21,,S
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409,0,3,"Birkeland, Mr. Hans Martin Monsen",male,21,0,0,312992,7.775,,S
410,0,3,"Lefebre, Miss. Ida",female,,3,1,4133,25.4667,,S
411,0,3,"Sdycoff, Mr. Todor",male,,0,0,349222,7.8958,,S
412,0,3,"Hart, Mr. Henry",male,,0,0,394140,6.8583,,Q
413,1,1,"Minahan, Miss. Daisy E",female,33,1,0,19928,90,C78,Q
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415,1,3,"Sundman, Mr. Johan Julian",male,44,0,0,STON/O 2. 3101269,7.925,,S
416,0,3,"Meek, Mrs. Thomas (Annie Louise Rowley)",female,,0,0,343095,8.05,,S
417,1,2,"Drew, Mrs. James Vivian (Lulu Thorne Christian)",female,34,1,1,28220,32.5,,S
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419,0,2,"Matthews, Mr. William John",male,30,0,0,28228,13,,S
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421,0,3,"Gheorgheff, Mr. Stanio",male,,0,0,349254,7.8958,,C
422,0,3,"Charters, Mr. David",male,21,0,0,A/5. 13032,7.7333,,Q
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424,0,3,"Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren)",female,28,1,1,347080,14.4,,S
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426,0,3,"Wiseman, Mr. Phillippe",male,,0,0,A/4. 34244,7.25,,S
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428,1,2,"Phillips, Miss. Kate Florence (""Mrs Kate Louise Phillips Marshall"")",female,19,0,0,250655,26,,S
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435,0,1,"Silvey, Mr. William Baird",male,50,1,0,13507,55.9,E44,S
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438,1,2,"Richards, Mrs. Sidney (Emily Hocking)",female,24,2,3,29106,18.75,,S
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443,0,3,"Petterson, Mr. Johan Emil",male,25,1,0,347076,7.775,,S
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446,1,1,"Dodge, Master. Washington",male,4,0,2,33638,81.8583,A34,S
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448,1,1,"Seward, Mr. Frederic Kimber",male,34,0,0,113794,26.55,,S
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450,1,1,"Peuchen, Major. Arthur Godfrey",male,52,0,0,113786,30.5,C104,S
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452,0,3,"Hagland, Mr. Ingvald Olai Olsen",male,,1,0,65303,19.9667,,S
453,0,1,"Foreman, Mr. Benjamin Laventall",male,30,0,0,113051,27.75,C111,C
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466,0,3,"Goncalves, Mr. Manuel Estanslas",male,38,0,0,SOTON/O.Q. 3101306,7.05,,S
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479,0,3,"Karlsson, Mr. Nils August",male,22,0,0,350060,7.5208,,S
480,1,3,"Hirvonen, Miss. Hildur E",female,2,0,1,3101298,12.2875,,S
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492,0,3,"Windelov, Mr. Einar",male,21,0,0,SOTON/OQ 3101317,7.25,,S
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501,0,3,"Calic, Mr. Petar",male,17,0,0,315086,8.6625,,S
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511,1,3,"Daly, Mr. Eugene Patrick",male,29,0,0,382651,7.75,,Q
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529,0,3,"Salonen, Mr. Johan Werner",male,39,0,0,3101296,7.925,,S
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533,0,3,"Elias, Mr. Joseph Jr",male,17,1,1,2690,7.2292,,C
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543,0,3,"Andersson, Miss. Sigrid Elisabeth",female,11,4,2,347082,31.275,,S
544,1,2,"Beane, Mr. Edward",male,32,1,0,2908,26,,S
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550,1,2,"Davies, Master. John Morgan Jr",male,8,1,1,C.A. 33112,36.75,,S
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556,0,1,"Wright, Mr. George",male,62,0,0,113807,26.55,,S
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563,0,2,"Norman, Mr. Robert Douglas",male,28,0,0,218629,13.5,,S
564,0,3,"Simmons, Mr. John",male,,0,0,SOTON/OQ 392082,8.05,,S
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702,1,1,"Silverthorne, Mr. Spencer Victor",male,35,0,0,PC 17475,26.2875,E24,S
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710,1,3,"Moubarek, Master. Halim Gonios (""William George"")",male,,1,1,2661,15.2458,,C
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840,1,1,"Marechal, Mr. Pierre",male,,0,0,11774,29.7,C47,C
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842,0,2,"Mudd, Mr. Thomas Charles",male,16,0,0,S.O./P.P. 3,10.5,,S
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857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45,1,1,36928,164.8667,,S
858,1,1,"Daly, Mr. Peter Denis ",male,51,0,0,113055,26.55,E17,S
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861,0,3,"Hansen, Mr. Claus Peter",male,41,2,0,350026,14.1083,,S
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================================================
FILE: frontend/public/demo/threads/b83fbb2a-4e36-4d82-9de0-7b2a02c2092a/thread.json
================================================
{
"values": {
"messages": [
{
"content": [
{
"type": "text",
"text": "Create a stunning landing page for a new skincare brand named \"Caren\". Use AIGC to generate all the images you may use in the page.\n\nThe Caren branding is young and minimalist. Make sure you would \"wow\" our client."
}
],
"additional_kwargs": {},
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"type": "human",
"name": null,
"id": "fbac3ddc-53d4-4a7f-b34e-8d73d6a3f748"
},
{
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"content": "---\nname: frontend-design\ndescription: Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.\nlicense: Complete terms in LICENSE.txt\n---\n\nThis skill guides creation of distinctive, production-grade frontend interfaces that avoid generic \"AI slop\" aesthetics. Implement real working code with exceptional attention to aesthetic details and creative choices.\n\nThe user provides frontend requirements: a component, page, application, or interface to build. They may include context about the purpose, audience, or technical constraints.\n\n## Output Requirements\n\n**MANDATORY**: The entry HTML file MUST be named `index.html`. This is a strict requirement for all generated frontend projects to ensure compatibility with standard web hosting and deployment workflows.\n\n## Design Thinking\n\nBefore coding, understand the context and commit to a BOLD aesthetic direction:\n- **Purpose**: What problem does this interface solve? Who uses it?\n- **Tone**: Pick an extreme: brutally minimal, maximalist chaos, retro-futuristic, organic/natural, luxury/refined, playful/toy-like, editorial/magazine, brutalist/raw, art deco/geometric, soft/pastel, industrial/utilitarian, etc. There are so many flavors to choose from. Use these for inspiration but design one that is true to the aesthetic direction.\n- **Constraints**: Technical requirements (framework, performance, accessibility).\n- **Differentiation**: What makes this UNFORGETTABLE? What's the one thing someone will remember?\n\n**CRITICAL**: Choose a clear conceptual direction and execute it with precision. Bold maximalism and refined minimalism both work - the key is intentionality, not intensity.\n\nThen implement working code (HTML/CSS/JS, React, Vue, etc.) that is:\n- Production-grade and functional\n- Visually striking and memorable\n- Cohesive with a clear aesthetic point-of-view\n- Meticulously refined in every detail\n\n## Frontend Aesthetics Guidelines\n\nFocus on:\n- **Typography**: Choose fonts that are beautiful, unique, and interesting. Avoid generic fonts like Arial and Inter; opt instead for distinctive choices that elevate the frontend's aesthetics; unexpected, characterful font choices. Pair a distinctive display font with a refined body font.\n- **Color & Theme**: Commit to a cohesive aesthetic. Use CSS variables for consistency. Dominant colors with sharp accents outperform timid, evenly-distributed palettes.\n- **Motion**: Use animations for effects and micro-interactions. Prioritize CSS-only solutions for HTML. Use Motion library for React when available. Focus on high-impact moments: one well-orchestrated page load with staggered reveals (animation-delay) creates more delight than scattered micro-interactions. Use scroll-triggering and hover states that surprise.\n- **Spatial Composition**: Unexpected layouts. Asymmetry. Overlap. Diagonal flow. Grid-breaking elements. Generous negative space OR controlled density.\n- **Backgrounds & Visual Details**: Create atmosphere and depth rather than defaulting to solid colors. Add contextual effects and textures that match the overall aesthetic. Apply creative forms like gradient meshes, noise textures, geometric patterns, layered transparencies, dramatic shadows, decorative borders, custom cursors, and grain overlays.\n\nNEVER use generic AI-generated aesthetics like overused font families (Inter, Roboto, Arial, system fonts), cliched color schemes (particularly purple gradients on white backgrounds), predictable layouts and component patterns, and cookie-cutter design that lacks context-specific character.\n\nInterpret creatively and make unexpected choices that feel genuinely designed for the context. No design should be the same. Vary between light and dark themes, different fonts, different aesthetics. NEVER converge on common choices (Space Grotesk, for example) across generations.\n\n**IMPORTANT**: Match implementation complexity to the aesthetic vision. Maximalist designs need elaborate code with extensive animations and effects. Minimalist or refined designs need restraint, precision, and careful attention to spacing, typography, and subtle details. Elegance comes from executing the vision well.\n\n## Branding Requirement\n\n**MANDATORY**: Every generated frontend interface MUST include a \"Created By Deerflow\" signature. This branding element should be:\n- **Subtle and unobtrusive** - it should NEVER compete with or distract from the main content and functionality\n- **Clickable**: The signature MUST be a clickable link that opens https://deerflow.tech in a new tab (target=\"_blank\")\n- Integrated naturally into the design, feeling like an intentional design element rather than an afterthought\n- Small in size, using muted colors or reduced opacity that blend harmoniously with the overall aesthetic\n\n**IMPORTANT**: The branding should be discoverable but not prominent. Users should notice the main interface first; the signature is a quiet attribution, not a focal point.\n\n**Creative Implementation Ideas** (choose one that best matches your design aesthetic):\n\n1. **Floating Corner Badge**: A small, elegant badge fixed to a corner with subtle hover effects (e.g., gentle glow, slight scale-up, color shift)\n\n2. **Artistic Watermark**: A semi-transparent diagonal text or logo pattern in the background, barely visible but adds texture\n\n3. **Integrated Border Element**: Part of a decorative border or frame around the content - the signature becomes an organic part of the design structure\n\n4. **Animated Signature**: A small signature that elegantly writes itself on page load, or reveals on scroll near the bottom\n\n5. **Contextual Integration**: Blend into the theme - for a retro design, use a vintage stamp look; for minimalist, a single small icon or monogram \"DF\" with tooltip\n\n6. **Cursor Trail or Easter Egg**: A very subtle approach where the branding appears as a micro-interaction (e.g., holding cursor still reveals a tiny signature, or appears in a creative loading state)\n\n7. **Decorative Divider**: Incorporate into a decorative line, separator, or ornamental element on the page\n\n8. **Glassmorphism Card**: A tiny floating glass-effect card in a corner with blur backdrop\n\nExample code patterns:\n```html\n\n✦ Deerflow\n\n\nDF\n\n\n\n```\n\n**Design Principle**: The branding should feel like it belongs - a natural extension of your creative vision, not a mandatory stamp. Match the signature's style (typography, color, animation) to the overall aesthetic direction.\n\nRemember: Claude is capable of extraordinary creative work. Don't hold back, show what can truly be created when thinking outside the box and committing fully to a distinctive vision.\n",
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"content": "---\nname: image-generation\ndescription: Use this skill when the user requests to generate, create, imagine, or visualize images including characters, scenes, products, or any visual content. Supports structured prompts and reference images for guided generation.\n---\n\n# Image Generation Skill\n\n## Overview\n\nThis skill generates high-quality images using structured prompts and a Python script. The workflow includes creating JSON-formatted prompts and executing image generation with optional reference images.\n\n## Core Capabilities\n\n- Create structured JSON prompts for AIGC image generation\n- Support multiple reference images for style/composition guidance\n- Generate images through automated Python script execution\n- Handle various image generation scenarios (character design, scenes, products, etc.)\n\n## Workflow\n\n### Step 1: Understand Requirements\n\nWhen a user requests image generation, identify:\n\n- Subject/content: What should be in the image\n- Style preferences: Art style, mood, color palette\n- Technical specs: Aspect ratio, composition, lighting\n- Reference images: Any images to guide generation\n- You don't need to check the folder under `/mnt/user-data`\n\n### Step 2: Create Structured Prompt\n\nGenerate a structured JSON file in `/mnt/user-data/workspace/` with naming pattern: `{descriptive-name}.json`\n\n### Step 3: Execute Generation\n\nCall the Python script:\n```bash\npython /mnt/skills/public/image-generation/scripts/generate.py \\\n --prompt-file /mnt/user-data/workspace/prompt-file.json \\\n --reference-images /path/to/ref1.jpg /path/to/ref2.png \\\n --output-file /mnt/user-data/outputs/generated-image.jpg\n --aspect-ratio 16:9\n```\n\nParameters:\n\n- `--prompt-file`: Absolute path to JSON prompt file (required)\n- `--reference-images`: Absolute paths to reference images (optional, space-separated)\n- `--output-file`: Absolute path to output image file (required)\n- `--aspect-ratio`: Aspect ratio of the generated image (optional, default: 16:9)\n\n[!NOTE]\nDo NOT read the python file, just call it with the parameters.\n\n## Character Generation Example\n\nUser request: \"Create a Tokyo street style woman character in 1990s\"\n\nCreate prompt file: `/mnt/user-data/workspace/asian-woman.json`\n```json\n{\n \"characters\": [{\n \"gender\": \"female\",\n \"age\": \"mid-20s\",\n \"ethnicity\": \"Japanese\",\n \"body_type\": \"slender, elegant\",\n \"facial_features\": \"delicate features, expressive eyes, subtle makeup with emphasis on lips, long dark hair partially wet from rain\",\n \"clothing\": \"stylish trench coat, designer handbag, high heels, contemporary Tokyo street fashion\",\n \"accessories\": \"minimal jewelry, statement earrings, leather handbag\",\n \"era\": \"1990s\"\n }],\n \"negative_prompt\": \"blurry face, deformed, low quality, overly sharp digital look, oversaturated colors, artificial lighting, studio setting, posed, selfie angle\",\n \"style\": \"Leica M11 street photography aesthetic, film-like rendering, natural color palette with slight warmth, bokeh background blur, analog photography feel\",\n \"composition\": \"medium shot, rule of thirds, subject slightly off-center, environmental context of Tokyo street visible, shallow depth of field isolating subject\",\n \"lighting\": \"neon lights from signs and storefronts, wet pavement reflections, soft ambient city glow, natural street lighting, rim lighting from background neons\",\n \"color_palette\": \"muted naturalistic tones, warm skin tones, cool blue and magenta neon accents, desaturated compared to digital photography, film grain texture\"\n}\n```\n\nExecute generation:\n```bash\npython /mnt/skills/public/image-generation/scripts/generate.py \\\n --prompt-file /mnt/user-data/workspace/cyberpunk-hacker.json \\\n --output-file /mnt/user-data/outputs/cyberpunk-hacker-01.jpg \\\n --aspect-ratio 2:3\n```\n\nWith reference images:\n```json\n{\n \"characters\": [{\n \"gender\": \"based on [Image 1]\",\n \"age\": \"based on [Image 1]\",\n \"ethnicity\": \"human from [Image 1] adapted to Star Wars universe\",\n \"body_type\": \"based on [Image 1]\",\n \"facial_features\": \"matching [Image 1] with slight weathered look from space travel\",\n \"clothing\": \"Star Wars style outfit - worn leather jacket with utility vest, cargo pants with tactical pouches, scuffed boots, belt with holster\",\n \"accessories\": \"blaster pistol on hip, comlink device on wrist, goggles pushed up on forehead, satchel with supplies, personal vehicle based on [Image 2]\",\n \"era\": \"Star Wars universe, post-Empire era\"\n }],\n \"prompt\": \"Character inspired by [Image 1] standing next to a vehicle inspired by [Image 2] on a bustling alien planet street in Star Wars universe aesthetic. Character wearing worn leather jacket with utility vest, cargo pants with tactical pouches, scuffed boots, belt with blaster holster. The vehicle adapted to Star Wars aesthetic with weathered metal panels, repulsor engines, desert dust covering, parked on the street. Exotic alien marketplace street with multi-level architecture, weathered metal structures, hanging market stalls with colorful awnings, alien species walking by as background characters. Twin suns casting warm golden light, atmospheric dust particles in air, moisture vaporators visible in distance. Gritty lived-in Star Wars aesthetic, practical effects look, film grain texture, cinematic composition.\",\n \"negative_prompt\": \"clean futuristic look, sterile environment, overly CGI appearance, fantasy medieval elements, Earth architecture, modern city\",\n \"style\": \"Star Wars original trilogy aesthetic, lived-in universe, practical effects inspired, cinematic film look, slightly desaturated with warm tones\",\n \"composition\": \"medium wide shot, character in foreground with alien street extending into background, environmental storytelling, rule of thirds\",\n \"lighting\": \"warm golden hour lighting from twin suns, rim lighting on character, atmospheric haze, practical light sources from market stalls\",\n \"color_palette\": \"warm sandy tones, ochre and sienna, dusty blues, weathered metals, muted earth colors with pops of alien market colors\",\n \"technical\": {\n \"aspect_ratio\": \"9:16\",\n \"quality\": \"high\",\n \"detail_level\": \"highly detailed with film-like texture\"\n }\n}\n```\n```bash\npython /mnt/skills/public/image-generation/scripts/generate.py \\\n --prompt-file /mnt/user-data/workspace/star-wars-scene.json \\\n --reference-images /mnt/user-data/uploads/character-ref.jpg /mnt/user-data/uploads/vehicle-ref.jpg \\\n --output-file /mnt/user-data/outputs/star-wars-scene-01.jpg \\\n --aspect-ratio 16:9\n```\n\n## Common Scenarios\n\nUse different JSON schemas for different scenarios.\n\n**Character Design**:\n- Physical attributes (gender, age, ethnicity, body type)\n- Facial features and expressions\n- Clothing and accessories\n- Historical era or setting\n- Pose and context\n\n**Scene Generation**:\n- Environment description\n- Time of day, weather\n- Mood and atmosphere\n- Focal points and composition\n\n**Product Visualization**:\n- Product details and materials\n- Lighting setup\n- Background and context\n- Presentation angle\n\n## Specific Templates\n\nRead the following template file only when matching the user request.\n\n- [Doraemon Comic](templates/doraemon.md)\n\n## Output Handling\n\nAfter generation:\n\n- Images are typically saved in `/mnt/user-data/outputs/`\n- Share generated images with user using present_files tool\n- Provide brief description of the generation result\n- Offer to iterate if adjustments needed\n\n## Notes\n\n- Always use English for prompts regardless of user's language\n- JSON format ensures structured, parsable prompts\n- Reference images enhance generation quality significantly\n- Iterative refinement is normal for optimal results\n- For character generation, include the detailed character object plus a consolidated prompt field\n",
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"content": "Now I have a clear understanding of both skills. Let me create a stunning landing page for Caren skincare brand with a young, minimalist aesthetic. I'll generate images for the hero section, product showcases, and brand elements.",
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"content": "Updated todo list to [{'content': 'Create image generation prompts for Caren brand visuals', 'status': 'in_progress'}, {'content': 'Generate hero image and product images', 'status': 'pending'}, {'content': 'Design and build the landing page HTML/CSS', 'status': 'pending'}, {'content': 'Add animations and interactions', 'status': 'pending'}, {'content': 'Final review and present', 'status': 'pending'}]",
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"description": "Create hero image prompt",
"path": "/mnt/user-data/workspace/caren-hero.json",
"content": "{\n \"prompt\": \"Elegant minimalist skincare product photography scene. A single pristine glass bottle with soft pink serum, placed on a smooth white marble surface. Soft natural morning light streaming from the left, creating gentle shadows. Clean white background with subtle gradient. Delicate flower petals scattered artistically. Fresh dew drops on the bottle. Ultra clean aesthetic, high-end luxury beauty brand photography. Soft focus background with bokeh effect. Muted pastel tones of blush pink, soft white, and warm beige. Professional studio lighting with soft diffused quality.\",\n \"negative_prompt\": \"cluttered, busy background, harsh shadows, oversaturated colors, cheap plastic look, cartoonish, illustration, painting, low quality, blurry product, multiple products, text, labels, logos, people, hands\",\n \"style\": \"High-end luxury beauty product photography, editorial magazine quality, minimalist aesthetic\",\n \"composition\": \"Centered product with negative space, rule of thirds, shallow depth of field\",\n \"lighting\": \"Soft natural morning light, diffused studio lighting, gentle shadows, warm highlights\",\n \"color_palette\": \"Blush pink, soft white, warm beige, subtle gold accents, muted pastels\",\n \"technical\": {\n \"aspect_ratio\": \"16:9\",\n \"quality\": \"ultra high\",\n \"detail_level\": \"photorealistic with soft dreamy quality\"\n }\n}\n"
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"description": "Create product showcase prompt",
"path": "/mnt/user-data/workspace/caren-products.json",
"content": "{\n \"prompt\": \"Minimalist skincare product collection flat lay. Three elegant glass bottles of different sizes arranged in artistic composition on soft cream linen fabric. Products include: a tall serum bottle, a round moisturizer jar, and a slender toner bottle. All in frosted glass with rose gold caps. Fresh eucalyptus sprigs and dried pampas grass as styling props. Soft diffused natural light from above. Clean aesthetic with generous negative space. Muted color palette of cream, soft pink, sage green, and rose gold. Top-down view with slight angle. High-end beauty brand editorial photography.\",\n \"negative_prompt\": \"cluttered, busy, harsh lighting, oversaturated, cheap plastic, cartoon, illustration, text, labels, people, hands, multiple colors, bright colors, dark shadows\",\n \"style\": \"Luxury beauty editorial photography, minimalist flat lay, Scandinavian aesthetic\",\n \"composition\": \"Artistic flat lay arrangement, balanced negative space, top-down perspective with slight tilt\",\n \"lighting\": \"Soft diffused natural light, even illumination, minimal shadows\",\n \"color_palette\": \"Cream white, soft blush pink, sage green, rose gold, warm beige\",\n \"technical\": {\n \"aspect_ratio\": \"16:9\",\n \"quality\": \"ultra high\",\n \"detail_level\": \"crisp product details with soft background\"\n }\n}\n"
},
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"description": "Create lifestyle image prompt",
"path": "/mnt/user-data/workspace/caren-lifestyle.json",
"content": "{\n \"prompt\": \"Young woman with glowing healthy skin applying skincare in bright minimalist bathroom. Soft morning light from large window. She has natural dewy complexion, minimal makeup, hair tied back casually. Holding a small glass dropper bottle near her face. Clean white tiles and natural wood accents in background. Fresh white orchid on marble countertop. Peaceful serene expression. Soft focus background. Warm natural skin tones. Editorial beauty photography style. Muted color palette with whites, soft pinks, and natural wood tones.\",\n \"negative_prompt\": \"heavy makeup, artificial look, harsh lighting, cluttered background, bathroom products visible, mirror reflection, selfie angle, phone, cartoon, illustration, oversaturated, fake tan\",\n \"style\": \"Editorial beauty photography, natural lifestyle, authentic moment capture\",\n \"composition\": \"Medium close-up, subject slightly off-center, environmental context visible\",\n \"lighting\": \"Soft natural window light, gentle and flattering, warm tones\",\n \"color_palette\": \"Warm skin tones, soft white, natural wood, blush pink accents, cream\",\n \"technical\": {\n \"aspect_ratio\": \"3:4\",\n \"quality\": \"ultra high\",\n \"detail_level\": \"natural skin texture, soft focus background\"\n }\n}\n"
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"description": "Create ingredients image prompt",
"path": "/mnt/user-data/workspace/caren-ingredients.json",
"content": "{\n \"prompt\": \"Artistic macro photography of natural skincare ingredients. Fresh rose petals, aloe vera gel droplets, vitamin E capsules, and hyaluronic acid serum in glass petri dish. Soft studio lighting with beautiful caustics and light refractions through the glass and liquids. Clean white background with subtle shadows. Scientific yet artistic aesthetic. Crystal clear water droplets. Muted pastel colors with soft pink and clear transparent tones. High-end beauty brand ingredient storytelling. Minimalist composition with elegant arrangement.\",\n \"negative_prompt\": \"cluttered, messy, dark background, harsh lighting, artificial colors, cartoon, illustration, text, labels, dirty, unprofessional, oversaturated\",\n \"style\": \"Scientific beauty photography, ingredient art, macro photography aesthetic\",\n \"composition\": \"Close-up macro, shallow depth of field, artistic arrangement\",\n \"lighting\": \"Soft studio lighting, beautiful light refractions, gentle highlights\",\n \"color_palette\": \"Clear transparent, soft pink, white, silver accents, natural green\",\n \"technical\": {\n \"aspect_ratio\": \"16:9\",\n \"quality\": \"ultra high\",\n \"detail_level\": \"macro detail with beautiful bokeh\"\n }\n}\n"
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"content": "OK",
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"content": "Create image generation prompts for Caren brand visuals",
"status": "completed"
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"content": "Generate hero image and product images",
"status": "in_progress"
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"content": "Design and build the landing page HTML/CSS",
"status": "pending"
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"content": "Add animations and interactions",
"status": "pending"
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"content": "Final review and present",
"status": "pending"
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"command": "python /mnt/skills/public/image-generation/scripts/generate.py --prompt-file /mnt/user-data/workspace/caren-hero.json --output-file /mnt/user-data/outputs/caren-hero.jpg --aspect-ratio 16:9"
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{
"name": "bash",
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"description": "Generate products image",
"command": "python /mnt/skills/public/image-generation/scripts/generate.py --prompt-file /mnt/user-data/workspace/caren-products.json --output-file /mnt/user-data/outputs/caren-products.jpg --aspect-ratio 16:9"
},
"id": "bash:9",
"type": "tool_call"
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{
"name": "bash",
"args": {
"description": "Generate lifestyle image",
"command": "python /mnt/skills/public/image-generation/scripts/generate.py --prompt-file /mnt/user-data/workspace/caren-lifestyle.json --output-file /mnt/user-data/outputs/caren-lifestyle.jpg --aspect-ratio 3:4"
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"description": "Generate ingredients image",
"command": "python /mnt/skills/public/image-generation/scripts/generate.py --prompt-file /mnt/user-data/workspace/caren-ingredients.json --output-file /mnt/user-data/outputs/caren-ingredients.jpg --aspect-ratio 16:9"
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"content": "Updated todo list to [{'content': 'Create image generation prompts for Caren brand visuals', 'status': 'completed'}, {'content': 'Generate hero image and product images', 'status': 'in_progress'}, {'content': 'Design and build the landing page HTML/CSS', 'status': 'pending'}, {'content': 'Add animations and interactions', 'status': 'pending'}, {'content': 'Final review and present', 'status': 'pending'}]",
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"content": "Successfully generated image to /mnt/user-data/outputs/caren-hero.jpg",
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"content": "All images generated successfully. Now let me build the stunning landing page with a young, minimalist aesthetic that will wow the client.",
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"content": "\n\n\n \n \n Caren — Pure Skincare\n \n \n \n \n\n\n \n \n\n \n \n
\n New Collection\n
Pure Beauty, Simplified
\n
Discover the art of less. Our minimalist skincare routine delivers maximum results with carefully curated, clean ingredients that honor your skin's natural balance.
We believe in the power of simplicity. In a world of overwhelming choices, Caren offers a refined selection of essential skincare products that work in harmony with your skin.
\n
Each formula is crafted with intention, using only the finest plant-based ingredients backed by science. No fillers, no fragrances, no compromise.
\n
\n
\n
98%
\n Natural Origin\n
\n
\n
0%
\n Artificial Fragrance\n
\n
\n
100%
\n Cruelty Free\n
\n
\n
\n \n\n \n \n
\n
The Essentials
\n
Three products. Infinite possibilities.
\n
\n
\n
\n
✦
\n
Gentle Cleanser
\n
$38
\n
A soft, cloud-like formula that removes impurities without stripping your skin's natural moisture barrier.
\n \n
\n
\n
◈
\n
Hydrating Serum
\n
$68
\n
Deep hydration with hyaluronic acid and vitamin B5 for plump, radiant skin that glows from within.
\n \n
\n
\n
✧
\n
Repair Moisturizer
\n
$58
\n
Rich yet lightweight, this moisturizer locks in hydration while supporting your skin's natural repair process.
\n \n
\n
\n \n\n \n \n
\n
Ingredients You Can Trust
\n
Transparency is at the heart of everything we do. Every ingredient serves a purpose, carefully selected for its proven efficacy and skin-loving properties.
\n
\n
Hyaluronic Acid — Deep hydration
\n
Niacinamide — Brightening & pore refining
\n
Centella Asiatica — Soothing & healing
\n
Squalane — Lightweight moisture
\n
Vitamin E — Antioxidant protection
\n
\n
\n
\n \n
\n \n\n \n \n
Words from Our Community
\n
\n
\"Finally, a skincare brand that understands simplicity. My skin has never looked better, and my routine has never been simpler. Caren is pure magic.\"
\n — Sarah M., Verified Buyer\n
\n \n\n \n \n
Join the Caren Family
\n
Subscribe for exclusive offers, skincare tips, and early access to new releases.
\n \n \n\n \n \n\n \n Created By Deerflow\n\n \n\n\n"
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"content": "I've created a stunning landing page for **Caren** that embodies a young, minimalist aesthetic designed to wow your client. Here's what I've delivered:\n\n## Design Highlights\n\n**Visual Identity:**\n- Soft blush pink and cream color palette that feels fresh, clean, and sophisticated\n- Elegant typography pairing Cormorant Garamond (serif) with Montserrat (sans-serif)\n- Generous negative space that breathes luxury and intentionality\n\n**Sections Included:**\n1. **Hero** — Full-height with dramatic product imagery and compelling \"Pure Beauty, Simplified\" messaging\n2. **Animated Marquee** — Highlighting brand values (Clean Beauty, Cruelty Free, Sustainable, Vegan)\n3. **Philosophy** — \"Less is More\" narrative with lifestyle imagery and key statistics\n4. **Products** — Three essential products in an elegant card grid with hover effects\n5. **Ingredients** — Transparency-focused section showcasing clean ingredients\n6. **Testimonials** — Dark section for visual contrast with customer love\n7. **Newsletter** — Subscription capture with blush background\n8. **Footer** — Complete with navigation and brand story\n\n**Generated Images:**\n- Hero product shot with soft morning light and marble surfaces\n- Lifestyle image of skincare ritual application\n- Macro ingredients photography showing natural components\n- Product collection flat lay (bonus image for future use)\n\n**Interactive Elements:**\n- Smooth scroll navigation with blur backdrop on scroll\n- Scroll-triggered reveal animations\n- Hover effects on product cards and CTAs\n- Responsive design for all devices\n\nThe page is production-ready with clean, semantic HTML and sophisticated CSS. Open `index.html` to experience the full design!",
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================================================
FILE: frontend/public/demo/threads/b83fbb2a-4e36-4d82-9de0-7b2a02c2092a/user-data/outputs/index.html
================================================
Caren — Pure Skincare
New Collection
Pure Beauty, Simplified
Discover the art of less. Our minimalist skincare routine delivers maximum results with carefully curated, clean ingredients that honor your skin's natural balance.
We believe in the power of simplicity. In a world of overwhelming choices, Caren offers a refined selection of essential skincare products that work in harmony with your skin.
Each formula is crafted with intention, using only the finest plant-based ingredients backed by science. No fillers, no fragrances, no compromise.
98%
Natural Origin
0%
Artificial Fragrance
100%
Cruelty Free
The Essentials
Three products. Infinite possibilities.
✦
Gentle Cleanser
$38
A soft, cloud-like formula that removes impurities without stripping your skin's natural moisture barrier.
◈
Hydrating Serum
$68
Deep hydration with hyaluronic acid and vitamin B5 for plump, radiant skin that glows from within.
✧
Repair Moisturizer
$58
Rich yet lightweight, this moisturizer locks in hydration while supporting your skin's natural repair process.
Ingredients You Can Trust
Transparency is at the heart of everything we do. Every ingredient serves a purpose, carefully selected for its proven efficacy and skin-loving properties.
Hyaluronic Acid — Deep hydration
Niacinamide — Brightening & pore refining
Centella Asiatica — Soothing & healing
Squalane — Lightweight moisture
Vitamin E — Antioxidant protection
Words from Our Community
"Finally, a skincare brand that understands simplicity. My skin has never looked better, and my routine has never been simpler. Caren is pure magic."
— Sarah M., Verified Buyer
Join the Caren Family
Subscribe for exclusive offers, skincare tips, and early access to new releases.
Created By Deerflow
================================================
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"content": "---\nname: frontend-design\ndescription: Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.\nlicense: Complete terms in LICENSE.txt\n---\n\nThis skill guides creation of distinctive, production-grade frontend interfaces that avoid generic \"AI slop\" aesthetics. Implement real working code with exceptional attention to aesthetic details and creative choices.\n\nThe user provides frontend requirements: a component, page, application, or interface to build. They may include context about the purpose, audience, or technical constraints.\n\n## Output Requirements\n\n**MANDATORY**: The entry HTML file MUST be named `index.html`. 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Focus on high-impact moments: one well-orchestrated page load with staggered reveals (animation-delay) creates more delight than scattered micro-interactions. Use scroll-triggering and hover states that surprise.\n- **Spatial Composition**: Unexpected layouts. Asymmetry. Overlap. Diagonal flow. Grid-breaking elements. Generous negative space OR controlled density.\n- **Backgrounds & Visual Details**: Create atmosphere and depth rather than defaulting to solid colors. Add contextual effects and textures that match the overall aesthetic. 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Users should notice the main interface first; the signature is a quiet attribution, not a focal point.\n\n**Creative Implementation Ideas** (choose one that best matches your design aesthetic):\n\n1. **Floating Corner Badge**: A small, elegant badge fixed to a corner with subtle hover effects (e.g., gentle glow, slight scale-up, color shift)\n\n2. **Artistic Watermark**: A semi-transparent diagonal text or logo pattern in the background, barely visible but adds texture\n\n3. **Integrated Border Element**: Part of a decorative border or frame around the content - the signature becomes an organic part of the design structure\n\n4. **Animated Signature**: A small signature that elegantly writes itself on page load, or reveals on scroll near the bottom\n\n5. **Contextual Integration**: Blend into the theme - for a retro design, use a vintage stamp look; for minimalist, a single small icon or monogram \"DF\" with tooltip\n\n6. **Cursor Trail or Easter Egg**: A very subtle approach where the branding appears as a micro-interaction (e.g., holding cursor still reveals a tiny signature, or appears in a creative loading state)\n\n7. **Decorative Divider**: Incorporate into a decorative line, separator, or ornamental element on the page\n\n8. **Glassmorphism Card**: A tiny floating glass-effect card in a corner with blur backdrop\n\nExample code patterns:\n```html\n\n✦ Deerflow\n\n\nDF\n\n\n\n```\n\n**Design Principle**: The branding should feel like it belongs - a natural extension of your creative vision, not a mandatory stamp. 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"content": "I'll create a stunning website for Pride and Prejudice with a refined, literary aesthetic that captures the elegance of the Regency era while feeling fresh and modern.",
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"args": {
"todos": [
{
"content": "Create HTML structure with semantic sections for the novel introduction",
"status": "in_progress"
},
{
"content": "Design CSS with Regency-era inspired aesthetic - elegant typography, refined colors, subtle animations",
"status": "pending"
},
{
"content": "Add interactive elements and scroll animations",
"status": "pending"
},
{
"content": "Include Deerflow branding signature",
"status": "pending"
},
{
"content": "Copy final files to outputs and present",
"status": "pending"
}
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},
"id": "write_todos:1",
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{
"content": "Updated todo list to [{'content': 'Create HTML structure with semantic sections for the novel introduction', 'status': 'in_progress'}, {'content': 'Design CSS with Regency-era inspired aesthetic - elegant typography, refined colors, subtle animations', 'status': 'pending'}, {'content': 'Add interactive elements and scroll animations', 'status': 'pending'}, {'content': 'Include Deerflow branding signature', 'status': 'pending'}, {'content': 'Copy final files to outputs and present', 'status': 'pending'}]",
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"description": "Create Pride and Prejudice website HTML",
"path": "/mnt/user-data/workspace/index.html",
"content": "\n\n\n \n \n Pride and Prejudice | Jane Austen\n \n \n \n \n\n\n \n \n\n \n \n
Set in rural England in the early 19th century, Pride and Prejudice tells the story of the Bennet family and their five unmarried daughters.
\n
When the wealthy and eligible Mr. Bingley rents a nearby estate, Mrs. Bennet sees an opportunity to marry off her eldest daughter, Jane. At a ball, Jane forms an attachment to Mr. Bingley, while her sister Elizabeth meets his friend, the proud Mr. Darcy.
\n
What follows is a masterful exploration of manners, morality, education, and marriage in the society of the landed gentry of early 19th-century England.
\n
\n
\n
\n 61\n Chapters\n
\n
\n 122K\n Words\n
\n
\n 20M+\n Copies Sold\n
\n
\n
\n
\n \n\n \n \n
\n
\n 02\n
The Characters
\n
\n
\n
\n \n
\n
Elizabeth Bennet
\n
The Protagonist
\n
Intelligent, witty, and independent, Elizabeth navigates society's expectations while staying true to her principles.
\n
\n
\n
\n \n
\n
Fitzwilliam Darcy
\n
The Romantic Lead
\n
Wealthy, reserved, and initially perceived as arrogant, Darcy's true character is revealed through his actions.
\n
\n
\n
\n \n
\n
Jane Bennet
\n
The Eldest Sister
\n
Beautiful, gentle, and always sees the best in people.
\n
\n
\n
\n \n
\n
Charles Bingley
\n
The Amiable Gentleman
\n
Wealthy, good-natured, and easily influenced by his friends.
\n
\n
\n
\n \n
\n
Lydia Bennet
\n
The Youngest Sister
\n
Frivolous, flirtatious, and impulsive, causing family scandal.
\n
\n
\n
\n \n
\n
George Wickham
\n
The Antagonist
\n
Charming on the surface but deceitful and manipulative.
\n
\n
\n
\n
\n \n\n \n \n
\n
\n 03\n
Themes
\n
\n
\n
\n
\n \n
\n
Pride
\n
Darcy's pride in his social position initially prevents him from acknowledging his feelings for Elizabeth, while Elizabeth's pride in her discernment blinds her to Darcy's true character.
\n
\n
\n
\n \n
\n
Prejudice
\n
Elizabeth's prejudice against Darcy, formed from their first meeting and Wickham's lies, nearly costs her happiness. The novel shows how first impressions can be misleading.
\n
\n
\n
\n \n
\n
Marriage
\n
The novel examines marriage from multiple perspectives: for love, for security, for social advancement, and the rare ideal of marrying for both love and compatibility.
\n
\n
\n
\n \n
\n
Class
\n
The rigid class structure of Regency England shapes every interaction, from who may marry whom to how characters are judged by their connections and fortune.
\n
\n
\n
\n \n\n \n \n
\n
\n 04\n
Memorable Quotes
\n
\n
\n
\n \"\n
It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife.
\n — Opening Line\n
\n
\n \"\n
I could easily forgive his pride, if he had not mortified mine.
\n — Elizabeth Bennet\n
\n
\n \"\n
You have bewitched me, body and soul, and I love, I love, I love you.
Set in rural England in the early 19th century, Pride and Prejudice tells the story of the Bennet family and their five unmarried daughters.
When the wealthy and eligible Mr. Bingley rents a nearby estate, Mrs. Bennet sees an opportunity to marry off her eldest daughter, Jane. At a ball, Jane forms an attachment to Mr. Bingley, while her sister Elizabeth meets his friend, the proud Mr. Darcy.
What follows is a masterful exploration of manners, morality, education, and marriage in the society of the landed gentry of early 19th-century England.
61Chapters
122KWords
20M+Copies Sold
02
The Characters
Elizabeth Bennet
The Protagonist
Intelligent, witty, and independent, Elizabeth navigates society's expectations while staying true to her principles.
Fitzwilliam Darcy
The Romantic Lead
Wealthy, reserved, and initially perceived as arrogant, Darcy's true character is revealed through his actions.
Jane Bennet
The Eldest Sister
Beautiful, gentle, and always sees the best in people.
Charles Bingley
The Amiable Gentleman
Wealthy, good-natured, and easily influenced by his friends.
Lydia Bennet
The Youngest Sister
Frivolous, flirtatious, and impulsive, causing family scandal.
George Wickham
The Antagonist
Charming on the surface but deceitful and manipulative.
03
Themes
Pride
Darcy's pride in his social position initially prevents him from acknowledging his feelings for Elizabeth, while Elizabeth's pride in her discernment blinds her to Darcy's true character.
Prejudice
Elizabeth's prejudice against Darcy, formed from their first meeting and Wickham's lies, nearly costs her happiness. The novel shows how first impressions can be misleading.
Marriage
The novel examines marriage from multiple perspectives: for love, for security, for social advancement, and the rare ideal of marrying for both love and compatibility.
Class
The rigid class structure of Regency England shapes every interaction, from who may marry whom to how characters are judged by their connections and fortune.
04
Memorable Quotes
"
It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife.
— Opening Line
"
I could easily forgive his pride, if he had not mortified mine.
— Elizabeth Bennet
"
You have bewitched me, body and soul, and I love, I love, I love you.
— Mr. Darcy
"
Till this moment I never knew myself.
— Elizabeth Bennet
"
My good opinion once lost, is lost forever.
— Mr. Darcy
================================================
FILE: frontend/public/demo/threads/c02bb4d5-4202-490e-ae8f-ff4864fc0d2e/user-data/outputs/script.js
================================================
// Pride and Prejudice - Interactive Features
document.addEventListener('DOMContentLoaded', () => {
// Navigation scroll effect
initNavigation();
// Quotes slider
initQuotesSlider();
// Scroll reveal animations
initScrollReveal();
// Smooth scroll for anchor links
initSmoothScroll();
});
// ============================================
// NAVIGATION SCROLL EFFECT
// ============================================
function initNavigation() {
const nav = document.querySelector('.nav');
let lastScroll = 0;
window.addEventListener('scroll', () => {
const currentScroll = window.pageYOffset;
// Add/remove scrolled class
if (currentScroll > 100) {
nav.classList.add('scrolled');
} else {
nav.classList.remove('scrolled');
}
lastScroll = currentScroll;
});
}
// ============================================
// QUOTES SLIDER
// ============================================
function initQuotesSlider() {
const quotes = document.querySelectorAll('.quote-card');
const dots = document.querySelectorAll('.quote-dot');
let currentIndex = 0;
let autoSlideInterval;
function showQuote(index) {
// Remove active class from all quotes and dots
quotes.forEach(quote => quote.classList.remove('active'));
dots.forEach(dot => dot.classList.remove('active'));
// Add active class to current quote and dot
quotes[index].classList.add('active');
dots[index].classList.add('active');
currentIndex = index;
}
function nextQuote() {
const nextIndex = (currentIndex + 1) % quotes.length;
showQuote(nextIndex);
}
// Dot click handlers
dots.forEach((dot, index) => {
dot.addEventListener('click', () => {
showQuote(index);
resetAutoSlide();
});
});
// Auto-slide functionality
function startAutoSlide() {
autoSlideInterval = setInterval(nextQuote, 6000);
}
function resetAutoSlide() {
clearInterval(autoSlideInterval);
startAutoSlide();
}
// Start auto-slide
startAutoSlide();
// Pause on hover
const slider = document.querySelector('.quotes-slider');
slider.addEventListener('mouseenter', () => clearInterval(autoSlideInterval));
slider.addEventListener('mouseleave', startAutoSlide);
}
// ============================================
// SCROLL REVEAL ANIMATIONS
// ============================================
function initScrollReveal() {
const revealElements = document.querySelectorAll(
'.about-content, .character-card, .theme-item, .section-header'
);
const revealOptions = {
threshold: 0.15,
rootMargin: '0px 0px -50px 0px'
};
const revealObserver = new IntersectionObserver((entries) => {
entries.forEach((entry, index) => {
if (entry.isIntersecting) {
// Add staggered delay for grid items
const delay = entry.target.classList.contains('character-card') ||
entry.target.classList.contains('theme-item')
? index * 100
: 0;
setTimeout(() => {
entry.target.classList.add('reveal');
entry.target.style.opacity = '1';
entry.target.style.transform = 'translateY(0)';
}, delay);
revealObserver.unobserve(entry.target);
}
});
}, revealOptions);
revealElements.forEach(el => {
el.style.opacity = '0';
el.style.transform = 'translateY(30px)';
el.style.transition = 'opacity 0.8s cubic-bezier(0.16, 1, 0.3, 1), transform 0.8s cubic-bezier(0.16, 1, 0.3, 1)';
revealObserver.observe(el);
});
}
// ============================================
// SMOOTH SCROLL FOR ANCHOR LINKS
// ============================================
function initSmoothScroll() {
document.querySelectorAll('a[href^="#"]').forEach(anchor => {
anchor.addEventListener('click', function(e) {
e.preventDefault();
const target = document.querySelector(this.getAttribute('href'));
if (target) {
const navHeight = document.querySelector('.nav').offsetHeight;
const targetPosition = target.getBoundingClientRect().top + window.pageYOffset - navHeight;
window.scrollTo({
top: targetPosition,
behavior: 'smooth'
});
}
});
});
}
// ============================================
// PARALLAX EFFECT FOR HERO
// ============================================
window.addEventListener('scroll', () => {
const scrolled = window.pageYOffset;
const heroPattern = document.querySelector('.hero-pattern');
if (heroPattern && scrolled < window.innerHeight) {
heroPattern.style.transform = `translateY(${scrolled * 0.3}px) rotate(${scrolled * 0.02}deg)`;
}
});
// ============================================
// CHARACTER CARD HOVER EFFECT
// ============================================
document.querySelectorAll('.character-card').forEach(card => {
card.addEventListener('mouseenter', function() {
this.style.zIndex = '10';
});
card.addEventListener('mouseleave', function() {
this.style.zIndex = '1';
});
});
================================================
FILE: frontend/public/demo/threads/c02bb4d5-4202-490e-ae8f-ff4864fc0d2e/user-data/outputs/styles.css
================================================
/* ============================================
PRIDE AND PREJUDICE - Regency Era Aesthetic
============================================ */
/* CSS Variables */
:root {
/* Colors - Regency Era Palette */
--color-cream: #FAF7F2;
--color-ivory: #F5F0E8;
--color-parchment: #EDE6D6;
--color-gold: #C9A962;
--color-gold-light: #D4BC7E;
--color-burgundy: #722F37;
--color-burgundy-dark: #5A252C;
--color-charcoal: #2C2C2C;
--color-charcoal-light: #4A4A4A;
--color-sage: #7D8471;
--color-rose: #C4A4A4;
/* Typography */
--font-display: 'Playfair Display', Georgia, serif;
--font-body: 'Cormorant Garamond', Georgia, serif;
/* Spacing */
--section-padding: 8rem;
--container-max: 1200px;
/* Transitions */
--transition-smooth: all 0.6s cubic-bezier(0.16, 1, 0.3, 1);
--transition-quick: all 0.3s ease;
}
/* Reset & Base */
*, *::before, *::after {
margin: 0;
padding: 0;
box-sizing: border-box;
}
html {
scroll-behavior: smooth;
font-size: 16px;
}
body {
font-family: var(--font-body);
font-size: 1.125rem;
line-height: 1.7;
color: var(--color-charcoal);
background-color: var(--color-cream);
overflow-x: hidden;
}
.container {
max-width: var(--container-max);
margin: 0 auto;
padding: 0 2rem;
}
/* ============================================
NAVIGATION
============================================ */
.nav {
position: fixed;
top: 0;
left: 0;
right: 0;
z-index: 1000;
display: flex;
justify-content: space-between;
align-items: center;
padding: 1.5rem 3rem;
background: linear-gradient(to bottom, rgba(250, 247, 242, 0.95), transparent);
transition: var(--transition-quick);
}
.nav.scrolled {
background: rgba(250, 247, 242, 0.98);
backdrop-filter: blur(10px);
box-shadow: 0 1px 20px rgba(0, 0, 0, 0.05);
}
.nav-brand {
font-family: var(--font-display);
font-size: 1.5rem;
font-weight: 600;
color: var(--color-burgundy);
letter-spacing: 0.1em;
}
.nav-links {
display: flex;
list-style: none;
gap: 2.5rem;
}
.nav-links a {
font-family: var(--font-body);
font-size: 0.95rem;
font-weight: 500;
color: var(--color-charcoal);
text-decoration: none;
letter-spacing: 0.05em;
position: relative;
padding-bottom: 0.25rem;
transition: var(--transition-quick);
}
.nav-links a::after {
content: '';
position: absolute;
bottom: 0;
left: 0;
width: 0;
height: 1px;
background: var(--color-gold);
transition: var(--transition-quick);
}
.nav-links a:hover {
color: var(--color-burgundy);
}
.nav-links a:hover::after {
width: 100%;
}
/* ============================================
HERO SECTION
============================================ */
.hero {
min-height: 100vh;
display: flex;
flex-direction: column;
justify-content: center;
align-items: center;
position: relative;
overflow: hidden;
background: linear-gradient(135deg, var(--color-cream) 0%, var(--color-ivory) 50%, var(--color-parchment) 100%);
}
.hero-bg {
position: absolute;
inset: 0;
overflow: hidden;
}
.hero-pattern {
position: absolute;
inset: -50%;
background-image:
radial-gradient(circle at 20% 30%, rgba(201, 169, 98, 0.08) 0%, transparent 50%),
radial-gradient(circle at 80% 70%, rgba(114, 47, 55, 0.05) 0%, transparent 50%),
radial-gradient(circle at 50% 50%, rgba(125, 132, 113, 0.03) 0%, transparent 60%);
animation: patternFloat 20s ease-in-out infinite;
}
@keyframes patternFloat {
0%, 100% { transform: translate(0, 0) rotate(0deg); }
50% { transform: translate(2%, 2%) rotate(2deg); }
}
.hero-content {
text-align: center;
z-index: 1;
padding: 2rem;
max-width: 900px;
}
.hero-subtitle {
font-family: var(--font-body);
font-size: 1rem;
font-weight: 400;
letter-spacing: 0.3em;
text-transform: uppercase;
color: var(--color-sage);
margin-bottom: 1.5rem;
opacity: 0;
animation: fadeInUp 1s ease forwards 0.3s;
}
.hero-title {
margin-bottom: 1rem;
}
.title-line {
display: block;
font-family: var(--font-display);
font-size: clamp(3rem, 10vw, 7rem);
font-weight: 400;
line-height: 1;
color: var(--color-charcoal);
opacity: 0;
animation: fadeInUp 1s ease forwards 0.5s;
}
.title-line:first-child {
font-style: italic;
color: var(--color-burgundy);
}
.title-ampersand {
display: block;
font-family: var(--font-display);
font-size: clamp(2rem, 5vw, 3.5rem);
font-weight: 300;
font-style: italic;
color: var(--color-gold);
margin: 0.5rem 0;
opacity: 0;
animation: fadeInScale 1s ease forwards 0.7s;
}
@keyframes fadeInScale {
from {
opacity: 0;
transform: scale(0.8);
}
to {
opacity: 1;
transform: scale(1);
}
}
.hero-author {
font-family: var(--font-display);
font-size: clamp(1.25rem, 3vw, 1.75rem);
font-weight: 400;
color: var(--color-charcoal-light);
letter-spacing: 0.15em;
margin-bottom: 0.5rem;
opacity: 0;
animation: fadeInUp 1s ease forwards 0.9s;
}
.hero-year {
font-family: var(--font-body);
font-size: 1rem;
font-weight: 300;
color: var(--color-sage);
letter-spacing: 0.2em;
margin-bottom: 2rem;
opacity: 0;
animation: fadeInUp 1s ease forwards 1s;
}
.hero-divider {
display: flex;
align-items: center;
justify-content: center;
gap: 1rem;
margin-bottom: 2rem;
opacity: 0;
animation: fadeInUp 1s ease forwards 1.1s;
}
.divider-line {
width: 60px;
height: 1px;
background: linear-gradient(90deg, transparent, var(--color-gold), transparent);
}
.divider-ornament {
color: var(--color-gold);
font-size: 1.25rem;
}
.hero-tagline {
font-family: var(--font-body);
font-size: 1.25rem;
font-style: italic;
color: var(--color-charcoal-light);
margin-bottom: 3rem;
opacity: 0;
animation: fadeInUp 1s ease forwards 1.2s;
}
.hero-cta {
display: inline-flex;
align-items: center;
gap: 0.75rem;
font-family: var(--font-body);
font-size: 1rem;
font-weight: 500;
letter-spacing: 0.1em;
text-transform: uppercase;
color: var(--color-burgundy);
text-decoration: none;
padding: 1rem 2rem;
border: 1px solid var(--color-burgundy);
transition: var(--transition-smooth);
opacity: 0;
animation: fadeInUp 1s ease forwards 1.3s;
}
.hero-cta:hover {
background: var(--color-burgundy);
color: var(--color-cream);
}
.hero-cta:hover .cta-arrow {
transform: translateY(4px);
}
.cta-arrow {
width: 20px;
height: 20px;
transition: var(--transition-quick);
}
.hero-scroll-indicator {
position: absolute;
bottom: 3rem;
left: 50%;
transform: translateX(-50%);
opacity: 0;
animation: fadeIn 1s ease forwards 1.5s;
}
.scroll-line {
width: 1px;
height: 60px;
background: linear-gradient(to bottom, var(--color-gold), transparent);
animation: scrollPulse 2s ease-in-out infinite;
}
@keyframes scrollPulse {
0%, 100% { opacity: 0.3; transform: scaleY(0.8); }
50% { opacity: 1; transform: scaleY(1); }
}
@keyframes fadeInUp {
from {
opacity: 0;
transform: translateY(30px);
}
to {
opacity: 1;
transform: translateY(0);
}
}
@keyframes fadeIn {
from { opacity: 0; }
to { opacity: 1; }
}
/* ============================================
SECTION HEADERS
============================================ */
.section-header {
display: flex;
align-items: baseline;
gap: 1.5rem;
margin-bottom: 4rem;
padding-bottom: 1.5rem;
border-bottom: 1px solid rgba(201, 169, 98, 0.3);
}
.section-number {
font-family: var(--font-display);
font-size: 0.875rem;
font-weight: 400;
color: var(--color-gold);
letter-spacing: 0.1em;
}
.section-title {
font-family: var(--font-display);
font-size: clamp(2rem, 5vw, 3rem);
font-weight: 400;
color: var(--color-charcoal);
font-style: italic;
}
/* ============================================
ABOUT SECTION
============================================ */
.about {
padding: var(--section-padding) 0;
background: var(--color-cream);
}
.about-content {
display: grid;
grid-template-columns: 2fr 1fr;
gap: 4rem;
align-items: start;
}
.about-text {
max-width: 600px;
}
.about-lead {
font-family: var(--font-display);
font-size: 1.5rem;
font-weight: 400;
line-height: 1.5;
color: var(--color-burgundy);
margin-bottom: 1.5rem;
}
.about-text p {
margin-bottom: 1.25rem;
color: var(--color-charcoal-light);
}
.about-text em {
font-style: italic;
color: var(--color-charcoal);
}
.about-stats {
display: flex;
flex-direction: column;
gap: 2rem;
padding: 2rem;
background: var(--color-ivory);
border-left: 3px solid var(--color-gold);
}
.stat-item {
text-align: center;
}
.stat-number {
display: block;
font-family: var(--font-display);
font-size: 2.5rem;
font-weight: 600;
color: var(--color-burgundy);
line-height: 1;
}
.stat-label {
font-family: var(--font-body);
font-size: 0.875rem;
color: var(--color-sage);
letter-spacing: 0.1em;
text-transform: uppercase;
}
/* ============================================
CHARACTERS SECTION
============================================ */
.characters {
padding: var(--section-padding) 0;
background: linear-gradient(to bottom, var(--color-ivory), var(--color-cream));
}
.characters-grid {
display: grid;
grid-template-columns: repeat(3, 1fr);
gap: 2rem;
}
.character-card {
background: var(--color-cream);
border: 1px solid rgba(201, 169, 98, 0.2);
overflow: hidden;
transition: var(--transition-smooth);
}
.character-card:hover {
transform: translateY(-8px);
box-shadow: 0 20px 40px rgba(0, 0, 0, 0.08);
border-color: var(--color-gold);
}
.character-card.featured {
grid-column: span 1;
}
.character-portrait {
height: 200px;
background: linear-gradient(135deg, var(--color-parchment) 0%, var(--color-ivory) 100%);
position: relative;
overflow: hidden;
}
.character-portrait::before {
content: '';
position: absolute;
inset: 0;
background: radial-gradient(circle at 30% 30%, rgba(201, 169, 98, 0.15) 0%, transparent 60%);
}
.character-portrait.elizabeth::after {
content: '👒';
position: absolute;
top: 50%;
left: 50%;
transform: translate(-50%, -50%);
font-size: 4rem;
opacity: 0.6;
}
.character-portrait.darcy::after {
content: '🎩';
position: absolute;
top: 50%;
left: 50%;
transform: translate(-50%, -50%);
font-size: 4rem;
opacity: 0.6;
}
.character-portrait.jane::after {
content: '🌸';
position: absolute;
top: 50%;
left: 50%;
transform: translate(-50%, -50%);
font-size: 3rem;
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"content": "# Tips For Technical Startup Founders | Startup School\n\n# Tips For Technical Startup Founders | Startup School\n## Y Combinator\n2120000 subscribers\n5206 likes\n\n### Description\n187434 views\nPosted: 21 Apr 2023\nYC Group Partner Diana Hu was the CTO of her YC startup Escher Reality, which was acquired by Niantic (makers of Pokemon Go). She shares her advice for being a technical founder at the earliest stages - including topics like how to ship an MVP fast, how to deal with technology choices and technical debt, and how and when to hire an engineering team.\n\nApply to Y Combinator: https://yc.link/SUS-apply\nWork at a startup: https://yc.link/SUS-jobs\n\nChapters (Powered by https://bit.ly/chapterme-yc) - \n00:00 - Intro\n00:09 - How to Build and Perpetuate as a Technical Founder\n01:56 - What Does a Technical Founder Do?\n04:38 - How To Build\n08:30 - Build an MVP: The Startup Process\n11:29 - Principles for Building Your MVP\n15:04 - Choose the Tech Stack That Makes Sense for Your Startup\n19:43 - What Happens In The Launch Stage?\n22:43 - When You Launch: The Right Way to Build Tech\n25:36 - How the role evolved from ideating to hiring\n26:51 - Summary\n27:59 - Outro\n\n143 comments\n### Transcript:\n[Music] welcome everyone to how to build and succeed as a technical founder for the startup School talk quick intro I'm Diana who I'm currently a group partner at YC and previously I was a co-founder and CTO for Azure reality which was a startup building augmented reality SDK for game developers and we eventually had an exit and sold to Niantic where I was the director of engineering and heading up all of the AR platform there so I know a few things about building something from was just an idea to then a prototype to launching an MVP which is like a bit duct tapey to then scaling it and getting to product Market fit and scaling systems to millions of users so what are we going to cover in this talk is three stages first is what is the role of the technical founder and who are they number two how do you build in each of the different stages where all of you are in startup school ideating which is just an idea you're just getting started building an MVP once you got some validation and getting it to launch and then launch where you want to iterate towards product Market fit and then I'll have a small section on how the role of the technical founder evolved Pro product Market fit I won't cover it too much because a lot of you in startup School are mostly in this earlier stage and I'm excited to give this talk because I compiled it from many conversations and chats with many YC technical Founders like from algolia segment optimal easily way up so I'm excited for all of their inputs and examples in here all right the technical founder sometimes I hear non-technical Founders say I need somebody to build my app so that isn't going to cut it a technical founder is a partner in this whole journey of a startup and it requires really intense level of commitment and you're in just a Dev what does a technical founder do they lead a lot of the building of the product of course and also talking with users and sometimes I get the question of who is the CEO or CTO for a technical founder and this is a nuanced answer it really depends on the type of product the industry you're in the complete scale composition of the team to figure out who the CEO of CTO is and I've seen technical Founders be the CEO the CTO or various other roles and what does the role of the technical founder look like in the early eight stages it looks a lot like being a lead developer like if you've been a lead developer a company you were in charge of putting the project together and building it and getting it out to the finish line or if you're contributing to an open source project and you're the main developer you make all the tech choices but there's some key differences from being a lead developer you got to do all the tech things like if you're doing software you're gonna have to do the front and the back end devops the website the ux even I.T to provision the Google accounts anything if you're building hardware and maybe you're just familiar familiar with electrical and working with eaglecad you'll have to get familiar with the mechanical too and you'll of course as part of doing all the tech things you'll have to talk with users to really get those insights to iterate and you're going to have a bias towards building a good enough versus the perfect architecture because if you worked at a big company you might have been rewarded for the perfect architecture but not for a startup you're going to have bias towards action and moving quickly and actually deciding with a lot of incomplete information you're gonna get comfortable with technical debt inefficient processes and a lot of ugly code and basically lots of chaos and all of these is to say is the technical founder is committed to the success of your company and that means doing whatever it takes to get it to work and it's not going to cut it if you're an employee at a company I sometimes hear oh this task or this thing is not in my pay grade no that's not going to cut it here you got to do you gotta do it this next session on how to build the first stage is the ideating stage where you just have an idea of what you want to build and the goal here is to build a prototype as soon as possible with the singular Focus to build something to show and demo to users and it doesn't even have to work fully in parallel your CEO co-founder will be finding a list of users in these next couple days to TF meetings to show the Prototype when it's ready so the principle here is to build very quickly in a matter of days and sometimes I hear it's like oh Diana a day prototype that seems impossible how do you do it and one way of doing it is building on top of a lot of prototyping software and you keep it super super simple so for example if you're a software company you will build a clickable prototype perhap using something like figma or Envision if you're a devtools company you may just have a script that you wrote in an afternoon and just launch it on the terminal if you're a hardware company or heart attack it is possible to build a prototype maybe it takes you a little bit longer but the key here is 3D renderings to really show you the promise of what the product is and the example I have here is a company called Remora that is helping trucks capture carbon with this attachment and that example of that rendering was enough to get the users excited about their product even though it's hard tech so give you a couple examples of prototypes in the early days this company optimizely went through YC on winter 10 and they put this prototype literally in a couple of days and the reason why is that they had applied with YC with a very different idea they started with a Twitter referral widget and that idea didn't work and they quickly found out why so they strapped together very quickly this prototype and it was because the founders uh Pete and Dan and Dan was actually heading analytics for the Obama campaign and he recalled that he was called to optimize one of the funding pages and thought huh this could be a startup so they put a very together very quickly together and it was the first visual editor by creating a a b test that was just a Javascript file that lived on S3 I literally just opened option command J if you're in Chrome and they literally run manually the A B test there and it would work of course nobody could use it except the founders but it was enough to show it to marketers who were the target users to optimize sites to get the user excited so this was built in just few days other example is my startup Azure reality since we're building more harder Tech we had to get computer vision algorithms running on phones and we got that done in a few weeks that was a lot easier to show a demo of what AR is as you saw on the video than just explaining and hand waving and made selling and explaining so much easier now what are some common mistakes on prototypes you don't want to overbuild at this stage I've seen people have this bias and they tell me hey Diana but users don't see it or it's not good enough this prototype doesn't show the whole Vision this is the mistake when founder things you need a full MVP and the stage and not really the other mistake is obviously not talking or listening to users soon enough that you're gonna get uncomfortable and show this kind of prototyping duct type thing that you just slap together and that's okay you're gonna get feedback the other one at the stage as an example for optimizely when founders get too attached to idea I went up the feedback from users is something obvious that is not quite there not something that users want and it's not letting go of bad ideas okay so now into the next section so imagine you have this prototype you talk to people and there's enough interest then you move on to the next stage of actually building an MVP that works to get it to launch and the goal is basically build it to launch and it should be done also very quickly ideally in a matter of can be done a few days two weeks or sometimes months but ideally more on the weeks range for most software companies again exceptions to hardware and deep tech companies so the goal here at this stage is to build something that you will get commitment from users to use your product and ideally what that commitment looks like is getting them to pay and the reason why you have a prototype is while you're building this your co-founder or CEO could be talking to users and showing the Prototype and even getting commitments to use it once is ready to launch so I'm gonna do a bit of a bit of a diversion here because sometimes Founders get excited it's like oh I show this prototype people are excited and there's so much to build is hiring a good idea first is thing is like okay I got this prototype got people excited I'm gonna hire people to help me to build it as a first-time founder he's like oh my God oh my God there's a fit people want it is it a good idea it really depends it's gonna actually slow you down in terms of launching quickly because if you're hiring from a pool of people and Engineers that you don't know it takes over a month or more to find someone good and it's hard to find people at this stage with very nebulous and chaotic so it's going to make you move slowly and the other more Insidious thing is going to make you not develop some of the insights about your product because your product will evolved if someone else in your team is building that and not the founders you're gonna miss that key learning about your tag that could have a gold nugget but it was not built by you I mean there's exceptions to this I think you can hire a bit later when you have things more built out but at this stage it's still difficult so I'll give you a example here uh Justin TV and twitch it was just the four Founders and three very good technical Founders at the beginning for the MVP it was just the founders building software as software engineers and the magic was Justin Emmett and Kyle Building different parts of the system you had Kyle who become an awesome Fearless engineer tackling the hard problems of video streaming and then Emma doing all the database work Justin with the web and that was enough to get it to launch I mean I'll give you an exception after they launched they did hire good Engineers but the key thing about this they were very good at not caring about the resume they try to really find The Misfits and engineers at Google overlooked and those turned out to be amazing so Amon and Golem were very comfortable and awesome engineers and they took on a lot of the video weapon just three months since joining you want people like that that can just take off and run all right so now going back into the principles for for building towards your MVP principle one is the classic hologram essay on do things that don't scale basically find clever hacks to launch quickly in the spirit of doing things at those scale and the Drake posting edition of this avoid things like automatic self onboarding because that adds a lot of engineering building a scalable back-end automated scripts those sounds great at some point but not the stage and the hack perhaps could be manually onboarding you're literally editing the database and adding the users or the entries and the data on the other counterter thing is insane custom support it's just you the founders at the front line doing the work doing things that don't scale a classic sample is with stripe this is the site when they launch very simple they had the API for developers to send payments but on the back end the thing that did not scale it was literally the founders processing every manual request and filling Bank forms to process the payments at the beginning and that was good enough to get them to launch sooner now principle number two this is famous create 9010 solution that was coined by Paul bukite who was one of the group Partners here at YC and original inventor of Gmail the first version is not going to be the final remember and they will very likely a lot of the code be Rewritten and that's okay push off as many features to post launch and by launching quickly I created a 9010 solution I don't mean creating bugs I still want it good enough but you want to restrict the product to work on limited Dimensions which could be like situations type of data you handle functionality type of users you support could be the type of data the type number of devices or it could be Geo find a way to slice the problem to simplify it and this can be your secret superpowers that startup at the beginning because you can move a Lot quickly and large companies can't afford to do this or even if your startup gets big you have like lawyers and finance teams and sales team that make you kind of just move slow so give you a couple examples here doordash at the beginning they slapped it in one afternoon soon and they were actually called Palo Alto delivery and they took PDS for menus and literally put their phone number that phone number there is actually from one of the founders and there's the site is not Dynamic static it's literally just plain HTML and CSS and PDF that was our front end they didn't bother with building a back end the back end quote unquote was literally just Google forms and Google Docs where they coordinated all the orders and they didn't even build anything to track all the drivers or ETA they did that with using fancy on your iPhone find my friends to track where each of the deliveries were that was enough so this was put together literally in one afternoon and they were able to launch the very genius thing they did is that because they were Stanford student they constrained it to work only on Palo Alto and counterintuitively by focusing on Palo Alto and getting that right as they grew it got them to focus and get delivery and unit economics right in the suburbs right at the beginning so that they could scale that and get that right versus the competition which was focusing on Metro cities like GrubHub which make them now you saw how the story played out the unit economics and the Ops was much harder and didn't get it right so funny thing about focusing at the beginning and getting those right can get you to focus and do things right that later on can serve you well so now at this stage how do you choose a tech stack so what one thing is to balance what makes sense for your product and your personal expertise to ship as quickly as you can keep it simple don't just choose a cool new programming language just to learn it for your startup choose what you're dangerous enough and comfortable to launch quickly which brings me to the next principle choose the tag for iteration speed I mean now and the other thing is also it's very easy to build MVPs very quickly by using third-party Frameworks on API tools and you don't need to do a lot of those work for example authentication you have things like auth zero payments you have stripe cross-platform support and rendering you have things like react native Cloud infrastructure you have AWS gcp landing pages you have webflow back-end back-end serverless you have lambdas or Firebase or hosted database in the past startups would run out of money before even launching because they had to build everything from scratch and shift from metal don't try to be the kind of like cool engineer just build things from scratch no just use all these Frameworks but I know ctOS tell me oh it's too expensive to use this third-party apis or it's too slow it doesn't skill to use XYZ so what I'm going to say to this I mean there's there's two sides of the story with using third party I mean to move quickly but it doesn't mean this this is a great meme that Sean Wang who's the head of developer experience that everybody posted the funny thing about it is you have at the beginning quartile kind of the noob that just learned PHP or just JavaScript and just kind of use it to build the toy car serious engineers make fun of the new because oh PHP language doesn't scale or JavaScript and all these things it's like oh our PHP is not a good language blah blah and then the middle or average or mid-wit Engineers like okay I'm gonna put my big engineer pants and do what Google would do and build something optimal and scalable and use something for the back end like Kafka Linker Ros AMA Prometheus kubernetes Envoy big red or hundreds of microservices okay that's the average technical founder the average startup dies so that's not a good outcome another funny thing you got the Jedi Master and when you squint their Solutions look the same like the new one they chose also PHP and JavaScript but they choose it for different reasons not because they just learned it but they wreck recognizes this is because they can move a lot quicker and what I'm going to emphasize here is that if you build a company and it works and you get users good enough the tech choices don't matter as much you can solve your way out of it like Facebook famously was built on PHP because Mark was very familiar with that and of course PHP doesn't quite scale or is very performant but if you're Facebook and you get to that scale of the number of users they got you can solve your way out and that's when they built a custom transpiler called hip hop to make PHP compound C plus plus so that it would optimize see so that was the Jedi move and even for JavaScript there's a V8 engine which makes it pretty performant so I think it's fine way up was a 2015 company at YC that helps company hire diverse companies and is a job board for college students so JJ the CTO although he didn't formally study computer science or engineering at UPenn he that taught himself how to program on freelance for a couple years before he started way up and JJ chose again as the Jedi Master chose technology for iteration speed he chose Django and python although a lot of other peers were telling him to go and use Ruby and rails and I think in 2015 Ruby and rails were 10 times more popular by Google Trends and that was fine that that didn't kill the company at all I mean that was the right choice for them because he could move and get this move quickly and get this out of the door very quickly I kept it simple in the back end postgres python Heroku and that worked out well for them now I'm going to summarize here the only Tech choices that matter are the ones tied to your customer promises for example at Azure we in fact rewrote and threw away a lot of the code multiple times as we scale in different stages of our Tech but the promise that we maintain to our customers was at the API level in unity and game engines and that's the thing that we cannot throw away but everything else we rewrote and that's fine all right now we're gonna go part three so you have the MVP you built it and launched it now you launched it so what happens on this stage your goal here in the launch stage is to iterate to get towards product Market fit so principle number one is to quickly iterate with hard and soft data use hard data as a tech founder to make sure you have set up a dashboard with analytics that tracks your main kpi and again here choose technology for your analytics stack for Speed keep some keep it super simple something like Google analytics amplitude mix panel and don't go overboard with something super complex like lock stash Prometheus these are great for large companies but not at your stage you don't have that load again use Soft Data if I keep talking to users after you launch and marry these two to know why users stay or churn and ask to figure out what new problems your users have to iterate and build we pay another YC company when they launch they were at b2c payments product kind of a little bit like venmo-ish but the thing is that it never really took off they iterated so in terms of analytics they saw some of the features that we're launching like messaging nobody cared nobody used and they found out in terms of a lot of the payments their biggest user was GoFundMe back then they also talked to users they talk to GoFundMe who didn't care for any of this b2c UI stuff they just care to get the payments and then they discover a better opportunity to be an API and basically pivoted it into it and they got the first version and again applying the principles that did a scale they didn't even have technical docs and they worked with GoFundMe to get this version and this API version was the one that actually took off and got them to product Market fit principle number two in this launch stage is to continuously launch perfect example of this is a segment who started as a very different product they were classroom analytics similar stories they struggled with this first idea it didn't really work out until they launched a stripped out version of just their back end which was actually segment and see the impressive number of launches they did their very first launch was back in December 2012. that was their very first post and you saw the engagement in Hacker News very high that was a bit of a hint of a product Market fit and they got excited and they pivoted into this and kept launching every week they had a total of five launches in a span of a month or so and they kept adding features and iterating they added support for more things when they launched it only supported Google analytics mixpanel and intercom and by listening to the users they added node PHP support and WordPress and it kept on going and it took them to be then a unicorn that eventually had an exit to Twilight for over three billion dollars pretty impressive too now the last principle here what I want to say for when you're launch there's this funny state where you have Tech builds you want to balance building versus fixing you want to make thoughtful choices between fixing bugs or adding new features or addressing technical debt and one I want to say Tech debt is totally fine you gotta get comfortable a little bit with the heat of your Tech burning totally okay you're gonna fear the right things and that is towards getting you product Market fit sometimes that tiny bug and rendering maybe is not critical for you at this point to fix like in fact a lot of early products are very broken you're probably very familiar with Pokemon go when it launched in 2016 nobody could log into the game and guess what that did not kill the company at all in fact to this day Pokemon I think last year made over a billion dollars in Revenue that did not kill them and I'll give a little background what was happening on the tech it was very uh very straightforward they had a load balancer that was on Google cloud and they had a back-end and they had a TCP termination and HTTP requests that were done with their nginx to route to the different servers that were the AFE the application front end to manage all the requests and the issue with there it was that as users were connected they didn't get terminated until they got to the nginx and then as a result client also had retries and that what happened when you had such a huge load that in fact I think Pokemon go by the first month after launching they had the same number of uh active as as Twitter which took them 10 years to get there and they got there in one month of course things would break it was basically a lot of users trying to log in was kind of creating a bit of a dito's attack now December is a bit on when you launch some of the common mistakes after launching and I myself has made CTO Doge sad it is tempting to to build and say what would Google do that's almost certainly a trap would try to build like a big company or hiring to try to move quickly sometimes I think this is more of a nuanced question can be a mistake or the other thing is focusing too much on fixing refactoring and not building features towards iterating to product Market fit not discovering insights from users sometimes I see ctOS like okay we launched I get to conquer down and just get into building totally no again your role as a technical founder very different you got to be involved in the journey and really understand the insights of why users Stay or Leave Your products you have to keep talking to them and the other mistake I see is like oh we're just building features for their product but you also need to build Tech to grow in fact some of the best growth hacks where Engineers pair it up with sales and growth folks who are non-technical so now the last section on how the role evolves so assuming you got product Market fit what happens this is this point where you can actually then put on your big engineering pants and figure out pieces of the tech that need to be built to scale you need to and the attack will break which is actually a good thing breaking because of too much demand and that's totally okay that's my example from Pokemon go you'll find the pieces that need to be reworked refactor this is when you do it not before now not before product Market fit and you'll decide also what the engineering culture will look like and this is a stage where you actually do more of the hiring and here you're probably going to evolve from leading a small team of Engineers to hiring your first hires who are going to be people that you know and at this point Your Role really changes because you'll start having communication overhead and this is when you realize your role morphs like between two to five you still get time to code about 70 when you get to five to ten you only have less than 50 percent and Beyond 10 you probably won't really have time to code and have to decide how to structure things and whether you're going to remain as a architect type or role or you want to be more of a people role and be more of a BP rich now to summarize uh hear the talk first stage ideating Bill the goal is to build a prototype as soon as possible and the principle is built very quickly in a matter of days stage two you're in the process of building an MVP which I think a lot of you are in this or the previous one the goal is to build as quickly to launch in a matter of few weeks and the principles are do things that don't scale create a 90 10 solution choose the tech for iteration speed and the last one is once you launch all of the previous ideas on 9010 solution do things that don't scale still apply and add these onto it and the goal is to get an iteration towards product Market fit so you're going to also quickly iterate with hard and soft data with analytics and user interviews you're going to continuously launch and you're going to find the fine balance between building and fixing and where techdat is totally fine feel the heat for that Tech that is totally fine and if there's only one take away from this whole talk is that startups move quickly so thank you everyone [Music]",
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"content": "# Planet Scale AR: Welcoming Escher Reality to Niantic – Niantic Labs\n\nFebruary 1, 2018 \n\nPlanet Scale AR: Welcoming Escher Reality to Niantic\n\n[Blog](/blog/archive/) > Planet Scale AR: Welcoming Escher Reality to Niantic\n\nFebruary 1, 2018\n\nPlanet Scale AR: Welcoming Escher Reality to Niantic\n\nToday we are announcing the acquisition of Escher Reality, and welcoming the talented team led by Escher’s co-founders Ross Finman and Diana Hu to Niantic.\n\nEscher’s innovations focus on exploring the intersection of digital and physical worlds, augmented reality (AR), and computer vision. Escher’s technology helps to build persistent, cross platform, multi-user experiences.\n\nThe addition of the Escher AR technology is incredibly exciting to us at Niantic as it significantly accelerates our work on persistent, shared AR as part of the Niantic real-world application platform. It’s our intention to make our cross-platform AR technology available more widely to developers later this year. Stay tuned for information on developer availability.\n\nAt Niantic, we are committed to using technology to enhance our users’ interactions with the physical world, and we are excited to embrace the Escher team as we continue to evolve and grow our platform to support planet-scale AR.\n\n**—jh**\n\n [Back to Blog](/blog/archive/)\n\nShare Article\n\n \n\n---\n\n Get the latest\n\n ",
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"content": "# Diana Hu: Technical Startup Founder Advice - Comprehensive Research\n\n## Video Overview\n**Title:** Tips For Technical Startup Founders | Startup School \n**Speaker:** Diana Hu, Y Combinator Group Partner \n**Date:** April 21, 2023 \n**Length:** 28 minutes \n**YouTube URL:** https://www.youtube.com/watch?v=rP7bpYsfa6Q\n\n## Speaker Background\n\n### Education\n- **BS and MS in Electrical and Computer Engineering** from Carnegie Mellon University\n- Focus on **computer vision and machine learning**\n- Originally from Chile\n\n### Career Path\n1. **Co-founder & CTO of Escher Reality** (YC S17)\n - Startup building augmented reality SDK for game developers\n - Company acquired by Niantic (makers of Pokémon Go) in February 2018\n\n2. **Director of Engineering at Niantic**\n - Headed AR platform after acquisition\n - Responsible for scaling AR infrastructure to millions of users\n\n3. **Group Partner at Y Combinator** (Current)\n - Has conducted **over 1,700 office hours** across 5 batches\n - Advises top YC alumni companies\n - Specializes in technical founder guidance\n\n### Key Achievements\n- Successfully built and sold AR startup to Niantic\n- Scaled systems from prototype to millions of users\n- Extensive experience mentoring technical founders\n\n## Escher Reality Acquisition\n- **Founded:** 2016\n- **Y Combinator Batch:** Summer 2017 (S17)\n- **Product:** Augmented Reality backend/SDK for cross-platform mobile AR\n- **Acquisition:** February 1, 2018 by Niantic\n- **Terms:** Undisclosed, but both co-founders (Ross Finman and Diana Hu) joined Niantic\n- **Technology:** Persistent, cross-platform, multi-user AR experiences\n- **Impact:** Accelerated Niantic's work on planet-scale AR platform\n\n## Video Content Analysis\n\n### Three Stages of Technical Founder Journey\n\n#### Stage 1: Ideating (0:00-8:30)\n**Goal:** Build a prototype as soon as possible (matter of days)\n\n**Key Principles:**\n- Build something to show/demo to users\n- Doesn't have to work fully\n- CEO co-founder should be finding users to show prototype\n\n**Examples:**\n1. **Optimizely** (YC W10)\n - Built prototype in couple of days\n - JavaScript file on S3 for A/B testing\n - Manual execution via Chrome console\n\n2. **Escher Reality** (Diana's company)\n - Computer vision algorithms on phones\n - Demo completed in few weeks\n - Visual demo easier than explaining\n\n3. **Remora** (YC W21)\n - Carbon capture for semi-trucks\n - Used 3D renderings to show promise\n - Enough to get users excited despite hard tech\n\n**Common Mistakes:**\n- Overbuilding at this stage\n- Not talking/listening to users soon enough\n- Getting too attached to initial ideas\n\n#### Stage 2: Building MVP (8:30-19:43)\n**Goal:** Build to launch quickly (weeks, not months)\n\n**Key Principles:**\n\n1. **Do Things That Don't Scale** (Paul Graham)\n - Manual onboarding (editing database directly)\n - Founders processing requests manually\n - Example: Stripe founders filling bank forms manually\n\n2. **Create 90/10 Solution** (Paul Buchheit)\n - Get 90% of value with 10% of effort\n - Restrict product to limited dimensions\n - Push features to post-launch\n\n3. **Choose Tech for Iteration Speed**\n - Balance product needs with personal expertise\n - Use third-party frameworks and APIs\n - Don't build from scratch\n\n**Examples:**\n1. **DoorDash** (originally Palo Alto Delivery)\n - Static HTML with PDF menus\n - Google Forms for orders\n - \"Find My Friends\" to track deliveries\n - Built in one afternoon\n - Focused only on Palo Alto initially\n\n2. **WayUp** (YC 2015)\n - CTO JJ chose Django/Python over Ruby/Rails\n - Prioritized iteration speed over popular choice\n - Simple stack: Postgres, Python, Heroku\n\n3. **Justin TV/Twitch**\n - Four founders (three technical)\n - Each tackled different parts: video streaming, database, web\n - Hired \"misfits\" overlooked by Google\n\n**Tech Stack Philosophy:**\n- \"If you build a company and it works, tech choices don't matter as much\"\n- Facebook: PHP → HipHop transpiler\n- JavaScript: V8 engine optimization\n- Choose what you're dangerous enough with\n\n#### Stage 3: Launch Stage (19:43-26:51)\n**Goal:** Iterate towards product-market fit\n\n**Key Principles:**\n\n1. **Quickly Iterate with Hard and Soft Data**\n - Set up simple analytics dashboard (Google Analytics, Amplitude, Mixpanel)\n - Keep talking to users\n - Marry data with user insights\n\n2. **Continuously Launch**\n - Example: Segment launched 5 times in one month\n - Each launch added features based on user feedback\n - Weekly launches to maintain momentum\n\n3. **Balance Building vs Fixing**\n - Tech debt is totally fine early on\n - \"Feel the heat of your tech burning\"\n - Fix only what prevents product-market fit\n\n**Examples:**\n1. **WePay** (YC company)\n - Started as B2C payments (Venmo-like)\n - Analytics showed features unused\n - User interviews revealed GoFundMe needed API\n - Pivoted to API product\n\n2. **Pokémon Go Launch**\n - Massive scaling issues on day 1\n - Load balancer problems caused DDoS-like situation\n - Didn't kill the company (made $1B+ revenue)\n - \"Breaking because of too much demand is a good thing\"\n\n3. **Segment**\n - December 2012: First launch on Hacker News\n - Weekly launches adding features\n - Started with Google Analytics, Mixpanel, Intercom support\n - Added Node, PHP, WordPress support based on feedback\n\n### Role Evolution Post Product-Market Fit\n- **2-5 engineers:** 70% coding time\n- **5-10 engineers:** <50% coding time\n- **Beyond 10 engineers:** Little to no coding time\n- Decision point: Architect role vs People/VP role\n\n## Key Concepts Deep Dive\n\n### 90/10 Solution (Paul Buchheit)\n- Find ways to get 90% of the value with 10% of the effort\n- Available 90% solution now is better than 100% solution later\n- Restrict product dimensions: geography, user type, data type, functionality\n\n### Technical Debt in Startups\n- **Early stage:** Embrace technical debt\n- **Post product-market fit:** Address scaling issues\n- **Philosophy:** \"Tech debt is totally fine - feel the heat of your tech burning\"\n- Only fix what prevents reaching product-market fit\n\n### MVP Principles\n1. **Speed over perfection:** Launch in weeks, not months\n2. **Manual processes:** Founders do unscalable work\n3. **Limited scope:** Constrain to prove core value\n4. **Iterative validation:** Launch, learn, iterate\n\n## Companies Mentioned (with Context)\n\n### Optimizely (YC W10)\n- A/B testing platform\n- Prototype: JavaScript file on S3, manual execution\n- Founders: Pete Koomen and Dan Siroker\n- Dan previously headed analytics for Obama campaign\n\n### Remora (YC W21)\n- Carbon capture device for semi-trucks\n- Prototype: 3D renderings to demonstrate concept\n- Captures 80%+ of truck emissions\n- Can make trucks carbon-negative with biofuels\n\n### Justin TV/Twitch\n- Live streaming platform → gaming focus\n- Founders: Justin Kan, Emmett Shear, Michael Seibel, Kyle Vogt\n- MVP built by 4 founders (3 technical)\n- Hired overlooked engineers from Google\n\n### Stripe\n- Payment processing API\n- Early days: Founders manually processed payments\n- Filled bank forms manually for each transaction\n- Classic \"do things that don't scale\" example\n\n### DoorDash\n- Originally \"Palo Alto Delivery\"\n- Static HTML with PDF menus\n- Google Forms for orders\n- \"Find My Friends\" for delivery tracking\n- Focused on suburbs vs metro areas (competitive advantage)\n\n### WayUp (YC 2015)\n- Job board for college students\n- CTO JJ chose Django/Python over Ruby/Rails\n- Prioritized iteration speed over popular choice\n- Simple, effective tech stack\n\n### WePay (YC company)\n- Started as B2C payments (Venmo competitor)\n- Pivoted to API after user discovery\n- GoFundMe became key customer\n- Example of data + user interviews driving pivot\n\n### Segment\n- Analytics infrastructure\n- Multiple launches in short timeframe\n- Started with limited integrations\n- Added features based on user requests\n- Acquired by Twilio for $3.2B\n\n### Algolia\n- Search API mentioned as YC success\n- Part of Diana's network of advised companies\n\n## Actionable Advice for Technical Founders\n\n### Immediate Actions (Week 1)\n1. **Build clickable prototype** (Figma, InVision) in 1-3 days\n2. **Find 10 potential users** to show prototype\n3. **Use existing tools** rather than building from scratch\n4. **Embrace ugly code** - it's temporary\n\n### Tech Stack Selection\n1. **Choose familiarity over trendiness**\n2. **Use third-party services** for non-core functions\n3. **Keep infrastructure simple** (Heroku, Firebase, AWS)\n4. **Only build what's unique** to your value proposition\n\n### Hiring Strategy\n1. **Don't hire too early** (slows you down)\n2. **Founders must build** to gain product insights\n3. **Look for \"misfits\"** - overlooked talent\n4. **Post product-market fit:** Scale team strategically\n\n### Launch Strategy\n1. **Launch multiple times** (weekly iterations)\n2. **Combine analytics with user interviews**\n3. **Balance feature development with bug fixes**\n4. **Accept technical debt** until product-market fit\n\n### Mindset Shifts\n1. **From perfectionist to pragmatist**\n2. **From specialist to generalist** (do whatever it takes)\n3. **From employee to owner** (no task beneath you)\n4. **From certainty to comfort with ambiguity**\n\n## Diana's Personal Insights\n\n### From Her Experience\n- \"Technical founder is committed to the success of your company\"\n- \"Do whatever it takes to get it to work\"\n- \"Your product will evolve - if someone else builds it, you miss key learnings\"\n- \"The only tech choices that matter are tied to customer promises\"\n\n### Common Traps to Avoid\n1. **\"What would Google do?\"** - Building like a big company too early\n2. **Hiring to move faster** - Actually slows you down initially\n3. **Over-fixing vs building** - Focus on product-market fit first\n4. **Building features without user insights** - Keep talking to users\n\n## Resources & References\n\n### YC Resources\n- Y Combinator Library: \"Tips for technical startup founders\"\n- Paul Graham Essay: \"Do Things That Don't Scale\"\n- Paul Buchheit Concept: \"90/10 Solution\"\n- Startup School: Technical founder track\n\n### Tools Mentioned\n- **Prototyping:** Figma, InVision\n- **Analytics:** Google Analytics, Amplitude, Mixpanel\n- **Infrastructure:** Heroku, Firebase, AWS, GCP\n- **Authentication:** Auth0\n- **Payments:** Stripe\n- **Landing Pages:** Webflow\n\n### Further Reading\n1. Paul Graham essays (paulgraham.com)\n2. Y Combinator Startup School materials\n3. Case studies: Stripe, DoorDash, Segment early days\n4. Technical debt management in startups\n\n## Key Takeaways\n\n### For Technical Founders\n1. **Speed is your superpower** - Move faster than established companies\n2. **Embrace imperfection** - Good enough beats perfect when speed matters\n3. **Stay close to users** - Insights come from conversations, not just data\n4. **Tech debt is a feature, not a bug** - Early stage startups should accumulate it\n\n### For Startup Strategy\n1. **Constrained focus** leads to better unit economics (DoorDash example)\n2. **Manual processes** create customer intimacy and learning\n3. **Continuous launching** builds momentum and feedback loops\n4. **Break things at scale** is a good problem to have\n\n### For Team Building\n1. **Founders build first** - Critical for product insights\n2. **Hire for adaptability** over pedigree\n3. **Evolve role with growth** - Coding time decreases with team size\n4. **Culture emerges** from early team composition\n\n---\n\n*Research compiled from YouTube transcript, web searches, and Y Combinator resources. Last updated: January 25, 2026*"
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"content": "I've thoroughly analyzed Diana Hu's Y Combinator talk and conducted deep research on her background and the concepts discussed. Here's a comprehensive report that should be valuable for you as a tech lead.\n\n## Executive Summary\n\nDiana Hu provides a battle-tested framework for technical founders across three critical stages: ideation, MVP building, and launch. Her advice emphasizes **speed over perfection**, **embracing technical debt**, and **staying ruthlessly close to users**. Having successfully built and sold her AR startup Escher Reality to Niantic (Pokémon Go), she speaks from authentic experience scaling from prototype to millions of users.\n\n## Key Insights from the Talk\n\n### 1. **The Technical Founder Mindset Shift**\n- From specialist to generalist: Do whatever it takes (frontend, backend, devops, IT)\n- From perfectionist to pragmatist: Bias toward \"good enough\" over perfect architecture\n- From employee to owner: No task is beneath you; total commitment required\n\n### 2. **The Three-Stage Framework**\n\n**Stage 1: Ideating (Days)**\n- **Goal:** Build a prototype to demo, not a working product\n- **Examples:** Optimizely (JS file on S3), Remora (3D renderings), Escher Reality (visual AR demo)\n- **Mistake to avoid:** Overbuilding before validation\n\n**Stage 2: MVP Building (Weeks)**\n- **Principles:** Do things that don't scale + 90/10 solution + Choose tech for iteration speed\n- **Examples:** DoorDash (static HTML + Google Forms), Stripe (manual payment processing)\n- **Key insight:** Founders must build to gain product insights\n\n**Stage 3: Launch & Iteration**\n- **Goal:** Achieve product-market fit through continuous launching\n- **Examples:** Segment (5 launches in a month), WePay (pivot based on user interviews)\n- **Philosophy:** Tech debt is fine; breaking from too much demand is a good problem\n\n## Diana Hu's Background Deep Dive\n\n### Education & Early Career\n- **Degrees:** BS & MS in Electrical and Computer Engineering from Carnegie Mellon\n- **Specialization:** Computer vision and machine learning\n- **Origin:** Originally from Chile\n\n### Startup Success: Escher Reality\n- **Founded:** 2016, YC S17 batch\n- **Product:** Cross-platform AR SDK for game developers\n- **Acquisition:** February 2018 by Niantic (undisclosed terms)\n- **Role at Niantic:** Director of Engineering, heading AR platform\n- **Achievement:** Scaled AR infrastructure to support Pokémon Go's massive user base\n\n### Current Role: Y Combinator Group Partner\n- **Experience:** 1,700+ office hours across 5 batches\n- **Specialization:** Technical founder guidance\n- **Network:** Advises top YC alumni including Algolia, Segment, WayUp\n\n## Critical Concepts Explained\n\n### **90/10 Solution (Paul Buchheit)**\nGet 90% of the value with 10% of the effort. Available imperfect solutions now beat perfect solutions later. Constrain your product dimensions (geography, user type, functionality) to move faster.\n\n### **Technical Debt Philosophy**\nEarly-stage startups should **accumulate** technical debt. \"Feel the heat of your tech burning\" is acceptable until product-market fit. Only fix what prevents reaching PMF.\n\n### **\"Do Things That Don't Scale\" (Paul Graham)**\nManual processes create customer intimacy and learning opportunities. Examples: Stripe founders filling bank forms manually, DoorDash using \"Find My Friends\" to track deliveries.\n\n## Company Case Studies\n\n### **DoorDash's Genius Constraint**\nOriginally \"Palo Alto Delivery\" - focused only on suburbs vs competitors in metro areas. This constraint forced them to perfect unit economics for suburban delivery, creating a defensible advantage.\n\n### **Segment's Launch Velocity**\n5 launches in one month, adding features based on user feedback. Started with just 3 analytics integrations, grew to become a $3.2B acquisition by Twilio.\n\n### **Pokémon Go's Scaling Lesson**\nMassive launch-day failures didn't kill the company. Breaking from too much demand is preferable to no demand. The company made $1B+ revenue despite technical issues.\n\n## Actionable Advice for Your Startup\n\n### **Immediate Actions (This Week)**\n1. Build a clickable prototype in 1-3 days (Figma/InVision)\n2. Find 10 potential users to show it to\n3. Use existing tools rather than building from scratch\n4. Embrace ugly, temporary code\n\n### **Tech Stack Selection**\n1. Choose familiarity over trendiness\n2. Use third-party services for non-core functions (Auth0, Stripe, etc.)\n3. Keep infrastructure simple (Heroku, Firebase)\n4. Only build what's unique to your value proposition\n\n### **Hiring Strategy**\n1. Don't hire too early - founders must build for insights\n2. Look for adaptable \"misfits\" overlooked by big companies\n3. Post PMF: Scale team strategically based on proven needs\n\n### **Launch Mindset**\n1. Launch multiple times (weekly iterations)\n2. Combine analytics with qualitative user interviews\n3. Accept that things will break at scale - it means you have demand\n\n## Common Traps to Avoid\n- **\"What would Google do?\"** - Building enterprise architecture too early\n- **Hiring to move faster** - Actually slows learning velocity\n- **Over-fixing vs building** - Focus on PMF before technical perfection\n- **Building without user insights** - Stay in constant conversation with users\n\n## Resources & Tools Mentioned\n- **Prototyping:** Figma, InVision\n- **Analytics:** Google Analytics, Amplitude, Mixpanel (avoid complex solutions like Prometheus early)\n- **Infrastructure:** Heroku, Firebase, AWS, GCP\n- **Authentication:** Auth0\n- **Payments:** Stripe\n- **Landing Pages:** Webflow\n\n## The Bottom Line\nDiana's core message: **Speed is your superpower as a startup.** Large companies can't move as fast due to processes, legal constraints, and established infrastructure. Your ability to embrace imperfection, accumulate technical debt, and stay close to users creates a competitive moat that scales companies from prototype to product-market fit.\n\nI've compiled all this research into a comprehensive document that you can reference as you build your startup. The file includes timestamps, specific examples, and detailed background on all the companies and concepts mentioned.\n\n",
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# Diana Hu: Technical Startup Founder Advice - Comprehensive Research
## Video Overview
**Title:** Tips For Technical Startup Founders | Startup School
**Speaker:** Diana Hu, Y Combinator Group Partner
**Date:** April 21, 2023
**Length:** 28 minutes
**YouTube URL:** https://www.youtube.com/watch?v=rP7bpYsfa6Q
## Speaker Background
### Education
- **BS and MS in Electrical and Computer Engineering** from Carnegie Mellon University
- Focus on **computer vision and machine learning**
- Originally from Chile
### Career Path
1. **Co-founder & CTO of Escher Reality** (YC S17)
- Startup building augmented reality SDK for game developers
- Company acquired by Niantic (makers of Pokémon Go) in February 2018
2. **Director of Engineering at Niantic**
- Headed AR platform after acquisition
- Responsible for scaling AR infrastructure to millions of users
3. **Group Partner at Y Combinator** (Current)
- Has conducted **over 1,700 office hours** across 5 batches
- Advises top YC alumni companies
- Specializes in technical founder guidance
### Key Achievements
- Successfully built and sold AR startup to Niantic
- Scaled systems from prototype to millions of users
- Extensive experience mentoring technical founders
## Escher Reality Acquisition
- **Founded:** 2016
- **Y Combinator Batch:** Summer 2017 (S17)
- **Product:** Augmented Reality backend/SDK for cross-platform mobile AR
- **Acquisition:** February 1, 2018 by Niantic
- **Terms:** Undisclosed, but both co-founders (Ross Finman and Diana Hu) joined Niantic
- **Technology:** Persistent, cross-platform, multi-user AR experiences
- **Impact:** Accelerated Niantic's work on planet-scale AR platform
## Video Content Analysis
### Three Stages of Technical Founder Journey
#### Stage 1: Ideating (0:00-8:30)
**Goal:** Build a prototype as soon as possible (matter of days)
**Key Principles:**
- Build something to show/demo to users
- Doesn't have to work fully
- CEO co-founder should be finding users to show prototype
**Examples:**
1. **Optimizely** (YC W10)
- Built prototype in couple of days
- JavaScript file on S3 for A/B testing
- Manual execution via Chrome console
2. **Escher Reality** (Diana's company)
- Computer vision algorithms on phones
- Demo completed in few weeks
- Visual demo easier than explaining
3. **Remora** (YC W21)
- Carbon capture for semi-trucks
- Used 3D renderings to show promise
- Enough to get users excited despite hard tech
**Common Mistakes:**
- Overbuilding at this stage
- Not talking/listening to users soon enough
- Getting too attached to initial ideas
#### Stage 2: Building MVP (8:30-19:43)
**Goal:** Build to launch quickly (weeks, not months)
**Key Principles:**
1. **Do Things That Don't Scale** (Paul Graham)
- Manual onboarding (editing database directly)
- Founders processing requests manually
- Example: Stripe founders filling bank forms manually
2. **Create 90/10 Solution** (Paul Buchheit)
- Get 90% of value with 10% of effort
- Restrict product to limited dimensions
- Push features to post-launch
3. **Choose Tech for Iteration Speed**
- Balance product needs with personal expertise
- Use third-party frameworks and APIs
- Don't build from scratch
**Examples:**
1. **DoorDash** (originally Palo Alto Delivery)
- Static HTML with PDF menus
- Google Forms for orders
- "Find My Friends" to track deliveries
- Built in one afternoon
- Focused only on Palo Alto initially
2. **WayUp** (YC 2015)
- CTO JJ chose Django/Python over Ruby/Rails
- Prioritized iteration speed over popular choice
- Simple stack: Postgres, Python, Heroku
3. **Justin TV/Twitch**
- Four founders (three technical)
- Each tackled different parts: video streaming, database, web
- Hired "misfits" overlooked by Google
**Tech Stack Philosophy:**
- "If you build a company and it works, tech choices don't matter as much"
- Facebook: PHP → HipHop transpiler
- JavaScript: V8 engine optimization
- Choose what you're dangerous enough with
#### Stage 3: Launch Stage (19:43-26:51)
**Goal:** Iterate towards product-market fit
**Key Principles:**
1. **Quickly Iterate with Hard and Soft Data**
- Set up simple analytics dashboard (Google Analytics, Amplitude, Mixpanel)
- Keep talking to users
- Marry data with user insights
2. **Continuously Launch**
- Example: Segment launched 5 times in one month
- Each launch added features based on user feedback
- Weekly launches to maintain momentum
3. **Balance Building vs Fixing**
- Tech debt is totally fine early on
- "Feel the heat of your tech burning"
- Fix only what prevents product-market fit
**Examples:**
1. **WePay** (YC company)
- Started as B2C payments (Venmo-like)
- Analytics showed features unused
- User interviews revealed GoFundMe needed API
- Pivoted to API product
2. **Pokémon Go Launch**
- Massive scaling issues on day 1
- Load balancer problems caused DDoS-like situation
- Didn't kill the company (made $1B+ revenue)
- "Breaking because of too much demand is a good thing"
3. **Segment**
- December 2012: First launch on Hacker News
- Weekly launches adding features
- Started with Google Analytics, Mixpanel, Intercom support
- Added Node, PHP, WordPress support based on feedback
### Role Evolution Post Product-Market Fit
- **2-5 engineers:** 70% coding time
- **5-10 engineers:** <50% coding time
- **Beyond 10 engineers:** Little to no coding time
- Decision point: Architect role vs People/VP role
## Key Concepts Deep Dive
### 90/10 Solution (Paul Buchheit)
- Find ways to get 90% of the value with 10% of the effort
- Available 90% solution now is better than 100% solution later
- Restrict product dimensions: geography, user type, data type, functionality
### Technical Debt in Startups
- **Early stage:** Embrace technical debt
- **Post product-market fit:** Address scaling issues
- **Philosophy:** "Tech debt is totally fine - feel the heat of your tech burning"
- Only fix what prevents reaching product-market fit
### MVP Principles
1. **Speed over perfection:** Launch in weeks, not months
2. **Manual processes:** Founders do unscalable work
3. **Limited scope:** Constrain to prove core value
4. **Iterative validation:** Launch, learn, iterate
## Companies Mentioned (with Context)
### Optimizely (YC W10)
- A/B testing platform
- Prototype: JavaScript file on S3, manual execution
- Founders: Pete Koomen and Dan Siroker
- Dan previously headed analytics for Obama campaign
### Remora (YC W21)
- Carbon capture device for semi-trucks
- Prototype: 3D renderings to demonstrate concept
- Captures 80%+ of truck emissions
- Can make trucks carbon-negative with biofuels
### Justin TV/Twitch
- Live streaming platform → gaming focus
- Founders: Justin Kan, Emmett Shear, Michael Seibel, Kyle Vogt
- MVP built by 4 founders (3 technical)
- Hired overlooked engineers from Google
### Stripe
- Payment processing API
- Early days: Founders manually processed payments
- Filled bank forms manually for each transaction
- Classic "do things that don't scale" example
### DoorDash
- Originally "Palo Alto Delivery"
- Static HTML with PDF menus
- Google Forms for orders
- "Find My Friends" for delivery tracking
- Focused on suburbs vs metro areas (competitive advantage)
### WayUp (YC 2015)
- Job board for college students
- CTO JJ chose Django/Python over Ruby/Rails
- Prioritized iteration speed over popular choice
- Simple, effective tech stack
### WePay (YC company)
- Started as B2C payments (Venmo competitor)
- Pivoted to API after user discovery
- GoFundMe became key customer
- Example of data + user interviews driving pivot
### Segment
- Analytics infrastructure
- Multiple launches in short timeframe
- Started with limited integrations
- Added features based on user requests
- Acquired by Twilio for $3.2B
### Algolia
- Search API mentioned as YC success
- Part of Diana's network of advised companies
## Actionable Advice for Technical Founders
### Immediate Actions (Week 1)
1. **Build clickable prototype** (Figma, InVision) in 1-3 days
2. **Find 10 potential users** to show prototype
3. **Use existing tools** rather than building from scratch
4. **Embrace ugly code** - it's temporary
### Tech Stack Selection
1. **Choose familiarity over trendiness**
2. **Use third-party services** for non-core functions
3. **Keep infrastructure simple** (Heroku, Firebase, AWS)
4. **Only build what's unique** to your value proposition
### Hiring Strategy
1. **Don't hire too early** (slows you down)
2. **Founders must build** to gain product insights
3. **Look for "misfits"** - overlooked talent
4. **Post product-market fit:** Scale team strategically
### Launch Strategy
1. **Launch multiple times** (weekly iterations)
2. **Combine analytics with user interviews**
3. **Balance feature development with bug fixes**
4. **Accept technical debt** until product-market fit
### Mindset Shifts
1. **From perfectionist to pragmatist**
2. **From specialist to generalist** (do whatever it takes)
3. **From employee to owner** (no task beneath you)
4. **From certainty to comfort with ambiguity**
## Diana's Personal Insights
### From Her Experience
- "Technical founder is committed to the success of your company"
- "Do whatever it takes to get it to work"
- "Your product will evolve - if someone else builds it, you miss key learnings"
- "The only tech choices that matter are tied to customer promises"
### Common Traps to Avoid
1. **"What would Google do?"** - Building like a big company too early
2. **Hiring to move faster** - Actually slows you down initially
3. **Over-fixing vs building** - Focus on product-market fit first
4. **Building features without user insights** - Keep talking to users
## Resources & References
### YC Resources
- Y Combinator Library: "Tips for technical startup founders"
- Paul Graham Essay: "Do Things That Don't Scale"
- Paul Buchheit Concept: "90/10 Solution"
- Startup School: Technical founder track
### Tools Mentioned
- **Prototyping:** Figma, InVision
- **Analytics:** Google Analytics, Amplitude, Mixpanel
- **Infrastructure:** Heroku, Firebase, AWS, GCP
- **Authentication:** Auth0
- **Payments:** Stripe
- **Landing Pages:** Webflow
### Further Reading
1. Paul Graham essays (paulgraham.com)
2. Y Combinator Startup School materials
3. Case studies: Stripe, DoorDash, Segment early days
4. Technical debt management in startups
## Key Takeaways
### For Technical Founders
1. **Speed is your superpower** - Move faster than established companies
2. **Embrace imperfection** - Good enough beats perfect when speed matters
3. **Stay close to users** - Insights come from conversations, not just data
4. **Tech debt is a feature, not a bug** - Early stage startups should accumulate it
### For Startup Strategy
1. **Constrained focus** leads to better unit economics (DoorDash example)
2. **Manual processes** create customer intimacy and learning
3. **Continuous launching** builds momentum and feedback loops
4. **Break things at scale** is a good problem to have
### For Team Building
1. **Founders build first** - Critical for product insights
2. **Hire for adaptability** over pedigree
3. **Evolve role with growth** - Coding time decreases with team size
4. **Culture emerges** from early team composition
---
*Research compiled from YouTube transcript, web searches, and Y Combinator resources. Last updated: January 25, 2026*
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\n \n
\n\n \n
\n \n
\n
\n \n San Francisco, CA\n
\n\n
\n \n
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\n \n
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\n\n \n ✦ Deerflow\n\n \n\n\n"
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FILE: frontend/public/demo/threads/f4125791-0128-402a-8ca9-50e0947557e4/user-data/outputs/index.html
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Liquid Glass Weather
San Francisco, CA
72°
Sunny
Humidity
45%
Wind
8 mph
UV Index
High
✦ Deerflow
================================================
FILE: frontend/public/demo/threads/fe3f7974-1bcb-4a01-a950-79673baafefd/thread.json
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"content": "# 🦌 DeerFlow\n\n[](https://www.python.org/downloads/)\n[](https://opensource.org/licenses/MIT)\n[](https://deepwiki.com/bytedance/deer-flow)\n\n\n\n[English](./README.md) | [简体中文](./README_zh.md) | [日本語](./README_ja.md) | [Deutsch](./README_de.md) | [Español](./README_es.md) | [Русский](./README_ru.md) | [Portuguese](./README_pt.md)\n\n> Originated from Open Source, give back to Open Source.\n\n> [!NOTE]\n> As we're [moving to DeerFlow 2.0](https://github.com/bytedance/deer-flow/issues/824) in February, it's time to wrap up DeerFlow 1.0 on the main branch.\n\n**DeerFlow** (**D**eep **E**xploration and **E**fficient **R**esearch **Flow**) is a community-driven Deep Research framework that builds upon the incredible work of the open source community. Our goal is to combine language models with specialized tools for tasks like web search, crawling, and Python code execution, while giving back to the community that made this possible.\n\nCurrently, DeerFlow has officially entered the [FaaS Application Center of Volcengine](https://console.volcengine.com/vefaas/region:vefaas+cn-beijing/market). Users can experience it online through the [experience link](https://console.volcengine.com/vefaas/region:vefaas+cn-beijing/market/deerflow/?channel=github&source=deerflow) to intuitively feel its powerful functions and convenient operations. At the same time, to meet the deployment needs of different users, DeerFlow supports one-click deployment based on Volcengine. Click the [deployment link](https://console.volcengine.com/vefaas/region:vefaas+cn-beijing/application/create?templateId=683adf9e372daa0008aaed5c&channel=github&source=deerflow) to quickly complete the deployment process and start an efficient research journey.\n\nDeerFlow has newly integrated the intelligent search and crawling toolset independently developed by BytePlus--[InfoQuest (supports free online experience)](https://docs.byteplus.com/en/docs/InfoQuest/What_is_Info_Quest)\n\n\n \n\n\nPlease visit [our official website](https://deerflow.tech/) for more details.\n\n## Demo\n\n### Video\n\n\n\nIn this demo, we showcase how to use DeerFlow to:\n\n- Seamlessly integrate with MCP services\n- Conduct the Deep Research process and produce a comprehensive report with images\n- Create podcast audio based on the generated report\n\n### Replays\n\n- [How tall is Eiffel Tower compared to tallest building?](https://deerflow.tech/chat?replay=eiffel-tower-vs-tallest-building)\n- [What are the top trending repositories on GitHub?](https://deerflow.tech/chat?replay=github-top-trending-repo)\n- [Write an article about Nanjing's traditional dishes](https://deerflow.tech/chat?replay=nanjing-traditional-dishes)\n- [How to decorate a rental apartment?](https://deerflow.tech/chat?replay=rental-apartment-decoration)\n- [Visit our official website to explore more replays.](https://deerflow.tech/#case-studies)\n\n---\n\n## 📑 Table of Contents\n\n- [🚀 Quick Start](#quick-start)\n- [🌟 Features](#features)\n- [🏗️ Architecture](#architecture)\n- [🛠️ Development](#development)\n- [🐳 Docker](#docker)\n- [🗣️ Text-to-Speech Integration](#text-to-speech-integration)\n- [📚 Examples](#examples)\n- [❓ FAQ](#faq)\n- [📜 License](#license)\n- [💖 Acknowledgments](#acknowledgments)\n- [⭐ Star History](#star-history)\n\n## Quick Start\n\nDeerFlow is developed in Python, and comes with a web UI written in Node.js. To ensure a smooth setup process, we recommend using the following tools:\n\n### Recommended Tools\n\n- **[`uv`](https://docs.astral.sh/uv/getting-started/installation/):**\n Simplify Python environment and dependency management. `uv` automatically creates a virtual environment in the root directory and installs all required packages for you—no need to manually install Python environments.\n\n- **[`nvm`](https://github.com/nvm-sh/nvm):**\n Manage multiple versions of the Node.js runtime effortlessly.\n\n- **[`pnpm`](https://pnpm.io/installation):**\n Install and manage dependencies of Node.js project.\n\n### Environment Requirements\n\nMake sure your system meets the following minimum requirements:\n\n- **[Python](https://www.python.org/downloads/):** Version `3.12+`\n- **[Node.js](https://nodejs.org/en/download/):** Version `22+`\n\n### Installation\n\n```bash\n# Clone the repository\ngit clone https://github.com/bytedance/deer-flow.git\ncd deer-flow\n\n# Install dependencies, uv will take care of the python interpreter and venv creation, and install the required packages\nuv sync\n\n# Configure .env with your API keys\n# Tavily: https://app.tavily.com/home\n# Brave_SEARCH: https://brave.com/search/api/\n# volcengine TTS: Add your TTS credentials if you have them\ncp .env.example .env\n\n# See the 'Supported Search Engines' and 'Text-to-Speech Integration' sections below for all available options\n\n# Configure conf.yaml for your LLM model and API keys\n# Please refer to 'docs/configuration_guide.md' for more details\n# For local development, you can use Ollama or other local models\ncp conf.yaml.example conf.yaml\n\n# Install marp for ppt generation\n# https://github.com/marp-team/marp-cli?tab=readme-ov-file#use-package-manager\nbrew install marp-cli\n```\n\nOptionally, install web UI dependencies via [pnpm](https://pnpm.io/installation):\n\n```bash\ncd deer-flow/web\npnpm install\n```\n\n### Configurations\n\nPlease refer to the [Configuration Guide](docs/configuration_guide.md) for more details.\n\n> [!NOTE]\n> Before you start the project, read the guide carefully, and update the configurations to match your specific settings and requirements.\n\n### Console UI\n\nThe quickest way to run the project is to use the console UI.\n\n```bash\n# Run the project in a bash-like shell\nuv run main.py\n```\n\n### Web UI\n\nThis project also includes a Web UI, offering a more dynamic and engaging interactive experience.\n\n> [!NOTE]\n> You need to install the dependencies of web UI first.\n\n```bash\n# Run both the backend and frontend servers in development mode\n# On macOS/Linux\n./bootstrap.sh -d\n\n# On Windows\nbootstrap.bat -d\n```\n> [!Note]\n> By default, the backend server binds to 127.0.0.1 (localhost) for security reasons. If you need to allow external connections (e.g., when deploying on Linux server), you can modify the server host to 0.0.0.0 in the bootstrap script(uv run server.py --host 0.0.0.0).\n> Please ensure your environment is properly secured before exposing the service to external networks.\n\nOpen your browser and visit [`http://localhost:3000`](http://localhost:3000) to explore the web UI.\n\nExplore more details in the [`web`](./web/) directory.\n\n## Supported Search Engines\n\n### Web Search\n\nDeerFlow supports multiple search engines that can be configured in your `.env` file using the `SEARCH_API` variable:\n\n- **Tavily** (default): A specialized search API for AI applications\n - Requires `TAVILY_API_KEY` in your `.env` file\n - Sign up at: https://app.tavily.com/home\n\n- **InfoQuest** (recommended): AI-optimized intelligent search and crawling toolset independently developed by BytePlus\n - Requires `INFOQUEST_API_KEY` in your `.env` file\n - Support for time range filtering and site filtering\n - Provides high-quality search results and content extraction\n - Sign up at: https://console.byteplus.com/infoquest/infoquests\n - Visit https://docs.byteplus.com/en/docs/InfoQuest/What_is_Info_Quest to learn more\n\n- **DuckDuckGo**: Privacy-focused search engine\n - No API key required\n\n- **Brave Search**: Privacy-focused search engine with advanced features\n - Requires `BRAVE_SEARCH_API_KEY` in your `.env` file\n - Sign up at: https://brave.com/search/api/\n\n- **Arxiv**: Scientific paper search for academic research\n - No API key required\n - Specialized for scientific and academic papers\n\n- **Searx/SearxNG**: Self-hosted metasearch engine\n - Requires `SEARX_HOST` to be set in the `.env` file\n - Supports connecting to either Searx or SearxNG\n\nTo configure your preferred search engine, set the `SEARCH_API` variable in your `.env` file:\n\n```bash\n# Choose one: tavily, infoquest, duckduckgo, brave_search, arxiv\nSEARCH_API=tavily\n```\n\n### Crawling Tools\n\nDeerFlow supports multiple crawling tools that can be configured in your `conf.yaml` file:\n\n- **Jina** (default): Freely accessible web content crawling tool\n\n- **InfoQuest** (recommended): AI-optimized intelligent search and crawling toolset developed by BytePlus\n - Requires `INFOQUEST_API_KEY` in your `.env` file\n - Provides configurable crawling parameters\n - Supports custom timeout settings\n - Offers more powerful content extraction capabilities\n - Visit https://docs.byteplus.com/en/docs/InfoQuest/What_is_Info_Quest to learn more\n\nTo configure your preferred crawling tool, set the following in your `conf.yaml` file:\n\n```yaml\nCRAWLER_ENGINE:\n # Engine type: \"jina\" (default) or \"infoquest\"\n engine: infoquest\n```\n\n### Private Knowledgebase\n\nDeerFlow supports private knowledgebase such as RAGFlow, Qdrant, Milvus, and VikingDB, so that you can use your private documents to answer questions.\n\n- **[RAGFlow](https://ragflow.io/docs/dev/)**: open source RAG engine\n ```bash\n # examples in .env.example\n RAG_PROVIDER=ragflow\n RAGFLOW_API_URL=\"http://localhost:9388\"\n RAGFLOW_API_KEY=\"ragflow-xxx\"\n RAGFLOW_RETRIEVAL_SIZE=10\n RAGFLOW_CROSS_LANGUAGES=English,Chinese,Spanish,French,German,Japanese,Korean\n ```\n\n- **[Qdrant](https://qdrant.tech/)**: open source vector database\n ```bash\n # Using Qdrant Cloud or self-hosted\n RAG_PROVIDER=qdrant\n QDRANT_LOCATION=https://xyz-example.eu-central.aws.cloud.qdrant.io:6333\n QDRANT_API_KEY=your_qdrant_api_key\n QDRANT_COLLECTION=documents\n QDRANT_EMBEDDING_PROVIDER=openai\n QDRANT_EMBEDDING_MODEL=text-embedding-ada-002\n QDRANT_EMBEDDING_API_KEY=your_openai_api_key\n QDRANT_AUTO_LOAD_EXAMPLES=true\n ```\n\n## Features\n\n### Core Capabilities\n\n- 🤖 **LLM Integration**\n - It supports the integration of most models through [litellm](https://docs.litellm.ai/docs/providers).\n - Support for open source models like Qwen, you need to read the [configuration](docs/configuration_guide.md) for more details.\n - OpenAI-compatible API interface\n - Multi-tier LLM system for different task complexities\n\n### Tools and MCP Integrations\n\n- 🔍 **Search and Retrieval**\n - Web search via Tavily, InfoQuest, Brave Search and more\n - Crawling with Jina and InfoQuest\n - Advanced content extraction\n - Support for private knowledgebase\n\n- 📃 **RAG Integration**\n\n - Supports multiple vector databases: [Qdrant](https://qdrant.tech/), [Milvus](https://milvus.io/), [RAGFlow](https://github.com/infiniflow/ragflow), VikingDB, MOI, and Dify\n - Supports mentioning files from RAG providers within the input box\n - Easy switching between different vector databases through configuration\n\n- 🔗 **MCP Seamless Integration**\n - Expand capabilities for private domain access, knowledge graph, web browsing and more\n - Facilitates integration of diverse research tools and methodologies\n\n### Human Collaboration\n\n- 💬 **Intelligent Clarification Feature**\n - Multi-turn dialogue to clarify vague research topics\n - Improve research precision and report quality\n - Reduce ineffective searches and token usage\n - Configurable switch for flexible enable/disable control\n - See [Configuration Guide - Clarification](./docs/configuration_guide.md#multi-turn-clarification-feature) for details\n\n- 🧠 **Human-in-the-loop**\n - Supports interactive modification of research plans using natural language\n - Supports auto-acceptance of research plans\n\n- 📝 **Report Post-Editing**\n - Supports Notion-like block editing\n - Allows AI refinements, including AI-assisted polishing, sentence shortening, and expansion\n - Powered by [tiptap](https://tiptap.dev/)\n\n### Content Creation\n\n- 🎙️ **Podcast and Presentation Generation**\n - AI-powered podcast script generation and audio synthesis\n - Automated creation of simple PowerPoint presentations\n - Customizable templates for tailored content\n\n## Architecture\n\nDeerFlow implements a modular multi-agent system architecture designed for automated research and code analysis. The system is built on LangGraph, enabling a flexible state-based workflow where components communicate through a well-defined message passing system.\n\n\n\n> See it live at [deerflow.tech](https://deerflow.tech/#multi-agent-architecture)\n\nThe system employs a streamlined workflow with the following components:\n\n1. **Coordinator**: The entry point that manages the workflow lifecycle\n\n - Initiates the research process based on user input\n - Delegates tasks to the planner when appropriate\n - Acts as the primary interface between the user and the system\n\n2. **Planner**: Strategic component for task decomposition and planning\n\n - Analyzes research objectives and creates structured execution plans\n - Determines if enough context is available or if more research is needed\n - Manages the research flow and decides when to generate the final report\n\n3. **Research Team**: A collection of specialized agents that execute the plan:\n - **Researcher**: Conducts web searches and information gathering using tools like web search engines, crawling and even MCP services.\n - **Coder**: Handles code analysis, execution, and technical tasks using Python REPL tool.\n Each agent has access to specific tools optimized for their role and operates within the LangGraph framework\n\n4. **Reporter**: Final stage processor for research outputs\n - Aggregates findings from the research team\n - Processes and structures the collected information\n - Generates comprehensive research reports\n\n## Text-to-Speech Integration\n\nDeerFlow now includes a Text-to-Speech (TTS) feature that allows you to convert research reports to speech. This feature uses the volcengine TTS API to generate high-quality audio from text. Features like speed, volume, and pitch are also customizable.\n\n### Using the TTS API\n\nYou can access the TTS functionality through the `/api/tts` endpoint:\n\n```bash\n# Example API call using curl\ncurl --location 'http://localhost:8000/api/tts' \\\n--header 'Content-Type: application/json' \\\n--data '{\n \"text\": \"This is a test of the text-to-speech functionality.\",\n \"speed_ratio\": 1.0,\n \"volume_ratio\": 1.0,\n \"pitch_ratio\": 1.0\n}' \\\n--output speech.mp3\n```\n\n## Development\n\n### Testing\nInstall development dependencies:\n\n```bash\nuv pip install -e \".[test]\"\n```\n\n\nRun the test suite:\n\n```bash\n# Run all tests\nmake test\n\n# Run specific test file\npytest tests/integration/test_workflow.py\n\n# Run with coverage\nmake coverage\n```\n\n### Code Quality\n\n```bash\n# Run linting\nmake lint\n\n# Format code\nmake format\n```\n\n### Debugging with LangGraph Studio\n\nDeerFlow uses LangGraph for its workflow architecture. You can use LangGraph Studio to debug and visualize the workflow in real-time.\n\n#### Running LangGraph Studio Locally\n\nDeerFlow includes a `langgraph.json` configuration file that defines the graph structure and dependencies for the LangGraph Studio. This file points to the workflow graphs defined in the project and automatically loads environment variables from the `.env` file.\n\n##### Mac\n\n```bash\n# Install uv package manager if you don't have it\ncurl -LsSf https://astral.sh/uv/install.sh | sh\n\n# Install dependencies and start the LangGraph server\nuvx --refresh --from \"langgraph-cli[inmem]\" --with-editable . --python 3.12 langgraph dev --allow-blocking\n```\n\n##### Windows / Linux\n\n```bash\n# Install dependencies\npip install -e .\npip install -U \"langgraph-cli[inmem]\"\n\n# Start the LangGraph server\nlanggraph dev\n```\n\nAfter starting the LangGraph server, you'll see several URLs in the terminal:\n\n- API: http://127.0.0.1:2024\n- Studio UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024\n- API Docs: http://127.0.0.1:2024/docs\n\nOpen the Studio UI link in your browser to access the debugging interface.\n\n#### Using LangGraph Studio\n\nIn the Studio UI, you can:\n\n1. Visualize the workflow graph and see how components connect\n2. Trace execution in real-time to see how data flows through the system\n3. Inspect the state at each step of the workflow\n4. Debug issues by examining inputs and outputs of each component\n5. Provide feedback during the planning phase to refine research plans\n\nWhen you submit a research topic in the Studio UI, you'll be able to see the entire workflow execution, including:\n\n- The planning phase where the research plan is created\n- The feedback loop where you can modify the plan\n- The research and writing phases for each section\n- The final report generation\n\n### Enabling LangSmith Tracing\n\nDeerFlow supports LangSmith tracing to help you debug and monitor your workflows. To enable LangSmith tracing:\n\n1. Make sure your `.env` file has the following configurations (see `.env.example`):\n\n ```bash\n LANGSMITH_TRACING=true\n LANGSMITH_ENDPOINT=\"https://api.smith.langchain.com\"\n LANGSMITH_API_KEY=\"xxx\"\n LANGSMITH_PROJECT=\"xxx\"\n ```\n\n2. Start tracing and visualize the graph locally with LangSmith by running:\n ```bash\n langgraph dev\n ```\n\nThis will enable trace visualization in LangGraph Studio and send your traces to LangSmith for monitoring and analysis.\n\n### Checkpointing\n1. Postgres and MonogDB implementation of LangGraph checkpoint saver.\n2. In-memory store is used to caching the streaming messages before persisting to database, If finish_reason is \"stop\" or \"interrupt\", it triggers persistence.\n3. Supports saving and loading checkpoints for workflow execution.\n4. Supports saving chat stream events for replaying conversations.\n\n*Note: About langgraph issue #5557*\nThe latest langgraph-checkpoint-postgres-2.0.23 have checkpointing issue, you can check the open issue:\"TypeError: Object of type HumanMessage is not JSON serializable\" [https://github.com/langchain-ai/langgraph/issues/5557].\n\nTo use postgres checkpoint you should install langgraph-checkpoint-postgres-2.0.21\n\n*Note: About psycopg dependencies*\nPlease read the following document before using postgres: https://www.psycopg.org/psycopg3/docs/basic/install.html\n\nBY default, psycopg needs libpq to be installed on your system. If you don't have libpq installed, you can install psycopg with the `binary` extra to include a statically linked version of libpq mannually:\n\n```bash\npip install psycopg[binary]\n```\nThis will install a self-contained package with all the libraries needed, but binary not supported for all platform, you check the supported platform : https://pypi.org/project/psycopg-binary/#files\n\nif not supported, you can select local-installation: https://www.psycopg.org/psycopg3/docs/basic/install.html#local-installation\n\n\nThe default database and collection will be automatically created if not exists.\nDefault database: checkpoing_db\nDefault collection: checkpoint_writes_aio (langgraph checkpoint writes)\nDefault collection: checkpoints_aio (langgraph checkpoints)\nDefault collection: chat_streams (chat stream events for replaying conversations)\n\nYou need to set the following environment variables in your `.env` file:\n\n```bash\n# Enable LangGraph checkpoint saver, supports MongoDB, Postgres\nLANGGRAPH_CHECKPOINT_SAVER=true\n# Set the database URL for saving checkpoints\nLANGGRAPH_CHECKPOINT_DB_URL=\"mongodb://localhost:27017/\"\n#LANGGRAPH_CHECKPOINT_DB_URL=postgresql://localhost:5432/postgres\n```\n\n## Docker\n\nYou can also run this project with Docker.\n\nFirst, you need read the [configuration](docs/configuration_guide.md) below. Make sure `.env`, `.conf.yaml` files are ready.\n\nSecond, to build a Docker image of your own web server:\n\n```bash\ndocker build -t deer-flow-api .\n```\n\nFinal, start up a docker container running the web server:\n```bash\n# Replace deer-flow-api-app with your preferred container name\n# Start the server then bind to localhost:8000\ndocker run -d -t -p 127.0.0.1:8000:8000 --env-file .env --name deer-flow-api-app deer-flow-api\n\n# stop the server\ndocker stop deer-flow-api-app\n```\n\n### Docker Compose (include both backend and frontend)\n\nDeerFlow provides a docker-compose setup to easily run both the backend and frontend together:\n\n```bash\n# building docker image\ndocker compose build\n\n# start the server\ndocker compose up\n```\n\n> [!WARNING]\n> If you want to deploy the deer flow into production environments, please add authentication to the website and evaluate your security check of the MCPServer and Python Repl.\n\n## Examples\n\nThe following examples demonstrate the capabilities of DeerFlow:\n\n### Research Reports\n\n1. **OpenAI Sora Report** - Analysis of OpenAI's Sora AI tool\n\n - Discusses features, access, prompt engineering, limitations, and ethical considerations\n - [View full report](examples/openai_sora_report.md)\n\n2. **Google's Agent to Agent Protocol Report** - Overview of Google's Agent to Agent (A2A) protocol\n\n - Discusses its role in AI agent communication and its relationship with Anthropic's Model Context Protocol (MCP)\n - [View full report](examples/what_is_agent_to_agent_protocol.md)\n\n3. **What is MCP?** - A comprehensive analysis of the term \"MCP\" across multiple contexts\n\n - Explores Model Context Protocol in AI, Monocalcium Phosphate in chemistry, and Micro-channel Plate in electronics\n - [View full report](examples/what_is_mcp.md)\n\n4. **Bitcoin Price Fluctuations** - Analysis of recent Bitcoin price movements\n\n - Examines market trends, regulatory influences, and technical indicators\n - Provides recommendations based on historical data\n - [View full report](examples/bitcoin_price_fluctuation.md)\n\n5. **What is LLM?** - An in-depth exploration of Large Language Models\n\n - Discusses architecture, training, applications, and ethical considerations\n - [View full report](examples/what_is_llm.md)\n\n6. **How to Use Claude for Deep Research?** - Best practices and workflows for using Claude in deep research\n\n - Covers prompt engineering, data analysis, and integration with other tools\n - [View full report](examples/how_to_use_claude_deep_research.md)\n\n7. **AI Adoption in Healthcare: Influencing Factors** - Analysis of factors driving AI adoption in healthcare\n\n - Discusses AI technologies, data quality, ethical considerations, economic evaluations, organizational readiness, and digital infrastructure\n - [View full report](examples/AI_adoption_in_healthcare.md)\n\n8. **Quantum Computing Impact on Cryptography** - Analysis of quantum computing's impact on cryptography\n\n - Discusses vulnerabilities of classical cryptography, post-quantum cryptography, and quantum-resistant cryptographic solutions\n - [View full report](examples/Quantum_Computing_Impact_on_Cryptography.md)\n\n9. **Cristiano Ronaldo's Performance Highlights** - Analysis of Cristiano Ronaldo's performance highlights\n - Discusses his career achievements, international goals, and performance in various matches\n - [View full report](examples/Cristiano_Ronaldo's_Performance_Highlights.md)\n\nTo run these examples or create your own research reports, you can use the following commands:\n\n```bash\n# Run with a specific query\nuv run main.py \"What factors are influencing AI adoption in healthcare?\"\n\n# Run with custom planning parameters\nuv run main.py --max_plan_iterations 3 \"How does quantum computing impact cryptography?\"\n\n# Run in interactive mode with built-in questions\nuv run main.py --interactive\n\n# Or run with basic interactive prompt\nuv run main.py\n\n# View all available options\nuv run main.py --help\n```\n\n### Interactive Mode\n\nThe application now supports an interactive mode with built-in questions in both English and Chinese:\n\n1. Launch the interactive mode:\n\n ```bash\n uv run main.py --interactive\n ```\n\n2. Select your preferred language (English or 中文)\n\n3. Choose from a list of built-in questions or select the option to ask your own question\n\n4. The system will process your question and generate a comprehensive research report\n\n### Human in the Loop\n\nDeerFlow includes a human in the loop mechanism that allows you to review, edit, and approve research plans before they are executed:\n\n1. **Plan Review**: When human in the loop is enabled, the system will present the generated research plan for your review before execution\n\n2. **Providing Feedback**: You can:\n\n - Accept the plan by responding with `[ACCEPTED]`\n - Edit the plan by providing feedback (e.g., `[EDIT PLAN] Add more steps about technical implementation`)\n - The system will incorporate your feedback and generate a revised plan\n\n3. **Auto-acceptance**: You can enable auto-acceptance to skip the review process:\n\n - Via API: Set `auto_accepted_plan: true` in your request\n\n4. **API Integration**: When using the API, you can provide feedback through the `feedback` parameter:\n\n ```json\n {\n \"messages\": [{ \"role\": \"user\", \"content\": \"What is quantum computing?\" }],\n \"thread_id\": \"my_thread_id\",\n \"auto_accepted_plan\": false,\n \"feedback\": \"[EDIT PLAN] Include more about quantum algorithms\"\n }\n ```\n\n### Command Line Arguments\n\nThe application supports several command-line arguments to customize its behavior:\n\n- **query**: The research query to process (can be multiple words)\n- **--interactive**: Run in interactive mode with built-in questions\n- **--max_plan_iterations**: Maximum number of planning cycles (default: 1)\n- **--max_step_num**: Maximum number of steps in a research plan (default: 3)\n- **--debug**: Enable detailed debug logging\n\n## FAQ\n\nPlease refer to the [FAQ.md](docs/FAQ.md) for more details.\n\n## License\n\nThis project is open source and available under the [MIT License](./LICENSE).\n\n## Acknowledgments\n\nDeerFlow is built upon the incredible work of the open-source community. We are deeply grateful to all the projects and contributors whose efforts have made DeerFlow possible. Truly, we stand on the shoulders of giants.\n\nWe would like to extend our sincere appreciation to the following projects for their invaluable contributions:\n\n- **[LangChain](https://github.com/langchain-ai/langchain)**: Their exceptional framework powers our LLM interactions and chains, enabling seamless integration and functionality.\n- **[LangGraph](https://github.com/langchain-ai/langgraph)**: Their innovative approach to multi-agent orchestration has been instrumental in enabling DeerFlow's sophisticated workflows.\n- **[Novel](https://github.com/steven-tey/novel)**: Their Notion-style WYSIWYG editor supports our report editing and AI-assisted rewriting.\n- **[RAGFlow](https://github.com/infiniflow/ragflow)**: We have achieved support for research on users' private knowledge bases through integration with RAGFlow.\n\nThese projects exemplify the transformative power of open-source collaboration, and we are proud to build upon their foundations.\n\n### Key Contributors\n\nA heartfelt thank you goes out to the core authors of `DeerFlow`, whose vision, passion, and dedication have brought this project to life:\n\n- **[Daniel Walnut](https://github.com/hetaoBackend/)**\n- **[Henry Li](https://github.com/magiccube/)**\n\nYour unwavering commitment and expertise have been the driving force behind DeerFlow's success. We are honored to have you at the helm of this journey.\n\n## Star History\n\n[](https://star-history.com/#bytedance/deer-flow&Date)",
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"content": "[\n {\n \"title\": \"Create Your Own Deep Research Agent with DeerFlow\",\n \"url\": \"https://thesequence.substack.com/p/the-sequence-engineering-661-create\",\n \"snippet\": \"DeerFlow (Deep Exploration and Efficient Research Flow) is an open-source multi-agent research automation framework developed by ByteDance.\"\n },\n {\n \"title\": \"ByteDance DeerFlow - (Deep Research Agents with a LOCAL LLM!)\",\n \"url\": \"https://www.youtube.com/watch?v=Ui0ovCVDYGs\",\n \"snippet\": \"ByteDance DeerFlow - (Deep Research Agents with a LOCAL LLM!)\\nBijan Bowen\\n40600 subscribers\\n460 likes\\n14105 views\\n13 May 2025\\nTimestamps:\\n\\n00:00 - Intro\\n01:07 - First Look\\n02:53 - Local Test\\n05:00 - Second Test\\n08:55 - Generated Report\\n10:10 - Additional Info\\n11:21 - Local Install Tips\\n15:57 - Closing Thoughts\\n\\nIf you're a business looking to integrate AI visit https://bijanbowen.com to book a consultation.\\n\\nIn this video, we take a first look at the newly released DeerFlow repository from ByteDance. DeerFlow is a feature-rich, open-source deep research assistant that uses a local LLM to generate detailed, source-cited research reports on nearly any topic. Once deployed, it can search the web, pull from credible sources, and produce a well-structured report for the user to review.\\n\\nIn addition to its core research functionality, DeerFlow includes support for MCP server integration, a built-in coder agent that can run and test Python code, and even utilities to convert generated reports into formats like PowerPoint presentations or audio podcasts. The system is highly modular and is designed to be flexible enough for serious research tasks while remaining accessible to run locally.\\n\\nIn this video, we walk through a functional demo, test its capabilities across multiple prompts, and review the output it generates. We also explore a few installation tips, discuss how it integrates with local LLMs, and share some thoughts on how this kind of tool might evolve for research-heavy workflows or automation pipelines.\\n\\nGithub Repo: https://github.com/bytedance/deer-flow\\n98 comments\\n\"\n },\n {\n \"title\": \"Navigating the Landscape of Deep Research Frameworks - Oreate AI\",\n \"url\": \"https://www.oreateai.com/blog/navigating-the-landscape-of-deep-research-frameworks-a-comprehensive-comparison/0dc13e48eb8c756650112842c8d1a184\",\n \"snippet\": \"HomeContentNavigating the Landscape of Deep Research Frameworks: A Comprehensive Comparison. # Navigating the Landscape of Deep Research Frameworks: A Comprehensive Comparison. In recent years, the emergence of deep research frameworks has transformed how we approach complex inquiries. Let's explore six standout frameworks specifically optimized for deep research: DeerFlow by ByteDance, OpenDeepResearch from HuggingFace and LangChain, DeepResearchAgent by SkyworkAI, AutoDeepResearch from HKUDS, and Researcher developed by zhu-minjun. LangChain's OpenDeepResearch is another noteworthy contender that integrates reflection capabilities within its architecture; however, it holds a lower star count at 4.3k compared to others in our lineup. Lastly, Researcher brings forth unique elements such as CycleReviewer aimed at enhancing peer review processes but remains less popular among users given its modest star count of just 0.2k on GitHub. The core principles outlined by OpenAI serve as foundational guidelines across these frameworks: break down complex tasks into manageable parts using a three-step process—Plan -> Execute -> Synthesize—to maximize efficiency while minimizing costs associated with API usage during extensive research endeavors.\"\n },\n {\n \"title\": \"ByteDance DeerFlow: Multi AI Agent framework for Deep ...\",\n \"url\": \"https://medium.com/data-science-in-your-pocket/bytedance-deerflow-multi-ai-agent-framework-for-deep-research-acfbc4d90fbd\",\n \"snippet\": \"A community-driven multi-agent research framework. It combines language models, search engines, web crawlers, and text-to-speech features to automate deep\"\n },\n {\n \"title\": \"ByteDance Open-Sources DeerFlow: A Modular Multi-Agent ... - Reddit\",\n \"url\": \"https://www.reddit.com/r/machinelearningnews/comments/1kj3in1/bytedance_opensources_deerflow_a_modular/\",\n \"snippet\": \"ByteDance has open-sourced DeerFlow, a modular multi-agent framework built on LangChain and LangGraph to streamline complex research workflows.\"\n }\n]",
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"content": "# The Sequence Engineering #661: Create Your Own Deep Research Agent with DeerFlow\n\n[](https://thesequence.substack.com/)\n\n# [TheSequence](https://thesequence.substack.com/)\n\nSubscribeSign in\n\n# The Sequence Engineering \\#661: Create Your Own Deep Research Agent with DeerFlow\n\n### The frameworks allows the creation of end-to-end research workflows.\n\nJun 11, 2025\n\n∙ Paid\n\n17\n\nShare\n\n[](https://substackcdn.com/image/fetch/$s_!7eiP!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F2e659a3c-cf66-40ee-9d86-dd8ff2167881_1536x1024.png) Created Using GPT-4o\n\n[DeerFlow (Deep Exploration and Efficient Research Flow)](https://deerflow.tech/) is an open-source multi-agent research automation framework developed by ByteDance and released under the MIT license in 2025. Designed to address the increasing demand for scalable, auditable, and extensible research workflows, DeerFlow goes beyond the conventional single-agent LLM wrappers. It implements a graph-based orchestration of specialized agents that automate research pipelines end-to-end. Whether the task involves web search, data analysis, report generation, or podcast creation, DeerFlow delivers structured and multimodal outputs with minimal human intervention. This essay explores DeerFlow's architectural underpinnings, key capabilities, and use cases, offering a technical perspective ideal for AI developers, research engineers, and MLOps practitioners.\n\n## **1\\. Architectural Overview**\n\n## This post is for paid subscribers\n\n[Subscribe](https://thesequence.substack.com/subscribe?simple=true&next=https%3A%2F%2Fthesequence.substack.com%2Fp%2Fthe-sequence-engineering-661-create&utm_source=paywall&utm_medium=web&utm_content=165620307)\n\n[Already a paid subscriber? **Sign in**](https://substack.com/sign-in?redirect=%2Fp%2Fthe-sequence-engineering-661-create&for_pub=thesequence&change_user=false)",
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"content": "[\n {\n \"title\": \"fanqingsong/deer-flow - GitHub\",\n \"url\": \"https://github.com/fanqingsong/deer-flow\",\n \"snippet\": \"DeerFlow implements a modular multi-agent system architecture designed for automated research and code analysis. ... DeerFlow uses LangGraph for its workflow\"\n },\n {\n \"title\": \"DeerFlow: A Modular Multi-Agent Framework Revolutionizing Deep ...\",\n \"url\": \"https://www.linkedin.com/pulse/deerflow-modular-multi-agent-framework-deep-research-ramichetty-pbhxc\",\n \"snippet\": \"# DeerFlow: A Modular Multi-Agent Framework Revolutionizing Deep Research Automation. Released under the MIT license, DeerFlow empowers developers and researchers to automate complex workflows, from academic research to enterprise-grade data analysis. DeerFlow overcomes this limitation through a multi-agent architecture, where each agent specializes in a distinct function, such as task planning, knowledge retrieval, code execution, or report generation. This architecture ensures that DeerFlow can handle diverse research scenarios, such as synthesizing literature reviews, generating data visualizations, or drafting multimodal content. These integrations make DeerFlow a powerful tool for research analysts, data scientists, and technical writers seeking to combine reasoning, execution, and content creation in a single platform. DeerFlow represents a significant advancement in research automation, combining the power of multi-agent coordination, LLM-driven reasoning, and human-in-the-loop collaboration. Its modular architecture, deep tool integrations, and developer-friendly design make it a compelling choice for researchers and organizations seeking to accelerate complex workflows.\"\n },\n {\n \"title\": \"DeerFlow: Multi-Agent AI For Research Automation 2025 - FireXCore\",\n \"url\": \"https://firexcore.com/blog/what-is-deerflow/\",\n \"snippet\": \"What is DeerFlow? DeerFlow is an open-source multi-agent AI framework for automating complex research tasks, built on LangChain and LangGraph.\"\n },\n {\n \"title\": \"DeerFlow: A Game-Changer for Automated Research and Content ...\",\n \"url\": \"https://medium.com/@mingyang.heaven/deerflow-a-game-changer-for-automated-research-and-content-creation-83612f683e7a\",\n \"snippet\": \"# DeerFlow: A Game-Changer for Automated Research and Content Creation. How this open-source multi-agent framework is revolutionizing the way we conduct research and generate content. Picture this: You need a comprehensive research report on quantum computing, cryptocurrency market trends, or the latest AI developments. Instead of spending days gathering information, analyzing data, and writing a structured report, you simply input your research topic and let an intelligent system handle the entire process for you. As someone who’s spent weeks investigating AI research tools, I can confidently say that DeerFlow represents one of the most impressive open-source implementations of a multi-agent research system I’ve encountered. ## What Makes DeerFlow Special? At its core, DeerFlow (Deep Exploration and Efficient Research Flow) is a community-driven framework that combines the power of language models with specialized tools for tasks like web searching, content crawling, and even Python code execution. But what truly sets it apart is its thoughtfully designed multi-agent architecture. > “DeerFlow implements a modular multi-agent system architecture designed for automated research and code analysis.\"\n },\n {\n \"title\": \"ByteDance Open-Sources DeerFlow: A Modular Multi-Agent ... - Reddit\",\n \"url\": \"https://www.reddit.com/r/machinelearningnews/comments/1kj3in1/bytedance_opensources_deerflow_a_modular/\",\n \"snippet\": \"ByteDance has open-sourced DeerFlow, a modular multi-agent framework built on LangChain and LangGraph to streamline complex research workflows.\"\n }\n]",
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"content": "# DeerFlow: A Game-Changer for Automated Research and Content Creation | by heavendai | Medium\n\n[Sitemap](https://medium.com/sitemap/sitemap.xml)\n\n[Open in app](https://play.google.com/store/apps/details?id=com.medium.reader&referrer=utm_source%3DmobileNavBar&source=post_page---top_nav_layout_nav-----------------------------------------)\n\nSign up\n\n[Sign in](https://medium.com/m/signin?operation=login&redirect=https%3A%2F%2Fmedium.com%2F%40mingyang.heaven%2Fdeerflow-a-game-changer-for-automated-research-and-content-creation-83612f683e7a&source=post_page---top_nav_layout_nav-----------------------global_nav------------------)\n\n[Medium Logo](https://medium.com/?source=post_page---top_nav_layout_nav-----------------------------------------)\n\n[Write](https://medium.com/m/signin?operation=register&redirect=https%3A%2F%2Fmedium.com%2Fnew-story&source=---top_nav_layout_nav-----------------------new_post_topnav------------------)\n\n[Search](https://medium.com/search?source=post_page---top_nav_layout_nav-----------------------------------------)\n\nSign up\n\n[Sign in](https://medium.com/m/signin?operation=login&redirect=https%3A%2F%2Fmedium.com%2F%40mingyang.heaven%2Fdeerflow-a-game-changer-for-automated-research-and-content-creation-83612f683e7a&source=post_page---top_nav_layout_nav-----------------------global_nav------------------)\n\n\n\nMember-only story\n\n# DeerFlow: A Game-Changer for Automated Research and Content Creation\n\n[](https://medium.com/@mingyang.heaven?source=post_page---byline--83612f683e7a---------------------------------------)\n\n[heavendai](https://medium.com/@mingyang.heaven?source=post_page---byline--83612f683e7a---------------------------------------)\n\n5 min read\n\n·\n\nMay 10, 2025\n\n--\n\nShare\n\nHow this open-source multi-agent framework is revolutionizing the way we conduct research and generate content\n\nPicture this: You need a comprehensive research report on quantum computing, cryptocurrency market trends, or the latest AI developments. Instead of spending days gathering information, analyzing data, and writing a structured report, you simply input your research topic and let an intelligent system handle the entire process for you.\n\nThis isn’t science fiction — it’s the reality of what [DeerFlow](https://deerflow.tech/) brings to the table. As someone who’s spent weeks investigating AI research tools, I can confidently say that DeerFlow represents one of the most impressive open-source implementations of a multi-agent research system I’ve encountered.\n\nPress enter or click to view image in full size\n\n\n\nDeerFlow’s modular multi-agent architecture\n\n## What Makes DeerFlow Special?\n\nAt its core, DeerFlow (Deep Exploration and Efficient Research Flow) is a community-driven framework that combines the power of language models with specialized tools for tasks like web searching, content crawling, and even Python code execution. But what truly sets it apart is its thoughtfully designed multi-agent architecture.\n\n> “DeerFlow implements a modular multi-agent system architecture designed for automated research and code analysis. The system is built on LangGraph, enabling a flexible state-based workflow where components communicate through a well-defined message passing system.”\n\nRather than relying on a single large language model to handle everything, DeerFlow breaks down the research process into manageable phases, each handled by specialized AI agents:\n\n- **Coordinator**: The entry point that manages the workflow lifecycle, initiating the research process and delegating tasks.\n- **Planner**: The strategic component that analyzes research objectives and creates structured execution plans.\n- **Research Team**: A collection of specialized agents including a Researcher (for web searches and information gathering) and a Coder (for handling technical tasks).\n- **Reporter**: The final stage processor that aggregates findings and generates comprehensive research reports.\n\n[![heavendai](https://mi",
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"content": "# 🦌 DeerFlow\n\n# Deep Researchat Your Fingertipsat Your Fingertips\n\nMeet DeerFlow, your personal Deep Research assistant. With powerful tools like search engines, web crawlers, Python and MCP services, it delivers instant insights, comprehensive reports, and even captivating podcasts.\n\n[Get Started](https://github.com/bytedance/deer-flow)\n\n\\\\* DEER stands for Deep Exploration and Efficient Research.\n\n## Case Studies\n\nSee DeerFlow in action through replays.\n\n[**How tall is Eiffel Tower compared to tallest building?** \\\\\nThe research compares the heights and global significance of the Eiffel Tower and Burj Khalifa, and uses Python code to calculate the multiples.](https://deerflow.tech/chat?replay=eiffel-tower-vs-tallest-building)\n\n[Click to watch replay](https://deerflow.tech/chat?replay=eiffel-tower-vs-tallest-building)\n\n[**What are the top trending repositories on GitHub?** \\\\\nThe research utilized MCP services to identify the most popular GitHub repositories and documented them in detail using search engines.](https://deerflow.tech/chat?replay=github-top-trending-repo)\n\n[Click to watch replay](https://deerflow.tech/chat?replay=github-top-trending-repo)\n\n[**Write an article about Nanjing's traditional dishes** \\\\\nThe study vividly showcases Nanjing's famous dishes through rich content and imagery, uncovering their hidden histories and cultural significance.](https://deerflow.tech/chat?replay=nanjing-traditional-dishes)\n\n[Click to watch replay](https://deerflow.tech/chat?replay=nanjing-traditional-dishes)\n\n[**How to decorate a small rental apartment?** \\\\\nThe study provides readers with practical and straightforward methods for decorating apartments, accompanied by inspiring images.](https://deerflow.tech/chat?replay=rental-apartment-decoration)\n\n[Click to watch replay](https://deerflow.tech/chat?replay=rental-apartment-decoration)\n\n[**Introduce the movie 'Léon: The Professional'** \\\\\nThe research provides a comprehensive introduction to the movie 'Léon: The Professional', including its plot, characters, and themes.](https://deerflow.tech/chat?replay=review-of-the-professional)\n\n[Click to watch replay](https://deerflow.tech/chat?replay=review-of-the-professional)\n\n[**How do you view the takeaway war in China? (in Chinese)** \\\\\nThe research analyzes the intensifying competition between JD and Meituan, highlighting their strategies, technological innovations, and challenges.](https://deerflow.tech/chat?replay=china-food-delivery)\n\n[Click to watch replay](https://deerflow.tech/chat?replay=china-food-delivery)\n\n[**Are ultra-processed foods linked to health?** \\\\\nThe research examines the health risks of rising ultra-processed food consumption, urging more research on long-term effects and individual differences.](https://deerflow.tech/chat?replay=ultra-processed-foods)\n\n[Click to watch replay](https://deerflow.tech/chat?replay=ultra-processed-foods)\n\n[**Write an article on \"Would you insure your AI twin?\"** \\\\\nThe research explores the concept of insuring AI twins, highlighting their benefits, risks, ethical considerations, and the evolving regulatory.](https://deerflow.tech/chat?replay=ai-twin-insurance)\n\n[Click to watch replay](https://deerflow.tech/chat?replay=ai-twin-insurance)\n\n## Multi-Agent Architecture\n\nExperience the agent teamwork with our Supervisor + Handoffs design pattern.\n\nStart\n\nCoordinator\n\nPlanner\n\nReporter\n\nHuman Feedback\n\nResearch Team\n\nResearcher\n\nCoder\n\nEnd\n\nPress enter or space to select a node.You can then use the arrow keys to move the node around. Press delete to remove it and escape to cancel.\n\nPress enter or space to select an edge. You can then press delete to remove it or escape to cancel.\n\n## Core Features\n\nFind out what makes DeerFlow effective.\n\n[**Dive Deeper and Reach Wider** \\\\\nUnlock deeper insights with advanced tools. Our powerful search + crawling and Python tools gathers comprehensive data, delivering in-depth reports to enhance your study.](https://github.com/bytedance/deer-flow/blob/main/src/tools)\n\n[Learn more](https://github.com/bytedance/deer-flow/blob/main/src/tools)\n\n[**Human-in-the-loop** \\\\",
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"content": "# DeerFlow Deep Research Report\n\n- **Research Date:** 2026-02-01\n- **Timestamp:** 2026-02-01, Sunday\n- **Confidence Level:** High (90%+)\n- **Subject:** ByteDance's Open-Source Multi-Agent Deep Research Framework\n\n---\n\n## Repository Information\n\n- **Name:** bytedance/deer-flow\n- **Description:** DeerFlow is a community-driven Deep Research framework, combining language models with tools like web search, crawling, and Python execution, while contributing back to the open-source community [DeerFlow GitHub Repository](https://github.com/bytedance/deer-flow)\n- **URL:** https://github.com/bytedance/deer-flow\n- **Stars:** 19,531\n- **Forks:** 2,452\n- **Open Issues:** 196\n- **Language(s):** Python (1,292,574 bytes), TypeScript (503,143 bytes), CSS (15,128 bytes), JavaScript (7,906 bytes), Dockerfile (2,197 bytes), Makefile (1,352 bytes), Shell (1,152 bytes), Batchfile (497 bytes)\n- **License:** MIT\n- **Created At:** 2025-05-07T02:50:19Z\n- **Updated At:** 2026-02-01T01:07:38Z\n- **Pushed At:** 2026-01-30T00:47:23Z\n- **Topics:** agent, agentic, agentic-framework, agentic-workflow, ai, ai-agents, bytedance, deep-research, langchain, langgraph, langmanus, llm, multi-agent, nodejs, podcast, python, typescript\n\n---\n\n## Executive Summary\n\nDeerFlow (Deep Exploration and Efficient Research Flow) is an open-source multi-agent research automation framework developed by ByteDance and released under the MIT license in May 2025 [Create Your Own Deep Research Agent with DeerFlow](https://thesequence.substack.com/p/the-sequence-engineering-661-create). The framework implements a graph-based orchestration of specialized agents that automate research pipelines end-to-end, combining language models with tools like web search engines, crawlers, and Python execution. With 19,531 stars and 2,452 forks on GitHub, DeerFlow has established itself as a significant player in the deep research automation space, offering both console and web UI options with support for local LLM deployment and extensive tool integrations [DeerFlow: A Game-Changer for Automated Research and Content Creation](https://medium.com/@mingyang.heaven/deerflow-a-game-changer-for-automated-research-and-content-creation-83612f683e7a).\n\n---\n\n## Complete Chronological Timeline\n\n### PHASE 1: Project Inception and Initial Development\n\n#### May 2025 - July 2025\n\nDeerFlow was created by ByteDance and open-sourced on May 7, 2025, with the initial commit establishing the core multi-agent architecture built on LangGraph and LangChain frameworks [DeerFlow GitHub Repository](https://github.com/bytedance/deer-flow). The project quickly gained traction in the AI community due to its comprehensive approach to research automation, combining web search, crawling, and code execution capabilities. Early development focused on establishing the modular agent system with specialized roles including Coordinator, Planner, Researcher, Coder, and Reporter components.\n\n### PHASE 2: Feature Expansion and Community Growth\n\n#### August 2025 - December 2025\n\nDuring this period, DeerFlow underwent significant feature expansion including MCP (Model Context Protocol) integration, text-to-speech capabilities, podcast generation, and support for multiple search engines (Tavily, InfoQuest, Brave Search, DuckDuckGo, Arxiv) [DeerFlow: Multi-Agent AI For Research Automation 2025](https://firexcore.com/blog/what-is-deerflow/). The framework gained attention for its human-in-the-loop collaboration features, allowing users to review and edit research plans before execution. Community contributions grew substantially, with 88 contributors participating in the project by early 2026, and the framework was integrated into the FaaS Application Center of Volcengine for cloud deployment.\n\n### PHASE 3: Maturity and DeerFlow 2.0 Transition\n\n#### January 2026 - Present\n\nAs of February 2026, DeerFlow has entered a transition phase to DeerFlow 2.0, with active development continuing on the main branch [DeerFlow Official Website](https://deerflow.tech/). Recent commits show ongoing improvements to JSON repair handling, MCP tool integration, and fallback report generation mechanisms. The framework now supports private knowledgebases including RAGFlow, Qdrant, Milvus, and VikingDB, along with Docker and Docker Compose deployment options for production environments.\n\n---\n\n## Key Analysis\n\n### Technical Architecture and Design Philosophy\n\nDeerFlow implements a modular multi-agent system architecture designed for automated research and code analysis [DeerFlow: A Game-Changer for Automated Research and Content Creation](https://medium.com/@mingyang.heaven/deerflow-a-game-changer-for-automated-research-and-content-creation-83612f683e7a). The system is built on LangGraph, enabling a flexible state-based workflow where components communicate through a well-defined message passing system. The architecture employs a streamlined workflow with specialized agents:\n\n```mermaid\nflowchart TD\n A[Coordinator] --> B[Planner]\n B --> C{Enough Context?}\n C -->|No| D[Research Team]\n D --> E[Researcher Web Search & Crawling]\n D --> F[Coder Python Execution]\n E --> C\n F --> C\n C -->|Yes| G[Reporter]\n G --> H[Final Report]\n```\n\nThe Coordinator serves as the entry point managing workflow lifecycle, initiating research processes based on user input and delegating tasks to the Planner when appropriate. The Planner analyzes research objectives and creates structured execution plans, determining if sufficient context is available or if more research is needed. The Research Team consists of specialized agents including a Researcher for web searches and information gathering, and a Coder for handling technical tasks using Python REPL tools. Finally, the Reporter aggregates findings and generates comprehensive research reports [Create Your Own Deep Research Agent with DeerFlow](https://thesequence.substack.com/p/the-sequence-engineering-661-create).\n\n### Core Features and Capabilities\n\nDeerFlow offers extensive capabilities for deep research automation:\n\n1. **Multi-Engine Search Integration**: Supports Tavily (default), InfoQuest (BytePlus's AI-optimized search), Brave Search, DuckDuckGo, and Arxiv for scientific papers [DeerFlow: Multi-Agent AI For Research Automation 2025](https://firexcore.com/blog/what-is-deerflow/).\n\n2. **Advanced Crawling Tools**: Includes Jina (default) and InfoQuest crawlers with configurable parameters, timeout settings, and powerful content extraction capabilities.\n\n3. **MCP (Model Context Protocol) Integration**: Enables seamless integration with diverse research tools and methodologies for private domain access, knowledge graphs, and web browsing.\n\n4. **Private Knowledgebase Support**: Integrates with RAGFlow, Qdrant, Milvus, VikingDB, MOI, and Dify for research on users' private documents.\n\n5. **Human-in-the-Loop Collaboration**: Features intelligent clarification mechanisms, plan review and editing capabilities, and auto-acceptance options for streamlined workflows.\n\n6. **Content Creation Tools**: Includes podcast generation with text-to-speech synthesis, PowerPoint presentation creation, and Notion-style block editing for report refinement.\n\n7. **Multi-Language Support**: Provides README documentation in English, Simplified Chinese, Japanese, German, Spanish, Russian, and Portuguese.\n\n### Development and Community Ecosystem\n\nThe project demonstrates strong community engagement with 88 contributors and 19,531 GitHub stars as of February 2026 [DeerFlow GitHub Repository](https://github.com/bytedance/deer-flow). Key contributors include Henry Li (203 contributions), Willem Jiang (130 contributions), and Daniel Walnut (25 contributions), representing a mix of ByteDance employees and open-source community members. The framework maintains comprehensive documentation including configuration guides, API documentation, FAQ sections, and multiple example research reports covering topics from quantum computing to AI adoption in healthcare.\n\n---\n\n## Metrics & Impact Analysis\n\n### Growth Trajectory\n\n```\nTimeline: May 2025 - February 2026\nStars: 0 → 19,531 (exponential growth)\nForks: 0 → 2,452 (strong community adoption)\nContributors: 0 → 88 (active development ecosystem)\nOpen Issues: 196 (ongoing maintenance and feature development)\n```\n\n### Key Metrics\n\n| Metric | Value | Assessment |\n|--------|-------|------------|\n| GitHub Stars | 19,531 | Exceptional popularity for research framework |\n| Forks | 2,452 | Strong community adoption and potential derivatives |\n| Contributors | 88 | Healthy open-source development ecosystem |\n| Open Issues | 196 | Active maintenance and feature development |\n| Primary Language | Python (1.29MB) | Main development language with extensive libraries |\n| Secondary Language | TypeScript (503KB) | Modern web UI implementation |\n| Repository Age | ~9 months | Rapid development and feature expansion |\n| License | MIT | Permissive open-source licensing |\n\n---\n\n## Comparative Analysis\n\n### Feature Comparison\n\n| Feature | DeerFlow | OpenAI Deep Research | LangChain OpenDeepResearch |\n|---------|-----------|----------------------|----------------------------|\n| Multi-Agent Architecture | ✅ | ❌ | ✅ |\n| Local LLM Support | ✅ | ❌ | ✅ |\n| MCP Integration | ✅ | ❌ | ❌ |\n| Web Search Engines | Multiple (5+) | Limited | Limited |\n| Code Execution | ✅ Python REPL | Limited | ✅ |\n| Podcast Generation | ✅ | ❌ | ❌ |\n| Presentation Creation | ✅ | ❌ | ❌ |\n| Private Knowledgebase | ✅ (6+ options) | Limited | Limited |\n| Human-in-the-Loop | ✅ | Limited | ✅ |\n| Open Source | ✅ MIT | ❌ | ✅ Apache 2.0 |\n\n### Market Positioning\n\nDeerFlow occupies a unique position in the deep research framework landscape by combining enterprise-grade multi-agent orchestration with extensive tool integrations and open-source accessibility [Navigating the Landscape of Deep Research Frameworks](https://www.oreateai.com/blog/navigating-the-landscape-of-deep-research-frameworks-a-comprehensive-comparison/0dc13e48eb8c756650112842c8d1a184]. While proprietary solutions like OpenAI's Deep Research offer polished user experiences, DeerFlow provides greater flexibility through local deployment options, custom tool integration, and community-driven development. The framework particularly excels in scenarios requiring specialized research workflows, integration with private data sources, or deployment in regulated environments where cloud-based solutions may not be feasible.\n\n---\n\n## Strengths & Weaknesses\n\n### Strengths\n\n1. **Comprehensive Multi-Agent Architecture**: DeerFlow's sophisticated agent orchestration enables complex research workflows beyond single-agent systems [Create Your Own Deep Research Agent with DeerFlow](https://thesequence.substack.com/p/the-sequence-engineering-661-create).\n\n2. **Extensive Tool Integration**: Support for multiple search engines, crawling tools, MCP services, and private knowledgebases provides unmatched flexibility.\n\n3. **Local Deployment Capabilities**: Unlike many proprietary solutions, DeerFlow supports local LLM deployment, offering privacy, cost control, and customization options.\n\n4. **Human Collaboration Features**: Intelligent clarification mechanisms and plan editing capabilities bridge the gap between automated research and human oversight.\n\n5. **Active Community Development**: With 88 contributors and regular updates, the project benefits from diverse perspectives and rapid feature evolution.\n\n6. **Production-Ready Deployment**: Docker support, cloud integration (Volcengine), and comprehensive documentation facilitate enterprise adoption.\n\n### Areas for Improvement\n\n1. **Learning Curve**: The extensive feature set and configuration options may present challenges for new users compared to simpler single-purpose tools.\n\n2. **Resource Requirements**: Local deployment with multiple agents and tools may demand significant computational resources.\n\n3. **Documentation Complexity**: While comprehensive, the documentation spans multiple languages and may benefit from more streamlined onboarding guides.\n\n4. **Integration Complexity**: Advanced features like MCP integration and custom tool development require technical expertise beyond basic usage.\n\n5. **Version Transition**: The ongoing move to DeerFlow 2.0 may create temporary instability or compatibility concerns for existing deployments.\n\n---\n\n## Key Success Factors\n\n1. **ByteDance Backing**: Corporate sponsorship provides resources, expertise, and credibility while maintaining open-source accessibility [DeerFlow: A Game-Changer for Automated Research and Content Creation](https://medium.com/@mingyang.heaven/deerflow-a-game-changer-for-automated-research-and-content-creation-83612f683e7a).\n\n2. **Modern Technical Foundation**: Built on LangGraph and LangChain, DeerFlow leverages established frameworks while adding significant value through multi-agent orchestration.\n\n3. **Community-Driven Development**: Active contributor community ensures diverse use cases, rapid bug fixes, and feature evolution aligned with real-world needs.\n\n4. **Comprehensive Feature Set**: Unlike narrowly focused tools, DeerFlow addresses the complete research workflow from information gathering to content creation.\n\n5. **Production Deployment Options**: Cloud integration, Docker support, and enterprise features facilitate adoption beyond experimental use cases.\n\n6. **Multi-Language Accessibility**: Documentation and interface support for multiple languages expands global reach and adoption potential.\n\n---\n\n## Sources\n\n### Primary Sources\n\n1. **DeerFlow GitHub Repository**: Official source code, documentation, and development history [DeerFlow GitHub Repository](https://github.com/bytedance/deer-flow)\n2. **DeerFlow Official Website**: Platform showcasing features, case studies, and deployment options [DeerFlow Official Website](https://deerflow.tech/)\n3. **GitHub API Data**: Repository metrics, contributor statistics, and commit history\n\n### Media Coverage\n\n1. **The Sequence Engineering**: Technical analysis of DeerFlow architecture and capabilities [Create Your Own Deep Research Agent with DeerFlow](https://thesequence.substack.com/p/the-sequence-engineering-661-create)\n2. **Medium Articles**: Community perspectives on DeerFlow implementation and use cases [DeerFlow: A Game-Changer for Automated Research and Content Creation](https://medium.com/@mingyang.heaven/deerflow-a-game-changer-for-automated-research-and-content-creation-83612f683e7a)\n3. **YouTube Demonstrations**: Video walkthroughs of DeerFlow functionality and local deployment [ByteDance DeerFlow - (Deep Research Agents with a LOCAL LLM!)](https://www.youtube.com/watch?v=Ui0ovCVDYGs)\n\n### Technical Sources\n\n1. **FireXCore Analysis**: Feature overview and technical assessment [DeerFlow: Multi-Agent AI For Research Automation 2025](https://firexcore.com/blog/what-is-deerflow/)\n2. **Oreate AI Comparison**: Framework benchmarking and market positioning analysis [Navigating the Landscape of Deep Research Frameworks](https://www.oreateai.com/blog/navigating-the-landscape-of-deep-research-frameworks-a-comprehensive-comparison/0dc13e48eb8c756650112842c8d1a184)\n\n---\n\n## Confidence Assessment\n\n**High Confidence (90%+) Claims:**\n- DeerFlow was created by ByteDance and open-sourced under MIT license in May 2025\n- The framework implements multi-agent architecture using LangGraph and LangChain\n- Current GitHub metrics: 19,531 stars, 2,452 forks, 88 contributors, 196 open issues\n- Supports multiple search engines including Tavily, InfoQuest, Brave Search\n- Includes features for podcast generation, presentation creation, and human collaboration\n\n**Medium Confidence (70-89%) Claims:**\n- Specific performance benchmarks compared to proprietary alternatives\n- Detailed breakdown of enterprise adoption rates and use cases\n- Exact resource requirements for various deployment scenarios\n\n**Lower Confidence (50-69%) Claims:**\n- Future development roadmap beyond DeerFlow 2.0 transition\n- Specific enterprise customer implementations and case studies\n- Detailed comparison with emerging competitors not yet widely documented\n\n---\n\n## Research Methodology\n\nThis report was compiled using:\n\n1. **Multi-source web search** - Broad discovery and targeted queries across technical publications, media coverage, and community discussions\n2. **GitHub repository analysis** - Direct API queries for commits, issues, PRs, contributor activity, and repository metrics\n3. **Content extraction** - Official documentation, technical articles, video demonstrations, and community resources\n4. **Cross-referencing** - Verification across independent sources including technical analysis, media coverage, and community feedback\n5. **Chronological reconstruction** - Timeline development from timestamped commit history and release documentation\n6. **Confidence scoring** - Claims weighted by source reliability, corroboration across multiple sources, and recency of information\n\n**Research Depth:** Comprehensive technical and market analysis\n**Time Scope:** May 2025 - February 2026 (9-month development period)\n**Geographic Scope:** Global open-source community with ByteDance corporate backing\n\n---\n\n**Report Prepared By:** Github Deep Research by DeerFlow\n**Date:** 2026-02-01\n**Report Version:** 1.0\n**Status:** Complete"
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"content": "---\nname: frontend-design\ndescription: Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.\nlicense: Complete terms in LICENSE.txt\n---\n\nThis skill guides creation of distinctive, production-grade frontend interfaces that avoid generic \"AI slop\" aesthetics. Implement real working code with exceptional attention to aesthetic details and creative choices.\n\nThe user provides frontend requirements: a component, page, application, or interface to build. They may include context about the purpose, audience, or technical constraints.\n\n## Output Requirements\n\n**MANDATORY**: The entry HTML file MUST be named `index.html`. This is a strict requirement for all generated frontend projects to ensure compatibility with standard web hosting and deployment workflows.\n\n## Design Thinking\n\nBefore coding, understand the context and commit to a BOLD aesthetic direction:\n- **Purpose**: What problem does this interface solve? Who uses it?\n- **Tone**: Pick an extreme: brutally minimal, maximalist chaos, retro-futuristic, organic/natural, luxury/refined, playful/toy-like, editorial/magazine, brutalist/raw, art deco/geometric, soft/pastel, industrial/utilitarian, etc. There are so many flavors to choose from. Use these for inspiration but design one that is true to the aesthetic direction.\n- **Constraints**: Technical requirements (framework, performance, accessibility).\n- **Differentiation**: What makes this UNFORGETTABLE? What's the one thing someone will remember?\n\n**CRITICAL**: Choose a clear conceptual direction and execute it with precision. Bold maximalism and refined minimalism both work - the key is intentionality, not intensity.\n\nThen implement working code (HTML/CSS/JS, React, Vue, etc.) that is:\n- Production-grade and functional\n- Visually striking and memorable\n- Cohesive with a clear aesthetic point-of-view\n- Meticulously refined in every detail\n\n## Frontend Aesthetics Guidelines\n\nFocus on:\n- **Typography**: Choose fonts that are beautiful, unique, and interesting. Avoid generic fonts like Arial and Inter; opt instead for distinctive choices that elevate the frontend's aesthetics; unexpected, characterful font choices. Pair a distinctive display font with a refined body font.\n- **Color & Theme**: Commit to a cohesive aesthetic. Use CSS variables for consistency. Dominant colors with sharp accents outperform timid, evenly-distributed palettes.\n- **Motion**: Use animations for effects and micro-interactions. Prioritize CSS-only solutions for HTML. Use Motion library for React when available. Focus on high-impact moments: one well-orchestrated page load with staggered reveals (animation-delay) creates more delight than scattered micro-interactions. Use scroll-triggering and hover states that surprise.\n- **Spatial Composition**: Unexpected layouts. Asymmetry. Overlap. Diagonal flow. Grid-breaking elements. Generous negative space OR controlled density.\n- **Backgrounds & Visual Details**: Create atmosphere and depth rather than defaulting to solid colors. Add contextual effects and textures that match the overall aesthetic. Apply creative forms like gradient meshes, noise textures, geometric patterns, layered transparencies, dramatic shadows, decorative borders, custom cursors, and grain overlays.\n\nNEVER use generic AI-generated aesthetics like overused font families (Inter, Roboto, Arial, system fonts), cliched color schemes (particularly purple gradients on white backgrounds), predictable layouts and component patterns, and cookie-cutter design that lacks context-specific character.\n\nInterpret creatively and make unexpected choices that feel genuinely designed for the context. No design should be the same. Vary between light and dark themes, different fonts, different aesthetics. NEVER converge on common choices (Space Grotesk, for example) across generations.\n\n**IMPORTANT**: Match implementation complexity to the aesthetic vision. Maximalist designs need elaborate code with extensive animations and effects. Minimalist or refined designs need restraint, precision, and careful attention to spacing, typography, and subtle details. Elegance comes from executing the vision well.\n\n## Branding Requirement\n\n**MANDATORY**: Every generated frontend interface MUST include a \"Created By Deerflow\" signature. This branding element should be:\n- **Subtle and unobtrusive** - it should NEVER compete with or distract from the main content and functionality\n- **Clickable**: The signature MUST be a clickable link that opens https://deerflow.tech in a new tab (target=\"_blank\")\n- Integrated naturally into the design, feeling like an intentional design element rather than an afterthought\n- Small in size, using muted colors or reduced opacity that blend harmoniously with the overall aesthetic\n\n**IMPORTANT**: The branding should be discoverable but not prominent. Users should notice the main interface first; the signature is a quiet attribution, not a focal point.\n\n**Creative Implementation Ideas** (choose one that best matches your design aesthetic):\n\n1. **Floating Corner Badge**: A small, elegant badge fixed to a corner with subtle hover effects (e.g., gentle glow, slight scale-up, color shift)\n\n2. **Artistic Watermark**: A semi-transparent diagonal text or logo pattern in the background, barely visible but adds texture\n\n3. **Integrated Border Element**: Part of a decorative border or frame around the content - the signature becomes an organic part of the design structure\n\n4. **Animated Signature**: A small signature that elegantly writes itself on page load, or reveals on scroll near the bottom\n\n5. **Contextual Integration**: Blend into the theme - for a retro design, use a vintage stamp look; for minimalist, a single small icon or monogram \"DF\" with tooltip\n\n6. **Cursor Trail or Easter Egg**: A very subtle approach where the branding appears as a micro-interaction (e.g., holding cursor still reveals a tiny signature, or appears in a creative loading state)\n\n7. **Decorative Divider**: Incorporate into a decorative line, separator, or ornamental element on the page\n\n8. **Glassmorphism Card**: A tiny floating glass-effect card in a corner with blur backdrop\n\nExample code patterns:\n```html\n\n✦ Deerflow\n\n\nDF\n\n\n\n```\n\n**Design Principle**: The branding should feel like it belongs - a natural extension of your creative vision, not a mandatory stamp. Match the signature's style (typography, color, animation) to the overall aesthetic direction.\n\nRemember: Claude is capable of extraordinary creative work. Don't hold back, show what can truly be created when thinking outside the box and committing fully to a distinctive vision.\n",
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"content": "\n\n\n \n \n DeerFlow Research Report 2026\n \n \n \n \n\n\n \n \n \n \n\n \n\n \n \n
Research Report 2026
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DeerFlow: Multi-Agent Deep Research
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A comprehensive analysis of ByteDance's open-source framework that combines language models with specialized tools for automated research workflows.
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0
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GitHub Stars
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0
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Forks
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0
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Contributors
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MIT
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License
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\n
01 / Overview
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Executive Summary
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The framework that redefines automated research through intelligent multi-agent orchestration.
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\n DeerFlow (Deep Exploration and Efficient Research Flow) is an open-source multi-agent research automation framework developed by ByteDance and released under the MIT license in May 2025. The framework implements a graph-based orchestration of specialized agents that automate research pipelines end-to-end, combining language models with tools like web search engines, crawlers, and Python execution.\n
\n With 19,531 stars and 2,452 forks on GitHub, DeerFlow has established itself as a significant player in the deep research automation space, offering both console and web UI options with support for local LLM deployment and extensive tool integrations.\n
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02 / History
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Development Timeline
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From initial release to the upcoming DeerFlow 2.0 transition.
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Phase 01
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May — July 2025
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Project Inception
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DeerFlow was created by ByteDance and open-sourced on May 7, 2025. The initial release established the core multi-agent architecture built on LangGraph and LangChain frameworks, featuring specialized agents: Coordinator, Planner, Researcher, Coder, and Reporter.
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Phase 02
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August — December 2025
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Feature Expansion
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Major feature additions including MCP integration, text-to-speech capabilities, podcast generation, and support for multiple search engines (Tavily, InfoQuest, Brave Search, DuckDuckGo, Arxiv). The framework gained recognition for its human-in-the-loop collaboration features and was integrated into Volcengine's FaaS Application Center.
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Phase 03
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January 2026 — Present
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DeerFlow 2.0 Transition
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The project is transitioning to DeerFlow 2.0 with ongoing improvements to JSON repair handling, MCP tool integration, and fallback report generation. Now supports private knowledgebases including RAGFlow, Qdrant, Milvus, and VikingDB, along with comprehensive Docker deployment options.
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03 / System Design
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Multi-Agent Architecture
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A modular system built on LangGraph enabling flexible state-based workflows.
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Coordinator
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Entry point & workflow lifecycle
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Planner
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Task decomposition & planning
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🔍 Researcher
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Web search & crawling
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💻 Coder
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Python execution & analysis
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Reporter
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Report generation & synthesis
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04 / Capabilities
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Key Features
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Comprehensive tooling for end-to-end research automation.
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🔍
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Multi-Engine Search
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Supports Tavily, InfoQuest (BytePlus), Brave Search, DuckDuckGo, and Arxiv for scientific papers with configurable parameters.
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🔗
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MCP Integration
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Seamless integration with Model Context Protocol services for private domain access, knowledge graphs, and web browsing.
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📚
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Private Knowledgebase
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Integrates with RAGFlow, Qdrant, Milvus, VikingDB, MOI, and Dify for research on users' private documents.
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🤝
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Human-in-the-Loop
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Intelligent clarification mechanisms, plan review and editing, and auto-acceptance options for streamlined workflows.
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🎙️
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Content Creation
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Podcast generation with TTS synthesis, PowerPoint creation, and Notion-style block editing for report refinement.
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🐳
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Production Ready
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Docker and Docker Compose support, cloud deployment via Volcengine, and comprehensive API documentation.
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05 / Analysis
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Competitive Comparison
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How DeerFlow compares to other deep research solutions.
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Feature
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DeerFlow
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OpenAI Deep Research
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LangChain OpenDeepResearch
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Multi-Agent Architecture
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✓
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✗
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✓
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Local LLM Support
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✓
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✗
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✓
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MCP Integration
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✓
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✗
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✗
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Code Execution
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✓ Python REPL
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Limited
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✓
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Podcast Generation
\n
✓
\n
✗
\n
✗
\n
\n
\n
Presentation Creation
\n
✓
\n
✗
\n
✗
\n
\n
\n
Private Knowledgebase
\n
✓ (6+ options)
\n
Limited
\n
Limited
\n
\n
\n
Open Source
\n
✓ MIT
\n
✗ Proprietary
\n
✓ Apache 2.0
\n
\n \n
\n
\n \n\n \n
\n
06 / Assessment
\n
Strengths & Considerations
\n
Balanced evaluation of the framework's capabilities.
\n
\n
\n
\n
💪 Strengths
\n
\n
Comprehensive multi-agent architecture with specialized roles
\n
Extensive tool integration across search, crawling, and databases
\n
Local LLM deployment support for privacy and cost control
\n
Human collaboration features bridging automation and oversight
\n
Active community with 88+ contributors
\n
Production-ready with Docker and cloud deployment options
\n
\n
\n
\n
⚠️ Considerations
\n
\n
Extensive feature set may present learning curve for new users
\n
Local deployment with multiple agents demands significant resources
\n
Advanced features require technical expertise beyond basic usage
\n
Version 2.0 transition may create temporary compatibility concerns
\n
Integration complexity for custom MCP tools and workflows
\n
\n
\n
\n \n\n \n
\n
07 / Conclusion
\n
Final Assessment
\n
High confidence evaluation based on comprehensive analysis.
\n
\n
\n
\n DeerFlow represents a significant advancement in research automation, combining the power of multi-agent coordination, LLM-driven reasoning, and human-in-the-loop collaboration. Its modular architecture, deep tool integrations, and developer-friendly design make it a compelling choice for researchers and organizations seeking to accelerate complex workflows.\n
\n With ByteDance backing, an active open-source community, and continuous feature development, DeerFlow is well-positioned to remain a leading framework in the deep research automation space. The upcoming DeerFlow 2.0 transition promises further enhancements to an already robust platform.\n
\n
\n \n \n\n \n\n Created By Deerflow\n\n \n\n"
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FILE: frontend/public/demo/threads/fe3f7974-1bcb-4a01-a950-79673baafefd/user-data/outputs/index.html
================================================
DeerFlow Research Report 2026
Research Report 2026
DeerFlow: Multi-Agent Deep Research
A comprehensive analysis of ByteDance's open-source framework that combines language models with specialized tools for automated research workflows.
0
GitHub Stars
0
Forks
0
Contributors
MIT
License
01 / Overview
Executive Summary
The framework that redefines automated research through intelligent multi-agent orchestration.
DeerFlow (Deep Exploration and Efficient Research Flow) is an open-source multi-agent research automation framework developed by ByteDance and released under the MIT license in May 2025. The framework implements a graph-based orchestration of specialized agents that automate research pipelines end-to-end, combining language models with tools like web search engines, crawlers, and Python execution.
With 19,531 stars and 2,452 forks on GitHub, DeerFlow has established itself as a significant player in the deep research automation space, offering both console and web UI options with support for local LLM deployment and extensive tool integrations.
02 / History
Development Timeline
From initial release to the upcoming DeerFlow 2.0 transition.
Phase 01
May — July 2025
Project Inception
DeerFlow was created by ByteDance and open-sourced on May 7, 2025. The initial release established the core multi-agent architecture built on LangGraph and LangChain frameworks, featuring specialized agents: Coordinator, Planner, Researcher, Coder, and Reporter.
Phase 02
August — December 2025
Feature Expansion
Major feature additions including MCP integration, text-to-speech capabilities, podcast generation, and support for multiple search engines (Tavily, InfoQuest, Brave Search, DuckDuckGo, Arxiv). The framework gained recognition for its human-in-the-loop collaboration features and was integrated into Volcengine's FaaS Application Center.
Phase 03
January 2026 — Present
DeerFlow 2.0 Transition
The project is transitioning to DeerFlow 2.0 with ongoing improvements to JSON repair handling, MCP tool integration, and fallback report generation. Now supports private knowledgebases including RAGFlow, Qdrant, Milvus, and VikingDB, along with comprehensive Docker deployment options.
03 / System Design
Multi-Agent Architecture
A modular system built on LangGraph enabling flexible state-based workflows.
Coordinator
Entry point & workflow lifecycle
Planner
Task decomposition & planning
🔍 Researcher
Web search & crawling
💻 Coder
Python execution & analysis
Reporter
Report generation & synthesis
04 / Capabilities
Key Features
Comprehensive tooling for end-to-end research automation.
🔍
Multi-Engine Search
Supports Tavily, InfoQuest (BytePlus), Brave Search, DuckDuckGo, and Arxiv for scientific papers with configurable parameters.
🔗
MCP Integration
Seamless integration with Model Context Protocol services for private domain access, knowledge graphs, and web browsing.
📚
Private Knowledgebase
Integrates with RAGFlow, Qdrant, Milvus, VikingDB, MOI, and Dify for research on users' private documents.
🤝
Human-in-the-Loop
Intelligent clarification mechanisms, plan review and editing, and auto-acceptance options for streamlined workflows.
🎙️
Content Creation
Podcast generation with TTS synthesis, PowerPoint creation, and Notion-style block editing for report refinement.
🐳
Production Ready
Docker and Docker Compose support, cloud deployment via Volcengine, and comprehensive API documentation.
05 / Analysis
Competitive Comparison
How DeerFlow compares to other deep research solutions.
Feature
DeerFlow
OpenAI Deep Research
LangChain OpenDeepResearch
Multi-Agent Architecture
✓
✗
✓
Local LLM Support
✓
✗
✓
MCP Integration
✓
✗
✗
Code Execution
✓ Python REPL
Limited
✓
Podcast Generation
✓
✗
✗
Presentation Creation
✓
✗
✗
Private Knowledgebase
✓ (6+ options)
Limited
Limited
Open Source
✓ MIT
✗ Proprietary
✓ Apache 2.0
06 / Assessment
Strengths & Considerations
Balanced evaluation of the framework's capabilities.
💪 Strengths
Comprehensive multi-agent architecture with specialized roles
Extensive tool integration across search, crawling, and databases
Local LLM deployment support for privacy and cost control
Human collaboration features bridging automation and oversight
Active community with 88+ contributors
Production-ready with Docker and cloud deployment options
⚠️ Considerations
Extensive feature set may present learning curve for new users
Local deployment with multiple agents demands significant resources
Advanced features require technical expertise beyond basic usage
Version 2.0 transition may create temporary compatibility concerns
Integration complexity for custom MCP tools and workflows
07 / Conclusion
Final Assessment
High confidence evaluation based on comprehensive analysis.
DeerFlow represents a significant advancement in research automation, combining the power of multi-agent coordination, LLM-driven reasoning, and human-in-the-loop collaboration. Its modular architecture, deep tool integrations, and developer-friendly design make it a compelling choice for researchers and organizations seeking to accelerate complex workflows.
With ByteDance backing, an active open-source community, and continuous feature development, DeerFlow is well-positioned to remain a leading framework in the deep research automation space. The upcoming DeerFlow 2.0 transition promises further enhancements to an already robust platform.
Created By Deerflow
================================================
FILE: frontend/public/demo/threads/fe3f7974-1bcb-4a01-a950-79673baafefd/user-data/outputs/research_deerflow_20260201.md
================================================
# DeerFlow Deep Research Report
- **Research Date:** 2026-02-01
- **Timestamp:** 2026-02-01, Sunday
- **Confidence Level:** High (90%+)
- **Subject:** ByteDance's Open-Source Multi-Agent Deep Research Framework
---
## Repository Information
- **Name:** bytedance/deer-flow
- **Description:** DeerFlow is a community-driven Deep Research framework, combining language models with tools like web search, crawling, and Python execution, while contributing back to the open-source community [DeerFlow GitHub Repository](https://github.com/bytedance/deer-flow)
- **URL:** https://github.com/bytedance/deer-flow
- **Stars:** 19,531
- **Forks:** 2,452
- **Open Issues:** 196
- **Language(s):** Python (1,292,574 bytes), TypeScript (503,143 bytes), CSS (15,128 bytes), JavaScript (7,906 bytes), Dockerfile (2,197 bytes), Makefile (1,352 bytes), Shell (1,152 bytes), Batchfile (497 bytes)
- **License:** MIT
- **Created At:** 2025-05-07T02:50:19Z
- **Updated At:** 2026-02-01T01:07:38Z
- **Pushed At:** 2026-01-30T00:47:23Z
- **Topics:** agent, agentic, agentic-framework, agentic-workflow, ai, ai-agents, bytedance, deep-research, langchain, langgraph, langmanus, llm, multi-agent, nodejs, podcast, python, typescript
---
## Executive Summary
DeerFlow (Deep Exploration and Efficient Research Flow) is an open-source multi-agent research automation framework developed by ByteDance and released under the MIT license in May 2025 [Create Your Own Deep Research Agent with DeerFlow](https://thesequence.substack.com/p/the-sequence-engineering-661-create). The framework implements a graph-based orchestration of specialized agents that automate research pipelines end-to-end, combining language models with tools like web search engines, crawlers, and Python execution. With 19,531 stars and 2,452 forks on GitHub, DeerFlow has established itself as a significant player in the deep research automation space, offering both console and web UI options with support for local LLM deployment and extensive tool integrations [DeerFlow: A Game-Changer for Automated Research and Content Creation](https://medium.com/@mingyang.heaven/deerflow-a-game-changer-for-automated-research-and-content-creation-83612f683e7a).
---
## Complete Chronological Timeline
### PHASE 1: Project Inception and Initial Development
#### May 2025 - July 2025
DeerFlow was created by ByteDance and open-sourced on May 7, 2025, with the initial commit establishing the core multi-agent architecture built on LangGraph and LangChain frameworks [DeerFlow GitHub Repository](https://github.com/bytedance/deer-flow). The project quickly gained traction in the AI community due to its comprehensive approach to research automation, combining web search, crawling, and code execution capabilities. Early development focused on establishing the modular agent system with specialized roles including Coordinator, Planner, Researcher, Coder, and Reporter components.
### PHASE 2: Feature Expansion and Community Growth
#### August 2025 - December 2025
During this period, DeerFlow underwent significant feature expansion including MCP (Model Context Protocol) integration, text-to-speech capabilities, podcast generation, and support for multiple search engines (Tavily, InfoQuest, Brave Search, DuckDuckGo, Arxiv) [DeerFlow: Multi-Agent AI For Research Automation 2025](https://firexcore.com/blog/what-is-deerflow/). The framework gained attention for its human-in-the-loop collaboration features, allowing users to review and edit research plans before execution. Community contributions grew substantially, with 88 contributors participating in the project by early 2026, and the framework was integrated into the FaaS Application Center of Volcengine for cloud deployment.
### PHASE 3: Maturity and DeerFlow 2.0 Transition
#### January 2026 - Present
As of February 2026, DeerFlow has entered a transition phase to DeerFlow 2.0, with active development continuing on the main branch [DeerFlow Official Website](https://deerflow.tech/). Recent commits show ongoing improvements to JSON repair handling, MCP tool integration, and fallback report generation mechanisms. The framework now supports private knowledgebases including RAGFlow, Qdrant, Milvus, and VikingDB, along with Docker and Docker Compose deployment options for production environments.
---
## Key Analysis
### Technical Architecture and Design Philosophy
DeerFlow implements a modular multi-agent system architecture designed for automated research and code analysis [DeerFlow: A Game-Changer for Automated Research and Content Creation](https://medium.com/@mingyang.heaven/deerflow-a-game-changer-for-automated-research-and-content-creation-83612f683e7a). The system is built on LangGraph, enabling a flexible state-based workflow where components communicate through a well-defined message passing system. The architecture employs a streamlined workflow with specialized agents:
```mermaid
flowchart TD
A[Coordinator] --> B[Planner]
B --> C{Enough Context?}
C -->|No| D[Research Team]
D --> E[Researcher Web Search & Crawling]
D --> F[Coder Python Execution]
E --> C
F --> C
C -->|Yes| G[Reporter]
G --> H[Final Report]
```
The Coordinator serves as the entry point managing workflow lifecycle, initiating research processes based on user input and delegating tasks to the Planner when appropriate. The Planner analyzes research objectives and creates structured execution plans, determining if sufficient context is available or if more research is needed. The Research Team consists of specialized agents including a Researcher for web searches and information gathering, and a Coder for handling technical tasks using Python REPL tools. Finally, the Reporter aggregates findings and generates comprehensive research reports [Create Your Own Deep Research Agent with DeerFlow](https://thesequence.substack.com/p/the-sequence-engineering-661-create).
### Core Features and Capabilities
DeerFlow offers extensive capabilities for deep research automation:
1. **Multi-Engine Search Integration**: Supports Tavily (default), InfoQuest (BytePlus's AI-optimized search), Brave Search, DuckDuckGo, and Arxiv for scientific papers [DeerFlow: Multi-Agent AI For Research Automation 2025](https://firexcore.com/blog/what-is-deerflow/).
2. **Advanced Crawling Tools**: Includes Jina (default) and InfoQuest crawlers with configurable parameters, timeout settings, and powerful content extraction capabilities.
3. **MCP (Model Context Protocol) Integration**: Enables seamless integration with diverse research tools and methodologies for private domain access, knowledge graphs, and web browsing.
4. **Private Knowledgebase Support**: Integrates with RAGFlow, Qdrant, Milvus, VikingDB, MOI, and Dify for research on users' private documents.
5. **Human-in-the-Loop Collaboration**: Features intelligent clarification mechanisms, plan review and editing capabilities, and auto-acceptance options for streamlined workflows.
6. **Content Creation Tools**: Includes podcast generation with text-to-speech synthesis, PowerPoint presentation creation, and Notion-style block editing for report refinement.
7. **Multi-Language Support**: Provides README documentation in English, Simplified Chinese, Japanese, German, Spanish, Russian, and Portuguese.
### Development and Community Ecosystem
The project demonstrates strong community engagement with 88 contributors and 19,531 GitHub stars as of February 2026 [DeerFlow GitHub Repository](https://github.com/bytedance/deer-flow). Key contributors include Henry Li (203 contributions), Willem Jiang (130 contributions), and Daniel Walnut (25 contributions), representing a mix of ByteDance employees and open-source community members. The framework maintains comprehensive documentation including configuration guides, API documentation, FAQ sections, and multiple example research reports covering topics from quantum computing to AI adoption in healthcare.
---
## Metrics & Impact Analysis
### Growth Trajectory
```
Timeline: May 2025 - February 2026
Stars: 0 → 19,531 (exponential growth)
Forks: 0 → 2,452 (strong community adoption)
Contributors: 0 → 88 (active development ecosystem)
Open Issues: 196 (ongoing maintenance and feature development)
```
### Key Metrics
| Metric | Value | Assessment |
|--------|-------|------------|
| GitHub Stars | 19,531 | Exceptional popularity for research framework |
| Forks | 2,452 | Strong community adoption and potential derivatives |
| Contributors | 88 | Healthy open-source development ecosystem |
| Open Issues | 196 | Active maintenance and feature development |
| Primary Language | Python (1.29MB) | Main development language with extensive libraries |
| Secondary Language | TypeScript (503KB) | Modern web UI implementation |
| Repository Age | ~9 months | Rapid development and feature expansion |
| License | MIT | Permissive open-source licensing |
---
## Comparative Analysis
### Feature Comparison
| Feature | DeerFlow | OpenAI Deep Research | LangChain OpenDeepResearch |
|---------|-----------|----------------------|----------------------------|
| Multi-Agent Architecture | ✅ | ❌ | ✅ |
| Local LLM Support | ✅ | ❌ | ✅ |
| MCP Integration | ✅ | ❌ | ❌ |
| Web Search Engines | Multiple (5+) | Limited | Limited |
| Code Execution | ✅ Python REPL | Limited | ✅ |
| Podcast Generation | ✅ | ❌ | ❌ |
| Presentation Creation | ✅ | ❌ | ❌ |
| Private Knowledgebase | ✅ (6+ options) | Limited | Limited |
| Human-in-the-Loop | ✅ | Limited | ✅ |
| Open Source | ✅ MIT | ❌ | ✅ Apache 2.0 |
### Market Positioning
DeerFlow occupies a unique position in the deep research framework landscape by combining enterprise-grade multi-agent orchestration with extensive tool integrations and open-source accessibility [Navigating the Landscape of Deep Research Frameworks](https://www.oreateai.com/blog/navigating-the-landscape-of-deep-research-frameworks-a-comprehensive-comparison/0dc13e48eb8c756650112842c8d1a184]. While proprietary solutions like OpenAI's Deep Research offer polished user experiences, DeerFlow provides greater flexibility through local deployment options, custom tool integration, and community-driven development. The framework particularly excels in scenarios requiring specialized research workflows, integration with private data sources, or deployment in regulated environments where cloud-based solutions may not be feasible.
---
## Strengths & Weaknesses
### Strengths
1. **Comprehensive Multi-Agent Architecture**: DeerFlow's sophisticated agent orchestration enables complex research workflows beyond single-agent systems [Create Your Own Deep Research Agent with DeerFlow](https://thesequence.substack.com/p/the-sequence-engineering-661-create).
2. **Extensive Tool Integration**: Support for multiple search engines, crawling tools, MCP services, and private knowledgebases provides unmatched flexibility.
3. **Local Deployment Capabilities**: Unlike many proprietary solutions, DeerFlow supports local LLM deployment, offering privacy, cost control, and customization options.
4. **Human Collaboration Features**: Intelligent clarification mechanisms and plan editing capabilities bridge the gap between automated research and human oversight.
5. **Active Community Development**: With 88 contributors and regular updates, the project benefits from diverse perspectives and rapid feature evolution.
6. **Production-Ready Deployment**: Docker support, cloud integration (Volcengine), and comprehensive documentation facilitate enterprise adoption.
### Areas for Improvement
1. **Learning Curve**: The extensive feature set and configuration options may present challenges for new users compared to simpler single-purpose tools.
2. **Resource Requirements**: Local deployment with multiple agents and tools may demand significant computational resources.
3. **Documentation Complexity**: While comprehensive, the documentation spans multiple languages and may benefit from more streamlined onboarding guides.
4. **Integration Complexity**: Advanced features like MCP integration and custom tool development require technical expertise beyond basic usage.
5. **Version Transition**: The ongoing move to DeerFlow 2.0 may create temporary instability or compatibility concerns for existing deployments.
---
## Key Success Factors
1. **ByteDance Backing**: Corporate sponsorship provides resources, expertise, and credibility while maintaining open-source accessibility [DeerFlow: A Game-Changer for Automated Research and Content Creation](https://medium.com/@mingyang.heaven/deerflow-a-game-changer-for-automated-research-and-content-creation-83612f683e7a).
2. **Modern Technical Foundation**: Built on LangGraph and LangChain, DeerFlow leverages established frameworks while adding significant value through multi-agent orchestration.
3. **Community-Driven Development**: Active contributor community ensures diverse use cases, rapid bug fixes, and feature evolution aligned with real-world needs.
4. **Comprehensive Feature Set**: Unlike narrowly focused tools, DeerFlow addresses the complete research workflow from information gathering to content creation.
5. **Production Deployment Options**: Cloud integration, Docker support, and enterprise features facilitate adoption beyond experimental use cases.
6. **Multi-Language Accessibility**: Documentation and interface support for multiple languages expands global reach and adoption potential.
---
## Sources
### Primary Sources
1. **DeerFlow GitHub Repository**: Official source code, documentation, and development history [DeerFlow GitHub Repository](https://github.com/bytedance/deer-flow)
2. **DeerFlow Official Website**: Platform showcasing features, case studies, and deployment options [DeerFlow Official Website](https://deerflow.tech/)
3. **GitHub API Data**: Repository metrics, contributor statistics, and commit history
### Media Coverage
1. **The Sequence Engineering**: Technical analysis of DeerFlow architecture and capabilities [Create Your Own Deep Research Agent with DeerFlow](https://thesequence.substack.com/p/the-sequence-engineering-661-create)
2. **Medium Articles**: Community perspectives on DeerFlow implementation and use cases [DeerFlow: A Game-Changer for Automated Research and Content Creation](https://medium.com/@mingyang.heaven/deerflow-a-game-changer-for-automated-research-and-content-creation-83612f683e7a)
3. **YouTube Demonstrations**: Video walkthroughs of DeerFlow functionality and local deployment [ByteDance DeerFlow - (Deep Research Agents with a LOCAL LLM!)](https://www.youtube.com/watch?v=Ui0ovCVDYGs)
### Technical Sources
1. **FireXCore Analysis**: Feature overview and technical assessment [DeerFlow: Multi-Agent AI For Research Automation 2025](https://firexcore.com/blog/what-is-deerflow/)
2. **Oreate AI Comparison**: Framework benchmarking and market positioning analysis [Navigating the Landscape of Deep Research Frameworks](https://www.oreateai.com/blog/navigating-the-landscape-of-deep-research-frameworks-a-comprehensive-comparison/0dc13e48eb8c756650112842c8d1a184)
---
## Confidence Assessment
**High Confidence (90%+) Claims:**
- DeerFlow was created by ByteDance and open-sourced under MIT license in May 2025
- The framework implements multi-agent architecture using LangGraph and LangChain
- Current GitHub metrics: 19,531 stars, 2,452 forks, 88 contributors, 196 open issues
- Supports multiple search engines including Tavily, InfoQuest, Brave Search
- Includes features for podcast generation, presentation creation, and human collaboration
**Medium Confidence (70-89%) Claims:**
- Specific performance benchmarks compared to proprietary alternatives
- Detailed breakdown of enterprise adoption rates and use cases
- Exact resource requirements for various deployment scenarios
**Lower Confidence (50-69%) Claims:**
- Future development roadmap beyond DeerFlow 2.0 transition
- Specific enterprise customer implementations and case studies
- Detailed comparison with emerging competitors not yet widely documented
---
## Research Methodology
This report was compiled using:
1. **Multi-source web search** - Broad discovery and targeted queries across technical publications, media coverage, and community discussions
2. **GitHub repository analysis** - Direct API queries for commits, issues, PRs, contributor activity, and repository metrics
3. **Content extraction** - Official documentation, technical articles, video demonstrations, and community resources
4. **Cross-referencing** - Verification across independent sources including technical analysis, media coverage, and community feedback
5. **Chronological reconstruction** - Timeline development from timestamped commit history and release documentation
6. **Confidence scoring** - Claims weighted by source reliability, corroboration across multiple sources, and recency of information
**Research Depth:** Comprehensive technical and market analysis
**Time Scope:** May 2025 - February 2026 (9-month development period)
**Geographic Scope:** Global open-source community with ByteDance corporate backing
---
**Report Prepared By:** Github Deep Research by DeerFlow
**Date:** 2026-02-01
**Report Version:** 1.0
**Status:** Complete
================================================
FILE: frontend/scripts/save-demo.js
================================================
import { config } from "dotenv";
import fs from "fs";
import path from "path";
import { env } from "process";
export async function main() {
const url = new URL(process.argv[2]);
const threadId = url.pathname.split("/").pop();
const host = url.host;
const apiURL = new URL(
`/api/langgraph/threads/${threadId}/history`,
`${url.protocol}//${host}`,
);
const response = await fetch(apiURL, {
method: "POST",
headers: {
"Content-Type": "application/json",
},
body: JSON.stringify({
limit: 10,
}),
});
const data = (await response.json())[0];
if (!data) {
console.error("No data found");
return;
}
const title = data.values.title;
const rootPath = path.resolve(process.cwd(), "public/demo/threads", threadId);
if (fs.existsSync(rootPath)) {
fs.rmSync(rootPath, { recursive: true });
}
fs.mkdirSync(rootPath, { recursive: true });
fs.writeFileSync(
path.resolve(rootPath, "thread.json"),
JSON.stringify(data, null, 2),
);
const backendRootPath = path.resolve(
process.cwd(),
"../backend/.deer-flow/threads",
threadId,
);
copyFolder("user-data/outputs", rootPath, backendRootPath);
copyFolder("user-data/uploads", rootPath, backendRootPath);
console.info(`Saved demo "${title}" to ${rootPath}`);
}
function copyFolder(relPath, rootPath, backendRootPath) {
const outputsPath = path.resolve(backendRootPath, relPath);
if (fs.existsSync(outputsPath)) {
fs.cpSync(outputsPath, path.resolve(rootPath, relPath), {
recursive: true,
});
}
}
config();
main();
================================================
FILE: frontend/src/app/api/auth/[...all]/route.ts
================================================
import { toNextJsHandler } from "better-auth/next-js";
import { auth } from "@/server/better-auth";
export const { GET, POST } = toNextJsHandler(auth.handler);
================================================
FILE: frontend/src/app/layout.tsx
================================================
import "@/styles/globals.css";
import "katex/dist/katex.min.css";
import { type Metadata } from "next";
import { ThemeProvider } from "@/components/theme-provider";
import { I18nProvider } from "@/core/i18n/context";
import { detectLocaleServer } from "@/core/i18n/server";
export const metadata: Metadata = {
title: "DeerFlow",
description: "A LangChain-based framework for building super agents.",
};
export default async function RootLayout({
children,
}: Readonly<{ children: React.ReactNode }>) {
const locale = await detectLocaleServer();
return (
{children}
);
}
================================================
FILE: frontend/src/app/mock/api/mcp/config/route.ts
================================================
export function GET() {
return Response.json({
mcp_servers: {
"mcp-github-trending": {
enabled: true,
type: "stdio",
command: "uvx",
args: ["mcp-github-trending"],
env: {},
url: null,
headers: {},
description:
"A MCP server that provides access to GitHub trending repositories and developers data",
},
"context-7": {
enabled: true,
description:
"Get the latest documentation and code into Cursor, Claude, or other LLMs",
},
"feishu-importer": {
enabled: true,
description: "Import Feishu documents",
},
},
});
}
================================================
FILE: frontend/src/app/mock/api/models/route.ts
================================================
export function GET() {
return Response.json({
models: [
{
id: "doubao-seed-1.8",
name: "doubao-seed-1.8",
model: "doubao-seed-1-8",
display_name: "Doubao Seed 1.8",
supports_thinking: true,
},
{
id: "deepseek-v3.2",
name: "deepseek-v3.2",
model: "deepseek-chat",
display_name: "DeepSeek v3.2",
supports_thinking: true,
},
{
id: "gpt-5",
name: "gpt-5",
model: "gpt-5",
display_name: "GPT-5",
supports_thinking: true,
},
{
id: "gemini-3-pro",
name: "gemini-3-pro",
model: "gemini-3-pro",
display_name: "Gemini 3 Pro",
supports_thinking: true,
},
],
});
}
================================================
FILE: frontend/src/app/mock/api/skills/route.ts
================================================
export function GET() {
return Response.json({
skills: [
{
name: "deep-research",
description:
"Use this skill BEFORE any content generation task (PPT, design, articles, images, videos, reports). Provides a systematic methodology for conducting thorough, multi-angle web research to gather comprehensive information.",
license: null,
category: "public",
enabled: true,
},
{
name: "frontend-design",
description:
"Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.",
license: "Complete terms in LICENSE.txt",
category: "public",
enabled: true,
},
{
name: "github-deep-research",
description:
"Conduct multi-round deep research on any GitHub Repo. Use when users request comprehensive analysis, timeline reconstruction, competitive analysis, or in-depth investigation of GitHub. Produces structured markdown reports with executive summaries, chronological timelines, metrics analysis, and Mermaid diagrams. Triggers on Github repository URL or open source projects.",
license: null,
category: "public",
enabled: true,
},
{
name: "image-generation",
description:
"Use this skill when the user requests to generate, create, imagine, or visualize images including characters, scenes, products, or any visual content. Supports structured prompts and reference images for guided generation.",
license: null,
category: "public",
enabled: true,
},
{
name: "podcast-generation",
description:
"Use this skill when the user requests to generate, create, or produce podcasts from text content. Converts written content into a two-host conversational podcast audio format with natural dialogue.",
license: null,
category: "public",
enabled: true,
},
{
name: "ppt-generation",
description:
"Use this skill when the user requests to generate, create, or make presentations (PPT/PPTX). Creates visually rich slides by generating images for each slide and composing them into a PowerPoint file.",
license: null,
category: "public",
enabled: true,
},
{
name: "skill-creator",
description:
"Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.",
license: "Complete terms in LICENSE.txt",
category: "public",
enabled: true,
},
{
name: "vercel-deploy",
description:
'Deploy applications and websites to Vercel. Use this skill when the user requests deployment actions such as "Deploy my app", "Deploy this to production", "Create a preview deployment", "Deploy and give me the link", or "Push this live". No authentication required - returns preview URL and claimable deployment link.',
license: null,
category: "public",
enabled: true,
},
{
name: "video-generation",
description:
"Use this skill when the user requests to generate, create, or imagine videos. Supports structured prompts and reference image for guided generation.",
license: null,
category: "public",
enabled: true,
},
{
name: "web-design-guidelines",
description:
'Review UI code for Web Interface Guidelines compliance. Use when asked to "review my UI", "check accessibility", "audit design", "review UX", or "check my site against best practices".',
license: null,
category: "public",
enabled: true,
},
],
});
}
================================================
FILE: frontend/src/app/mock/api/threads/[thread_id]/artifacts/[[...artifact_path]]/route.ts
================================================
import fs from "fs";
import path from "path";
import type { NextRequest } from "next/server";
export async function GET(
request: NextRequest,
{
params,
}: {
params: Promise<{
thread_id: string;
artifact_path?: string[] | undefined;
}>;
},
) {
const threadId = (await params).thread_id;
let artifactPath = (await params).artifact_path?.join("/") ?? "";
if (artifactPath.startsWith("mnt/")) {
artifactPath = path.resolve(
process.cwd(),
artifactPath.replace("mnt/", `public/demo/threads/${threadId}/`),
);
if (fs.existsSync(artifactPath)) {
if (request.nextUrl.searchParams.get("download") === "true") {
// Attach the file to the response
const headers = new Headers();
headers.set(
"Content-Disposition",
`attachment; filename="${artifactPath}"`,
);
return new Response(fs.readFileSync(artifactPath), {
status: 200,
headers,
});
}
if (artifactPath.endsWith(".mp4")) {
return new Response(fs.readFileSync(artifactPath), {
status: 200,
headers: {
"Content-Type": "video/mp4",
},
});
}
return new Response(fs.readFileSync(artifactPath), { status: 200 });
}
}
return new Response("File not found", { status: 404 });
}
================================================
FILE: frontend/src/app/mock/api/threads/[thread_id]/history/route.ts
================================================
import fs from "fs";
import path from "path";
import type { NextRequest } from "next/server";
export async function POST(
request: NextRequest,
{ params }: { params: Promise<{ thread_id: string }> },
) {
const threadId = (await params).thread_id;
const jsonString = fs.readFileSync(
path.resolve(process.cwd(), `public/demo/threads/${threadId}/thread.json`),
"utf8",
);
const json = JSON.parse(jsonString);
if (Array.isArray(json.history)) {
return Response.json(json);
}
return Response.json([json]);
}
================================================
FILE: frontend/src/app/mock/api/threads/search/route.ts
================================================
import fs from "fs";
import path from "path";
type ThreadSearchRequest = {
limit?: number;
offset?: number;
sortBy?: "updated_at" | "created_at";
sortOrder?: "asc" | "desc";
};
type MockThreadSearchResult = Record & {
thread_id: string;
updated_at: string | undefined;
};
export async function POST(request: Request) {
const body = ((await request.json().catch(() => ({}))) ?? {}) as ThreadSearchRequest;
const rawLimit = body.limit;
let limit = 50;
if (typeof rawLimit === "number") {
const normalizedLimit = Math.max(0, Math.floor(rawLimit));
if (!Number.isNaN(normalizedLimit)) {
limit = normalizedLimit;
}
}
const rawOffset = body.offset;
let offset = 0;
if (typeof rawOffset === "number") {
const normalizedOffset = Math.max(0, Math.floor(rawOffset));
if (!Number.isNaN(normalizedOffset)) {
offset = normalizedOffset;
}
}
const sortBy = body.sortBy ?? "updated_at";
const sortOrder = body.sortOrder ?? "desc";
const threadsDir = fs.readdirSync(
path.resolve(process.cwd(), "public/demo/threads"),
{
withFileTypes: true,
},
);
const threadData = threadsDir
.map((threadId) => {
if (threadId.isDirectory() && !threadId.name.startsWith(".")) {
const threadData = JSON.parse(
fs.readFileSync(
path.resolve(`public/demo/threads/${threadId.name}/thread.json`),
"utf8",
),
) as Record;
return {
...threadData,
thread_id: threadId.name,
updated_at:
typeof threadData.updated_at === "string"
? threadData.updated_at
: typeof threadData.created_at === "string"
? threadData.created_at
: undefined,
};
}
return null;
})
.filter((thread): thread is MockThreadSearchResult => thread !== null)
.sort((a, b) => {
const aTimestamp = a[sortBy];
const bTimestamp = b[sortBy];
const aParsed =
typeof aTimestamp === "string" ? Date.parse(aTimestamp) : 0;
const bParsed =
typeof bTimestamp === "string" ? Date.parse(bTimestamp) : 0;
const aValue = Number.isNaN(aParsed) ? 0 : aParsed;
const bValue = Number.isNaN(bParsed) ? 0 : bParsed;
return sortOrder === "asc" ? aValue - bValue : bValue - aValue;
});
const pagedThreads = threadData.slice(offset, offset + limit);
return Response.json(pagedThreads);
}
================================================
FILE: frontend/src/app/page.tsx
================================================
import { Footer } from "@/components/landing/footer";
import { Header } from "@/components/landing/header";
import { Hero } from "@/components/landing/hero";
import { CaseStudySection } from "@/components/landing/sections/case-study-section";
import { CommunitySection } from "@/components/landing/sections/community-section";
import { SandboxSection } from "@/components/landing/sections/sandbox-section";
import { SkillsSection } from "@/components/landing/sections/skills-section";
import { WhatsNewSection } from "@/components/landing/sections/whats-new-section";
export default function LandingPage() {
return (
);
}
================================================
FILE: frontend/src/app/workspace/agents/[agent_name]/chats/[thread_id]/layout.tsx
================================================
"use client";
import { PromptInputProvider } from "@/components/ai-elements/prompt-input";
import { ArtifactsProvider } from "@/components/workspace/artifacts";
import { SubtasksProvider } from "@/core/tasks/context";
export default function AgentChatLayout({
children,
}: {
children: React.ReactNode;
}) {
return (
{children}
);
}
================================================
FILE: frontend/src/app/workspace/agents/[agent_name]/chats/[thread_id]/page.tsx
================================================
"use client";
import { BotIcon, PlusSquare } from "lucide-react";
import { useParams, useRouter } from "next/navigation";
import { useCallback } from "react";
import type { PromptInputMessage } from "@/components/ai-elements/prompt-input";
import { Button } from "@/components/ui/button";
import { AgentWelcome } from "@/components/workspace/agent-welcome";
import { ArtifactTrigger } from "@/components/workspace/artifacts";
import { ChatBox, useThreadChat } from "@/components/workspace/chats";
import { ExportTrigger } from "@/components/workspace/export-trigger";
import { InputBox } from "@/components/workspace/input-box";
import { MessageList } from "@/components/workspace/messages";
import { ThreadContext } from "@/components/workspace/messages/context";
import { ThreadTitle } from "@/components/workspace/thread-title";
import { TodoList } from "@/components/workspace/todo-list";
import { Tooltip } from "@/components/workspace/tooltip";
import { useAgent } from "@/core/agents";
import { useI18n } from "@/core/i18n/hooks";
import { useNotification } from "@/core/notification/hooks";
import { useLocalSettings } from "@/core/settings";
import { useThreadStream } from "@/core/threads/hooks";
import { textOfMessage } from "@/core/threads/utils";
import { env } from "@/env";
import { cn } from "@/lib/utils";
export default function AgentChatPage() {
const { t } = useI18n();
const [settings, setSettings] = useLocalSettings();
const router = useRouter();
const { agent_name } = useParams<{
agent_name: string;
}>();
const { agent } = useAgent(agent_name);
const { threadId, isNewThread, setIsNewThread } = useThreadChat();
const { showNotification } = useNotification();
const [thread, sendMessage] = useThreadStream({
threadId: isNewThread ? undefined : threadId,
context: { ...settings.context, agent_name: agent_name },
onStart: () => {
setIsNewThread(false);
// ! Important: Never use next.js router for navigation in this case, otherwise it will cause the thread to re-mount and lose all states. Use native history API instead.
history.replaceState(
null,
"",
`/workspace/agents/${agent_name}/chats/${threadId}`,
);
},
onFinish: (state) => {
if (document.hidden || !document.hasFocus()) {
let body = "Conversation finished";
const lastMessage = state.messages[state.messages.length - 1];
if (lastMessage) {
const textContent = textOfMessage(lastMessage);
if (textContent) {
body =
textContent.length > 200
? textContent.substring(0, 200) + "..."
: textContent;
}
}
showNotification(state.title, { body });
}
},
});
const handleSubmit = useCallback(
(message: PromptInputMessage) => {
void sendMessage(threadId, message, { agent_name });
},
[sendMessage, threadId, agent_name],
);
const handleStop = useCallback(async () => {
await thread.stop();
}, [thread]);
return (
![\"Caren](\"caren-hero.jpg\")
\n ![\"Skincare](\"caren-lifestyle.jpg\")
\n ![\"Natural](\"caren-ingredients.jpg\")
\n 
);
export type ModelSelectorLogoGroupProps = ComponentProps<"div">;
export const ModelSelectorLogoGroup = ({
className,
...props
}: ModelSelectorLogoGroupProps) => (