Repository: browser-use/web-ui Branch: main Commit: 7eb62d4d5572 Files: 39 Total size: 235.9 KB Directory structure: gitextract_5jhmkoc6/ ├── .dockerignore ├── .github/ │ └── workflows/ │ └── build.yml ├── .gitignore ├── .vscode/ │ └── settings.json ├── Dockerfile ├── LICENSE ├── README.md ├── SECURITY.md ├── docker-compose.yml ├── requirements.txt ├── src/ │ ├── __init__.py │ ├── agent/ │ │ ├── __init__.py │ │ ├── browser_use/ │ │ │ └── browser_use_agent.py │ │ └── deep_research/ │ │ └── deep_research_agent.py │ ├── browser/ │ │ ├── __init__.py │ │ ├── custom_browser.py │ │ └── custom_context.py │ ├── controller/ │ │ ├── __init__.py │ │ └── custom_controller.py │ ├── utils/ │ │ ├── __init__.py │ │ ├── config.py │ │ ├── llm_provider.py │ │ ├── mcp_client.py │ │ └── utils.py │ └── webui/ │ ├── __init__.py │ ├── components/ │ │ ├── __init__.py │ │ ├── agent_settings_tab.py │ │ ├── browser_settings_tab.py │ │ ├── browser_use_agent_tab.py │ │ ├── deep_research_agent_tab.py │ │ └── load_save_config_tab.py │ ├── interface.py │ └── webui_manager.py ├── supervisord.conf ├── tests/ │ ├── test_agents.py │ ├── test_controller.py │ ├── test_llm_api.py │ └── test_playwright.py └── webui.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .dockerignore ================================================ data tmp results .env ================================================ FILE: .github/workflows/build.yml ================================================ name: Build Docker Image on: release: types: [published] push: branches: [main] env: GITHUB_CR_REPO: ghcr.io/${{ github.repository }} jobs: build: runs-on: ubuntu-latest strategy: fail-fast: false matrix: platform: - linux/amd64 - linux/arm64 steps: - name: Prepare run: | platform=${{ matrix.platform }} echo "PLATFORM_PAIR=${platform//\//-}" >> $GITHUB_ENV - name: Docker meta id: meta uses: docker/metadata-action@v5 with: images: | ${{ env.GITHUB_CR_REPO }} - name: Login to GHCR uses: docker/login-action@v3 with: registry: ghcr.io username: ${{ github.repository_owner }} password: ${{ secrets.GITHUB_TOKEN }} - name: Set up QEMU uses: docker/setup-qemu-action@v3 - name: Set up Docker Buildx uses: docker/setup-buildx-action@v3 - name: Build and push by digest id: build uses: docker/build-push-action@v6 with: platforms: ${{ matrix.platform }} labels: ${{ steps.meta.outputs.labels }} tags: | ${{ env.GITHUB_CR_REPO }} build-args: | TARGETPLATFORM=${{ matrix.platform }} outputs: type=image,push-by-digest=true,name-canonical=true,push=true - name: Export digest run: | mkdir -p ${{ runner.temp }}/digests digest="${{ steps.build.outputs.digest }}" touch "${{ runner.temp }}/digests/${digest#sha256:}" - name: Upload digest uses: actions/upload-artifact@v4 with: name: digests-${{ env.PLATFORM_PAIR }} path: ${{ runner.temp }}/digests/* if-no-files-found: error retention-days: 1 merge: runs-on: ubuntu-latest needs: - build steps: - name: Download digests uses: actions/download-artifact@v4 with: path: ${{ runner.temp }}/digests pattern: digests-* merge-multiple: true - name: Login to GHCR uses: docker/login-action@v3 with: registry: ghcr.io username: ${{ github.repository_owner }} password: ${{ secrets.GITHUB_TOKEN }} - name: Set up Docker Buildx uses: docker/setup-buildx-action@v3 - name: Docker meta id: meta uses: docker/metadata-action@v5 with: images: | ${{ env.GITHUB_CR_REPO }} tags: | type=ref,event=branch type=ref,event=pr type=semver,pattern={{version}} type=semver,pattern={{major}} - name: Docker tags run: | tags=$(jq -cr '.tags | map("-t " + .) | join(" ")' <<< "$DOCKER_METADATA_OUTPUT_JSON") if [ -z "$tags" ]; then echo "DOCKER_METADATA_OUTPUT_VERSION=${{ github.ref_name }}" >> $GITHUB_ENV tags="-t ${{ env.GITHUB_CR_REPO }}:${{ github.ref_name }}" fi echo "DOCKER_METADATA_TAGS=$tags" >> $GITHUB_ENV - name: Create manifest list and push working-directory: ${{ runner.temp }}/digests run: | docker buildx imagetools create ${{ env.DOCKER_METADATA_TAGS }} \ $(printf '${{ env.GITHUB_CR_REPO }}@sha256:%s ' *) - name: Inspect image run: | docker buildx imagetools inspect ${{ env.GITHUB_CR_REPO }}:${{ env.DOCKER_METADATA_OUTPUT_VERSION }} ================================================ FILE: .gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ cover/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder .pybuilder/ target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: # .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # poetry # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. # This is especially recommended for binary packages to ensure reproducibility, and is more # commonly ignored for libraries. # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control #poetry.lock # pdm # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. #pdm.lock # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it # in version control. # https://pdm.fming.dev/latest/usage/project/#working-with-version-control .pdm.toml .pdm-python .pdm-build/ # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ test_env/ myenv # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ # pytype static type analyzer .pytype/ # Cython debug symbols cython_debug/ # PyCharm # JetBrains specific template is maintained in a separate JetBrains.gitignore that can # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. .idea/ temp tmp .DS_Store private_example.py private_example browser_cookies.json cookies.json AgentHistory.json cv_04_24.pdf AgentHistoryList.json *.gif # For Sharing (.pem files) .gradio/ # For Docker data/ # For Config Files (Current Settings) .config.pkl *.pdf workflow ================================================ FILE: .vscode/settings.json ================================================ { "python.analysis.typeCheckingMode": "basic", "[python]": { "editor.defaultFormatter": "charliermarsh.ruff", "editor.formatOnSave": true, "editor.codeActionsOnSave": { "source.fixAll.ruff": "explicit", "source.organizeImports.ruff": "explicit" } } } ================================================ FILE: Dockerfile ================================================ FROM python:3.11-slim-bookworm # Set platform for multi-arch builds (Docker Buildx will set this) ARG TARGETPLATFORM ARG NODE_MAJOR=20 # Install system dependencies (removed libgconf-2-4) RUN apt-get update && apt-get install -y \ wget \ netcat-traditional \ gnupg \ curl \ unzip \ xvfb \ libxss1 \ libnss3 \ libnspr4 \ libasound2 \ libatk1.0-0 \ libatk-bridge2.0-0 \ libcups2 \ libdbus-1-3 \ libdrm2 \ libgbm1 \ libgtk-3-0 \ libxcomposite1 \ libxdamage1 \ libxfixes3 \ libxrandr2 \ xdg-utils \ fonts-liberation \ fonts-noto-color-emoji \ fonts-unifont \ dbus \ xauth \ x11vnc \ tigervnc-tools \ supervisor \ net-tools \ procps \ git \ python3-numpy \ fontconfig \ fonts-dejavu \ fonts-dejavu-core \ fonts-dejavu-extra \ vim \ && rm -rf /var/lib/apt/lists/* # Install noVNC RUN git clone https://github.com/novnc/noVNC.git /opt/novnc \ && git clone https://github.com/novnc/websockify /opt/novnc/utils/websockify \ && ln -s /opt/novnc/vnc.html /opt/novnc/index.html # Install Node.js using NodeSource PPA RUN mkdir -p /etc/apt/keyrings \ && curl -fsSL https://deb.nodesource.com/gpgkey/nodesource-repo.gpg.key | gpg --dearmor -o /etc/apt/keyrings/nodesource.gpg \ && echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_$NODE_MAJOR.x nodistro main" | tee /etc/apt/sources.list.d/nodesource.list \ && apt-get update \ && apt-get install -y nodejs \ && rm -rf /var/lib/apt/lists/* # Verify Node.js and npm installation RUN node -v && npm -v && npx -v # Set up working directory WORKDIR /app # Copy requirements and install Python dependencies COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt # Playwright setup ENV PLAYWRIGHT_BROWSERS_PATH=/ms-browsers RUN mkdir -p $PLAYWRIGHT_BROWSERS_PATH # Install Chromium via Playwright without --with-deps RUN PLAYWRIGHT_SKIP_BROWSER_DOWNLOAD=0 playwright install chromium # Copy application code COPY . . # Set up supervisor configuration RUN mkdir -p /var/log/supervisor COPY supervisord.conf /etc/supervisor/conf.d/supervisord.conf EXPOSE 7788 6080 5901 9222 CMD ["/usr/bin/supervisord", "-c", "/etc/supervisor/conf.d/supervisord.conf"] ================================================ FILE: LICENSE ================================================ MIT License Copyright (c) 2024 Browser Use Inc. Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: README.md ================================================ Browser Use Web UI
[![GitHub stars](https://img.shields.io/github/stars/browser-use/web-ui?style=social)](https://github.com/browser-use/web-ui/stargazers) [![Discord](https://img.shields.io/discord/1303749220842340412?color=7289DA&label=Discord&logo=discord&logoColor=white)](https://link.browser-use.com/discord) [![Documentation](https://img.shields.io/badge/Documentation-📕-blue)](https://docs.browser-use.com) [![WarmShao](https://img.shields.io/twitter/follow/warmshao?style=social)](https://x.com/warmshao) This project builds upon the foundation of the [browser-use](https://github.com/browser-use/browser-use), which is designed to make websites accessible for AI agents. We would like to officially thank [WarmShao](https://github.com/warmshao) for his contribution to this project. **WebUI:** is built on Gradio and supports most of `browser-use` functionalities. This UI is designed to be user-friendly and enables easy interaction with the browser agent. **Expanded LLM Support:** We've integrated support for various Large Language Models (LLMs), including: Google, OpenAI, Azure OpenAI, Anthropic, DeepSeek, Ollama etc. And we plan to add support for even more models in the future. **Custom Browser Support:** You can use your own browser with our tool, eliminating the need to re-login to sites or deal with other authentication challenges. This feature also supports high-definition screen recording. **Persistent Browser Sessions:** You can choose to keep the browser window open between AI tasks, allowing you to see the complete history and state of AI interactions. ## Installation Guide ### Option 1: Local Installation Read the [quickstart guide](https://docs.browser-use.com/quickstart#prepare-the-environment) or follow the steps below to get started. #### Step 1: Clone the Repository ```bash git clone https://github.com/browser-use/web-ui.git cd web-ui ``` #### Step 2: Set Up Python Environment We recommend using [uv](https://docs.astral.sh/uv/) for managing the Python environment. Using uv (recommended): ```bash uv venv --python 3.11 ``` Activate the virtual environment: - Windows (Command Prompt): ```cmd .venv\Scripts\activate ``` - Windows (PowerShell): ```powershell .\.venv\Scripts\Activate.ps1 ``` - macOS/Linux: ```bash source .venv/bin/activate ``` #### Step 3: Install Dependencies Install Python packages: ```bash uv pip install -r requirements.txt ``` Install Browsers in playwright. ```bash playwright install --with-deps ``` Or you can install specific browsers by running: ```bash playwright install chromium --with-deps ``` #### Step 4: Configure Environment 1. Create a copy of the example environment file: - Windows (Command Prompt): ```bash copy .env.example .env ``` - macOS/Linux/Windows (PowerShell): ```bash cp .env.example .env ``` 2. Open `.env` in your preferred text editor and add your API keys and other settings #### Step 5: Enjoy the web-ui 1. **Run the WebUI:** ```bash python webui.py --ip 127.0.0.1 --port 7788 ``` 2. **Access the WebUI:** Open your web browser and navigate to `http://127.0.0.1:7788`. 3. **Using Your Own Browser(Optional):** - Set `BROWSER_PATH` to the executable path of your browser and `BROWSER_USER_DATA` to the user data directory of your browser. Leave `BROWSER_USER_DATA` empty if you want to use local user data. - Windows ```env BROWSER_PATH="C:\Program Files\Google\Chrome\Application\chrome.exe" BROWSER_USER_DATA="C:\Users\YourUsername\AppData\Local\Google\Chrome\User Data" ``` > Note: Replace `YourUsername` with your actual Windows username for Windows systems. - Mac ```env BROWSER_PATH="/Applications/Google Chrome.app/Contents/MacOS/Google Chrome" BROWSER_USER_DATA="/Users/YourUsername/Library/Application Support/Google/Chrome" ``` - Close all Chrome windows - Open the WebUI in a non-Chrome browser, such as Firefox or Edge. This is important because the persistent browser context will use the Chrome data when running the agent. - Check the "Use Own Browser" option within the Browser Settings. ### Option 2: Docker Installation #### Prerequisites - Docker and Docker Compose installed - [Docker Desktop](https://www.docker.com/products/docker-desktop/) (For Windows/macOS) - [Docker Engine](https://docs.docker.com/engine/install/) and [Docker Compose](https://docs.docker.com/compose/install/) (For Linux) #### Step 1: Clone the Repository ```bash git clone https://github.com/browser-use/web-ui.git cd web-ui ``` #### Step 2: Configure Environment 1. Create a copy of the example environment file: - Windows (Command Prompt): ```bash copy .env.example .env ``` - macOS/Linux/Windows (PowerShell): ```bash cp .env.example .env ``` 2. Open `.env` in your preferred text editor and add your API keys and other settings #### Step 3: Docker Build and Run ```bash docker compose up --build ``` For ARM64 systems (e.g., Apple Silicon Macs), please run follow command: ```bash TARGETPLATFORM=linux/arm64 docker compose up --build ``` #### Step 4: Enjoy the web-ui and vnc - Web-UI: Open `http://localhost:7788` in your browser - VNC Viewer (for watching browser interactions): Open `http://localhost:6080/vnc.html` - Default VNC password: "youvncpassword" - Can be changed by setting `VNC_PASSWORD` in your `.env` file ## Changelog - [x] **2025/01/26:** Thanks to @vvincent1234. Now browser-use-webui can combine with DeepSeek-r1 to engage in deep thinking! - [x] **2025/01/10:** Thanks to @casistack. Now we have Docker Setup option and also Support keep browser open between tasks.[Video tutorial demo](https://github.com/browser-use/web-ui/issues/1#issuecomment-2582511750). - [x] **2025/01/06:** Thanks to @richard-devbot. A New and Well-Designed WebUI is released. [Video tutorial demo](https://github.com/warmshao/browser-use-webui/issues/1#issuecomment-2573393113). ================================================ FILE: SECURITY.md ================================================ ## Reporting Security Issues If you believe you have found a security vulnerability in browser-use, please report it through coordinated disclosure. **Please do not report security vulnerabilities through the repository issues, discussions, or pull requests.** Instead, please open a new [Github security advisory](https://github.com/browser-use/web-ui/security/advisories/new). Please include as much of the information listed below as you can to help me better understand and resolve the issue: * The type of issue (e.g., buffer overflow, SQL injection, or cross-site scripting) * Full paths of source file(s) related to the manifestation of the issue * The location of the affected source code (tag/branch/commit or direct URL) * Any special configuration required to reproduce the issue * Step-by-step instructions to reproduce the issue * Proof-of-concept or exploit code (if possible) * Impact of the issue, including how an attacker might exploit the issue This information will help me triage your report more quickly. ================================================ FILE: docker-compose.yml ================================================ services: # debug: docker compose run --rm -it browser-use-webui bash browser-use-webui: # image: ghcr.io/browser-use/web-ui # Using precompiled image build: context: . dockerfile: Dockerfile args: TARGETPLATFORM: ${TARGETPLATFORM:-linux/amd64} ports: - "7788:7788" - "6080:6080" - "5901:5901" - "9222:9222" environment: # LLM API Keys & Endpoints - OPENAI_ENDPOINT=${OPENAI_ENDPOINT:-https://api.openai.com/v1} - OPENAI_API_KEY=${OPENAI_API_KEY:-} - ANTHROPIC_ENDPOINT=${ANTHROPIC_ENDPOINT:-https://api.anthropic.com} - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY:-} - GOOGLE_API_KEY=${GOOGLE_API_KEY:-} - AZURE_OPENAI_ENDPOINT=${AZURE_OPENAI_ENDPOINT:-} - AZURE_OPENAI_API_KEY=${AZURE_OPENAI_API_KEY:-} - AZURE_OPENAI_API_VERSION=${AZURE_OPENAI_API_VERSION:-2025-01-01-preview} - DEEPSEEK_ENDPOINT=${DEEPSEEK_ENDPOINT:-https://api.deepseek.com} - DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY:-} - OLLAMA_ENDPOINT=${OLLAMA_ENDPOINT:-http://localhost:11434} - MISTRAL_ENDPOINT=${MISTRAL_ENDPOINT:-https://api.mistral.ai/v1} - MISTRAL_API_KEY=${MISTRAL_API_KEY:-} - ALIBABA_ENDPOINT=${ALIBABA_ENDPOINT:-https://dashscope.aliyuncs.com/compatible-mode/v1} - ALIBABA_API_KEY=${ALIBABA_API_KEY:-} - MOONSHOT_ENDPOINT=${MOONSHOT_ENDPOINT:-https://api.moonshot.cn/v1} - MOONSHOT_API_KEY=${MOONSHOT_API_KEY:-} - UNBOUND_ENDPOINT=${UNBOUND_ENDPOINT:-https://api.getunbound.ai} - UNBOUND_API_KEY=${UNBOUND_API_KEY:-} - SiliconFLOW_ENDPOINT=${SiliconFLOW_ENDPOINT:-https://api.siliconflow.cn/v1/} - SiliconFLOW_API_KEY=${SiliconFLOW_API_KEY:-} - IBM_ENDPOINT=${IBM_ENDPOINT:-https://us-south.ml.cloud.ibm.com} - IBM_API_KEY=${IBM_API_KEY:-} - IBM_PROJECT_ID=${IBM_PROJECT_ID:-} # Application Settings - ANONYMIZED_TELEMETRY=${ANONYMIZED_TELEMETRY:-false} - BROWSER_USE_LOGGING_LEVEL=${BROWSER_USE_LOGGING_LEVEL:-info} # Browser Settings - BROWSER_PATH= - BROWSER_USER_DATA= - BROWSER_DEBUGGING_PORT=${BROWSER_DEBUGGING_PORT:-9222} - BROWSER_DEBUGGING_HOST=localhost - USE_OWN_BROWSER=false - KEEP_BROWSER_OPEN=true - BROWSER_CDP=${BROWSER_CDP:-} # e.g., http://localhost:9222 # Display Settings - DISPLAY=:99 # This ENV is used by the Dockerfile during build time if playwright respects it. # It's not strictly needed at runtime by docker-compose unless your app or scripts also read it. - PLAYWRIGHT_BROWSERS_PATH=/ms-browsers # Matches Dockerfile ENV - RESOLUTION=${RESOLUTION:-1920x1080x24} - RESOLUTION_WIDTH=${RESOLUTION_WIDTH:-1920} - RESOLUTION_HEIGHT=${RESOLUTION_HEIGHT:-1080} # VNC Settings - VNC_PASSWORD=${VNC_PASSWORD:-youvncpassword} volumes: - /tmp/.X11-unix:/tmp/.X11-unix # - ./my_chrome_data:/app/data/chrome_data # Optional: persist browser data restart: unless-stopped shm_size: "2gb" cap_add: - SYS_ADMIN tmpfs: - /tmp healthcheck: test: ["CMD", "nc", "-z", "localhost", "5901"] # VNC port interval: 10s timeout: 5s retries: 3 ================================================ FILE: requirements.txt ================================================ browser-use==0.1.48 pyperclip==1.9.0 gradio==5.27.0 json-repair langchain-mistralai==0.2.4 MainContentExtractor==0.0.4 langchain-ibm==0.3.10 langchain_mcp_adapters==0.0.9 langgraph==0.3.34 langchain-community ================================================ FILE: src/__init__.py ================================================ ================================================ FILE: src/agent/__init__.py ================================================ ================================================ FILE: src/agent/browser_use/browser_use_agent.py ================================================ from __future__ import annotations import asyncio import logging import os # from lmnr.sdk.decorators import observe from browser_use.agent.gif import create_history_gif from browser_use.agent.service import Agent, AgentHookFunc from browser_use.agent.views import ( ActionResult, AgentHistory, AgentHistoryList, AgentStepInfo, ToolCallingMethod, ) from browser_use.browser.views import BrowserStateHistory from browser_use.utils import time_execution_async from dotenv import load_dotenv from browser_use.agent.message_manager.utils import is_model_without_tool_support load_dotenv() logger = logging.getLogger(__name__) SKIP_LLM_API_KEY_VERIFICATION = ( os.environ.get("SKIP_LLM_API_KEY_VERIFICATION", "false").lower()[0] in "ty1" ) class BrowserUseAgent(Agent): def _set_tool_calling_method(self) -> ToolCallingMethod | None: tool_calling_method = self.settings.tool_calling_method if tool_calling_method == 'auto': if is_model_without_tool_support(self.model_name): return 'raw' elif self.chat_model_library == 'ChatGoogleGenerativeAI': return None elif self.chat_model_library == 'ChatOpenAI': return 'function_calling' elif self.chat_model_library == 'AzureChatOpenAI': return 'function_calling' else: return None else: return tool_calling_method @time_execution_async("--run (agent)") async def run( self, max_steps: int = 100, on_step_start: AgentHookFunc | None = None, on_step_end: AgentHookFunc | None = None ) -> AgentHistoryList: """Execute the task with maximum number of steps""" loop = asyncio.get_event_loop() # Set up the Ctrl+C signal handler with callbacks specific to this agent from browser_use.utils import SignalHandler signal_handler = SignalHandler( loop=loop, pause_callback=self.pause, resume_callback=self.resume, custom_exit_callback=None, # No special cleanup needed on forced exit exit_on_second_int=True, ) signal_handler.register() try: self._log_agent_run() # Execute initial actions if provided if self.initial_actions: result = await self.multi_act(self.initial_actions, check_for_new_elements=False) self.state.last_result = result for step in range(max_steps): # Check if waiting for user input after Ctrl+C if self.state.paused: signal_handler.wait_for_resume() signal_handler.reset() # Check if we should stop due to too many failures if self.state.consecutive_failures >= self.settings.max_failures: logger.error(f'❌ Stopping due to {self.settings.max_failures} consecutive failures') break # Check control flags before each step if self.state.stopped: logger.info('Agent stopped') break while self.state.paused: await asyncio.sleep(0.2) # Small delay to prevent CPU spinning if self.state.stopped: # Allow stopping while paused break if on_step_start is not None: await on_step_start(self) step_info = AgentStepInfo(step_number=step, max_steps=max_steps) await self.step(step_info) if on_step_end is not None: await on_step_end(self) if self.state.history.is_done(): if self.settings.validate_output and step < max_steps - 1: if not await self._validate_output(): continue await self.log_completion() break else: error_message = 'Failed to complete task in maximum steps' self.state.history.history.append( AgentHistory( model_output=None, result=[ActionResult(error=error_message, include_in_memory=True)], state=BrowserStateHistory( url='', title='', tabs=[], interacted_element=[], screenshot=None, ), metadata=None, ) ) logger.info(f'❌ {error_message}') return self.state.history except KeyboardInterrupt: # Already handled by our signal handler, but catch any direct KeyboardInterrupt as well logger.info('Got KeyboardInterrupt during execution, returning current history') return self.state.history finally: # Unregister signal handlers before cleanup signal_handler.unregister() if self.settings.save_playwright_script_path: logger.info( f'Agent run finished. Attempting to save Playwright script to: {self.settings.save_playwright_script_path}' ) try: # Extract sensitive data keys if sensitive_data is provided keys = list(self.sensitive_data.keys()) if self.sensitive_data else None # Pass browser and context config to the saving method self.state.history.save_as_playwright_script( self.settings.save_playwright_script_path, sensitive_data_keys=keys, browser_config=self.browser.config, context_config=self.browser_context.config, ) except Exception as script_gen_err: # Log any error during script generation/saving logger.error(f'Failed to save Playwright script: {script_gen_err}', exc_info=True) await self.close() if self.settings.generate_gif: output_path: str = 'agent_history.gif' if isinstance(self.settings.generate_gif, str): output_path = self.settings.generate_gif create_history_gif(task=self.task, history=self.state.history, output_path=output_path) ================================================ FILE: src/agent/deep_research/deep_research_agent.py ================================================ import asyncio import json import logging import os import threading import uuid from pathlib import Path from typing import Any, Dict, List, Optional, TypedDict from browser_use.browser.browser import BrowserConfig from langchain_community.tools.file_management import ( ListDirectoryTool, ReadFileTool, WriteFileTool, ) # Langchain imports from langchain_core.messages import ( AIMessage, BaseMessage, HumanMessage, SystemMessage, ToolMessage, ) from langchain_core.prompts import ChatPromptTemplate from langchain_core.tools import StructuredTool, Tool # Langgraph imports from langgraph.graph import StateGraph from pydantic import BaseModel, Field from browser_use.browser.context import BrowserContextConfig from src.agent.browser_use.browser_use_agent import BrowserUseAgent from src.browser.custom_browser import CustomBrowser from src.controller.custom_controller import CustomController from src.utils.mcp_client import setup_mcp_client_and_tools logger = logging.getLogger(__name__) # Constants REPORT_FILENAME = "report.md" PLAN_FILENAME = "research_plan.md" SEARCH_INFO_FILENAME = "search_info.json" _AGENT_STOP_FLAGS = {} _BROWSER_AGENT_INSTANCES = {} async def run_single_browser_task( task_query: str, task_id: str, llm: Any, # Pass the main LLM browser_config: Dict[str, Any], stop_event: threading.Event, use_vision: bool = False, ) -> Dict[str, Any]: """ Runs a single BrowserUseAgent task. Manages browser creation and closing for this specific task. """ if not BrowserUseAgent: return { "query": task_query, "error": "BrowserUseAgent components not available.", } # --- Browser Setup --- # These should ideally come from the main agent's config headless = browser_config.get("headless", False) window_w = browser_config.get("window_width", 1280) window_h = browser_config.get("window_height", 1100) browser_user_data_dir = browser_config.get("user_data_dir", None) use_own_browser = browser_config.get("use_own_browser", False) browser_binary_path = browser_config.get("browser_binary_path", None) wss_url = browser_config.get("wss_url", None) cdp_url = browser_config.get("cdp_url", None) disable_security = browser_config.get("disable_security", False) bu_browser = None bu_browser_context = None try: logger.info(f"Starting browser task for query: {task_query}") extra_args = [] if use_own_browser: browser_binary_path = os.getenv("BROWSER_PATH", None) or browser_binary_path if browser_binary_path == "": browser_binary_path = None browser_user_data = browser_user_data_dir or os.getenv("BROWSER_USER_DATA", None) if browser_user_data: extra_args += [f"--user-data-dir={browser_user_data}"] else: browser_binary_path = None bu_browser = CustomBrowser( config=BrowserConfig( headless=headless, browser_binary_path=browser_binary_path, extra_browser_args=extra_args, wss_url=wss_url, cdp_url=cdp_url, new_context_config=BrowserContextConfig( window_width=window_w, window_height=window_h, ) ) ) context_config = BrowserContextConfig( save_downloads_path="./tmp/downloads", window_height=window_h, window_width=window_w, force_new_context=True, ) bu_browser_context = await bu_browser.new_context(config=context_config) # Simple controller example, replace with your actual implementation if needed bu_controller = CustomController() # Construct the task prompt for BrowserUseAgent # Instruct it to find specific info and return title/URL bu_task_prompt = f""" Research Task: {task_query} Objective: Find relevant information answering the query. Output Requirements: For each relevant piece of information found, please provide: 1. A concise summary of the information. 2. The title of the source page or document. 3. The URL of the source. Focus on accuracy and relevance. Avoid irrelevant details. PDF cannot directly extract _content, please try to download first, then using read_file, if you can't save or read, please try other methods. """ bu_agent_instance = BrowserUseAgent( task=bu_task_prompt, llm=llm, # Use the passed LLM browser=bu_browser, browser_context=bu_browser_context, controller=bu_controller, use_vision=use_vision, source="webui", ) # Store instance for potential stop() call task_key = f"{task_id}_{uuid.uuid4()}" _BROWSER_AGENT_INSTANCES[task_key] = bu_agent_instance # --- Run with Stop Check --- # BrowserUseAgent needs to internally check a stop signal or have a stop method. # We simulate checking before starting and assume `run` might be interruptible # or have its own stop mechanism we can trigger via bu_agent_instance.stop(). if stop_event.is_set(): logger.info(f"Browser task for '{task_query}' cancelled before start.") return {"query": task_query, "result": None, "status": "cancelled"} # The run needs to be awaitable and ideally accept a stop signal or have a .stop() method # result = await bu_agent_instance.run(max_steps=max_steps) # Add max_steps if applicable # Let's assume a simplified run for now logger.info(f"Running BrowserUseAgent for: {task_query}") result = await bu_agent_instance.run() # Assuming run is the main method logger.info(f"BrowserUseAgent finished for: {task_query}") final_data = result.final_result() if stop_event.is_set(): logger.info(f"Browser task for '{task_query}' stopped during execution.") return {"query": task_query, "result": final_data, "status": "stopped"} else: logger.info(f"Browser result for '{task_query}': {final_data}") return {"query": task_query, "result": final_data, "status": "completed"} except Exception as e: logger.error( f"Error during browser task for query '{task_query}': {e}", exc_info=True ) return {"query": task_query, "error": str(e), "status": "failed"} finally: if bu_browser_context: try: await bu_browser_context.close() bu_browser_context = None logger.info("Closed browser context.") except Exception as e: logger.error(f"Error closing browser context: {e}") if bu_browser: try: await bu_browser.close() bu_browser = None logger.info("Closed browser.") except Exception as e: logger.error(f"Error closing browser: {e}") if task_key in _BROWSER_AGENT_INSTANCES: del _BROWSER_AGENT_INSTANCES[task_key] class BrowserSearchInput(BaseModel): queries: List[str] = Field( description="List of distinct search queries to find information relevant to the research task." ) async def _run_browser_search_tool( queries: List[str], task_id: str, # Injected dependency llm: Any, # Injected dependency browser_config: Dict[str, Any], stop_event: threading.Event, max_parallel_browsers: int = 1, ) -> List[Dict[str, Any]]: """ Internal function to execute parallel browser searches based on LLM-provided queries. Handles concurrency and stop signals. """ # Limit queries just in case LLM ignores the description queries = queries[:max_parallel_browsers] logger.info( f"[Browser Tool {task_id}] Running search for {len(queries)} queries: {queries}" ) results = [] semaphore = asyncio.Semaphore(max_parallel_browsers) async def task_wrapper(query): async with semaphore: if stop_event.is_set(): logger.info( f"[Browser Tool {task_id}] Skipping task due to stop signal: {query}" ) return {"query": query, "result": None, "status": "cancelled"} # Pass necessary injected configs and the stop event return await run_single_browser_task( query, task_id, llm, # Pass the main LLM (or a dedicated one if needed) browser_config, stop_event, # use_vision could be added here if needed ) tasks = [task_wrapper(query) for query in queries] search_results = await asyncio.gather(*tasks, return_exceptions=True) processed_results = [] for i, res in enumerate(search_results): query = queries[i] # Get corresponding query if isinstance(res, Exception): logger.error( f"[Browser Tool {task_id}] Gather caught exception for query '{query}': {res}", exc_info=True, ) processed_results.append( {"query": query, "error": str(res), "status": "failed"} ) elif isinstance(res, dict): processed_results.append(res) else: logger.error( f"[Browser Tool {task_id}] Unexpected result type for query '{query}': {type(res)}" ) processed_results.append( {"query": query, "error": "Unexpected result type", "status": "failed"} ) logger.info( f"[Browser Tool {task_id}] Finished search. Results count: {len(processed_results)}" ) return processed_results def create_browser_search_tool( llm: Any, browser_config: Dict[str, Any], task_id: str, stop_event: threading.Event, max_parallel_browsers: int = 1, ) -> StructuredTool: """Factory function to create the browser search tool with necessary dependencies.""" # Use partial to bind the dependencies that aren't part of the LLM call arguments from functools import partial bound_tool_func = partial( _run_browser_search_tool, task_id=task_id, llm=llm, browser_config=browser_config, stop_event=stop_event, max_parallel_browsers=max_parallel_browsers, ) return StructuredTool.from_function( coroutine=bound_tool_func, name="parallel_browser_search", description=f"""Use this tool to actively search the web for information related to a specific research task or question. It runs up to {max_parallel_browsers} searches in parallel using a browser agent for better results than simple scraping. Provide a list of distinct search queries(up to {max_parallel_browsers}) that are likely to yield relevant information.""", args_schema=BrowserSearchInput, ) # --- Langgraph State Definition --- class ResearchTaskItem(TypedDict): # step: int # Maybe step within category, or just implicit by order task_description: str status: str # "pending", "completed", "failed" queries: Optional[List[str]] result_summary: Optional[str] class ResearchCategoryItem(TypedDict): category_name: str tasks: List[ResearchTaskItem] # Optional: category_status: str # Could be "pending", "in_progress", "completed" class DeepResearchState(TypedDict): task_id: str topic: str research_plan: List[ResearchCategoryItem] # CHANGED search_results: List[Dict[str, Any]] llm: Any tools: List[Tool] output_dir: Path browser_config: Dict[str, Any] final_report: Optional[str] current_category_index: int current_task_index_in_category: int stop_requested: bool error_message: Optional[str] messages: List[BaseMessage] # --- Langgraph Nodes --- def _load_previous_state(task_id: str, output_dir: str) -> Dict[str, Any]: state_updates = {} plan_file = os.path.join(output_dir, PLAN_FILENAME) search_file = os.path.join(output_dir, SEARCH_INFO_FILENAME) loaded_plan: List[ResearchCategoryItem] = [] next_cat_idx, next_task_idx = 0, 0 found_pending = False if os.path.exists(plan_file): try: with open(plan_file, "r", encoding="utf-8") as f: current_category: Optional[ResearchCategoryItem] = None lines = f.readlines() cat_counter = 0 task_counter_in_cat = 0 for line_num, line_content in enumerate(lines): line = line_content.strip() if line.startswith("## "): # Category if current_category: # Save previous category loaded_plan.append(current_category) if not found_pending: # If previous category was all done, advance cat counter cat_counter += 1 task_counter_in_cat = 0 category_name = line[line.find(" "):].strip() # Get text after "## X. " current_category = ResearchCategoryItem(category_name=category_name, tasks=[]) elif (line.startswith("- [ ]") or line.startswith("- [x]") or line.startswith( "- [-]")) and current_category: # Task status = "pending" if line.startswith("- [x]"): status = "completed" elif line.startswith("- [-]"): status = "failed" task_desc = line[5:].strip() current_category["tasks"].append( ResearchTaskItem(task_description=task_desc, status=status, queries=None, result_summary=None) ) if status == "pending" and not found_pending: next_cat_idx = cat_counter next_task_idx = task_counter_in_cat found_pending = True if not found_pending: # only increment if previous tasks were completed/failed task_counter_in_cat += 1 if current_category: # Append last category loaded_plan.append(current_category) if loaded_plan: state_updates["research_plan"] = loaded_plan if not found_pending and loaded_plan: # All tasks were completed or failed next_cat_idx = len(loaded_plan) # Points beyond the last category next_task_idx = 0 state_updates["current_category_index"] = next_cat_idx state_updates["current_task_index_in_category"] = next_task_idx logger.info( f"Loaded hierarchical research plan from {plan_file}. " f"Next task: Category {next_cat_idx}, Task {next_task_idx} in category." ) else: logger.warning(f"Plan file {plan_file} was empty or malformed.") except Exception as e: logger.error(f"Failed to load or parse research plan {plan_file}: {e}", exc_info=True) state_updates["error_message"] = f"Failed to load research plan: {e}" else: logger.info(f"Plan file {plan_file} not found. Will start fresh.") if os.path.exists(search_file): try: with open(search_file, "r", encoding="utf-8") as f: state_updates["search_results"] = json.load(f) logger.info(f"Loaded search results from {search_file}") except Exception as e: logger.error(f"Failed to load search results {search_file}: {e}") state_updates["error_message"] = ( state_updates.get("error_message", "") + f" Failed to load search results: {e}").strip() return state_updates def _save_plan_to_md(plan: List[ResearchCategoryItem], output_dir: str): plan_file = os.path.join(output_dir, PLAN_FILENAME) try: with open(plan_file, "w", encoding="utf-8") as f: f.write(f"# Research Plan\n\n") for cat_idx, category in enumerate(plan): f.write(f"## {cat_idx + 1}. {category['category_name']}\n\n") for task_idx, task in enumerate(category['tasks']): marker = "- [x]" if task["status"] == "completed" else "- [ ]" if task[ "status"] == "pending" else "- [-]" # [-] for failed f.write(f" {marker} {task['task_description']}\n") f.write("\n") logger.info(f"Hierarchical research plan saved to {plan_file}") except Exception as e: logger.error(f"Failed to save research plan to {plan_file}: {e}") def _save_search_results_to_json(results: List[Dict[str, Any]], output_dir: str): """Appends or overwrites search results to a JSON file.""" search_file = os.path.join(output_dir, SEARCH_INFO_FILENAME) try: # Simple overwrite for now, could be append with open(search_file, "w", encoding="utf-8") as f: json.dump(results, f, indent=2, ensure_ascii=False) logger.info(f"Search results saved to {search_file}") except Exception as e: logger.error(f"Failed to save search results to {search_file}: {e}") def _save_report_to_md(report: str, output_dir: Path): """Saves the final report to a markdown file.""" report_file = os.path.join(output_dir, REPORT_FILENAME) try: with open(report_file, "w", encoding="utf-8") as f: f.write(report) logger.info(f"Final report saved to {report_file}") except Exception as e: logger.error(f"Failed to save final report to {report_file}: {e}") async def planning_node(state: DeepResearchState) -> Dict[str, Any]: logger.info("--- Entering Planning Node ---") if state.get("stop_requested"): logger.info("Stop requested, skipping planning.") return {"stop_requested": True} llm = state["llm"] topic = state["topic"] existing_plan = state.get("research_plan") output_dir = state["output_dir"] if existing_plan and ( state.get("current_category_index", 0) > 0 or state.get("current_task_index_in_category", 0) > 0): logger.info("Resuming with existing plan.") _save_plan_to_md(existing_plan, output_dir) # Ensure it's saved initially # current_category_index and current_task_index_in_category should be set by _load_previous_state return {"research_plan": existing_plan} logger.info(f"Generating new research plan for topic: {topic}") prompt_text = f"""You are a meticulous research assistant. Your goal is to create a hierarchical research plan to thoroughly investigate the topic: "{topic}". The plan should be structured into several main research categories. Each category should contain a list of specific, actionable research tasks or questions. Format the output as a JSON list of objects. Each object represents a research category and should have: 1. "category_name": A string for the name of the research category. 2. "tasks": A list of strings, where each string is a specific research task for that category. Example JSON Output: [ {{ "category_name": "Understanding Core Concepts and Definitions", "tasks": [ "Define the primary terminology associated with '{topic}'.", "Identify the fundamental principles and theories underpinning '{topic}'." ] }}, {{ "category_name": "Historical Development and Key Milestones", "tasks": [ "Trace the historical evolution of '{topic}'.", "Identify key figures, events, or breakthroughs in the development of '{topic}'." ] }}, {{ "category_name": "Current State-of-the-Art and Applications", "tasks": [ "Analyze the current advancements and prominent applications of '{topic}'.", "Investigate ongoing research and active areas of development related to '{topic}'." ] }}, {{ "category_name": "Challenges, Limitations, and Future Outlook", "tasks": [ "Identify the major challenges and limitations currently facing '{topic}'.", "Explore potential future trends, ethical considerations, and societal impacts of '{topic}'." ] }} ] Generate a plan with 3-10 categories, and 2-6 tasks per category for the topic: "{topic}" according to the complexity of the topic. Ensure the output is a valid JSON array. """ messages = [ SystemMessage(content="You are a research planning assistant outputting JSON."), HumanMessage(content=prompt_text) ] try: response = await llm.ainvoke(messages) raw_content = response.content # The LLM might wrap the JSON in backticks if raw_content.strip().startswith("```json"): raw_content = raw_content.strip()[7:-3].strip() elif raw_content.strip().startswith("```"): raw_content = raw_content.strip()[3:-3].strip() logger.debug(f"LLM response for plan: {raw_content}") parsed_plan_from_llm = json.loads(raw_content) new_plan: List[ResearchCategoryItem] = [] for cat_idx, category_data in enumerate(parsed_plan_from_llm): if not isinstance(category_data, dict) or "category_name" not in category_data or "tasks" not in category_data: logger.warning(f"Skipping invalid category data: {category_data}") continue tasks: List[ResearchTaskItem] = [] for task_idx, task_desc in enumerate(category_data["tasks"]): if isinstance(task_desc, str): tasks.append( ResearchTaskItem( task_description=task_desc, status="pending", queries=None, result_summary=None, ) ) else: # Sometimes LLM puts tasks as {"task": "description"} if isinstance(task_desc, dict) and "task_description" in task_desc: tasks.append( ResearchTaskItem( task_description=task_desc["task_description"], status="pending", queries=None, result_summary=None, ) ) elif isinstance(task_desc, dict) and "task" in task_desc: # common LLM mistake tasks.append( ResearchTaskItem( task_description=task_desc["task"], status="pending", queries=None, result_summary=None, ) ) else: logger.warning( f"Skipping invalid task data: {task_desc} in category {category_data['category_name']}") new_plan.append( ResearchCategoryItem( category_name=category_data["category_name"], tasks=tasks, ) ) if not new_plan: logger.error("LLM failed to generate a valid plan structure from JSON.") return {"error_message": "Failed to generate research plan structure."} logger.info(f"Generated research plan with {len(new_plan)} categories.") _save_plan_to_md(new_plan, output_dir) # Save the hierarchical plan return { "research_plan": new_plan, "current_category_index": 0, "current_task_index_in_category": 0, "search_results": [], } except json.JSONDecodeError as e: logger.error(f"Failed to parse JSON from LLM for plan: {e}. Response was: {raw_content}", exc_info=True) return {"error_message": f"LLM generated invalid JSON for research plan: {e}"} except Exception as e: logger.error(f"Error during planning: {e}", exc_info=True) return {"error_message": f"LLM Error during planning: {e}"} async def research_execution_node(state: DeepResearchState) -> Dict[str, Any]: logger.info("--- Entering Research Execution Node ---") if state.get("stop_requested"): logger.info("Stop requested, skipping research execution.") return { "stop_requested": True, "current_category_index": state["current_category_index"], "current_task_index_in_category": state["current_task_index_in_category"], } plan = state["research_plan"] cat_idx = state["current_category_index"] task_idx = state["current_task_index_in_category"] llm = state["llm"] tools = state["tools"] output_dir = str(state["output_dir"]) task_id = state["task_id"] # For _AGENT_STOP_FLAGS # This check should ideally be handled by `should_continue` if not plan or cat_idx >= len(plan): logger.info("Research plan complete or categories exhausted.") return {} # should route to synthesis current_category = plan[cat_idx] if task_idx >= len(current_category["tasks"]): logger.info(f"All tasks in category '{current_category['category_name']}' completed. Moving to next category.") # This logic is now effectively handled by should_continue and the index updates below # The next iteration will be caught by should_continue or this node with updated indices return { "current_category_index": cat_idx + 1, "current_task_index_in_category": 0, "messages": state["messages"] # Pass messages along } current_task = current_category["tasks"][task_idx] if current_task["status"] == "completed": logger.info( f"Task '{current_task['task_description']}' in category '{current_category['category_name']}' already completed. Skipping.") # Logic to find next task next_task_idx = task_idx + 1 next_cat_idx = cat_idx if next_task_idx >= len(current_category["tasks"]): next_cat_idx += 1 next_task_idx = 0 return { "current_category_index": next_cat_idx, "current_task_index_in_category": next_task_idx, "messages": state["messages"] # Pass messages along } logger.info( f"Executing research task: '{current_task['task_description']}' (Category: '{current_category['category_name']}')" ) llm_with_tools = llm.bind_tools(tools) # Construct messages for LLM invocation task_prompt_content = ( f"Current Research Category: {current_category['category_name']}\n" f"Specific Task: {current_task['task_description']}\n\n" "Please use the available tools, especially 'parallel_browser_search', to gather information for this specific task. " "Provide focused search queries relevant ONLY to this task. " "If you believe you have sufficient information from previous steps for this specific task, you can indicate that you are ready to summarize or that no further search is needed." ) current_task_message_history = [ HumanMessage(content=task_prompt_content) ] if not state["messages"]: # First actual execution message invocation_messages = [ SystemMessage( content="You are a research assistant executing one task of a research plan. Focus on the current task only."), ] + current_task_message_history else: invocation_messages = state["messages"] + current_task_message_history try: logger.info(f"Invoking LLM with tools for task: {current_task['task_description']}") ai_response: BaseMessage = await llm_with_tools.ainvoke(invocation_messages) logger.info("LLM invocation complete.") tool_results = [] executed_tool_names = [] current_search_results = state.get("search_results", []) # Get existing search results if not isinstance(ai_response, AIMessage) or not ai_response.tool_calls: logger.warning( f"LLM did not call any tool for task '{current_task['task_description']}'. Response: {ai_response.content[:100]}..." ) current_task["status"] = "pending" # Or "completed_no_tool" if LLM explains it's done current_task["result_summary"] = f"LLM did not use a tool. Response: {ai_response.content}" current_task["current_category_index"] = cat_idx current_task["current_task_index_in_category"] = task_idx return current_task # We still save the plan and advance. else: # Process tool calls for tool_call in ai_response.tool_calls: tool_name = tool_call.get("name") tool_args = tool_call.get("args", {}) tool_call_id = tool_call.get("id") logger.info(f"LLM requested tool call: {tool_name} with args: {tool_args}") executed_tool_names.append(tool_name) selected_tool = next((t for t in tools if t.name == tool_name), None) if not selected_tool: logger.error(f"LLM called tool '{tool_name}' which is not available.") tool_results.append( ToolMessage(content=f"Error: Tool '{tool_name}' not found.", tool_call_id=tool_call_id)) continue try: stop_event = _AGENT_STOP_FLAGS.get(task_id) if stop_event and stop_event.is_set(): logger.info(f"Stop requested before executing tool: {tool_name}") current_task["status"] = "pending" # Or a new "stopped" status _save_plan_to_md(plan, output_dir) return {"stop_requested": True, "research_plan": plan, "current_category_index": cat_idx, "current_task_index_in_category": task_idx} logger.info(f"Executing tool: {tool_name}") tool_output = await selected_tool.ainvoke(tool_args) logger.info(f"Tool '{tool_name}' executed successfully.") if tool_name == "parallel_browser_search": current_search_results.extend(tool_output) # tool_output is List[Dict] else: # For other tools, we might need specific handling or just log logger.info(f"Result from tool '{tool_name}': {str(tool_output)[:200]}...") # Storing non-browser results might need a different structure or key in search_results current_search_results.append( {"tool_name": tool_name, "args": tool_args, "output": str(tool_output), "status": "completed"}) tool_results.append(ToolMessage(content=json.dumps(tool_output), tool_call_id=tool_call_id)) except Exception as e: logger.error(f"Error executing tool '{tool_name}': {e}", exc_info=True) tool_results.append( ToolMessage(content=f"Error executing tool {tool_name}: {e}", tool_call_id=tool_call_id)) current_search_results.append( {"tool_name": tool_name, "args": tool_args, "status": "failed", "error": str(e)}) # After processing all tool calls for this task step_failed_tool_execution = any("Error:" in str(tr.content) for tr in tool_results) # Consider a task successful if a browser search was attempted and didn't immediately error out during call # The browser search itself returns status for each query. browser_tool_attempted_successfully = "parallel_browser_search" in executed_tool_names and not step_failed_tool_execution if step_failed_tool_execution: current_task["status"] = "failed" current_task[ "result_summary"] = f"Tool execution failed. Errors: {[tr.content for tr in tool_results if 'Error' in str(tr.content)]}" elif executed_tool_names: # If any tool was called current_task["status"] = "completed" current_task["result_summary"] = f"Executed tool(s): {', '.join(executed_tool_names)}." # TODO: Could ask LLM to summarize the tool_results for this task if needed, rather than just listing tools. else: # No tool calls but AI response had .tool_calls structure (empty) current_task["status"] = "failed" # Or a more specific status current_task["result_summary"] = "LLM prepared for tool call but provided no tools." # Save progress _save_plan_to_md(plan, output_dir) _save_search_results_to_json(current_search_results, output_dir) # Determine next indices next_task_idx = task_idx + 1 next_cat_idx = cat_idx if next_task_idx >= len(current_category["tasks"]): next_cat_idx += 1 next_task_idx = 0 updated_messages = state["messages"] + current_task_message_history + [ai_response] + tool_results return { "research_plan": plan, "search_results": current_search_results, "current_category_index": next_cat_idx, "current_task_index_in_category": next_task_idx, "messages": updated_messages, } except Exception as e: logger.error(f"Unhandled error during research execution for task '{current_task['task_description']}': {e}", exc_info=True) current_task["status"] = "failed" _save_plan_to_md(plan, output_dir) # Determine next indices even on error to attempt to move on next_task_idx = task_idx + 1 next_cat_idx = cat_idx if next_task_idx >= len(current_category["tasks"]): next_cat_idx += 1 next_task_idx = 0 return { "research_plan": plan, "current_category_index": next_cat_idx, "current_task_index_in_category": next_task_idx, "error_message": f"Core Execution Error on task '{current_task['task_description']}': {e}", "messages": state["messages"] + current_task_message_history # Preserve messages up to error } async def synthesis_node(state: DeepResearchState) -> Dict[str, Any]: """Synthesizes the final report from the collected search results.""" logger.info("--- Entering Synthesis Node ---") if state.get("stop_requested"): logger.info("Stop requested, skipping synthesis.") return {"stop_requested": True} llm = state["llm"] topic = state["topic"] search_results = state.get("search_results", []) output_dir = state["output_dir"] plan = state["research_plan"] # Include plan for context if not search_results: logger.warning("No search results found to synthesize report.") report = f"# Research Report: {topic}\n\nNo information was gathered during the research process." _save_report_to_md(report, output_dir) return {"final_report": report} logger.info( f"Synthesizing report from {len(search_results)} collected search result entries." ) # Prepare context for the LLM # Format search results nicely, maybe group by query or original plan step formatted_results = "" references = {} ref_count = 1 for i, result_entry in enumerate(search_results): query = result_entry.get("query", "Unknown Query") # From parallel_browser_search tool_name = result_entry.get("tool_name") # From other tools status = result_entry.get("status", "unknown") result_data = result_entry.get("result") # From BrowserUseAgent's final_result tool_output_str = result_entry.get("output") # From other tools if tool_name == "parallel_browser_search" and status == "completed" and result_data: # result_data is the summary from BrowserUseAgent formatted_results += f'### Finding from Web Search Query: "{query}"\n' formatted_results += f"- **Summary:**\n{result_data}\n" # result_data is already a summary string here # If result_data contained title/URL, you'd format them here. # The current BrowserUseAgent returns a string summary directly as 'final_data' in run_single_browser_task formatted_results += "---\n" elif tool_name != "parallel_browser_search" and status == "completed" and tool_output_str: formatted_results += f'### Finding from Tool: "{tool_name}" (Args: {result_entry.get("args")})\n' formatted_results += f"- **Output:**\n{tool_output_str}\n" formatted_results += "---\n" elif status == "failed": error = result_entry.get("error") q_or_t = f"Query: \"{query}\"" if query != "Unknown Query" else f"Tool: \"{tool_name}\"" formatted_results += f'### Failed {q_or_t}\n' formatted_results += f"- **Error:** {error}\n" formatted_results += "---\n" # Prepare the research plan context plan_summary = "\nResearch Plan Followed:\n" for cat_idx, category in enumerate(plan): plan_summary += f"\n#### Category {cat_idx + 1}: {category['category_name']}\n" for task_idx, task in enumerate(category['tasks']): marker = "[x]" if task["status"] == "completed" else "[ ]" if task["status"] == "pending" else "[-]" plan_summary += f" - {marker} {task['task_description']}\n" synthesis_prompt = ChatPromptTemplate.from_messages( [ ( "system", """You are a professional researcher tasked with writing a comprehensive and well-structured report based on collected findings. The report should address the research topic thoroughly, synthesizing the information gathered from various sources. Structure the report logically: 1. Briefly introduce the topic and the report's scope (mentioning the research plan followed, including categories and tasks, is good). 2. Discuss the key findings, organizing them thematically, possibly aligning with the research categories. Analyze, compare, and contrast information. 3. Summarize the main points and offer concluding thoughts. Ensure the tone is objective and professional. If findings are contradictory or incomplete, acknowledge this. """, # Removed citation part for simplicity for now, as browser agent returns summaries. ), ( "human", f""" **Research Topic:** {topic} {plan_summary} **Collected Findings:** ``` {formatted_results} ``` Please generate the final research report in Markdown format based **only** on the information above. """, ), ] ) try: response = await llm.ainvoke( synthesis_prompt.format_prompt( topic=topic, plan_summary=plan_summary, formatted_results=formatted_results, ).to_messages() ) final_report_md = response.content # Append the reference list automatically to the end of the generated markdown if references: report_references_section = "\n\n## References\n\n" # Sort refs by ID for consistent output sorted_refs = sorted(references.values(), key=lambda x: x["id"]) for ref in sorted_refs: report_references_section += ( f"[{ref['id']}] {ref['title']} - {ref['url']}\n" ) final_report_md += report_references_section logger.info("Successfully synthesized the final report.") _save_report_to_md(final_report_md, output_dir) return {"final_report": final_report_md} except Exception as e: logger.error(f"Error during report synthesis: {e}", exc_info=True) return {"error_message": f"LLM Error during synthesis: {e}"} # --- Langgraph Edges and Conditional Logic --- def should_continue(state: DeepResearchState) -> str: logger.info("--- Evaluating Condition: Should Continue? ---") if state.get("stop_requested"): logger.info("Stop requested, routing to END.") return "end_run" if state.get("error_message") and "Core Execution Error" in state["error_message"]: # Critical error in node logger.warning(f"Critical error detected: {state['error_message']}. Routing to END.") return "end_run" plan = state.get("research_plan") cat_idx = state.get("current_category_index", 0) task_idx = state.get("current_task_index_in_category", 0) # This is the *next* task to check if not plan: logger.warning("No research plan found. Routing to END.") return "end_run" # Check if the current indices point to a valid pending task if cat_idx < len(plan): current_category = plan[cat_idx] if task_idx < len(current_category["tasks"]): # We are trying to execute the task at plan[cat_idx]["tasks"][task_idx] # The research_execution_node will handle if it's already completed. logger.info( f"Plan has potential pending tasks (next up: Category {cat_idx}, Task {task_idx}). Routing to Research Execution." ) return "execute_research" else: # task_idx is out of bounds for current category, means we need to check next category if cat_idx + 1 < len(plan): # If there is a next category logger.info( f"Finished tasks in category {cat_idx}. Moving to category {cat_idx + 1}. Routing to Research Execution." ) # research_execution_node will update state to {current_category_index: cat_idx + 1, current_task_index_in_category: 0} # Or rather, the previous execution node already set these indices to the start of the next category. return "execute_research" # If we've gone through all categories and tasks (cat_idx >= len(plan)) logger.info("All plan categories and tasks processed or current indices are out of bounds. Routing to Synthesis.") return "synthesize_report" # --- DeepSearchAgent Class --- class DeepResearchAgent: def __init__( self, llm: Any, browser_config: Dict[str, Any], mcp_server_config: Optional[Dict[str, Any]] = None, ): """ Initializes the DeepSearchAgent. Args: llm: The Langchain compatible language model instance. browser_config: Configuration dictionary for the BrowserUseAgent tool. Example: {"headless": True, "window_width": 1280, ...} mcp_server_config: Optional configuration for the MCP client. """ self.llm = llm self.browser_config = browser_config self.mcp_server_config = mcp_server_config self.mcp_client = None self.stopped = False self.graph = self._compile_graph() self.current_task_id: Optional[str] = None self.stop_event: Optional[threading.Event] = None self.runner: Optional[asyncio.Task] = None # To hold the asyncio task for run async def _setup_tools( self, task_id: str, stop_event: threading.Event, max_parallel_browsers: int = 1 ) -> List[Tool]: """Sets up the basic tools (File I/O) and optional MCP tools.""" tools = [ WriteFileTool(), ReadFileTool(), ListDirectoryTool(), ] # Basic file operations browser_use_tool = create_browser_search_tool( llm=self.llm, browser_config=self.browser_config, task_id=task_id, stop_event=stop_event, max_parallel_browsers=max_parallel_browsers, ) tools += [browser_use_tool] # Add MCP tools if config is provided if self.mcp_server_config: try: logger.info("Setting up MCP client and tools...") if not self.mcp_client: self.mcp_client = await setup_mcp_client_and_tools( self.mcp_server_config ) mcp_tools = self.mcp_client.get_tools() logger.info(f"Loaded {len(mcp_tools)} MCP tools.") tools.extend(mcp_tools) except Exception as e: logger.error(f"Failed to set up MCP tools: {e}", exc_info=True) elif self.mcp_server_config: logger.warning( "MCP server config provided, but setup function unavailable." ) tools_map = {tool.name: tool for tool in tools} return tools_map.values() async def close_mcp_client(self): if self.mcp_client: await self.mcp_client.__aexit__(None, None, None) self.mcp_client = None def _compile_graph(self) -> StateGraph: """Compiles the Langgraph state machine.""" workflow = StateGraph(DeepResearchState) # Add nodes workflow.add_node("plan_research", planning_node) workflow.add_node("execute_research", research_execution_node) workflow.add_node("synthesize_report", synthesis_node) workflow.add_node( "end_run", lambda state: logger.info("--- Reached End Run Node ---") or {} ) # Simple end node # Define edges workflow.set_entry_point("plan_research") workflow.add_edge( "plan_research", "execute_research" ) # Always execute after planning # Conditional edge after execution workflow.add_conditional_edges( "execute_research", should_continue, { "execute_research": "execute_research", # Loop back if more steps "synthesize_report": "synthesize_report", # Move to synthesis if done "end_run": "end_run", # End if stop requested or error }, ) workflow.add_edge("synthesize_report", "end_run") # End after synthesis app = workflow.compile() return app async def run( self, topic: str, task_id: Optional[str] = None, save_dir: str = "./tmp/deep_research", max_parallel_browsers: int = 1, ) -> Dict[str, Any]: """ Starts the deep research process (Async Generator Version). Args: topic: The research topic. task_id: Optional existing task ID to resume. If None, a new ID is generated. Yields: Intermediate state updates or messages during execution. """ if self.runner and not self.runner.done(): logger.warning( "Agent is already running. Please stop the current task first." ) # Return an error status instead of yielding return { "status": "error", "message": "Agent already running.", "task_id": self.current_task_id, } self.current_task_id = task_id if task_id else str(uuid.uuid4()) safe_root_dir = "./tmp/deep_research" normalized_save_dir = os.path.normpath(save_dir) if not normalized_save_dir.startswith(os.path.abspath(safe_root_dir)): logger.warning(f"Unsafe save_dir detected: {save_dir}. Using default directory.") normalized_save_dir = os.path.abspath(safe_root_dir) output_dir = os.path.join(normalized_save_dir, self.current_task_id) os.makedirs(output_dir, exist_ok=True) logger.info( f"[AsyncGen] Starting research task ID: {self.current_task_id} for topic: '{topic}'" ) logger.info(f"[AsyncGen] Output directory: {output_dir}") self.stop_event = threading.Event() _AGENT_STOP_FLAGS[self.current_task_id] = self.stop_event agent_tools = await self._setup_tools( self.current_task_id, self.stop_event, max_parallel_browsers ) initial_state: DeepResearchState = { "task_id": self.current_task_id, "topic": topic, "research_plan": [], "search_results": [], "messages": [], "llm": self.llm, "tools": agent_tools, "output_dir": Path(output_dir), "browser_config": self.browser_config, "final_report": None, "current_category_index": 0, "current_task_index_in_category": 0, "stop_requested": False, "error_message": None, } if task_id: logger.info(f"Attempting to resume task {task_id}...") loaded_state = _load_previous_state(task_id, output_dir) initial_state.update(loaded_state) if loaded_state.get("research_plan"): logger.info( f"Resuming with {len(loaded_state['research_plan'])} plan categories " f"and {len(loaded_state.get('search_results', []))} existing results. " f"Next task: Cat {initial_state['current_category_index']}, Task {initial_state['current_task_index_in_category']}" ) initial_state["topic"] = ( topic # Allow overriding topic even when resuming? Or use stored topic? Let's use new one. ) else: logger.warning( f"Resume requested for {task_id}, but no previous plan found. Starting fresh." ) # --- Execute Graph using ainvoke --- final_state = None status = "unknown" message = None try: logger.info(f"Invoking graph execution for task {self.current_task_id}...") self.runner = asyncio.create_task(self.graph.ainvoke(initial_state)) final_state = await self.runner logger.info(f"Graph execution finished for task {self.current_task_id}.") # Determine status based on final state if self.stop_event and self.stop_event.is_set(): status = "stopped" message = "Research process was stopped by request." logger.info(message) elif final_state and final_state.get("error_message"): status = "error" message = final_state["error_message"] logger.error(f"Graph execution completed with error: {message}") elif final_state and final_state.get("final_report"): status = "completed" message = "Research process completed successfully." logger.info(message) else: # If it ends without error/report (e.g., empty plan, stopped before synthesis) status = "finished_incomplete" message = "Research process finished, but may be incomplete (no final report generated)." logger.warning(message) except asyncio.CancelledError: status = "cancelled" message = f"Agent run task cancelled for {self.current_task_id}." logger.info(message) # final_state will remain None or the state before cancellation if checkpointing was used except Exception as e: status = "error" message = f"Unhandled error during graph execution for {self.current_task_id}: {e}" logger.error(message, exc_info=True) # final_state will remain None or the state before the error finally: logger.info(f"Cleaning up resources for task {self.current_task_id}") task_id_to_clean = self.current_task_id self.stop_event = None self.current_task_id = None self.runner = None # Mark runner as finished if self.mcp_client: await self.mcp_client.__aexit__(None, None, None) # Return a result dictionary including the status and the final state if available return { "status": status, "message": message, "task_id": task_id_to_clean, # Use the stored task_id "final_state": final_state if final_state else {}, # Return the final state dict } async def _stop_lingering_browsers(self, task_id): """Attempts to stop any BrowserUseAgent instances associated with the task_id.""" keys_to_stop = [ key for key in _BROWSER_AGENT_INSTANCES if key.startswith(f"{task_id}_") ] if not keys_to_stop: return logger.warning( f"Found {len(keys_to_stop)} potentially lingering browser agents for task {task_id}. Attempting stop..." ) for key in keys_to_stop: agent_instance = _BROWSER_AGENT_INSTANCES.get(key) try: if agent_instance: # Assuming BU agent has an async stop method await agent_instance.stop() logger.info(f"Called stop() on browser agent instance {key}") except Exception as e: logger.error( f"Error calling stop() on browser agent instance {key}: {e}" ) async def stop(self): """Signals the currently running agent task to stop.""" if not self.current_task_id or not self.stop_event: logger.info("No agent task is currently running.") return logger.info(f"Stop requested for task ID: {self.current_task_id}") self.stop_event.set() # Signal the stop event self.stopped = True await self._stop_lingering_browsers(self.current_task_id) def close(self): self.stopped = False ================================================ FILE: src/browser/__init__.py ================================================ ================================================ FILE: src/browser/custom_browser.py ================================================ import asyncio import pdb from playwright.async_api import Browser as PlaywrightBrowser from playwright.async_api import ( BrowserContext as PlaywrightBrowserContext, ) from playwright.async_api import ( Playwright, async_playwright, ) from browser_use.browser.browser import Browser, IN_DOCKER from browser_use.browser.context import BrowserContext, BrowserContextConfig from playwright.async_api import BrowserContext as PlaywrightBrowserContext import logging from browser_use.browser.chrome import ( CHROME_ARGS, CHROME_DETERMINISTIC_RENDERING_ARGS, CHROME_DISABLE_SECURITY_ARGS, CHROME_DOCKER_ARGS, CHROME_HEADLESS_ARGS, ) from browser_use.browser.context import BrowserContext, BrowserContextConfig from browser_use.browser.utils.screen_resolution import get_screen_resolution, get_window_adjustments from browser_use.utils import time_execution_async import socket from .custom_context import CustomBrowserContext logger = logging.getLogger(__name__) class CustomBrowser(Browser): async def new_context(self, config: BrowserContextConfig | None = None) -> CustomBrowserContext: """Create a browser context""" browser_config = self.config.model_dump() if self.config else {} context_config = config.model_dump() if config else {} merged_config = {**browser_config, **context_config} return CustomBrowserContext(config=BrowserContextConfig(**merged_config), browser=self) async def _setup_builtin_browser(self, playwright: Playwright) -> PlaywrightBrowser: """Sets up and returns a Playwright Browser instance with anti-detection measures.""" assert self.config.browser_binary_path is None, 'browser_binary_path should be None if trying to use the builtin browsers' # Use the configured window size from new_context_config if available if ( not self.config.headless and hasattr(self.config, 'new_context_config') and hasattr(self.config.new_context_config, 'window_width') and hasattr(self.config.new_context_config, 'window_height') ): screen_size = { 'width': self.config.new_context_config.window_width, 'height': self.config.new_context_config.window_height, } offset_x, offset_y = get_window_adjustments() elif self.config.headless: screen_size = {'width': 1920, 'height': 1080} offset_x, offset_y = 0, 0 else: screen_size = get_screen_resolution() offset_x, offset_y = get_window_adjustments() chrome_args = { f'--remote-debugging-port={self.config.chrome_remote_debugging_port}', *CHROME_ARGS, *(CHROME_DOCKER_ARGS if IN_DOCKER else []), *(CHROME_HEADLESS_ARGS if self.config.headless else []), *(CHROME_DISABLE_SECURITY_ARGS if self.config.disable_security else []), *(CHROME_DETERMINISTIC_RENDERING_ARGS if self.config.deterministic_rendering else []), f'--window-position={offset_x},{offset_y}', f'--window-size={screen_size["width"]},{screen_size["height"]}', *self.config.extra_browser_args, } # check if chrome remote debugging port is already taken, # if so remove the remote-debugging-port arg to prevent conflicts with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: if s.connect_ex(('localhost', self.config.chrome_remote_debugging_port)) == 0: chrome_args.remove(f'--remote-debugging-port={self.config.chrome_remote_debugging_port}') browser_class = getattr(playwright, self.config.browser_class) args = { 'chromium': list(chrome_args), 'firefox': [ *{ '-no-remote', *self.config.extra_browser_args, } ], 'webkit': [ *{ '--no-startup-window', *self.config.extra_browser_args, } ], } browser = await browser_class.launch( channel='chromium', # https://github.com/microsoft/playwright/issues/33566 headless=self.config.headless, args=args[self.config.browser_class], proxy=self.config.proxy.model_dump() if self.config.proxy else None, handle_sigterm=False, handle_sigint=False, ) return browser ================================================ FILE: src/browser/custom_context.py ================================================ import json import logging import os from browser_use.browser.browser import Browser, IN_DOCKER from browser_use.browser.context import BrowserContext, BrowserContextConfig from playwright.async_api import Browser as PlaywrightBrowser from playwright.async_api import BrowserContext as PlaywrightBrowserContext from typing import Optional from browser_use.browser.context import BrowserContextState logger = logging.getLogger(__name__) class CustomBrowserContext(BrowserContext): def __init__( self, browser: 'Browser', config: BrowserContextConfig | None = None, state: Optional[BrowserContextState] = None, ): super(CustomBrowserContext, self).__init__(browser=browser, config=config, state=state) ================================================ FILE: src/controller/__init__.py ================================================ ================================================ FILE: src/controller/custom_controller.py ================================================ import pdb import pyperclip from typing import Optional, Type, Callable, Dict, Any, Union, Awaitable, TypeVar from pydantic import BaseModel from browser_use.agent.views import ActionResult from browser_use.browser.context import BrowserContext from browser_use.controller.service import Controller, DoneAction from browser_use.controller.registry.service import Registry, RegisteredAction from main_content_extractor import MainContentExtractor from browser_use.controller.views import ( ClickElementAction, DoneAction, ExtractPageContentAction, GoToUrlAction, InputTextAction, OpenTabAction, ScrollAction, SearchGoogleAction, SendKeysAction, SwitchTabAction, ) import logging import inspect import asyncio import os from langchain_core.language_models.chat_models import BaseChatModel from browser_use.agent.views import ActionModel, ActionResult from src.utils.mcp_client import create_tool_param_model, setup_mcp_client_and_tools from browser_use.utils import time_execution_sync logger = logging.getLogger(__name__) Context = TypeVar('Context') class CustomController(Controller): def __init__(self, exclude_actions: list[str] = [], output_model: Optional[Type[BaseModel]] = None, ask_assistant_callback: Optional[Union[Callable[[str, BrowserContext], Dict[str, Any]], Callable[ [str, BrowserContext], Awaitable[Dict[str, Any]]]]] = None, ): super().__init__(exclude_actions=exclude_actions, output_model=output_model) self._register_custom_actions() self.ask_assistant_callback = ask_assistant_callback self.mcp_client = None self.mcp_server_config = None def _register_custom_actions(self): """Register all custom browser actions""" @self.registry.action( "When executing tasks, prioritize autonomous completion. However, if you encounter a definitive blocker " "that prevents you from proceeding independently – such as needing credentials you don't possess, " "requiring subjective human judgment, needing a physical action performed, encountering complex CAPTCHAs, " "or facing limitations in your capabilities – you must request human assistance." ) async def ask_for_assistant(query: str, browser: BrowserContext): if self.ask_assistant_callback: if inspect.iscoroutinefunction(self.ask_assistant_callback): user_response = await self.ask_assistant_callback(query, browser) else: user_response = self.ask_assistant_callback(query, browser) msg = f"AI ask: {query}. User response: {user_response['response']}" logger.info(msg) return ActionResult(extracted_content=msg, include_in_memory=True) else: return ActionResult(extracted_content="Human cannot help you. Please try another way.", include_in_memory=True) @self.registry.action( 'Upload file to interactive element with file path ', ) async def upload_file(index: int, path: str, browser: BrowserContext, available_file_paths: list[str]): if path not in available_file_paths: return ActionResult(error=f'File path {path} is not available') if not os.path.exists(path): return ActionResult(error=f'File {path} does not exist') dom_el = await browser.get_dom_element_by_index(index) file_upload_dom_el = dom_el.get_file_upload_element() if file_upload_dom_el is None: msg = f'No file upload element found at index {index}' logger.info(msg) return ActionResult(error=msg) file_upload_el = await browser.get_locate_element(file_upload_dom_el) if file_upload_el is None: msg = f'No file upload element found at index {index}' logger.info(msg) return ActionResult(error=msg) try: await file_upload_el.set_input_files(path) msg = f'Successfully uploaded file to index {index}' logger.info(msg) return ActionResult(extracted_content=msg, include_in_memory=True) except Exception as e: msg = f'Failed to upload file to index {index}: {str(e)}' logger.info(msg) return ActionResult(error=msg) @time_execution_sync('--act') async def act( self, action: ActionModel, browser_context: Optional[BrowserContext] = None, # page_extraction_llm: Optional[BaseChatModel] = None, sensitive_data: Optional[Dict[str, str]] = None, available_file_paths: Optional[list[str]] = None, # context: Context | None = None, ) -> ActionResult: """Execute an action""" try: for action_name, params in action.model_dump(exclude_unset=True).items(): if params is not None: if action_name.startswith("mcp"): # this is a mcp tool logger.debug(f"Invoke MCP tool: {action_name}") mcp_tool = self.registry.registry.actions.get(action_name).function result = await mcp_tool.ainvoke(params) else: result = await self.registry.execute_action( action_name, params, browser=browser_context, page_extraction_llm=page_extraction_llm, sensitive_data=sensitive_data, available_file_paths=available_file_paths, context=context, ) if isinstance(result, str): return ActionResult(extracted_content=result) elif isinstance(result, ActionResult): return result elif result is None: return ActionResult() else: raise ValueError(f'Invalid action result type: {type(result)} of {result}') return ActionResult() except Exception as e: raise e async def setup_mcp_client(self, mcp_server_config: Optional[Dict[str, Any]] = None): self.mcp_server_config = mcp_server_config if self.mcp_server_config: self.mcp_client = await setup_mcp_client_and_tools(self.mcp_server_config) self.register_mcp_tools() def register_mcp_tools(self): """ Register the MCP tools used by this controller. """ if self.mcp_client: for server_name in self.mcp_client.server_name_to_tools: for tool in self.mcp_client.server_name_to_tools[server_name]: tool_name = f"mcp.{server_name}.{tool.name}" self.registry.registry.actions[tool_name] = RegisteredAction( name=tool_name, description=tool.description, function=tool, param_model=create_tool_param_model(tool), ) logger.info(f"Add mcp tool: {tool_name}") logger.debug( f"Registered {len(self.mcp_client.server_name_to_tools[server_name])} mcp tools for {server_name}") else: logger.warning(f"MCP client not started.") async def close_mcp_client(self): if self.mcp_client: await self.mcp_client.__aexit__(None, None, None) ================================================ FILE: src/utils/__init__.py ================================================ ================================================ FILE: src/utils/config.py ================================================ PROVIDER_DISPLAY_NAMES = { "openai": "OpenAI", "azure_openai": "Azure OpenAI", "anthropic": "Anthropic", "deepseek": "DeepSeek", "google": "Google", "alibaba": "Alibaba", "moonshot": "MoonShot", "unbound": "Unbound AI", "ibm": "IBM", "grok": "Grok", } # Predefined model names for common providers model_names = { "anthropic": ["claude-3-5-sonnet-20241022", "claude-3-5-sonnet-20240620", "claude-3-opus-20240229"], "openai": ["gpt-4o", "gpt-4", "gpt-3.5-turbo", "o3-mini"], "deepseek": ["deepseek-chat", "deepseek-reasoner"], "google": ["gemini-2.0-flash", "gemini-2.0-flash-thinking-exp", "gemini-1.5-flash-latest", "gemini-1.5-flash-8b-latest", "gemini-2.0-flash-thinking-exp-01-21", "gemini-2.0-pro-exp-02-05", "gemini-2.5-pro-preview-03-25", "gemini-2.5-flash-preview-04-17"], "ollama": ["qwen2.5:7b", "qwen2.5:14b", "qwen2.5:32b", "qwen2.5-coder:14b", "qwen2.5-coder:32b", "llama2:7b", "deepseek-r1:14b", "deepseek-r1:32b"], "azure_openai": ["gpt-4o", "gpt-4", "gpt-3.5-turbo"], "mistral": ["pixtral-large-latest", "mistral-large-latest", "mistral-small-latest", "ministral-8b-latest"], "alibaba": ["qwen-plus", "qwen-max", "qwen-vl-max", "qwen-vl-plus", "qwen-turbo", "qwen-long"], "moonshot": ["moonshot-v1-32k-vision-preview", "moonshot-v1-8k-vision-preview"], "unbound": ["gemini-2.0-flash", "gpt-4o-mini", "gpt-4o", "gpt-4.5-preview"], "grok": [ "grok-3", "grok-3-fast", "grok-3-mini", "grok-3-mini-fast", "grok-2-vision", "grok-2-image", "grok-2", ], "siliconflow": [ "deepseek-ai/DeepSeek-R1", "deepseek-ai/DeepSeek-V3", "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", "deepseek-ai/DeepSeek-V2.5", "deepseek-ai/deepseek-vl2", "Qwen/Qwen2.5-72B-Instruct-128K", "Qwen/Qwen2.5-72B-Instruct", "Qwen/Qwen2.5-32B-Instruct", "Qwen/Qwen2.5-14B-Instruct", "Qwen/Qwen2.5-7B-Instruct", "Qwen/Qwen2.5-Coder-32B-Instruct", "Qwen/Qwen2.5-Coder-7B-Instruct", "Qwen/Qwen2-7B-Instruct", "Qwen/Qwen2-1.5B-Instruct", "Qwen/QwQ-32B-Preview", "Qwen/Qwen2-VL-72B-Instruct", "Qwen/Qwen2.5-VL-32B-Instruct", "Qwen/Qwen2.5-VL-72B-Instruct", "TeleAI/TeleChat2", "THUDM/glm-4-9b-chat", "Vendor-A/Qwen/Qwen2.5-72B-Instruct", "internlm/internlm2_5-7b-chat", "internlm/internlm2_5-20b-chat", "Pro/Qwen/Qwen2.5-7B-Instruct", "Pro/Qwen/Qwen2-7B-Instruct", "Pro/Qwen/Qwen2-1.5B-Instruct", "Pro/THUDM/chatglm3-6b", "Pro/THUDM/glm-4-9b-chat", ], "ibm": ["ibm/granite-vision-3.1-2b-preview", "meta-llama/llama-4-maverick-17b-128e-instruct-fp8", "meta-llama/llama-3-2-90b-vision-instruct"], "modelscope":[ "Qwen/Qwen2.5-Coder-32B-Instruct", "Qwen/Qwen2.5-Coder-14B-Instruct", "Qwen/Qwen2.5-Coder-7B-Instruct", "Qwen/Qwen2.5-72B-Instruct", "Qwen/Qwen2.5-32B-Instruct", "Qwen/Qwen2.5-14B-Instruct", "Qwen/Qwen2.5-7B-Instruct", "Qwen/QwQ-32B-Preview", "Qwen/Qwen2.5-VL-3B-Instruct", "Qwen/Qwen2.5-VL-7B-Instruct", "Qwen/Qwen2.5-VL-32B-Instruct", "Qwen/Qwen2.5-VL-72B-Instruct", "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B", "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B", "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B", "deepseek-ai/DeepSeek-R1", "deepseek-ai/DeepSeek-V3", "Qwen/Qwen3-1.7B", "Qwen/Qwen3-4B", "Qwen/Qwen3-8B", "Qwen/Qwen3-14B", "Qwen/Qwen3-30B-A3B", "Qwen/Qwen3-32B", "Qwen/Qwen3-235B-A22B", ], } ================================================ FILE: src/utils/llm_provider.py ================================================ from openai import OpenAI import pdb from langchain_openai import ChatOpenAI from langchain_core.globals import get_llm_cache from langchain_core.language_models.base import ( BaseLanguageModel, LangSmithParams, LanguageModelInput, ) import os from langchain_core.load import dumpd, dumps from langchain_core.messages import ( AIMessage, SystemMessage, AnyMessage, BaseMessage, BaseMessageChunk, HumanMessage, convert_to_messages, message_chunk_to_message, ) from langchain_core.outputs import ( ChatGeneration, ChatGenerationChunk, ChatResult, LLMResult, RunInfo, ) from langchain_ollama import ChatOllama from langchain_core.output_parsers.base import OutputParserLike from langchain_core.runnables import Runnable, RunnableConfig from langchain_core.tools import BaseTool from typing import ( TYPE_CHECKING, Any, Callable, Literal, Optional, Union, cast, List, ) from langchain_anthropic import ChatAnthropic from langchain_mistralai import ChatMistralAI from langchain_google_genai import ChatGoogleGenerativeAI from langchain_ollama import ChatOllama from langchain_openai import AzureChatOpenAI, ChatOpenAI from langchain_ibm import ChatWatsonx from langchain_aws import ChatBedrock from pydantic import SecretStr from src.utils import config class DeepSeekR1ChatOpenAI(ChatOpenAI): def __init__(self, *args: Any, **kwargs: Any) -> None: super().__init__(*args, **kwargs) self.client = OpenAI( base_url=kwargs.get("base_url"), api_key=kwargs.get("api_key") ) async def ainvoke( self, input: LanguageModelInput, config: Optional[RunnableConfig] = None, *, stop: Optional[list[str]] = None, **kwargs: Any, ) -> AIMessage: message_history = [] for input_ in input: if isinstance(input_, SystemMessage): message_history.append({"role": "system", "content": input_.content}) elif isinstance(input_, AIMessage): message_history.append({"role": "assistant", "content": input_.content}) else: message_history.append({"role": "user", "content": input_.content}) response = self.client.chat.completions.create( model=self.model_name, messages=message_history ) reasoning_content = response.choices[0].message.reasoning_content content = response.choices[0].message.content return AIMessage(content=content, reasoning_content=reasoning_content) def invoke( self, input: LanguageModelInput, config: Optional[RunnableConfig] = None, *, stop: Optional[list[str]] = None, **kwargs: Any, ) -> AIMessage: message_history = [] for input_ in input: if isinstance(input_, SystemMessage): message_history.append({"role": "system", "content": input_.content}) elif isinstance(input_, AIMessage): message_history.append({"role": "assistant", "content": input_.content}) else: message_history.append({"role": "user", "content": input_.content}) response = self.client.chat.completions.create( model=self.model_name, messages=message_history ) reasoning_content = response.choices[0].message.reasoning_content content = response.choices[0].message.content return AIMessage(content=content, reasoning_content=reasoning_content) class DeepSeekR1ChatOllama(ChatOllama): async def ainvoke( self, input: LanguageModelInput, config: Optional[RunnableConfig] = None, *, stop: Optional[list[str]] = None, **kwargs: Any, ) -> AIMessage: org_ai_message = await super().ainvoke(input=input) org_content = org_ai_message.content reasoning_content = org_content.split("")[0].replace("", "") content = org_content.split("")[1] if "**JSON Response:**" in content: content = content.split("**JSON Response:**")[-1] return AIMessage(content=content, reasoning_content=reasoning_content) def invoke( self, input: LanguageModelInput, config: Optional[RunnableConfig] = None, *, stop: Optional[list[str]] = None, **kwargs: Any, ) -> AIMessage: org_ai_message = super().invoke(input=input) org_content = org_ai_message.content reasoning_content = org_content.split("")[0].replace("", "") content = org_content.split("")[1] if "**JSON Response:**" in content: content = content.split("**JSON Response:**")[-1] return AIMessage(content=content, reasoning_content=reasoning_content) def get_llm_model(provider: str, **kwargs): """ Get LLM model :param provider: LLM provider :param kwargs: :return: """ if provider not in ["ollama", "bedrock"]: env_var = f"{provider.upper()}_API_KEY" api_key = kwargs.get("api_key", "") or os.getenv(env_var, "") if not api_key: provider_display = config.PROVIDER_DISPLAY_NAMES.get(provider, provider.upper()) error_msg = f"💥 {provider_display} API key not found! 🔑 Please set the `{env_var}` environment variable or provide it in the UI." raise ValueError(error_msg) kwargs["api_key"] = api_key if provider == "anthropic": if not kwargs.get("base_url", ""): base_url = "https://api.anthropic.com" else: base_url = kwargs.get("base_url") return ChatAnthropic( model=kwargs.get("model_name", "claude-3-5-sonnet-20241022"), temperature=kwargs.get("temperature", 0.0), base_url=base_url, api_key=api_key, ) elif provider == 'mistral': if not kwargs.get("base_url", ""): base_url = os.getenv("MISTRAL_ENDPOINT", "https://api.mistral.ai/v1") else: base_url = kwargs.get("base_url") if not kwargs.get("api_key", ""): api_key = os.getenv("MISTRAL_API_KEY", "") else: api_key = kwargs.get("api_key") return ChatMistralAI( model=kwargs.get("model_name", "mistral-large-latest"), temperature=kwargs.get("temperature", 0.0), base_url=base_url, api_key=api_key, ) elif provider == "openai": if not kwargs.get("base_url", ""): base_url = os.getenv("OPENAI_ENDPOINT", "https://api.openai.com/v1") else: base_url = kwargs.get("base_url") return ChatOpenAI( model=kwargs.get("model_name", "gpt-4o"), temperature=kwargs.get("temperature", 0.0), base_url=base_url, api_key=api_key, ) elif provider == "grok": if not kwargs.get("base_url", ""): base_url = os.getenv("GROK_ENDPOINT", "https://api.x.ai/v1") else: base_url = kwargs.get("base_url") return ChatOpenAI( model=kwargs.get("model_name", "grok-3"), temperature=kwargs.get("temperature", 0.0), base_url=base_url, api_key=api_key, ) elif provider == "deepseek": if not kwargs.get("base_url", ""): base_url = os.getenv("DEEPSEEK_ENDPOINT", "") else: base_url = kwargs.get("base_url") if kwargs.get("model_name", "deepseek-chat") == "deepseek-reasoner": return DeepSeekR1ChatOpenAI( model=kwargs.get("model_name", "deepseek-reasoner"), temperature=kwargs.get("temperature", 0.0), base_url=base_url, api_key=api_key, ) else: return ChatOpenAI( model=kwargs.get("model_name", "deepseek-chat"), temperature=kwargs.get("temperature", 0.0), base_url=base_url, api_key=api_key, ) elif provider == "google": return ChatGoogleGenerativeAI( model=kwargs.get("model_name", "gemini-2.0-flash-exp"), temperature=kwargs.get("temperature", 0.0), api_key=api_key, ) elif provider == "ollama": if not kwargs.get("base_url", ""): base_url = os.getenv("OLLAMA_ENDPOINT", "http://localhost:11434") else: base_url = kwargs.get("base_url") if "deepseek-r1" in kwargs.get("model_name", "qwen2.5:7b"): return DeepSeekR1ChatOllama( model=kwargs.get("model_name", "deepseek-r1:14b"), temperature=kwargs.get("temperature", 0.0), num_ctx=kwargs.get("num_ctx", 32000), base_url=base_url, ) else: return ChatOllama( model=kwargs.get("model_name", "qwen2.5:7b"), temperature=kwargs.get("temperature", 0.0), num_ctx=kwargs.get("num_ctx", 32000), num_predict=kwargs.get("num_predict", 1024), base_url=base_url, ) elif provider == "azure_openai": if not kwargs.get("base_url", ""): base_url = os.getenv("AZURE_OPENAI_ENDPOINT", "") else: base_url = kwargs.get("base_url") api_version = kwargs.get("api_version", "") or os.getenv("AZURE_OPENAI_API_VERSION", "2025-01-01-preview") return AzureChatOpenAI( model=kwargs.get("model_name", "gpt-4o"), temperature=kwargs.get("temperature", 0.0), api_version=api_version, azure_endpoint=base_url, api_key=api_key, ) elif provider == "alibaba": if not kwargs.get("base_url", ""): base_url = os.getenv("ALIBABA_ENDPOINT", "https://dashscope.aliyuncs.com/compatible-mode/v1") else: base_url = kwargs.get("base_url") return ChatOpenAI( model=kwargs.get("model_name", "qwen-plus"), temperature=kwargs.get("temperature", 0.0), base_url=base_url, api_key=api_key, ) elif provider == "ibm": parameters = { "temperature": kwargs.get("temperature", 0.0), "max_tokens": kwargs.get("num_ctx", 32000) } if not kwargs.get("base_url", ""): base_url = os.getenv("IBM_ENDPOINT", "https://us-south.ml.cloud.ibm.com") else: base_url = kwargs.get("base_url") return ChatWatsonx( model_id=kwargs.get("model_name", "ibm/granite-vision-3.1-2b-preview"), url=base_url, project_id=os.getenv("IBM_PROJECT_ID"), apikey=os.getenv("IBM_API_KEY"), params=parameters ) elif provider == "moonshot": return ChatOpenAI( model=kwargs.get("model_name", "moonshot-v1-32k-vision-preview"), temperature=kwargs.get("temperature", 0.0), base_url=os.getenv("MOONSHOT_ENDPOINT"), api_key=os.getenv("MOONSHOT_API_KEY"), ) elif provider == "unbound": return ChatOpenAI( model=kwargs.get("model_name", "gpt-4o-mini"), temperature=kwargs.get("temperature", 0.0), base_url=os.getenv("UNBOUND_ENDPOINT", "https://api.getunbound.ai"), api_key=api_key, ) elif provider == "siliconflow": if not kwargs.get("api_key", ""): api_key = os.getenv("SiliconFLOW_API_KEY", "") else: api_key = kwargs.get("api_key") if not kwargs.get("base_url", ""): base_url = os.getenv("SiliconFLOW_ENDPOINT", "") else: base_url = kwargs.get("base_url") return ChatOpenAI( api_key=api_key, base_url=base_url, model_name=kwargs.get("model_name", "Qwen/QwQ-32B"), temperature=kwargs.get("temperature", 0.0), ) elif provider == "modelscope": if not kwargs.get("api_key", ""): api_key = os.getenv("MODELSCOPE_API_KEY", "") else: api_key = kwargs.get("api_key") if not kwargs.get("base_url", ""): base_url = os.getenv("MODELSCOPE_ENDPOINT", "") else: base_url = kwargs.get("base_url") return ChatOpenAI( api_key=api_key, base_url=base_url, model_name=kwargs.get("model_name", "Qwen/QwQ-32B"), temperature=kwargs.get("temperature", 0.0), extra_body = {"enable_thinking": False} ) else: raise ValueError(f"Unsupported provider: {provider}") ================================================ FILE: src/utils/mcp_client.py ================================================ import inspect import logging import uuid from datetime import date, datetime, time from enum import Enum from typing import Any, Dict, List, Optional, Set, Type, Union, get_type_hints from browser_use.controller.registry.views import ActionModel from langchain.tools import BaseTool from langchain_mcp_adapters.client import MultiServerMCPClient from pydantic import BaseModel, Field, create_model from pydantic.v1 import BaseModel, Field logger = logging.getLogger(__name__) async def setup_mcp_client_and_tools(mcp_server_config: Dict[str, Any]) -> Optional[MultiServerMCPClient]: """ Initializes the MultiServerMCPClient, connects to servers, fetches tools, filters them, and returns a flat list of usable tools and the client instance. Returns: A tuple containing: - list[BaseTool]: The filtered list of usable LangChain tools. - MultiServerMCPClient | None: The initialized and started client instance, or None on failure. """ logger.info("Initializing MultiServerMCPClient...") if not mcp_server_config: logger.error("No MCP server configuration provided.") return None try: if "mcpServers" in mcp_server_config: mcp_server_config = mcp_server_config["mcpServers"] client = MultiServerMCPClient(mcp_server_config) await client.__aenter__() return client except Exception as e: logger.error(f"Failed to setup MCP client or fetch tools: {e}", exc_info=True) return None def create_tool_param_model(tool: BaseTool) -> Type[BaseModel]: """Creates a Pydantic model from a LangChain tool's schema""" # Get tool schema information json_schema = tool.args_schema tool_name = tool.name # If the tool already has a schema defined, convert it to a new param_model if json_schema is not None: # Create new parameter model params = {} # Process properties if they exist if 'properties' in json_schema: # Find required fields required_fields: Set[str] = set(json_schema.get('required', [])) for prop_name, prop_details in json_schema['properties'].items(): field_type = resolve_type(prop_details, f"{tool_name}_{prop_name}") # Check if parameter is required is_required = prop_name in required_fields # Get default value and description default_value = prop_details.get('default', ... if is_required else None) description = prop_details.get('description', '') # Add field constraints field_kwargs = {'default': default_value} if description: field_kwargs['description'] = description # Add additional constraints if present if 'minimum' in prop_details: field_kwargs['ge'] = prop_details['minimum'] if 'maximum' in prop_details: field_kwargs['le'] = prop_details['maximum'] if 'minLength' in prop_details: field_kwargs['min_length'] = prop_details['minLength'] if 'maxLength' in prop_details: field_kwargs['max_length'] = prop_details['maxLength'] if 'pattern' in prop_details: field_kwargs['pattern'] = prop_details['pattern'] # Add to parameters dictionary params[prop_name] = (field_type, Field(**field_kwargs)) return create_model( f'{tool_name}_parameters', __base__=ActionModel, **params, # type: ignore ) # If no schema is defined, extract parameters from the _run method run_method = tool._run sig = inspect.signature(run_method) # Get type hints for better type information try: type_hints = get_type_hints(run_method) except Exception: type_hints = {} params = {} for name, param in sig.parameters.items(): # Skip 'self' parameter and any other parameters you want to exclude if name == 'self': continue # Get annotation from type hints if available, otherwise from signature annotation = type_hints.get(name, param.annotation) if annotation == inspect.Parameter.empty: annotation = Any # Use default value if available, otherwise make it required if param.default != param.empty: params[name] = (annotation, param.default) else: params[name] = (annotation, ...) return create_model( f'{tool_name}_parameters', __base__=ActionModel, **params, # type: ignore ) def resolve_type(prop_details: Dict[str, Any], prefix: str = "") -> Any: """Recursively resolves JSON schema type to Python/Pydantic type""" # Handle reference types if '$ref' in prop_details: # In a real application, reference resolution would be needed return Any # Basic type mapping type_mapping = { 'string': str, 'integer': int, 'number': float, 'boolean': bool, 'array': List, 'object': Dict, 'null': type(None), } # Handle formatted strings if prop_details.get('type') == 'string' and 'format' in prop_details: format_mapping = { 'date-time': datetime, 'date': date, 'time': time, 'email': str, 'uri': str, 'url': str, 'uuid': uuid.UUID, 'binary': bytes, } return format_mapping.get(prop_details['format'], str) # Handle enum types if 'enum' in prop_details: enum_values = prop_details['enum'] # Create dynamic enum class with safe names enum_dict = {} for i, v in enumerate(enum_values): # Ensure enum names are valid Python identifiers if isinstance(v, str): key = v.upper().replace(' ', '_').replace('-', '_') if not key.isidentifier(): key = f"VALUE_{i}" else: key = f"VALUE_{i}" enum_dict[key] = v # Only create enum if we have values if enum_dict: return Enum(f"{prefix}_Enum", enum_dict) return str # Fallback # Handle array types if prop_details.get('type') == 'array' and 'items' in prop_details: item_type = resolve_type(prop_details['items'], f"{prefix}_item") return List[item_type] # type: ignore # Handle object types with properties if prop_details.get('type') == 'object' and 'properties' in prop_details: nested_params = {} for nested_name, nested_details in prop_details['properties'].items(): nested_type = resolve_type(nested_details, f"{prefix}_{nested_name}") # Get required field info required_fields = prop_details.get('required', []) is_required = nested_name in required_fields default_value = nested_details.get('default', ... if is_required else None) description = nested_details.get('description', '') field_kwargs = {'default': default_value} if description: field_kwargs['description'] = description nested_params[nested_name] = (nested_type, Field(**field_kwargs)) # Create nested model nested_model = create_model(f"{prefix}_Model", **nested_params) return nested_model # Handle union types (oneOf, anyOf) if 'oneOf' in prop_details or 'anyOf' in prop_details: union_schema = prop_details.get('oneOf') or prop_details.get('anyOf') union_types = [] for i, t in enumerate(union_schema): union_types.append(resolve_type(t, f"{prefix}_{i}")) if union_types: return Union.__getitem__(tuple(union_types)) # type: ignore return Any # Handle allOf (intersection types) if 'allOf' in prop_details: nested_params = {} for i, schema_part in enumerate(prop_details['allOf']): if 'properties' in schema_part: for nested_name, nested_details in schema_part['properties'].items(): nested_type = resolve_type(nested_details, f"{prefix}_allOf_{i}_{nested_name}") # Check if required required_fields = schema_part.get('required', []) is_required = nested_name in required_fields nested_params[nested_name] = (nested_type, ... if is_required else None) # Create composite model if nested_params: composite_model = create_model(f"{prefix}_CompositeModel", **nested_params) return composite_model return Dict # Default to basic types schema_type = prop_details.get('type', 'string') if isinstance(schema_type, list): # Handle multiple types (e.g., ["string", "null"]) non_null_types = [t for t in schema_type if t != 'null'] if non_null_types: primary_type = type_mapping.get(non_null_types[0], Any) if 'null' in schema_type: return Optional[primary_type] # type: ignore return primary_type return Any return type_mapping.get(schema_type, Any) ================================================ FILE: src/utils/utils.py ================================================ import base64 import os import time from pathlib import Path from typing import Dict, Optional import requests import json import gradio as gr import uuid def encode_image(img_path): if not img_path: return None with open(img_path, "rb") as fin: image_data = base64.b64encode(fin.read()).decode("utf-8") return image_data def get_latest_files(directory: str, file_types: list = ['.webm', '.zip']) -> Dict[str, Optional[str]]: """Get the latest recording and trace files""" latest_files: Dict[str, Optional[str]] = {ext: None for ext in file_types} if not os.path.exists(directory): os.makedirs(directory, exist_ok=True) return latest_files for file_type in file_types: try: matches = list(Path(directory).rglob(f"*{file_type}")) if matches: latest = max(matches, key=lambda p: p.stat().st_mtime) # Only return files that are complete (not being written) if time.time() - latest.stat().st_mtime > 1.0: latest_files[file_type] = str(latest) except Exception as e: print(f"Error getting latest {file_type} file: {e}") return latest_files ================================================ FILE: src/webui/__init__.py ================================================ ================================================ FILE: src/webui/components/__init__.py ================================================ ================================================ FILE: src/webui/components/agent_settings_tab.py ================================================ import json import os import gradio as gr from gradio.components import Component from typing import Any, Dict, Optional from src.webui.webui_manager import WebuiManager from src.utils import config import logging from functools import partial logger = logging.getLogger(__name__) def update_model_dropdown(llm_provider): """ Update the model name dropdown with predefined models for the selected provider. """ # Use predefined models for the selected provider if llm_provider in config.model_names: return gr.Dropdown(choices=config.model_names[llm_provider], value=config.model_names[llm_provider][0], interactive=True) else: return gr.Dropdown(choices=[], value="", interactive=True, allow_custom_value=True) async def update_mcp_server(mcp_file: str, webui_manager: WebuiManager): """ Update the MCP server. """ if hasattr(webui_manager, "bu_controller") and webui_manager.bu_controller: logger.warning("⚠️ Close controller because mcp file has changed!") await webui_manager.bu_controller.close_mcp_client() webui_manager.bu_controller = None if not mcp_file or not os.path.exists(mcp_file) or not mcp_file.endswith('.json'): logger.warning(f"{mcp_file} is not a valid MCP file.") return None, gr.update(visible=False) with open(mcp_file, 'r') as f: mcp_server = json.load(f) return json.dumps(mcp_server, indent=2), gr.update(visible=True) def create_agent_settings_tab(webui_manager: WebuiManager): """ Creates an agent settings tab. """ input_components = set(webui_manager.get_components()) tab_components = {} with gr.Group(): with gr.Column(): override_system_prompt = gr.Textbox(label="Override system prompt", lines=4, interactive=True) extend_system_prompt = gr.Textbox(label="Extend system prompt", lines=4, interactive=True) with gr.Group(): mcp_json_file = gr.File(label="MCP server json", interactive=True, file_types=[".json"]) mcp_server_config = gr.Textbox(label="MCP server", lines=6, interactive=True, visible=False) with gr.Group(): with gr.Row(): llm_provider = gr.Dropdown( choices=[provider for provider, model in config.model_names.items()], label="LLM Provider", value=os.getenv("DEFAULT_LLM", "openai"), info="Select LLM provider for LLM", interactive=True ) llm_model_name = gr.Dropdown( label="LLM Model Name", choices=config.model_names[os.getenv("DEFAULT_LLM", "openai")], value=config.model_names[os.getenv("DEFAULT_LLM", "openai")][0], interactive=True, allow_custom_value=True, info="Select a model in the dropdown options or directly type a custom model name" ) with gr.Row(): llm_temperature = gr.Slider( minimum=0.0, maximum=2.0, value=0.6, step=0.1, label="LLM Temperature", info="Controls randomness in model outputs", interactive=True ) use_vision = gr.Checkbox( label="Use Vision", value=True, info="Enable Vision(Input highlighted screenshot into LLM)", interactive=True ) ollama_num_ctx = gr.Slider( minimum=2 ** 8, maximum=2 ** 16, value=16000, step=1, label="Ollama Context Length", info="Controls max context length model needs to handle (less = faster)", visible=False, interactive=True ) with gr.Row(): llm_base_url = gr.Textbox( label="Base URL", value="", info="API endpoint URL (if required)" ) llm_api_key = gr.Textbox( label="API Key", type="password", value="", info="Your API key (leave blank to use .env)" ) with gr.Group(): with gr.Row(): planner_llm_provider = gr.Dropdown( choices=[provider for provider, model in config.model_names.items()], label="Planner LLM Provider", info="Select LLM provider for LLM", value=None, interactive=True ) planner_llm_model_name = gr.Dropdown( label="Planner LLM Model Name", interactive=True, allow_custom_value=True, info="Select a model in the dropdown options or directly type a custom model name" ) with gr.Row(): planner_llm_temperature = gr.Slider( minimum=0.0, maximum=2.0, value=0.6, step=0.1, label="Planner LLM Temperature", info="Controls randomness in model outputs", interactive=True ) planner_use_vision = gr.Checkbox( label="Use Vision(Planner LLM)", value=False, info="Enable Vision(Input highlighted screenshot into LLM)", interactive=True ) planner_ollama_num_ctx = gr.Slider( minimum=2 ** 8, maximum=2 ** 16, value=16000, step=1, label="Ollama Context Length", info="Controls max context length model needs to handle (less = faster)", visible=False, interactive=True ) with gr.Row(): planner_llm_base_url = gr.Textbox( label="Base URL", value="", info="API endpoint URL (if required)" ) planner_llm_api_key = gr.Textbox( label="API Key", type="password", value="", info="Your API key (leave blank to use .env)" ) with gr.Row(): max_steps = gr.Slider( minimum=1, maximum=1000, value=100, step=1, label="Max Run Steps", info="Maximum number of steps the agent will take", interactive=True ) max_actions = gr.Slider( minimum=1, maximum=100, value=10, step=1, label="Max Number of Actions", info="Maximum number of actions the agent will take per step", interactive=True ) with gr.Row(): max_input_tokens = gr.Number( label="Max Input Tokens", value=128000, precision=0, interactive=True ) tool_calling_method = gr.Dropdown( label="Tool Calling Method", value="auto", interactive=True, allow_custom_value=True, choices=['function_calling', 'json_mode', 'raw', 'auto', 'tools', "None"], visible=True ) tab_components.update(dict( override_system_prompt=override_system_prompt, extend_system_prompt=extend_system_prompt, llm_provider=llm_provider, llm_model_name=llm_model_name, llm_temperature=llm_temperature, use_vision=use_vision, ollama_num_ctx=ollama_num_ctx, llm_base_url=llm_base_url, llm_api_key=llm_api_key, planner_llm_provider=planner_llm_provider, planner_llm_model_name=planner_llm_model_name, planner_llm_temperature=planner_llm_temperature, planner_use_vision=planner_use_vision, planner_ollama_num_ctx=planner_ollama_num_ctx, planner_llm_base_url=planner_llm_base_url, planner_llm_api_key=planner_llm_api_key, max_steps=max_steps, max_actions=max_actions, max_input_tokens=max_input_tokens, tool_calling_method=tool_calling_method, mcp_json_file=mcp_json_file, mcp_server_config=mcp_server_config, )) webui_manager.add_components("agent_settings", tab_components) llm_provider.change( fn=lambda x: gr.update(visible=x == "ollama"), inputs=llm_provider, outputs=ollama_num_ctx ) llm_provider.change( lambda provider: update_model_dropdown(provider), inputs=[llm_provider], outputs=[llm_model_name] ) planner_llm_provider.change( fn=lambda x: gr.update(visible=x == "ollama"), inputs=[planner_llm_provider], outputs=[planner_ollama_num_ctx] ) planner_llm_provider.change( lambda provider: update_model_dropdown(provider), inputs=[planner_llm_provider], outputs=[planner_llm_model_name] ) async def update_wrapper(mcp_file): """Wrapper for handle_pause_resume.""" update_dict = await update_mcp_server(mcp_file, webui_manager) yield update_dict mcp_json_file.change( update_wrapper, inputs=[mcp_json_file], outputs=[mcp_server_config, mcp_server_config] ) ================================================ FILE: src/webui/components/browser_settings_tab.py ================================================ import os from distutils.util import strtobool import gradio as gr import logging from gradio.components import Component from src.webui.webui_manager import WebuiManager from src.utils import config logger = logging.getLogger(__name__) async def close_browser(webui_manager: WebuiManager): """ Close browser """ if webui_manager.bu_current_task and not webui_manager.bu_current_task.done(): webui_manager.bu_current_task.cancel() webui_manager.bu_current_task = None if webui_manager.bu_browser_context: logger.info("⚠️ Closing browser context when changing browser config.") await webui_manager.bu_browser_context.close() webui_manager.bu_browser_context = None if webui_manager.bu_browser: logger.info("⚠️ Closing browser when changing browser config.") await webui_manager.bu_browser.close() webui_manager.bu_browser = None def create_browser_settings_tab(webui_manager: WebuiManager): """ Creates a browser settings tab. """ input_components = set(webui_manager.get_components()) tab_components = {} with gr.Group(): with gr.Row(): browser_binary_path = gr.Textbox( label="Browser Binary Path", lines=1, interactive=True, placeholder="e.g. '/Applications/Google\\ Chrome.app/Contents/MacOS/Google\\ Chrome'" ) browser_user_data_dir = gr.Textbox( label="Browser User Data Dir", lines=1, interactive=True, placeholder="Leave it empty if you use your default user data", ) with gr.Group(): with gr.Row(): use_own_browser = gr.Checkbox( label="Use Own Browser", value=bool(strtobool(os.getenv("USE_OWN_BROWSER", "false"))), info="Use your existing browser instance", interactive=True ) keep_browser_open = gr.Checkbox( label="Keep Browser Open", value=bool(strtobool(os.getenv("KEEP_BROWSER_OPEN", "true"))), info="Keep Browser Open between Tasks", interactive=True ) headless = gr.Checkbox( label="Headless Mode", value=False, info="Run browser without GUI", interactive=True ) disable_security = gr.Checkbox( label="Disable Security", value=False, info="Disable browser security", interactive=True ) with gr.Group(): with gr.Row(): window_w = gr.Number( label="Window Width", value=1280, info="Browser window width", interactive=True ) window_h = gr.Number( label="Window Height", value=1100, info="Browser window height", interactive=True ) with gr.Group(): with gr.Row(): cdp_url = gr.Textbox( label="CDP URL", value=os.getenv("BROWSER_CDP", None), info="CDP URL for browser remote debugging", interactive=True, ) wss_url = gr.Textbox( label="WSS URL", info="WSS URL for browser remote debugging", interactive=True, ) with gr.Group(): with gr.Row(): save_recording_path = gr.Textbox( label="Recording Path", placeholder="e.g. ./tmp/record_videos", info="Path to save browser recordings", interactive=True, ) save_trace_path = gr.Textbox( label="Trace Path", placeholder="e.g. ./tmp/traces", info="Path to save Agent traces", interactive=True, ) with gr.Row(): save_agent_history_path = gr.Textbox( label="Agent History Save Path", value="./tmp/agent_history", info="Specify the directory where agent history should be saved.", interactive=True, ) save_download_path = gr.Textbox( label="Save Directory for browser downloads", value="./tmp/downloads", info="Specify the directory where downloaded files should be saved.", interactive=True, ) tab_components.update( dict( browser_binary_path=browser_binary_path, browser_user_data_dir=browser_user_data_dir, use_own_browser=use_own_browser, keep_browser_open=keep_browser_open, headless=headless, disable_security=disable_security, save_recording_path=save_recording_path, save_trace_path=save_trace_path, save_agent_history_path=save_agent_history_path, save_download_path=save_download_path, cdp_url=cdp_url, wss_url=wss_url, window_h=window_h, window_w=window_w, ) ) webui_manager.add_components("browser_settings", tab_components) async def close_wrapper(): """Wrapper for handle_clear.""" await close_browser(webui_manager) headless.change(close_wrapper) keep_browser_open.change(close_wrapper) disable_security.change(close_wrapper) use_own_browser.change(close_wrapper) ================================================ FILE: src/webui/components/browser_use_agent_tab.py ================================================ import asyncio import json import logging import os import uuid from typing import Any, AsyncGenerator, Dict, Optional import gradio as gr # from browser_use.agent.service import Agent from browser_use.agent.views import ( AgentHistoryList, AgentOutput, ) from browser_use.browser.browser import BrowserConfig from browser_use.browser.context import BrowserContext, BrowserContextConfig from browser_use.browser.views import BrowserState from gradio.components import Component from langchain_core.language_models.chat_models import BaseChatModel from src.agent.browser_use.browser_use_agent import BrowserUseAgent from src.browser.custom_browser import CustomBrowser from src.controller.custom_controller import CustomController from src.utils import llm_provider from src.webui.webui_manager import WebuiManager logger = logging.getLogger(__name__) # --- Helper Functions --- (Defined at module level) async def _initialize_llm( provider: Optional[str], model_name: Optional[str], temperature: float, base_url: Optional[str], api_key: Optional[str], num_ctx: Optional[int] = None, ) -> Optional[BaseChatModel]: """Initializes the LLM based on settings. Returns None if provider/model is missing.""" if not provider or not model_name: logger.info("LLM Provider or Model Name not specified, LLM will be None.") return None try: # Use your actual LLM provider logic here logger.info( f"Initializing LLM: Provider={provider}, Model={model_name}, Temp={temperature}" ) # Example using a placeholder function llm = llm_provider.get_llm_model( provider=provider, model_name=model_name, temperature=temperature, base_url=base_url or None, api_key=api_key or None, # Add other relevant params like num_ctx for ollama num_ctx=num_ctx if provider == "ollama" else None, ) return llm except Exception as e: logger.error(f"Failed to initialize LLM: {e}", exc_info=True) gr.Warning( f"Failed to initialize LLM '{model_name}' for provider '{provider}'. Please check settings. Error: {e}" ) return None def _get_config_value( webui_manager: WebuiManager, comp_dict: Dict[gr.components.Component, Any], comp_id_suffix: str, default: Any = None, ) -> Any: """Safely get value from component dictionary using its ID suffix relative to the tab.""" # Assumes component ID format is "tab_name.comp_name" tab_name = "browser_use_agent" # Hardcode or derive if needed comp_id = f"{tab_name}.{comp_id_suffix}" # Need to find the component object first using the ID from the manager try: comp = webui_manager.get_component_by_id(comp_id) return comp_dict.get(comp, default) except KeyError: # Try accessing settings tabs as well for prefix in ["agent_settings", "browser_settings"]: try: comp_id = f"{prefix}.{comp_id_suffix}" comp = webui_manager.get_component_by_id(comp_id) return comp_dict.get(comp, default) except KeyError: continue logger.warning( f"Component with suffix '{comp_id_suffix}' not found in manager for value lookup." ) return default def _format_agent_output(model_output: AgentOutput) -> str: """Formats AgentOutput for display in the chatbot using JSON.""" content = "" if model_output: try: # Directly use model_dump if actions and current_state are Pydantic models action_dump = [ action.model_dump(exclude_none=True) for action in model_output.action ] state_dump = model_output.current_state.model_dump(exclude_none=True) model_output_dump = { "current_state": state_dump, "action": action_dump, } # Dump to JSON string with indentation json_string = json.dumps(model_output_dump, indent=4, ensure_ascii=False) # Wrap in 
 for proper display in HTML
            content = f"
{json_string}
"

        except AttributeError as ae:
            logger.error(
                f"AttributeError during model dump: {ae}. Check if 'action' or 'current_state' or their items support 'model_dump'."
            )
            content = f"
Error: Could not format agent output (AttributeError: {ae}).\nRaw output: {str(model_output)}
"
        except Exception as e:
            logger.error(f"Error formatting agent output: {e}", exc_info=True)
            # Fallback to simple string representation on error
            content = f"
Error formatting agent output.\nRaw output:\n{str(model_output)}
"

    return content.strip()


# --- Updated Callback Implementation ---


async def _handle_new_step(
        webui_manager: WebuiManager, state: BrowserState, output: AgentOutput, step_num: int
):
    """Callback for each step taken by the agent, including screenshot display."""

    # Use the correct chat history attribute name from the user's code
    if not hasattr(webui_manager, "bu_chat_history"):
        logger.error(
            "Attribute 'bu_chat_history' not found in webui_manager! Cannot add chat message."
        )
        # Initialize it maybe? Or raise an error? For now, log and potentially skip chat update.
        webui_manager.bu_chat_history = []  # Initialize if missing (consider if this is the right place)
        # return # Or stop if this is critical
    step_num -= 1
    logger.info(f"Step {step_num} completed.")

    # --- Screenshot Handling ---
    screenshot_html = ""
    # Ensure state.screenshot exists and is not empty before proceeding
    # Use getattr for safer access
    screenshot_data = getattr(state, "screenshot", None)
    if screenshot_data:
        try:
            # Basic validation: check if it looks like base64
            if (
                    isinstance(screenshot_data, str) and len(screenshot_data) > 100
            ):  # Arbitrary length check
                # *** UPDATED STYLE: Removed centering, adjusted width ***
                img_tag = f'Step {step_num} Screenshot'
                screenshot_html = (
                        img_tag + "
"
                )  # Use 
 for line break after inline-block image
            else:
                logger.warning(
                    f"Screenshot for step {step_num} seems invalid (type: {type(screenshot_data)}, len: {len(screenshot_data) if isinstance(screenshot_data, str) else 'N/A'})."
                )
                screenshot_html = "**[Invalid screenshot data]**
"

        except Exception as e:
            logger.error(
                f"Error processing or formatting screenshot for step {step_num}: {e}",
                exc_info=True,
            )
            screenshot_html = "**[Error displaying screenshot]**
"
    else:
        logger.debug(f"No screenshot available for step {step_num}.")

    # --- Format Agent Output ---
    formatted_output = _format_agent_output(output)  # Use the updated function

    # --- Combine and Append to Chat ---
    step_header = f"--- **Step {step_num}** ---"
    # Combine header, image (with line break), and JSON block
    final_content = step_header + "
" + screenshot_html + formatted_output

    chat_message = {
        "role": "assistant",
        "content": final_content.strip(),  # Remove leading/trailing whitespace
    }

    # Append to the correct chat history list
    webui_manager.bu_chat_history.append(chat_message)

    await asyncio.sleep(0.05)


def _handle_done(webui_manager: WebuiManager, history: AgentHistoryList):
    """Callback when the agent finishes the task (success or failure)."""
    logger.info(
        f"Agent task finished. Duration: {history.total_duration_seconds():.2f}s, Tokens: {history.total_input_tokens()}"
    )
    final_summary = "**Task Completed**\n"
    final_summary += f"- Duration: {history.total_duration_seconds():.2f} seconds\n"
    final_summary += f"- Total Input Tokens: {history.total_input_tokens()}\n"  # Or total tokens if available

    final_result = history.final_result()
    if final_result:
        final_summary += f"- Final Result: {final_result}\n"

    errors = history.errors()
    if errors and any(errors):
        final_summary += f"- **Errors:**\n```\n{errors}\n```\n"
    else:
        final_summary += "- Status: Success\n"

    webui_manager.bu_chat_history.append(
        {"role": "assistant", "content": final_summary}
    )


async def _ask_assistant_callback(
        webui_manager: WebuiManager, query: str, browser_context: BrowserContext
) -> Dict[str, Any]:
    """Callback triggered by the agent's ask_for_assistant action."""
    logger.info("Agent requires assistance. Waiting for user input.")

    if not hasattr(webui_manager, "_chat_history"):
        logger.error("Chat history not found in webui_manager during ask_assistant!")
        return {"response": "Internal Error: Cannot display help request."}

    webui_manager.bu_chat_history.append(
        {
            "role": "assistant",
            "content": f"**Need Help:** {query}\nPlease provide information or perform the required action in the browser, then type your response/confirmation below and click 'Submit Response'.",
        }
    )

    # Use state stored in webui_manager
    webui_manager.bu_response_event = asyncio.Event()
    webui_manager.bu_user_help_response = None  # Reset previous response

    try:
        logger.info("Waiting for user response event...")
        await asyncio.wait_for(
            webui_manager.bu_response_event.wait(), timeout=3600.0
        )  # Long timeout
        logger.info("User response event received.")
    except asyncio.TimeoutError:
        logger.warning("Timeout waiting for user assistance.")
        webui_manager.bu_chat_history.append(
            {
                "role": "assistant",
                "content": "**Timeout:** No response received. Trying to proceed.",
            }
        )
        webui_manager.bu_response_event = None  # Clear the event
        return {"response": "Timeout: User did not respond."}  # Inform the agent

    response = webui_manager.bu_user_help_response
    webui_manager.bu_chat_history.append(
        {"role": "user", "content": response}
    )  # Show user response in chat
    webui_manager.bu_response_event = (
        None  # Clear the event for the next potential request
    )
    return {"response": response}


# --- Core Agent Execution Logic --- (Needs access to webui_manager)


async def run_agent_task(
        webui_manager: WebuiManager, components: Dict[gr.components.Component, Any]
) -> AsyncGenerator[Dict[gr.components.Component, Any], None]:
    """Handles the entire lifecycle of initializing and running the agent."""

    # --- Get Components ---
    # Need handles to specific UI components to update them
    user_input_comp = webui_manager.get_component_by_id("browser_use_agent.user_input")
    run_button_comp = webui_manager.get_component_by_id("browser_use_agent.run_button")
    stop_button_comp = webui_manager.get_component_by_id(
        "browser_use_agent.stop_button"
    )
    pause_resume_button_comp = webui_manager.get_component_by_id(
        "browser_use_agent.pause_resume_button"
    )
    clear_button_comp = webui_manager.get_component_by_id(
        "browser_use_agent.clear_button"
    )
    chatbot_comp = webui_manager.get_component_by_id("browser_use_agent.chatbot")
    history_file_comp = webui_manager.get_component_by_id(
        "browser_use_agent.agent_history_file"
    )
    gif_comp = webui_manager.get_component_by_id("browser_use_agent.recording_gif")
    browser_view_comp = webui_manager.get_component_by_id(
        "browser_use_agent.browser_view"
    )

    # --- 1. Get Task and Initial UI Update ---
    task = components.get(user_input_comp, "").strip()
    if not task:
        gr.Warning("Please enter a task.")
        yield {run_button_comp: gr.update(interactive=True)}
        return

    # Set running state indirectly via _current_task
    webui_manager.bu_chat_history.append({"role": "user", "content": task})

    yield {
        user_input_comp: gr.Textbox(
            value="", interactive=False, placeholder="Agent is running..."
        ),
        run_button_comp: gr.Button(value="⏳ Running...", interactive=False),
        stop_button_comp: gr.Button(interactive=True),
        pause_resume_button_comp: gr.Button(value="⏸️ Pause", interactive=True),
        clear_button_comp: gr.Button(interactive=False),
        chatbot_comp: gr.update(value=webui_manager.bu_chat_history),
        history_file_comp: gr.update(value=None),
        gif_comp: gr.update(value=None),
    }

    # --- Agent Settings ---
    # Access settings values via components dict, getting IDs from webui_manager
    def get_setting(key, default=None):
        comp = webui_manager.id_to_component.get(f"agent_settings.{key}")
        return components.get(comp, default) if comp else default

    override_system_prompt = get_setting("override_system_prompt") or None
    extend_system_prompt = get_setting("extend_system_prompt") or None
    llm_provider_name = get_setting(
        "llm_provider", None
    )  # Default to None if not found
    llm_model_name = get_setting("llm_model_name", None)
    llm_temperature = get_setting("llm_temperature", 0.6)
    use_vision = get_setting("use_vision", True)
    ollama_num_ctx = get_setting("ollama_num_ctx", 16000)
    llm_base_url = get_setting("llm_base_url") or None
    llm_api_key = get_setting("llm_api_key") or None
    max_steps = get_setting("max_steps", 100)
    max_actions = get_setting("max_actions", 10)
    max_input_tokens = get_setting("max_input_tokens", 128000)
    tool_calling_str = get_setting("tool_calling_method", "auto")
    tool_calling_method = tool_calling_str if tool_calling_str != "None" else None
    mcp_server_config_comp = webui_manager.id_to_component.get(
        "agent_settings.mcp_server_config"
    )
    mcp_server_config_str = (
        components.get(mcp_server_config_comp) if mcp_server_config_comp else None
    )
    mcp_server_config = (
        json.loads(mcp_server_config_str) if mcp_server_config_str else None
    )

    # Planner LLM Settings (Optional)
    planner_llm_provider_name = get_setting("planner_llm_provider") or None
    planner_llm = None
    planner_use_vision = False
    if planner_llm_provider_name:
        planner_llm_model_name = get_setting("planner_llm_model_name")
        planner_llm_temperature = get_setting("planner_llm_temperature", 0.6)
        planner_ollama_num_ctx = get_setting("planner_ollama_num_ctx", 16000)
        planner_llm_base_url = get_setting("planner_llm_base_url") or None
        planner_llm_api_key = get_setting("planner_llm_api_key") or None
        planner_use_vision = get_setting("planner_use_vision", False)

        planner_llm = await _initialize_llm(
            planner_llm_provider_name,
            planner_llm_model_name,
            planner_llm_temperature,
            planner_llm_base_url,
            planner_llm_api_key,
            planner_ollama_num_ctx if planner_llm_provider_name == "ollama" else None,
        )

    # --- Browser Settings ---
    def get_browser_setting(key, default=None):
        comp = webui_manager.id_to_component.get(f"browser_settings.{key}")
        return components.get(comp, default) if comp else default

    browser_binary_path = get_browser_setting("browser_binary_path") or None
    browser_user_data_dir = get_browser_setting("browser_user_data_dir") or None
    use_own_browser = get_browser_setting(
        "use_own_browser", False
    )  # Logic handled by CDP/WSS presence
    keep_browser_open = get_browser_setting("keep_browser_open", False)
    headless = get_browser_setting("headless", False)
    disable_security = get_browser_setting("disable_security", False)
    window_w = int(get_browser_setting("window_w", 1280))
    window_h = int(get_browser_setting("window_h", 1100))
    cdp_url = get_browser_setting("cdp_url") or None
    wss_url = get_browser_setting("wss_url") or None
    save_recording_path = get_browser_setting("save_recording_path") or None
    save_trace_path = get_browser_setting("save_trace_path") or None
    save_agent_history_path = get_browser_setting(
        "save_agent_history_path", "./tmp/agent_history"
    )
    save_download_path = get_browser_setting("save_download_path", "./tmp/downloads")

    stream_vw = 70
    stream_vh = int(70 * window_h // window_w)

    os.makedirs(save_agent_history_path, exist_ok=True)
    if save_recording_path:
        os.makedirs(save_recording_path, exist_ok=True)
    if save_trace_path:
        os.makedirs(save_trace_path, exist_ok=True)
    if save_download_path:
        os.makedirs(save_download_path, exist_ok=True)

    # --- 2. Initialize LLM ---
    main_llm = await _initialize_llm(
        llm_provider_name,
        llm_model_name,
        llm_temperature,
        llm_base_url,
        llm_api_key,
        ollama_num_ctx if llm_provider_name == "ollama" else None,
    )

    # Pass the webui_manager instance to the callback when wrapping it
    async def ask_callback_wrapper(
            query: str, browser_context: BrowserContext
    ) -> Dict[str, Any]:
        return await _ask_assistant_callback(webui_manager, query, browser_context)

    if not webui_manager.bu_controller:
        webui_manager.bu_controller = CustomController(
            ask_assistant_callback=ask_callback_wrapper
        )
        await webui_manager.bu_controller.setup_mcp_client(mcp_server_config)

    # --- 4. Initialize Browser and Context ---
    should_close_browser_on_finish = not keep_browser_open

    try:
        # Close existing resources if not keeping open
        if not keep_browser_open:
            if webui_manager.bu_browser_context:
                logger.info("Closing previous browser context.")
                await webui_manager.bu_browser_context.close()
                webui_manager.bu_browser_context = None
            if webui_manager.bu_browser:
                logger.info("Closing previous browser.")
                await webui_manager.bu_browser.close()
                webui_manager.bu_browser = None

        # Create Browser if needed
        if not webui_manager.bu_browser:
            logger.info("Launching new browser instance.")
            extra_args = []
            if use_own_browser:
                browser_binary_path = os.getenv("BROWSER_PATH", None) or browser_binary_path
                if browser_binary_path == "":
                    browser_binary_path = None
                browser_user_data = browser_user_data_dir or os.getenv("BROWSER_USER_DATA", None)
                if browser_user_data:
                    extra_args += [f"--user-data-dir={browser_user_data}"]
            else:
                browser_binary_path = None

            webui_manager.bu_browser = CustomBrowser(
                config=BrowserConfig(
                    headless=headless,
                    disable_security=disable_security,
                    browser_binary_path=browser_binary_path,
                    extra_browser_args=extra_args,
                    wss_url=wss_url,
                    cdp_url=cdp_url,
                    new_context_config=BrowserContextConfig(
                        window_width=window_w,
                        window_height=window_h,
                    )
                )
            )

        # Create Context if needed
        if not webui_manager.bu_browser_context:
            logger.info("Creating new browser context.")
            context_config = BrowserContextConfig(
                trace_path=save_trace_path if save_trace_path else None,
                save_recording_path=save_recording_path
                if save_recording_path
                else None,
                save_downloads_path=save_download_path if save_download_path else None,
                window_height=window_h,
                window_width=window_w,
            )
            if not webui_manager.bu_browser:
                raise ValueError("Browser not initialized, cannot create context.")
            webui_manager.bu_browser_context = (
                await webui_manager.bu_browser.new_context(config=context_config)
            )

        # --- 5. Initialize or Update Agent ---
        webui_manager.bu_agent_task_id = str(uuid.uuid4())  # New ID for this task run
        os.makedirs(
            os.path.join(save_agent_history_path, webui_manager.bu_agent_task_id),
            exist_ok=True,
        )
        history_file = os.path.join(
            save_agent_history_path,
            webui_manager.bu_agent_task_id,
            f"{webui_manager.bu_agent_task_id}.json",
        )
        gif_path = os.path.join(
            save_agent_history_path,
            webui_manager.bu_agent_task_id,
            f"{webui_manager.bu_agent_task_id}.gif",
        )

        # Pass the webui_manager to callbacks when wrapping them
        async def step_callback_wrapper(
                state: BrowserState, output: AgentOutput, step_num: int
        ):
            await _handle_new_step(webui_manager, state, output, step_num)

        def done_callback_wrapper(history: AgentHistoryList):
            _handle_done(webui_manager, history)

        if not webui_manager.bu_agent:
            logger.info(f"Initializing new agent for task: {task}")
            if not webui_manager.bu_browser or not webui_manager.bu_browser_context:
                raise ValueError(
                    "Browser or Context not initialized, cannot create agent."
                )
            webui_manager.bu_agent = BrowserUseAgent(
                task=task,
                llm=main_llm,
                browser=webui_manager.bu_browser,
                browser_context=webui_manager.bu_browser_context,
                controller=webui_manager.bu_controller,
                register_new_step_callback=step_callback_wrapper,
                register_done_callback=done_callback_wrapper,
                use_vision=use_vision,
                override_system_message=override_system_prompt,
                extend_system_message=extend_system_prompt,
                max_input_tokens=max_input_tokens,
                max_actions_per_step=max_actions,
                tool_calling_method=tool_calling_method,
                planner_llm=planner_llm,
                use_vision_for_planner=planner_use_vision if planner_llm else False,
                source="webui",
            )
            webui_manager.bu_agent.state.agent_id = webui_manager.bu_agent_task_id
            webui_manager.bu_agent.settings.generate_gif = gif_path
        else:
            webui_manager.bu_agent.state.agent_id = webui_manager.bu_agent_task_id
            webui_manager.bu_agent.add_new_task(task)
            webui_manager.bu_agent.settings.generate_gif = gif_path
            webui_manager.bu_agent.browser = webui_manager.bu_browser
            webui_manager.bu_agent.browser_context = webui_manager.bu_browser_context
            webui_manager.bu_agent.controller = webui_manager.bu_controller

        # --- 6. Run Agent Task and Stream Updates ---
        agent_run_coro = webui_manager.bu_agent.run(max_steps=max_steps)
        agent_task = asyncio.create_task(agent_run_coro)
        webui_manager.bu_current_task = agent_task  # Store the task

        last_chat_len = len(webui_manager.bu_chat_history)
        while not agent_task.done():
            is_paused = webui_manager.bu_agent.state.paused
            is_stopped = webui_manager.bu_agent.state.stopped

            # Check for pause state
            if is_paused:
                yield {
                    pause_resume_button_comp: gr.update(
                        value="▶️ Resume", interactive=True
                    ),
                    stop_button_comp: gr.update(interactive=True),
                }
                # Wait until pause is released or task is stopped/done
                while is_paused and not agent_task.done():
                    # Re-check agent state in loop
                    is_paused = webui_manager.bu_agent.state.paused
                    is_stopped = webui_manager.bu_agent.state.stopped
                    if is_stopped:  # Stop signal received while paused
                        break
                    await asyncio.sleep(0.2)

                if (
                        agent_task.done() or is_stopped
                ):  # If stopped or task finished while paused
                    break

                # If resumed, yield UI update
                yield {
                    pause_resume_button_comp: gr.update(
                        value="⏸️ Pause", interactive=True
                    ),
                    run_button_comp: gr.update(
                        value="⏳ Running...", interactive=False
                    ),
                }

            # Check if agent stopped itself or stop button was pressed (which sets agent.state.stopped)
            if is_stopped:
                logger.info("Agent has stopped (internally or via stop button).")
                if not agent_task.done():
                    # Ensure the task coroutine finishes if agent just set flag
                    try:
                        await asyncio.wait_for(
                            agent_task, timeout=1.0
                        )  # Give it a moment to exit run()
                    except asyncio.TimeoutError:
                        logger.warning(
                            "Agent task did not finish quickly after stop signal, cancelling."
                        )
                        agent_task.cancel()
                    except Exception:  # Catch task exceptions if it errors on stop
                        pass
                break  # Exit the streaming loop

            # Check if agent is asking for help (via response_event)
            update_dict = {}
            if webui_manager.bu_response_event is not None:
                update_dict = {
                    user_input_comp: gr.update(
                        placeholder="Agent needs help. Enter response and submit.",
                        interactive=True,
                    ),
                    run_button_comp: gr.update(
                        value="✔️ Submit Response", interactive=True
                    ),
                    pause_resume_button_comp: gr.update(interactive=False),
                    stop_button_comp: gr.update(interactive=False),
                    chatbot_comp: gr.update(value=webui_manager.bu_chat_history),
                }
                last_chat_len = len(webui_manager.bu_chat_history)
                yield update_dict
                # Wait until response is submitted or task finishes
                await webui_manager.bu_response_event.wait()

                # Restore UI after response submitted or if task ended unexpectedly
                if not agent_task.done():
                    yield {
                        user_input_comp: gr.update(
                            placeholder="Agent is running...", interactive=False
                        ),
                        run_button_comp: gr.update(
                            value="⏳ Running...", interactive=False
                        ),
                        pause_resume_button_comp: gr.update(interactive=True),
                        stop_button_comp: gr.update(interactive=True),
                    }
                else:
                    break  # Task finished while waiting for response

            # Update Chatbot if new messages arrived via callbacks
            if len(webui_manager.bu_chat_history) > last_chat_len:
                update_dict[chatbot_comp] = gr.update(
                    value=webui_manager.bu_chat_history
                )
                last_chat_len = len(webui_manager.bu_chat_history)

            # Update Browser View
            if headless and webui_manager.bu_browser_context:
                try:
                    screenshot_b64 = (
                        await webui_manager.bu_browser_context.take_screenshot()
                    )
                    if screenshot_b64:
                        html_content = f''
                        update_dict[browser_view_comp] = gr.update(
                            value=html_content, visible=True
                        )
                    else:
                        html_content = f"
Waiting for browser session...
"
                        update_dict[browser_view_comp] = gr.update(
                            value=html_content, visible=True
                        )
                except Exception as e:
                    logger.debug(f"Failed to capture screenshot: {e}")
                    update_dict[browser_view_comp] = gr.update(
                        value="
Error loading view...
",
                        visible=True,
                    )
            else:
                update_dict[browser_view_comp] = gr.update(visible=False)

            # Yield accumulated updates
            if update_dict:
                yield update_dict

            await asyncio.sleep(0.1)  # Polling interval

        # --- 7. Task Finalization ---
        webui_manager.bu_agent.state.paused = False
        webui_manager.bu_agent.state.stopped = False
        final_update = {}
        try:
            logger.info("Agent task completing...")
            # Await the task ensure completion and catch exceptions if not already caught
            if not agent_task.done():
                await agent_task  # Retrieve result/exception
            elif agent_task.exception():  # Check if task finished with exception
                agent_task.result()  # Raise the exception to be caught below
            logger.info("Agent task completed processing.")

            logger.info(f"Explicitly saving agent history to: {history_file}")
            webui_manager.bu_agent.save_history(history_file)

            if os.path.exists(history_file):
                final_update[history_file_comp] = gr.File(value=history_file)

            if gif_path and os.path.exists(gif_path):
                logger.info(f"GIF found at: {gif_path}")
                final_update[gif_comp] = gr.Image(value=gif_path)

        except asyncio.CancelledError:
            logger.info("Agent task was cancelled.")
            if not any(
                    "Cancelled" in msg.get("content", "")
                    for msg in webui_manager.bu_chat_history
                    if msg.get("role") == "assistant"
            ):
                webui_manager.bu_chat_history.append(
                    {"role": "assistant", "content": "**Task Cancelled**."}
                )
            final_update[chatbot_comp] = gr.update(value=webui_manager.bu_chat_history)
        except Exception as e:
            logger.error(f"Error during agent execution: {e}", exc_info=True)
            error_message = (
                f"**Agent Execution Error:**\n```\n{type(e).__name__}: {e}\n```"
            )
            if not any(
                    error_message in msg.get("content", "")
                    for msg in webui_manager.bu_chat_history
                    if msg.get("role") == "assistant"
            ):
                webui_manager.bu_chat_history.append(
                    {"role": "assistant", "content": error_message}
                )
            final_update[chatbot_comp] = gr.update(value=webui_manager.bu_chat_history)
            gr.Error(f"Agent execution failed: {e}")

        finally:
            webui_manager.bu_current_task = None  # Clear the task reference

            # Close browser/context if requested
            if should_close_browser_on_finish:
                if webui_manager.bu_browser_context:
                    logger.info("Closing browser context after task.")
                    await webui_manager.bu_browser_context.close()
                    webui_manager.bu_browser_context = None
                if webui_manager.bu_browser:
                    logger.info("Closing browser after task.")
                    await webui_manager.bu_browser.close()
                    webui_manager.bu_browser = None

            # --- 8. Final UI Update ---
            final_update.update(
                {
                    user_input_comp: gr.update(
                        value="",
                        interactive=True,
                        placeholder="Enter your next task...",
                    ),
                    run_button_comp: gr.update(value="▶️ Submit Task", interactive=True),
                    stop_button_comp: gr.update(value="⏹️ Stop", interactive=False),
                    pause_resume_button_comp: gr.update(
                        value="⏸️ Pause", interactive=False
                    ),
                    clear_button_comp: gr.update(interactive=True),
                    # Ensure final chat history is shown
                    chatbot_comp: gr.update(value=webui_manager.bu_chat_history),
                }
            )
            yield final_update

    except Exception as e:
        # Catch errors during setup (before agent run starts)
        logger.error(f"Error setting up agent task: {e}", exc_info=True)
        webui_manager.bu_current_task = None  # Ensure state is reset
        yield {
            user_input_comp: gr.update(
                interactive=True, placeholder="Error during setup. Enter task..."
            ),
            run_button_comp: gr.update(value="▶️ Submit Task", interactive=True),
            stop_button_comp: gr.update(value="⏹️ Stop", interactive=False),
            pause_resume_button_comp: gr.update(value="⏸️ Pause", interactive=False),
            clear_button_comp: gr.update(interactive=True),
            chatbot_comp: gr.update(
                value=webui_manager.bu_chat_history
                      + [{"role": "assistant", "content": f"**Setup Error:** {e}"}]
            ),
        }


# --- Button Click Handlers --- (Need access to webui_manager)


async def handle_submit(
        webui_manager: WebuiManager, components: Dict[gr.components.Component, Any]
):
    """Handles clicks on the main 'Submit' button."""
    user_input_comp = webui_manager.get_component_by_id("browser_use_agent.user_input")
    user_input_value = components.get(user_input_comp, "").strip()

    # Check if waiting for user assistance
    if webui_manager.bu_response_event and not webui_manager.bu_response_event.is_set():
        logger.info(f"User submitted assistance: {user_input_value}")
        webui_manager.bu_user_help_response = (
            user_input_value if user_input_value else "User provided no text response."
        )
        webui_manager.bu_response_event.set()
        # UI updates handled by the main loop reacting to the event being set
        yield {
            user_input_comp: gr.update(
                value="",
                interactive=False,
                placeholder="Waiting for agent to continue...",
            ),
            webui_manager.get_component_by_id(
                "browser_use_agent.run_button"
            ): gr.update(value="⏳ Running...", interactive=False),
        }
    # Check if a task is currently running (using _current_task)
    elif webui_manager.bu_current_task and not webui_manager.bu_current_task.done():
        logger.warning(
            "Submit button clicked while agent is already running and not asking for help."
        )
        gr.Info("Agent is currently running. Please wait or use Stop/Pause.")
        yield {}  # No change
    else:
        # Handle submission for a new task
        logger.info("Submit button clicked for new task.")
        # Use async generator to stream updates from run_agent_task
        async for update in run_agent_task(webui_manager, components):
            yield update


async def handle_stop(webui_manager: WebuiManager):
    """Handles clicks on the 'Stop' button."""
    logger.info("Stop button clicked.")
    agent = webui_manager.bu_agent
    task = webui_manager.bu_current_task

    if agent and task and not task.done():
        # Signal the agent to stop by setting its internal flag
        agent.state.stopped = True
        agent.state.paused = False  # Ensure not paused if stopped
        return {
            webui_manager.get_component_by_id(
                "browser_use_agent.stop_button"
            ): gr.update(interactive=False, value="⏹️ Stopping..."),
            webui_manager.get_component_by_id(
                "browser_use_agent.pause_resume_button"
            ): gr.update(interactive=False),
            webui_manager.get_component_by_id(
                "browser_use_agent.run_button"
            ): gr.update(interactive=False),
        }
    else:
        logger.warning("Stop clicked but agent is not running or task is already done.")
        # Reset UI just in case it's stuck
        return {
            webui_manager.get_component_by_id(
                "browser_use_agent.run_button"
            ): gr.update(interactive=True),
            webui_manager.get_component_by_id(
                "browser_use_agent.stop_button"
            ): gr.update(interactive=False),
            webui_manager.get_component_by_id(
                "browser_use_agent.pause_resume_button"
            ): gr.update(interactive=False),
            webui_manager.get_component_by_id(
                "browser_use_agent.clear_button"
            ): gr.update(interactive=True),
        }


async def handle_pause_resume(webui_manager: WebuiManager):
    """Handles clicks on the 'Pause/Resume' button."""
    agent = webui_manager.bu_agent
    task = webui_manager.bu_current_task

    if agent and task and not task.done():
        if agent.state.paused:
            logger.info("Resume button clicked.")
            agent.resume()
            # UI update happens in main loop
            return {
                webui_manager.get_component_by_id(
                    "browser_use_agent.pause_resume_button"
                ): gr.update(value="⏸️ Pause", interactive=True)
            }  # Optimistic update
        else:
            logger.info("Pause button clicked.")
            agent.pause()
            return {
                webui_manager.get_component_by_id(
                    "browser_use_agent.pause_resume_button"
                ): gr.update(value="▶️ Resume", interactive=True)
            }  # Optimistic update
    else:
        logger.warning(
            "Pause/Resume clicked but agent is not running or doesn't support state."
        )
        return {}  # No change


async def handle_clear(webui_manager: WebuiManager):
    """Handles clicks on the 'Clear' button."""
    logger.info("Clear button clicked.")

    # Stop any running task first
    task = webui_manager.bu_current_task
    if task and not task.done():
        logger.info("Clearing requires stopping the current task.")
        webui_manager.bu_agent.stop()
        task.cancel()
        try:
            await asyncio.wait_for(task, timeout=2.0)  # Wait briefly
        except (asyncio.CancelledError, asyncio.TimeoutError):
            pass
        except Exception as e:
            logger.warning(f"Error stopping task on clear: {e}")
    webui_manager.bu_current_task = None

    if webui_manager.bu_controller:
        await webui_manager.bu_controller.close_mcp_client()
        webui_manager.bu_controller = None
    webui_manager.bu_agent = None

    # Reset state stored in manager
    webui_manager.bu_chat_history = []
    webui_manager.bu_response_event = None
    webui_manager.bu_user_help_response = None
    webui_manager.bu_agent_task_id = None

    logger.info("Agent state and browser resources cleared.")

    # Reset UI components
    return {
        webui_manager.get_component_by_id("browser_use_agent.chatbot"): gr.update(
            value=[]
        ),
        webui_manager.get_component_by_id("browser_use_agent.user_input"): gr.update(
            value="", placeholder="Enter your task here..."
        ),
        webui_manager.get_component_by_id(
            "browser_use_agent.agent_history_file"
        ): gr.update(value=None),
        webui_manager.get_component_by_id("browser_use_agent.recording_gif"): gr.update(
            value=None
        ),
        webui_manager.get_component_by_id("browser_use_agent.browser_view"): gr.update(
            value="
Browser Cleared
"
        ),
        webui_manager.get_component_by_id("browser_use_agent.run_button"): gr.update(
            value="▶️ Submit Task", interactive=True
        ),
        webui_manager.get_component_by_id("browser_use_agent.stop_button"): gr.update(
            interactive=False
        ),
        webui_manager.get_component_by_id(
            "browser_use_agent.pause_resume_button"
        ): gr.update(value="⏸️ Pause", interactive=False),
        webui_manager.get_component_by_id("browser_use_agent.clear_button"): gr.update(
            interactive=True
        ),
    }


# --- Tab Creation Function ---


def create_browser_use_agent_tab(webui_manager: WebuiManager):
    """
    Create the run agent tab, defining UI, state, and handlers.
    """
    webui_manager.init_browser_use_agent()

    # --- Define UI Components ---
    tab_components = {}
    with gr.Column():
        chatbot = gr.Chatbot(
            lambda: webui_manager.bu_chat_history,  # Load history dynamically
            elem_id="browser_use_chatbot",
            label="Agent Interaction",
            type="messages",
            height=600,
            show_copy_button=True,
        )
        user_input = gr.Textbox(
            label="Your Task or Response",
            placeholder="Enter your task here or provide assistance when asked.",
            lines=3,
            interactive=True,
            elem_id="user_input",
        )
        with gr.Row():
            stop_button = gr.Button(
                "⏹️ Stop", interactive=False, variant="stop", scale=2
            )
            pause_resume_button = gr.Button(
                "⏸️ Pause", interactive=False, variant="secondary", scale=2, visible=True
            )
            clear_button = gr.Button(
                "🗑️ Clear", interactive=True, variant="secondary", scale=2
            )
            run_button = gr.Button("▶️ Submit Task", variant="primary", scale=3)

        browser_view = gr.HTML(
            value="
Browser View (Requires Headless=True)
",
            label="Browser Live View",
            elem_id="browser_view",
            visible=False,
        )
        with gr.Column():
            gr.Markdown("### Task Outputs")
            agent_history_file = gr.File(label="Agent History JSON", interactive=False)
            recording_gif = gr.Image(
                label="Task Recording GIF",
                format="gif",
                interactive=False,
                type="filepath",
            )

    # --- Store Components in Manager ---
    tab_components.update(
        dict(
            chatbot=chatbot,
            user_input=user_input,
            clear_button=clear_button,
            run_button=run_button,
            stop_button=stop_button,
            pause_resume_button=pause_resume_button,
            agent_history_file=agent_history_file,
            recording_gif=recording_gif,
            browser_view=browser_view,
        )
    )
    webui_manager.add_components(
        "browser_use_agent", tab_components
    )  # Use "browser_use_agent" as tab_name prefix

    all_managed_components = set(
        webui_manager.get_components()
    )  # Get all components known to manager
    run_tab_outputs = list(tab_components.values())

    async def submit_wrapper(
            components_dict: Dict[Component, Any],
    ) -> AsyncGenerator[Dict[Component, Any], None]:
        """Wrapper for handle_submit that yields its results."""
        async for update in handle_submit(webui_manager, components_dict):
            yield update

    async def stop_wrapper() -> AsyncGenerator[Dict[Component, Any], None]:
        """Wrapper for handle_stop."""
        update_dict = await handle_stop(webui_manager)
        yield update_dict

    async def pause_resume_wrapper() -> AsyncGenerator[Dict[Component, Any], None]:
        """Wrapper for handle_pause_resume."""
        update_dict = await handle_pause_resume(webui_manager)
        yield update_dict

    async def clear_wrapper() -> AsyncGenerator[Dict[Component, Any], None]:
        """Wrapper for handle_clear."""
        update_dict = await handle_clear(webui_manager)
        yield update_dict

    # --- Connect Event Handlers using the Wrappers --
    run_button.click(
        fn=submit_wrapper, inputs=all_managed_components, outputs=run_tab_outputs, trigger_mode="multiple"
    )
    user_input.submit(
        fn=submit_wrapper, inputs=all_managed_components, outputs=run_tab_outputs
    )
    stop_button.click(fn=stop_wrapper, inputs=None, outputs=run_tab_outputs)
    pause_resume_button.click(
        fn=pause_resume_wrapper, inputs=None, outputs=run_tab_outputs
    )
    clear_button.click(fn=clear_wrapper, inputs=None, outputs=run_tab_outputs)


================================================
FILE: src/webui/components/deep_research_agent_tab.py
================================================
import gradio as gr
from gradio.components import Component
from functools import partial

from src.webui.webui_manager import WebuiManager
from src.utils import config
import logging
import os
from typing import Any, Dict, AsyncGenerator, Optional, Tuple, Union
import asyncio
import json
from src.agent.deep_research.deep_research_agent import DeepResearchAgent
from src.utils import llm_provider

logger = logging.getLogger(__name__)


async def _initialize_llm(provider: Optional[str], model_name: Optional[str], temperature: float,
                          base_url: Optional[str], api_key: Optional[str], num_ctx: Optional[int] = None):
    """Initializes the LLM based on settings. Returns None if provider/model is missing."""
    if not provider or not model_name:
        logger.info("LLM Provider or Model Name not specified, LLM will be None.")
        return None
    try:
        logger.info(f"Initializing LLM: Provider={provider}, Model={model_name}, Temp={temperature}")
        # Use your actual LLM provider logic here
        llm = llm_provider.get_llm_model(
            provider=provider,
            model_name=model_name,
            temperature=temperature,
            base_url=base_url or None,
            api_key=api_key or None,
            num_ctx=num_ctx if provider == "ollama" else None
        )
        return llm
    except Exception as e:
        logger.error(f"Failed to initialize LLM: {e}", exc_info=True)
        gr.Warning(
            f"Failed to initialize LLM '{model_name}' for provider '{provider}'. Please check settings. Error: {e}")
        return None


def _read_file_safe(file_path: str) -> Optional[str]:
    """Safely read a file, returning None if it doesn't exist or on error."""
    if not os.path.exists(file_path):
        return None
    try:
        with open(file_path, 'r', encoding='utf-8') as f:
            return f.read()
    except Exception as e:
        logger.error(f"Error reading file {file_path}: {e}")
        return None


# --- Deep Research Agent Specific Logic ---

async def run_deep_research(webui_manager: WebuiManager, components: Dict[Component, Any]) -> AsyncGenerator[
    Dict[Component, Any], None]:
    """Handles initializing and running the DeepResearchAgent."""

    # --- Get Components ---
    research_task_comp = webui_manager.get_component_by_id("deep_research_agent.research_task")
    resume_task_id_comp = webui_manager.get_component_by_id("deep_research_agent.resume_task_id")
    parallel_num_comp = webui_manager.get_component_by_id("deep_research_agent.parallel_num")
    save_dir_comp = webui_manager.get_component_by_id(
        "deep_research_agent.max_query")  # Note: component ID seems misnamed in original code
    start_button_comp = webui_manager.get_component_by_id("deep_research_agent.start_button")
    stop_button_comp = webui_manager.get_component_by_id("deep_research_agent.stop_button")
    markdown_display_comp = webui_manager.get_component_by_id("deep_research_agent.markdown_display")
    markdown_download_comp = webui_manager.get_component_by_id("deep_research_agent.markdown_download")
    mcp_server_config_comp = webui_manager.get_component_by_id("deep_research_agent.mcp_server_config")

    # --- 1. Get Task and Settings ---
    task_topic = components.get(research_task_comp, "").strip()
    task_id_to_resume = components.get(resume_task_id_comp, "").strip() or None
    max_parallel_agents = int(components.get(parallel_num_comp, 1))
    base_save_dir = components.get(save_dir_comp, "./tmp/deep_research").strip()
    safe_root_dir = "./tmp/deep_research"
    normalized_base_save_dir = os.path.abspath(os.path.normpath(base_save_dir))
    if os.path.commonpath([normalized_base_save_dir, os.path.abspath(safe_root_dir)]) != os.path.abspath(safe_root_dir):
        logger.warning(f"Unsafe base_save_dir detected: {base_save_dir}. Using default directory.")
        normalized_base_save_dir = os.path.abspath(safe_root_dir)
    base_save_dir = normalized_base_save_dir
    mcp_server_config_str = components.get(mcp_server_config_comp)
    mcp_config = json.loads(mcp_server_config_str) if mcp_server_config_str else None

    if not task_topic:
        gr.Warning("Please enter a research task.")
        yield {start_button_comp: gr.update(interactive=True)}  # Re-enable start button
        return

    # Store base save dir for stop handler
    webui_manager.dr_save_dir = base_save_dir
    os.makedirs(base_save_dir, exist_ok=True)

    # --- 2. Initial UI Update ---
    yield {
        start_button_comp: gr.update(value="⏳ Running...", interactive=False),
        stop_button_comp: gr.update(interactive=True),
        research_task_comp: gr.update(interactive=False),
        resume_task_id_comp: gr.update(interactive=False),
        parallel_num_comp: gr.update(interactive=False),
        save_dir_comp: gr.update(interactive=False),
        markdown_display_comp: gr.update(value="Starting research..."),
        markdown_download_comp: gr.update(value=None, interactive=False)
    }

    agent_task = None
    running_task_id = None
    plan_file_path = None
    report_file_path = None
    last_plan_content = None
    last_plan_mtime = 0

    try:
        # --- 3. Get LLM and Browser Config from other tabs ---
        # Access settings values via components dict, getting IDs from webui_manager
        def get_setting(tab: str, key: str, default: Any = None):
            comp = webui_manager.id_to_component.get(f"{tab}.{key}")
            return components.get(comp, default) if comp else default

        # LLM Config (from agent_settings tab)
        llm_provider_name = get_setting("agent_settings", "llm_provider")
        llm_model_name = get_setting("agent_settings", "llm_model_name")
        llm_temperature = max(get_setting("agent_settings", "llm_temperature", 0.5), 0.5)
        llm_base_url = get_setting("agent_settings", "llm_base_url")
        llm_api_key = get_setting("agent_settings", "llm_api_key")
        ollama_num_ctx = get_setting("agent_settings", "ollama_num_ctx")

        llm = await _initialize_llm(
            llm_provider_name, llm_model_name, llm_temperature, llm_base_url, llm_api_key,
            ollama_num_ctx if llm_provider_name == "ollama" else None
        )
        if not llm:
            raise ValueError("LLM Initialization failed. Please check Agent Settings.")

        # Browser Config (from browser_settings tab)
        # Note: DeepResearchAgent constructor takes a dict, not full Browser/Context objects
        browser_config_dict = {
            "headless": get_setting("browser_settings", "headless", False),
            "disable_security": get_setting("browser_settings", "disable_security", False),
            "browser_binary_path": get_setting("browser_settings", "browser_binary_path"),
            "user_data_dir": get_setting("browser_settings", "browser_user_data_dir"),
            "window_width": int(get_setting("browser_settings", "window_w", 1280)),
            "window_height": int(get_setting("browser_settings", "window_h", 1100)),
            # Add other relevant fields if DeepResearchAgent accepts them
        }

        # --- 4. Initialize or Get Agent ---
        if not webui_manager.dr_agent:
            webui_manager.dr_agent = DeepResearchAgent(
                llm=llm,
                browser_config=browser_config_dict,
                mcp_server_config=mcp_config
            )
            logger.info("DeepResearchAgent initialized.")

        # --- 5. Start Agent Run ---
        agent_run_coro = webui_manager.dr_agent.run(
            topic=task_topic,
            task_id=task_id_to_resume,
            save_dir=base_save_dir,
            max_parallel_browsers=max_parallel_agents
        )
        agent_task = asyncio.create_task(agent_run_coro)
        webui_manager.dr_current_task = agent_task

        # Wait briefly for the agent to start and potentially create the task ID/folder
        await asyncio.sleep(1.0)

        # Determine the actual task ID being used (agent sets this)
        running_task_id = webui_manager.dr_agent.current_task_id
        if not running_task_id:
            # Agent might not have set it yet, try to get from result later? Risky.
            # Or derive from resume_task_id if provided?
            running_task_id = task_id_to_resume
            if not running_task_id:
                logger.warning("Could not determine running task ID immediately.")
                # We can still monitor, but might miss initial plan if ID needed for path
            else:
                logger.info(f"Assuming task ID based on resume ID: {running_task_id}")
        else:
            logger.info(f"Agent started with Task ID: {running_task_id}")

        webui_manager.dr_task_id = running_task_id  # Store for stop handler

        # --- 6. Monitor Progress via research_plan.md ---
        if running_task_id:
            task_specific_dir = os.path.join(base_save_dir, str(running_task_id))
            plan_file_path = os.path.join(task_specific_dir, "research_plan.md")
            report_file_path = os.path.join(task_specific_dir, "report.md")
            logger.info(f"Monitoring plan file: {plan_file_path}")
        else:
            logger.warning("Cannot monitor plan file: Task ID unknown.")
            plan_file_path = None
        last_plan_content = None
        while not agent_task.done():
            update_dict = {}
            update_dict[resume_task_id_comp] = gr.update(value=running_task_id)
            agent_stopped = getattr(webui_manager.dr_agent, 'stopped', False)
            if agent_stopped:
                logger.info("Stop signal detected from agent state.")
                break  # Exit monitoring loop

            # Check and update research plan display
            if plan_file_path:
                try:
                    current_mtime = os.path.getmtime(plan_file_path) if os.path.exists(plan_file_path) else 0
                    if current_mtime > last_plan_mtime:
                        logger.info(f"Detected change in {plan_file_path}")
                        plan_content = _read_file_safe(plan_file_path)
                        if last_plan_content is None or (
                                plan_content is not None and plan_content != last_plan_content):
                            update_dict[markdown_display_comp] = gr.update(value=plan_content)
                            last_plan_content = plan_content
                            last_plan_mtime = current_mtime
                        elif plan_content is None:
                            # File might have been deleted or became unreadable
                            last_plan_mtime = 0  # Reset to force re-read attempt later
                except Exception as e:
                    logger.warning(f"Error checking/reading plan file {plan_file_path}: {e}")
                    # Avoid continuous logging for the same error
                    await asyncio.sleep(2.0)

            # Yield updates if any
            if update_dict:
                yield update_dict

            await asyncio.sleep(1.0)  # Check file changes every second

        # --- 7. Task Finalization ---
        logger.info("Agent task processing finished. Awaiting final result...")
        final_result_dict = await agent_task  # Get result or raise exception
        logger.info(f"Agent run completed. Result keys: {final_result_dict.keys() if final_result_dict else 'None'}")

        # Try to get task ID from result if not known before
        if not running_task_id and final_result_dict and 'task_id' in final_result_dict:
            running_task_id = final_result_dict['task_id']
            webui_manager.dr_task_id = running_task_id
            task_specific_dir = os.path.join(base_save_dir, str(running_task_id))
            report_file_path = os.path.join(task_specific_dir, "report.md")
            logger.info(f"Task ID confirmed from result: {running_task_id}")

        final_ui_update = {}
        if report_file_path and os.path.exists(report_file_path):
            logger.info(f"Loading final report from: {report_file_path}")
            report_content = _read_file_safe(report_file_path)
            if report_content:
                final_ui_update[markdown_display_comp] = gr.update(value=report_content)
                final_ui_update[markdown_download_comp] = gr.File(value=report_file_path,
                                                                  label=f"Report ({running_task_id}.md)",
                                                                  interactive=True)
            else:
                final_ui_update[markdown_display_comp] = gr.update(
                    value="# Research Complete\n\n*Error reading final report file.*")
        elif final_result_dict and 'report' in final_result_dict:
            logger.info("Using report content directly from agent result.")
            # If agent directly returns report content
            final_ui_update[markdown_display_comp] = gr.update(value=final_result_dict['report'])
            # Cannot offer download if only content is available
            final_ui_update[markdown_download_comp] = gr.update(value=None, label="Download Research Report",
                                                                interactive=False)
        else:
            logger.warning("Final report file not found and not in result dict.")
            final_ui_update[markdown_display_comp] = gr.update(value="# Research Complete\n\n*Final report not found.*")

        yield final_ui_update


    except Exception as e:
        logger.error(f"Error during Deep Research Agent execution: {e}", exc_info=True)
        gr.Error(f"Research failed: {e}")
        yield {markdown_display_comp: gr.update(value=f"# Research Failed\n\n**Error:**\n```\n{e}\n```")}

    finally:
        # --- 8. Final UI Reset ---
        webui_manager.dr_current_task = None  # Clear task reference
        webui_manager.dr_task_id = None  # Clear running task ID

        yield {
            start_button_comp: gr.update(value="▶️ Run", interactive=True),
            stop_button_comp: gr.update(interactive=False),
            research_task_comp: gr.update(interactive=True),
            resume_task_id_comp: gr.update(value="", interactive=True),
            parallel_num_comp: gr.update(interactive=True),
            save_dir_comp: gr.update(interactive=True),
            # Keep download button enabled if file exists
            markdown_download_comp: gr.update() if report_file_path and os.path.exists(report_file_path) else gr.update(
                interactive=False)
        }


async def stop_deep_research(webui_manager: WebuiManager) -> Dict[Component, Any]:
    """Handles the Stop button click."""
    logger.info("Stop button clicked for Deep Research.")
    agent = webui_manager.dr_agent
    task = webui_manager.dr_current_task
    task_id = webui_manager.dr_task_id
    base_save_dir = webui_manager.dr_save_dir

    stop_button_comp = webui_manager.get_component_by_id("deep_research_agent.stop_button")
    start_button_comp = webui_manager.get_component_by_id("deep_research_agent.start_button")
    markdown_display_comp = webui_manager.get_component_by_id("deep_research_agent.markdown_display")
    markdown_download_comp = webui_manager.get_component_by_id("deep_research_agent.markdown_download")

    final_update = {
        stop_button_comp: gr.update(interactive=False, value="⏹️ Stopping...")
    }

    if agent and task and not task.done():
        logger.info("Signalling DeepResearchAgent to stop.")
        try:
            # Assuming stop is synchronous or sets a flag quickly
            await agent.stop()
        except Exception as e:
            logger.error(f"Error calling agent.stop(): {e}")

        # The run_deep_research loop should detect the stop and exit.
        # We yield an intermediate "Stopping..." state. The final reset is done by run_deep_research.

        # Try to show the final report if available after stopping
        await asyncio.sleep(1.5)  # Give agent a moment to write final files potentially
        report_file_path = None
        if task_id and base_save_dir:
            report_file_path = os.path.join(base_save_dir, str(task_id), "report.md")

        if report_file_path and os.path.exists(report_file_path):
            report_content = _read_file_safe(report_file_path)
            if report_content:
                final_update[markdown_display_comp] = gr.update(
                    value=report_content + "\n\n---\n*Research stopped by user.*")
                final_update[markdown_download_comp] = gr.File(value=report_file_path, label=f"Report ({task_id}.md)",
                                                               interactive=True)
            else:
                final_update[markdown_display_comp] = gr.update(
                    value="# Research Stopped\n\n*Error reading final report file after stop.*")
        else:
            final_update[markdown_display_comp] = gr.update(value="# Research Stopped by User")

        # Keep start button disabled, run_deep_research finally block will re-enable it.
        final_update[start_button_comp] = gr.update(interactive=False)

    else:
        logger.warning("Stop clicked but no active research task found.")
        # Reset UI state just in case
        final_update = {
            start_button_comp: gr.update(interactive=True),
            stop_button_comp: gr.update(interactive=False),
            webui_manager.get_component_by_id("deep_research_agent.research_task"): gr.update(interactive=True),
            webui_manager.get_component_by_id("deep_research_agent.resume_task_id"): gr.update(interactive=True),
            webui_manager.get_component_by_id("deep_research_agent.max_iteration"): gr.update(interactive=True),
            webui_manager.get_component_by_id("deep_research_agent.max_query"): gr.update(interactive=True),
        }

    return final_update


async def update_mcp_server(mcp_file: str, webui_manager: WebuiManager):
    """
    Update the MCP server.
    """
    if hasattr(webui_manager, "dr_agent") and webui_manager.dr_agent:
        logger.warning("⚠️ Close controller because mcp file has changed!")
        await webui_manager.dr_agent.close_mcp_client()

    if not mcp_file or not os.path.exists(mcp_file) or not mcp_file.endswith('.json'):
        logger.warning(f"{mcp_file} is not a valid MCP file.")
        return None, gr.update(visible=False)

    with open(mcp_file, 'r') as f:
        mcp_server = json.load(f)

    return json.dumps(mcp_server, indent=2), gr.update(visible=True)


def create_deep_research_agent_tab(webui_manager: WebuiManager):
    """
    Creates a deep research agent tab
    """
    input_components = set(webui_manager.get_components())
    tab_components = {}

    with gr.Group():
        with gr.Row():
            mcp_json_file = gr.File(label="MCP server json", interactive=True, file_types=[".json"])
            mcp_server_config = gr.Textbox(label="MCP server", lines=6, interactive=True, visible=False)

    with gr.Group():
        research_task = gr.Textbox(label="Research Task", lines=5,
                                   value="Give me a detailed travel plan to Switzerland from June 1st to 10th.",
                                   interactive=True)
        with gr.Row():
            resume_task_id = gr.Textbox(label="Resume Task ID", value="",
                                        interactive=True)
            parallel_num = gr.Number(label="Parallel Agent Num", value=1,
                                     precision=0,
                                     interactive=True)
            max_query = gr.Textbox(label="Research Save Dir", value="./tmp/deep_research",
                                   interactive=True)
    with gr.Row():
        stop_button = gr.Button("⏹️ Stop", variant="stop", scale=2)
        start_button = gr.Button("▶️ Run", variant="primary", scale=3)
    with gr.Group():
        markdown_display = gr.Markdown(label="Research Report")
        markdown_download = gr.File(label="Download Research Report", interactive=False)
    tab_components.update(
        dict(
            research_task=research_task,
            parallel_num=parallel_num,
            max_query=max_query,
            start_button=start_button,
            stop_button=stop_button,
            markdown_display=markdown_display,
            markdown_download=markdown_download,
            resume_task_id=resume_task_id,
            mcp_json_file=mcp_json_file,
            mcp_server_config=mcp_server_config,
        )
    )
    webui_manager.add_components("deep_research_agent", tab_components)
    webui_manager.init_deep_research_agent()

    async def update_wrapper(mcp_file):
        """Wrapper for handle_pause_resume."""
        update_dict = await update_mcp_server(mcp_file, webui_manager)
        yield update_dict

    mcp_json_file.change(
        update_wrapper,
        inputs=[mcp_json_file],
        outputs=[mcp_server_config, mcp_server_config]
    )

    dr_tab_outputs = list(tab_components.values())
    all_managed_inputs = set(webui_manager.get_components())

    # --- Define Event Handler Wrappers ---
    async def start_wrapper(comps: Dict[Component, Any]) -> AsyncGenerator[Dict[Component, Any], None]:
        async for update in run_deep_research(webui_manager, comps):
            yield update

    async def stop_wrapper() -> AsyncGenerator[Dict[Component, Any], None]:
        update_dict = await stop_deep_research(webui_manager)
        yield update_dict

    # --- Connect Handlers ---
    start_button.click(
        fn=start_wrapper,
        inputs=all_managed_inputs,
        outputs=dr_tab_outputs
    )

    stop_button.click(
        fn=stop_wrapper,
        inputs=None,
        outputs=dr_tab_outputs
    )


================================================
FILE: src/webui/components/load_save_config_tab.py
================================================
import gradio as gr
from gradio.components import Component

from src.webui.webui_manager import WebuiManager
from src.utils import config


def create_load_save_config_tab(webui_manager: WebuiManager):
    """
    Creates a load and save config tab.
    """
    input_components = set(webui_manager.get_components())
    tab_components = {}

    config_file = gr.File(
        label="Load UI Settings from json",
        file_types=[".json"],
        interactive=True
    )
    with gr.Row():
        load_config_button = gr.Button("Load Config", variant="primary")
        save_config_button = gr.Button("Save UI Settings", variant="primary")

    config_status = gr.Textbox(
        label="Status",
        lines=2,
        interactive=False
    )

    tab_components.update(dict(
        load_config_button=load_config_button,
        save_config_button=save_config_button,
        config_status=config_status,
        config_file=config_file,
    ))

    webui_manager.add_components("load_save_config", tab_components)

    save_config_button.click(
        fn=webui_manager.save_config,
        inputs=set(webui_manager.get_components()),
        outputs=[config_status]
    )

    load_config_button.click(
        fn=webui_manager.load_config,
        inputs=[config_file],
        outputs=webui_manager.get_components(),
    )



================================================
FILE: src/webui/interface.py
================================================
import gradio as gr

from src.webui.webui_manager import WebuiManager
from src.webui.components.agent_settings_tab import create_agent_settings_tab
from src.webui.components.browser_settings_tab import create_browser_settings_tab
from src.webui.components.browser_use_agent_tab import create_browser_use_agent_tab
from src.webui.components.deep_research_agent_tab import create_deep_research_agent_tab
from src.webui.components.load_save_config_tab import create_load_save_config_tab

theme_map = {
    "Default": gr.themes.Default(),
    "Soft": gr.themes.Soft(),
    "Monochrome": gr.themes.Monochrome(),
    "Glass": gr.themes.Glass(),
    "Origin": gr.themes.Origin(),
    "Citrus": gr.themes.Citrus(),
    "Ocean": gr.themes.Ocean(),
    "Base": gr.themes.Base()
}


def create_ui(theme_name="Ocean"):
    css = """
    .gradio-container {
        width: 70vw !important; 
        max-width: 70% !important; 
        margin-left: auto !important;
        margin-right: auto !important;
        padding-top: 10px !important;
    }
    .header-text {
        text-align: center;
        margin-bottom: 20px;
    }
    .tab-header-text {
        text-align: center;
    }
    .theme-section {
        margin-bottom: 10px;
        padding: 15px;
        border-radius: 10px;
    }
    """

    # dark mode in default
    js_func = """
    function refresh() {
        const url = new URL(window.location);

        if (url.searchParams.get('__theme') !== 'dark') {
            url.searchParams.set('__theme', 'dark');
            window.location.href = url.href;
        }
    }
    """

    ui_manager = WebuiManager()

    with gr.Blocks(
            title="Browser Use WebUI", theme=theme_map[theme_name], css=css, js=js_func,
    ) as demo:
        with gr.Row():
            gr.Markdown(
                """
                # 🌐 Browser Use WebUI
                ### Control your browser with AI assistance
                """,
                elem_classes=["header-text"],
            )

        with gr.Tabs() as tabs:
            with gr.TabItem("⚙️ Agent Settings"):
                create_agent_settings_tab(ui_manager)

            with gr.TabItem("🌐 Browser Settings"):
                create_browser_settings_tab(ui_manager)

            with gr.TabItem("🤖 Run Agent"):
                create_browser_use_agent_tab(ui_manager)

            with gr.TabItem("🎁 Agent Marketplace"):
                gr.Markdown(
                    """
                    ### Agents built on Browser-Use
                    """,
                    elem_classes=["tab-header-text"],
                )
                with gr.Tabs():
                    with gr.TabItem("Deep Research"):
                        create_deep_research_agent_tab(ui_manager)

            with gr.TabItem("📁 Load & Save Config"):
                create_load_save_config_tab(ui_manager)

    return demo


================================================
FILE: src/webui/webui_manager.py
================================================
import json
from collections.abc import Generator
from typing import TYPE_CHECKING
import os
import gradio as gr
from datetime import datetime
from typing import Optional, Dict, List
import uuid
import asyncio
import time

from gradio.components import Component
from browser_use.browser.browser import Browser
from browser_use.browser.context import BrowserContext
from browser_use.agent.service import Agent
from src.browser.custom_browser import CustomBrowser
from src.browser.custom_context import CustomBrowserContext
from src.controller.custom_controller import CustomController
from src.agent.deep_research.deep_research_agent import DeepResearchAgent


class WebuiManager:
    def __init__(self, settings_save_dir: str = "./tmp/webui_settings"):
        self.id_to_component: dict[str, Component] = {}
        self.component_to_id: dict[Component, str] = {}

        self.settings_save_dir = settings_save_dir
        os.makedirs(self.settings_save_dir, exist_ok=True)

    def init_browser_use_agent(self) -> None:
        """
        init browser use agent
        """
        self.bu_agent: Optional[Agent] = None
        self.bu_browser: Optional[CustomBrowser] = None
        self.bu_browser_context: Optional[CustomBrowserContext] = None
        self.bu_controller: Optional[CustomController] = None
        self.bu_chat_history: List[Dict[str, Optional[str]]] = []
        self.bu_response_event: Optional[asyncio.Event] = None
        self.bu_user_help_response: Optional[str] = None
        self.bu_current_task: Optional[asyncio.Task] = None
        self.bu_agent_task_id: Optional[str] = None

    def init_deep_research_agent(self) -> None:
        """
        init deep research agent
        """
        self.dr_agent: Optional[DeepResearchAgent] = None
        self.dr_current_task = None
        self.dr_agent_task_id: Optional[str] = None
        self.dr_save_dir: Optional[str] = None

    def add_components(self, tab_name: str, components_dict: dict[str, "Component"]) -> None:
        """
        Add tab components
        """
        for comp_name, component in components_dict.items():
            comp_id = f"{tab_name}.{comp_name}"
            self.id_to_component[comp_id] = component
            self.component_to_id[component] = comp_id

    def get_components(self) -> list["Component"]:
        """
        Get all components
        """
        return list(self.id_to_component.values())

    def get_component_by_id(self, comp_id: str) -> "Component":
        """
        Get component by id
        """
        return self.id_to_component[comp_id]

    def get_id_by_component(self, comp: "Component") -> str:
        """
        Get id by component
        """
        return self.component_to_id[comp]

    def save_config(self, components: Dict["Component", str]) -> None:
        """
        Save config
        """
        cur_settings = {}
        for comp in components:
            if not isinstance(comp, gr.Button) and not isinstance(comp, gr.File) and str(
                    getattr(comp, "interactive", True)).lower() != "false":
                comp_id = self.get_id_by_component(comp)
                cur_settings[comp_id] = components[comp]

        config_name = datetime.now().strftime("%Y%m%d-%H%M%S")
        with open(os.path.join(self.settings_save_dir, f"{config_name}.json"), "w") as fw:
            json.dump(cur_settings, fw, indent=4)

        return os.path.join(self.settings_save_dir, f"{config_name}.json")

    def load_config(self, config_path: str):
        """
        Load config
        """
        with open(config_path, "r") as fr:
            ui_settings = json.load(fr)

        update_components = {}
        for comp_id, comp_val in ui_settings.items():
            if comp_id in self.id_to_component:
                comp = self.id_to_component[comp_id]
                if comp.__class__.__name__ == "Chatbot":
                    update_components[comp] = comp.__class__(value=comp_val, type="messages")
                else:
                    update_components[comp] = comp.__class__(value=comp_val)
                    if comp_id == "agent_settings.planner_llm_provider":
                        yield update_components  # yield provider, let callback run
                        time.sleep(0.1)  # wait for Gradio UI callback

        config_status = self.id_to_component["load_save_config.config_status"]
        update_components.update(
            {
                config_status: config_status.__class__(value=f"Successfully loaded config: {config_path}")
            }
        )
        yield update_components


================================================
FILE: supervisord.conf
================================================
[supervisord]
user=root
nodaemon=true
logfile=/dev/stdout
logfile_maxbytes=0
loglevel=error

[program:xvfb]
command=Xvfb :99 -screen 0 %(ENV_RESOLUTION)s -ac +extension GLX +render -noreset
autorestart=true
stdout_logfile=/dev/stdout
stdout_logfile_maxbytes=0
stderr_logfile=/dev/stderr
stderr_logfile_maxbytes=0
priority=100
startsecs=3
stopsignal=TERM
stopwaitsecs=10

[program:vnc_setup]
command=bash -c "mkdir -p ~/.vnc && echo '%(ENV_VNC_PASSWORD)s' | vncpasswd -f > ~/.vnc/passwd && chmod 600 ~/.vnc/passwd && ls -la ~/.vnc/passwd"
autorestart=false
startsecs=0
priority=150
stdout_logfile=/dev/stdout
stdout_logfile_maxbytes=0
stderr_logfile=/dev/stderr
stderr_logfile_maxbytes=0

[program:x11vnc]
command=bash -c "mkdir -p /var/log && touch /var/log/x11vnc.log && chmod 666 /var/log/x11vnc.log && sleep 5 && DISPLAY=:99 x11vnc -display :99 -forever -shared -rfbauth /root/.vnc/passwd -rfbport 5901 -o /var/log/x11vnc.log"
autorestart=true
stdout_logfile=/dev/stdout
stdout_logfile_maxbytes=0
stderr_logfile=/dev/stderr
stderr_logfile_maxbytes=0
priority=200
startretries=10
startsecs=10
stopsignal=TERM
stopwaitsecs=10
depends_on=vnc_setup,xvfb

[program:x11vnc_log]
command=bash -c "mkdir -p /var/log && touch /var/log/x11vnc.log && tail -f /var/log/x11vnc.log"
autorestart=true
stdout_logfile=/dev/stdout
stdout_logfile_maxbytes=0
stderr_logfile=/dev/stderr
stderr_logfile_maxbytes=0
priority=250
stopsignal=TERM
stopwaitsecs=5
depends_on=x11vnc

[program:novnc]
command=bash -c "sleep 5 && cd /opt/novnc && ./utils/novnc_proxy --vnc localhost:5901 --listen 0.0.0.0:6080 --web /opt/novnc"
autorestart=true
stdout_logfile=/dev/stdout
stdout_logfile_maxbytes=0
stderr_logfile=/dev/stderr
stderr_logfile_maxbytes=0
priority=300
startretries=5
startsecs=3
depends_on=x11vnc

[program:webui]
command=python webui.py --ip 0.0.0.0 --port 7788
directory=/app
autorestart=true
stdout_logfile=/dev/stdout
stdout_logfile_maxbytes=0
stderr_logfile=/dev/stderr
stderr_logfile_maxbytes=0
priority=400
startretries=3
startsecs=3
stopsignal=TERM
stopwaitsecs=10

================================================
FILE: tests/test_agents.py
================================================
import pdb

from dotenv import load_dotenv

load_dotenv()
import sys

sys.path.append(".")
import asyncio
import os
import sys
from pprint import pprint

from browser_use import Agent
from browser_use.agent.views import AgentHistoryList

from src.utils import utils


async def test_browser_use_agent():
    from browser_use.browser.browser import Browser, BrowserConfig
    from browser_use.browser.context import (
        BrowserContextConfig
    )
    from browser_use.agent.service import Agent

    from src.browser.custom_browser import CustomBrowser
    from src.controller.custom_controller import CustomController
    from src.utils import llm_provider
    from src.agent.browser_use.browser_use_agent import BrowserUseAgent

    llm = llm_provider.get_llm_model(
        provider="openai",
        model_name="gpt-4o",
        temperature=0.8,
    )

    # llm = llm_provider.get_llm_model(
    #     provider="google",
    #     model_name="gemini-2.0-flash",
    #     temperature=0.6,
    #     api_key=os.getenv("GOOGLE_API_KEY", "")
    # )

    # llm = utils.get_llm_model(
    #     provider="deepseek",
    #     model_name="deepseek-reasoner",
    #     temperature=0.8
    # )

    # llm = utils.get_llm_model(
    #     provider="deepseek",
    #     model_name="deepseek-chat",
    #     temperature=0.8
    # )

    # llm = utils.get_llm_model(
    #     provider="ollama", model_name="qwen2.5:7b", temperature=0.5
    # )

    # llm = utils.get_llm_model(
    #     provider="ollama", model_name="deepseek-r1:14b", temperature=0.5
    # )

    window_w, window_h = 1280, 1100

    # llm = llm_provider.get_llm_model(
    #     provider="azure_openai",
    #     model_name="gpt-4o",
    #     temperature=0.5,
    #     base_url=os.getenv("AZURE_OPENAI_ENDPOINT", ""),
    #     api_key=os.getenv("AZURE_OPENAI_API_KEY", ""),
    # )

    mcp_server_config = {
        "mcpServers": {
            # "markitdown": {
            #     "command": "docker",
            #     "args": [
            #         "run",
            #         "--rm",
            #         "-i",
            #         "markitdown-mcp:latest"
            #     ]
            # },
            "desktop-commander": {
                "command": "npx",
                "args": [
                    "-y",
                    "@wonderwhy-er/desktop-commander"
                ]
            },
        }
    }
    controller = CustomController()
    await controller.setup_mcp_client(mcp_server_config)
    use_own_browser = True
    use_vision = True  # Set to False when using DeepSeek

    max_actions_per_step = 10
    browser = None
    browser_context = None

    try:
        extra_browser_args = []
        if use_own_browser:
            browser_binary_path = os.getenv("BROWSER_PATH", None)
            if browser_binary_path == "":
                browser_binary_path = None
            browser_user_data = os.getenv("BROWSER_USER_DATA", None)
            if browser_user_data:
                extra_browser_args += [f"--user-data-dir={browser_user_data}"]
        else:
            browser_binary_path = None
        browser = CustomBrowser(
            config=BrowserConfig(
                headless=False,
                browser_binary_path=browser_binary_path,
                extra_browser_args=extra_browser_args,
                new_context_config=BrowserContextConfig(
                    window_width=window_w,
                    window_height=window_h,
                )
            )
        )
        browser_context = await browser.new_context(
            config=BrowserContextConfig(
                trace_path=None,
                save_recording_path=None,
                save_downloads_path="./tmp/downloads",
                window_height=window_h,
                window_width=window_w,
            )
        )
        agent = BrowserUseAgent(
            # task="download pdf from https://arxiv.org/pdf/2311.16498 and rename this pdf to 'mcp-test.pdf'",
            task="give me nvidia stock price",
            llm=llm,
            browser=browser,
            browser_context=browser_context,
            controller=controller,
            use_vision=use_vision,
            max_actions_per_step=max_actions_per_step,
            generate_gif=True
        )
        history: AgentHistoryList = await agent.run(max_steps=100)

        print("Final Result:")
        pprint(history.final_result(), indent=4)

        print("\nErrors:")
        pprint(history.errors(), indent=4)

    except Exception:
        import traceback
        traceback.print_exc()
    finally:
        if browser_context:
            await browser_context.close()
        if browser:
            await browser.close()
        if controller:
            await controller.close_mcp_client()


async def test_browser_use_parallel():
    from browser_use.browser.browser import Browser, BrowserConfig
    from browser_use.browser.context import (
        BrowserContextConfig,
    )
    from browser_use.agent.service import Agent

    from src.browser.custom_browser import CustomBrowser
    from src.controller.custom_controller import CustomController
    from src.utils import llm_provider
    from src.agent.browser_use.browser_use_agent import BrowserUseAgent

    # llm = utils.get_llm_model(
    #     provider="openai",
    #     model_name="gpt-4o",
    #     temperature=0.8,
    #     base_url=os.getenv("OPENAI_ENDPOINT", ""),
    #     api_key=os.getenv("OPENAI_API_KEY", ""),
    # )

    # llm = utils.get_llm_model(
    #     provider="google",
    #     model_name="gemini-2.0-flash",
    #     temperature=0.6,
    #     api_key=os.getenv("GOOGLE_API_KEY", "")
    # )

    # llm = utils.get_llm_model(
    #     provider="deepseek",
    #     model_name="deepseek-reasoner",
    #     temperature=0.8
    # )

    # llm = utils.get_llm_model(
    #     provider="deepseek",
    #     model_name="deepseek-chat",
    #     temperature=0.8
    # )

    # llm = utils.get_llm_model(
    #     provider="ollama", model_name="qwen2.5:7b", temperature=0.5
    # )

    # llm = utils.get_llm_model(
    #     provider="ollama", model_name="deepseek-r1:14b", temperature=0.5
    # )

    window_w, window_h = 1280, 1100

    llm = llm_provider.get_llm_model(
        provider="azure_openai",
        model_name="gpt-4o",
        temperature=0.5,
        base_url=os.getenv("AZURE_OPENAI_ENDPOINT", ""),
        api_key=os.getenv("AZURE_OPENAI_API_KEY", ""),
    )

    mcp_server_config = {
        "mcpServers": {
            # "markitdown": {
            #     "command": "docker",
            #     "args": [
            #         "run",
            #         "--rm",
            #         "-i",
            #         "markitdown-mcp:latest"
            #     ]
            # },
            "desktop-commander": {
                "command": "npx",
                "args": [
                    "-y",
                    "@wonderwhy-er/desktop-commander"
                ]
            },
            # "filesystem": {
            #     "command": "npx",
            #     "args": [
            #         "-y",
            #         "@modelcontextprotocol/server-filesystem",
            #         "/Users/xxx/ai_workspace",
            #     ]
            # },
        }
    }
    controller = CustomController()
    await controller.setup_mcp_client(mcp_server_config)
    use_own_browser = True
    use_vision = True  # Set to False when using DeepSeek

    max_actions_per_step = 10
    browser = None
    browser_context = None

    try:
        extra_browser_args = []
        if use_own_browser:
            browser_binary_path = os.getenv("BROWSER_PATH", None)
            if browser_binary_path == "":
                browser_binary_path = None
            browser_user_data = os.getenv("BROWSER_USER_DATA", None)
            if browser_user_data:
                extra_browser_args += [f"--user-data-dir={browser_user_data}"]
        else:
            browser_binary_path = None
        browser = CustomBrowser(
            config=BrowserConfig(
                headless=False,
                browser_binary_path=browser_binary_path,
                extra_browser_args=extra_browser_args,
                new_context_config=BrowserContextConfig(
                    window_width=window_w,
                    window_height=window_h,
                )
            )
        )
        browser_context = await browser.new_context(
            config=BrowserContextConfig(
                trace_path=None,
                save_recording_path=None,
                save_downloads_path="./tmp/downloads",
                window_height=window_h,
                window_width=window_w,
                force_new_context=True
            )
        )
        agents = [
            BrowserUseAgent(task=task, llm=llm, browser=browser, controller=controller)
            for task in [
                'Search Google for weather in Tokyo',
                # 'Check Reddit front page title',
                # 'Find NASA image of the day',
                # 'Check top story on CNN',
                # 'Search latest SpaceX launch date',
                # 'Look up population of Paris',
                'Find current time in Sydney',
                'Check who won last Super Bowl',
                # 'Search trending topics on Twitter',
            ]
        ]

        history = await asyncio.gather(*[agent.run() for agent in agents])
        print("Final Result:")
        pprint(history.final_result(), indent=4)

        print("\nErrors:")
        pprint(history.errors(), indent=4)

        pdb.set_trace()

    except Exception:
        import traceback

        traceback.print_exc()
    finally:
        if browser_context:
            await browser_context.close()
        if browser:
            await browser.close()
        if controller:
            await controller.close_mcp_client()


async def test_deep_research_agent():
    from src.agent.deep_research.deep_research_agent import DeepResearchAgent, PLAN_FILENAME, REPORT_FILENAME
    from src.utils import llm_provider

    llm = llm_provider.get_llm_model(
        provider="openai",
        model_name="gpt-4o",
        temperature=0.5
    )

    # llm = llm_provider.get_llm_model(
    #     provider="bedrock",
    # )

    mcp_server_config = {
        "mcpServers": {
            "desktop-commander": {
                "command": "npx",
                "args": [
                    "-y",
                    "@wonderwhy-er/desktop-commander"
                ]
            },
        }
    }

    browser_config = {"headless": False, "window_width": 1280, "window_height": 1100, "use_own_browser": False}
    agent = DeepResearchAgent(llm=llm, browser_config=browser_config, mcp_server_config=mcp_server_config)
    research_topic = "Give me investment advices of nvidia and tesla."
    task_id_to_resume = ""  # Set this to resume a previous task ID

    print(f"Starting research on: {research_topic}")

    try:
        # Call run and wait for the final result dictionary
        result = await agent.run(research_topic,
                                 task_id=task_id_to_resume,
                                 save_dir="./tmp/deep_research",
                                 max_parallel_browsers=1,
                                 )

        print("\n--- Research Process Ended ---")
        print(f"Status: {result.get('status')}")
        print(f"Message: {result.get('message')}")
        print(f"Task ID: {result.get('task_id')}")

        # Check the final state for the report
        final_state = result.get('final_state', {})
        if final_state:
            print("\n--- Final State Summary ---")
            print(
                f"  Plan Steps Completed: {sum(1 for item in final_state.get('research_plan', []) if item.get('status') == 'completed')}")
            print(f"  Total Search Results Logged: {len(final_state.get('search_results', []))}")
            if final_state.get("final_report"):
                print("  Final Report: Generated (content omitted). You can find it in the output directory.")
                # print("\n--- Final Report ---") # Optionally print report
                # print(final_state["final_report"])
            else:
                print("  Final Report: Not generated.")
        else:
            print("Final state information not available.")


    except Exception as e:
        print(f"\n--- An unhandled error occurred outside the agent run ---")
        print(e)


if __name__ == "__main__":
    asyncio.run(test_browser_use_agent())
    # asyncio.run(test_browser_use_parallel())
    # asyncio.run(test_deep_research_agent())


================================================
FILE: tests/test_controller.py
================================================
import asyncio
import pdb
import sys
import time

sys.path.append(".")

from dotenv import load_dotenv

load_dotenv()


async def test_mcp_client():
    from src.utils.mcp_client import setup_mcp_client_and_tools, create_tool_param_model

    test_server_config = {
        "mcpServers": {
            # "markitdown": {
            #     "command": "docker",
            #     "args": [
            #         "run",
            #         "--rm",
            #         "-i",
            #         "markitdown-mcp:latest"
            #     ]
            # },
            "desktop-commander": {
                "command": "npx",
                "args": [
                    "-y",
                    "@wonderwhy-er/desktop-commander"
                ]
            },
            # "filesystem": {
            #     "command": "npx",
            #     "args": [
            #         "-y",
            #         "@modelcontextprotocol/server-filesystem",
            #         "/Users/xxx/ai_workspace",
            #     ]
            # },
        }
    }

    mcp_tools, mcp_client = await setup_mcp_client_and_tools(test_server_config)

    for tool in mcp_tools:
        tool_param_model = create_tool_param_model(tool)
        print(tool.name)
        print(tool.description)
        print(tool_param_model.model_json_schema())
    pdb.set_trace()


async def test_controller_with_mcp():
    import os
    from src.controller.custom_controller import CustomController
    from browser_use.controller.registry.views import ActionModel

    mcp_server_config = {
        "mcpServers": {
            # "markitdown": {
            #     "command": "docker",
            #     "args": [
            #         "run",
            #         "--rm",
            #         "-i",
            #         "markitdown-mcp:latest"
            #     ]
            # },
            "desktop-commander": {
                "command": "npx",
                "args": [
                    "-y",
                    "@wonderwhy-er/desktop-commander"
                ]
            },
            # "filesystem": {
            #     "command": "npx",
            #     "args": [
            #         "-y",
            #         "@modelcontextprotocol/server-filesystem",
            #         "/Users/xxx/ai_workspace",
            #     ]
            # },
        }
    }

    controller = CustomController()
    await controller.setup_mcp_client(mcp_server_config)
    action_name = "mcp.desktop-commander.execute_command"
    action_info = controller.registry.registry.actions[action_name]
    param_model = action_info.param_model
    print(param_model.model_json_schema())
    params = {"command": f"python ./tmp/test.py"
              }
    validated_params = param_model(**params)
    ActionModel_ = controller.registry.create_action_model()
    # Create ActionModel instance with the validated parameters
    action_model = ActionModel_(**{action_name: validated_params})
    result = await controller.act(action_model)
    result = result.extracted_content
    print(result)
    if result and "Command is still running. Use read_output to get more output." in result and "PID" in \
            result.split("\n")[0]:
        pid = int(result.split("\n")[0].split("PID")[-1].strip())
        action_name = "mcp.desktop-commander.read_output"
        action_info = controller.registry.registry.actions[action_name]
        param_model = action_info.param_model
        print(param_model.model_json_schema())
        params = {"pid": pid}
        validated_params = param_model(**params)
        action_model = ActionModel_(**{action_name: validated_params})
        output_result = ""
        while True:
            time.sleep(1)
            result = await controller.act(action_model)
            result = result.extracted_content
            if result:
                pdb.set_trace()
                output_result = result
                break
        print(output_result)
        pdb.set_trace()
    await controller.close_mcp_client()
    pdb.set_trace()


if __name__ == '__main__':
    # asyncio.run(test_mcp_client())
    asyncio.run(test_controller_with_mcp())


================================================
FILE: tests/test_llm_api.py
================================================
import os
import pdb
from dataclasses import dataclass

from dotenv import load_dotenv
from langchain_core.messages import HumanMessage, SystemMessage
from langchain_ollama import ChatOllama

load_dotenv()

import sys

sys.path.append(".")


@dataclass
class LLMConfig:
    provider: str
    model_name: str
    temperature: float = 0.8
    base_url: str = None
    api_key: str = None


def create_message_content(text, image_path=None):
    content = [{"type": "text", "text": text}]
    image_format = "png" if image_path and image_path.endswith(".png") else "jpeg"
    if image_path:
        from src.utils import utils
        image_data = utils.encode_image(image_path)
        content.append({
            "type": "image_url",
            "image_url": {"url": f"data:image/{image_format};base64,{image_data}"}
        })
    return content


def get_env_value(key, provider):
    env_mappings = {
        "openai": {"api_key": "OPENAI_API_KEY", "base_url": "OPENAI_ENDPOINT"},
        "azure_openai": {"api_key": "AZURE_OPENAI_API_KEY", "base_url": "AZURE_OPENAI_ENDPOINT"},
        "google": {"api_key": "GOOGLE_API_KEY"},
        "deepseek": {"api_key": "DEEPSEEK_API_KEY", "base_url": "DEEPSEEK_ENDPOINT"},
        "mistral": {"api_key": "MISTRAL_API_KEY", "base_url": "MISTRAL_ENDPOINT"},
        "alibaba": {"api_key": "ALIBABA_API_KEY", "base_url": "ALIBABA_ENDPOINT"},
        "moonshot": {"api_key": "MOONSHOT_API_KEY", "base_url": "MOONSHOT_ENDPOINT"},
        "ibm": {"api_key": "IBM_API_KEY", "base_url": "IBM_ENDPOINT"}
    }

    if provider in env_mappings and key in env_mappings[provider]:
        return os.getenv(env_mappings[provider][key], "")
    return ""


def test_llm(config, query, image_path=None, system_message=None):
    from src.utils import utils, llm_provider

    # Special handling for Ollama-based models
    if config.provider == "ollama":
        if "deepseek-r1" in config.model_name:
            from src.utils.llm_provider import DeepSeekR1ChatOllama
            llm = DeepSeekR1ChatOllama(model=config.model_name)
        else:
            llm = ChatOllama(model=config.model_name)

        ai_msg = llm.invoke(query)
        print(ai_msg.content)
        if "deepseek-r1" in config.model_name:
            pdb.set_trace()
        return

    # For other providers, use the standard configuration
    llm = llm_provider.get_llm_model(
        provider=config.provider,
        model_name=config.model_name,
        temperature=config.temperature,
        base_url=config.base_url or get_env_value("base_url", config.provider),
        api_key=config.api_key or get_env_value("api_key", config.provider)
    )

    # Prepare messages for non-Ollama models
    messages = []
    if system_message:
        messages.append(SystemMessage(content=create_message_content(system_message)))
    messages.append(HumanMessage(content=create_message_content(query, image_path)))
    ai_msg = llm.invoke(messages)

    # Handle different response types
    if hasattr(ai_msg, "reasoning_content"):
        print(ai_msg.reasoning_content)
    print(ai_msg.content)

def test_openai_model():
    config = LLMConfig(provider="openai", model_name="gpt-4o")
    test_llm(config, "Describe this image", "assets/examples/test.png")


def test_google_model():
    # Enable your API key first if you haven't: https://ai.google.dev/palm_docs/oauth_quickstart
    config = LLMConfig(provider="google", model_name="gemini-2.0-flash-exp")
    test_llm(config, "Describe this image", "assets/examples/test.png")


def test_azure_openai_model():
    config = LLMConfig(provider="azure_openai", model_name="gpt-4o")
    test_llm(config, "Describe this image", "assets/examples/test.png")


def test_deepseek_model():
    config = LLMConfig(provider="deepseek", model_name="deepseek-chat")
    test_llm(config, "Who are you?")


def test_deepseek_r1_model():
    config = LLMConfig(provider="deepseek", model_name="deepseek-reasoner")
    test_llm(config, "Which is greater, 9.11 or 9.8?", system_message="You are a helpful AI assistant.")


def test_ollama_model():
    config = LLMConfig(provider="ollama", model_name="qwen2.5:7b")
    test_llm(config, "Sing a ballad of LangChain.")


def test_deepseek_r1_ollama_model():
    config = LLMConfig(provider="ollama", model_name="deepseek-r1:14b")
    test_llm(config, "How many 'r's are in the word 'strawberry'?")


def test_mistral_model():
    config = LLMConfig(provider="mistral", model_name="pixtral-large-latest")
    test_llm(config, "Describe this image", "assets/examples/test.png")


def test_moonshot_model():
    config = LLMConfig(provider="moonshot", model_name="moonshot-v1-32k-vision-preview")
    test_llm(config, "Describe this image", "assets/examples/test.png")


def test_ibm_model():
    config = LLMConfig(provider="ibm", model_name="meta-llama/llama-4-maverick-17b-128e-instruct-fp8")
    test_llm(config, "Describe this image", "assets/examples/test.png")


def test_qwen_model():
    config = LLMConfig(provider="alibaba", model_name="qwen-vl-max")
    test_llm(config, "How many 'r's are in the word 'strawberry'?")


if __name__ == "__main__":
    # test_openai_model()
    # test_google_model()
    test_azure_openai_model()
    # test_deepseek_model()
    # test_ollama_model()
    # test_deepseek_r1_model()
    # test_deepseek_r1_ollama_model()
    # test_mistral_model()
    # test_ibm_model()
    # test_qwen_model()


================================================
FILE: tests/test_playwright.py
================================================
import pdb
from dotenv import load_dotenv

load_dotenv()


def test_connect_browser():
    import os
    from playwright.sync_api import sync_playwright

    chrome_exe = os.getenv("CHROME_PATH", "")
    chrome_use_data = os.getenv("CHROME_USER_DATA", "")

    with sync_playwright() as p:
        browser = p.chromium.launch_persistent_context(
            user_data_dir=chrome_use_data,
            executable_path=chrome_exe,
            headless=False  # Keep browser window visible
        )

        page = browser.new_page()
        page.goto("https://mail.google.com/mail/u/0/#inbox")
        page.wait_for_load_state()

        input("Press the Enter key to close the browser...")

        browser.close()


if __name__ == '__main__':
    test_connect_browser()


================================================
FILE: webui.py
================================================
from dotenv import load_dotenv
load_dotenv()
import argparse
from src.webui.interface import theme_map, create_ui


def main():
    parser = argparse.ArgumentParser(description="Gradio WebUI for Browser Agent")
    parser.add_argument("--ip", type=str, default="127.0.0.1", help="IP address to bind to")
    parser.add_argument("--port", type=int, default=7788, help="Port to listen on")
    parser.add_argument("--theme", type=str, default="Ocean", choices=theme_map.keys(), help="Theme to use for the UI")
    args = parser.parse_args()

    demo = create_ui(theme_name=args.theme)
    demo.queue().launch(server_name=args.ip, server_port=args.port)


if __name__ == '__main__':
    main()