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Repository: Jenqyang/Awesome-AI-Agents
Branch: main
Commit: cc55b8229229
Files: 18
Total size: 297.3 KB
Directory structure:
gitextract_spf2vu1r/
├── .github/
│ ├── ISSUE_TEMPLATE/
│ │ ├── config.yml
│ │ ├── new-resource-submission.yml
│ │ └── update-existing-entry.yml
│ ├── pull_request_template.md
│ └── workflows/
│ └── pages.yml
├── CONTRIBUTING.md
├── README.md
├── scripts/
│ └── build-site.mjs
└── site/
├── .nojekyll
├── app.js
├── fonts/
│ ├── OFL.txt
│ └── README.md
├── icons/
│ └── README.md
├── index.html
├── resources.json
├── robots.txt
├── sitemap.xml
└── styles.css
================================================
FILE CONTENTS
================================================
================================================
FILE: .github/ISSUE_TEMPLATE/config.yml
================================================
blank_issues_enabled: false
contact_links:
- name: Submit via Pull Request (Preferred)
url: https://github.com/Jenqyang/Awesome-AI-Agents/pulls
about: Open a PR directly for faster review.
================================================
FILE: .github/ISSUE_TEMPLATE/new-resource-submission.yml
================================================
name: New Resource Submission
description: Propose a new resource for this list (project, benchmark, paper-list, or blog).
title: "Submission: <name>"
labels:
- submission
body:
- type: markdown
attributes:
value: |
Thanks for your submission.
Please provide concise, evidence-based information. Marketing-heavy submissions may be closed.
If this is mainly a gateway to a paid hosted service, explain the standalone OSS value clearly or it may be rejected.
- type: dropdown
id: section
attributes:
label: Target section
description: Choose the section where this resource should be added.
options:
- Applications / Autonomous Agent Task Solver Projects
- Applications / Multi-Agent Task Solver Projects
- Applications / Agent Society Simulation
- Applications / Advanced Components
- Applications / Tools
- Frameworks
- Benchmark/Evaluator
- Platforms/API
- Related / Survey
- Related / Paper-List Repo
- Related / Blog
- Reference Repo
validations:
required: true
- type: input
id: name
attributes:
label: Resource name
placeholder: e.g. OpenPaw
validations:
required: true
- type: input
id: url
attributes:
label: Primary URL
description: Repository URL or canonical page.
placeholder: https://github.com/org/repo
validations:
required: true
- type: input
id: license
attributes:
label: License (if open source)
placeholder: MIT / Apache-2.0 / GPL-3.0 / N/A
validations:
required: false
- type: textarea
id: value
attributes:
label: Why this is valuable for the agent ecosystem
description: Share concrete value, integration/use cases, and who benefits.
placeholder: |
- What practical problem does it solve?
- How does it help agent builders/users?
- Any adoption or usage evidence?
validations:
required: true
- type: textarea
id: readme
attributes:
label: README/documentation quality check
description: Confirm docs are complete enough for users to evaluate and use.
placeholder: |
Include links or notes for:
- Install/Setup
- Usage examples
- Architecture/features
- Limitations or constraints (if any)
validations:
required: true
- type: textarea
id: commercialization
attributes:
label: Commercial dependency check
description: Required if this project depends on a paid API, SaaS, credits, or a closed hosted backend.
placeholder: |
Explain briefly:
- What part is open source and usable on its own?
- What part depends on the paid/closed service?
- Why this is still not mainly an advertisement for the hosted product?
validations:
required: false
- type: textarea
id: line
attributes:
label: Suggested one-line entry (neutral tone)
description: Use concise, factual language.
placeholder: "- [Project](https://github.com/org/repo) - One-line neutral description."
validations:
required: true
- type: checkboxes
id: checklist
attributes:
label: Checklist
options:
- label: I searched README and confirmed this is not a duplicate.
required: true
- label: This submission is not an advertisement.
required: true
- label: The description avoids unverifiable claims (e.g., "best", "first") unless clearly cited.
required: true
- label: If this depends on a paid or closed hosted service, the open-source part still has meaningful standalone value.
required: true
================================================
FILE: .github/ISSUE_TEMPLATE/update-existing-entry.yml
================================================
name: Update Existing Entry
description: Request a correction, removal, or significant update for an existing entry.
title: "Update: <existing entry name>"
labels:
- curation
body:
- type: input
id: entry
attributes:
label: Existing entry name
placeholder: e.g. AgentGuard
validations:
required: true
- type: input
id: location
attributes:
label: Section in README
placeholder: e.g. Tools / Frameworks / Benchmark/Evaluator
validations:
required: true
- type: dropdown
id: change_type
attributes:
label: Change type
options:
- Fix typo/format
- Update URL/repo move
- Update description
- Remove outdated or inactive entry
- Reclassify to a different section
validations:
required: true
- type: textarea
id: reason
attributes:
label: Reason and evidence
description: Include links, screenshots, archived URLs, or concrete evidence.
placeholder: Explain what changed and why this update is necessary.
validations:
required: true
- type: textarea
id: suggested
attributes:
label: Suggested replacement line (if applicable)
placeholder: "- [Project](https://github.com/org/repo) - Updated one-line description."
validations:
required: false
- type: checkboxes
id: checks
attributes:
label: Checklist
options:
- label: I verified this request against the current README.
required: true
- label: I included concrete evidence for this change.
required: true
================================================
FILE: .github/pull_request_template.md
================================================
## Summary
Describe what this PR adds/updates in 1-3 lines.
## Type of change
- [ ] Add new resource entry
- [ ] Update existing entry
- [ ] Remove outdated/invalid entry
- [ ] Formatting/typo only
## Target section
Specify where this change belongs in `README.md`:
- [ ] Applications / Autonomous Agent Task Solver Projects
- [ ] Applications / Multi-Agent Task Solver Projects
- [ ] Applications / Agent Society Simulation
- [ ] Applications / Advanced Components
- [ ] Applications / Tools
- [ ] Frameworks
- [ ] Benchmark/Evaluator
- [ ] Platforms/API
- [ ] Related / Survey
- [ ] Related / Paper-List Repo
- [ ] Related / Blog
- [ ] Reference Repo
## Quality checks (required)
- [ ] I searched `README.md` and confirmed this is not a duplicate.
- [ ] The submission is relevant to the AI agent ecosystem.
- [ ] The description is neutral and evidence-based (not promotional).
- [ ] I removed unverifiable claims (e.g., "best", "first") or provided clear evidence.
- [ ] If this is open source, license information is clear.
- [ ] The linked project/resource has usable documentation (README/docs).
- [ ] If this project depends on a paid or closed hosted service, the OSS artifact still has clear standalone value and this PR does not function mainly as service promotion.
## Proposed entry line
Paste the exact line added/updated:
```md
- [ProjectName](https://github.com/org/repo) - Neutral one-line description. 
```
## Evidence (optional but recommended)
Add links proving relevance/quality (docs, examples, benchmark pages, usage references, etc.).
If the project integrates with a paid or closed backend, include 1-3 lines explaining what remains materially useful from the open-source part alone.
================================================
FILE: .github/workflows/pages.yml
================================================
name: Deploy website to GitHub Pages
on:
push:
branches: [main]
paths:
- "README.md"
- "site/**"
- "scripts/build-site.mjs"
- ".github/workflows/pages.yml"
schedule:
- cron: "17 3 * * *"
workflow_dispatch:
permissions:
contents: read
pages: write
id-token: write
concurrency:
group: pages
cancel-in-progress: true
jobs:
build:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v6
- name: Generate resource data from README
run: node scripts/build-site.mjs
env:
GITHUB_TOKEN: ${{ github.token }}
- name: Configure GitHub Pages
uses: actions/configure-pages@v5
- name: Upload website artifact
uses: actions/upload-pages-artifact@v4
with:
path: site
deploy:
environment:
name: github-pages
url: ${{ steps.deployment.outputs.page_url }}
runs-on: ubuntu-latest
needs: build
steps:
- name: Deploy to GitHub Pages
id: deployment
uses: actions/deploy-pages@v4
================================================
FILE: CONTRIBUTING.md
================================================
# Contributing to Awesome-AI-Agents
Thanks for helping improve this list.
## Scope
This repository curates high-quality resources for AI agents, including:
- Applications
- Frameworks
- Tools
- Benchmark/Evaluator
- Platforms/API
- Related resources (Survey, Paper-List Repo, Blog, Reference Repo)
## Quality Bar
We prioritize quality over quantity.
- Standard OSS by default: entries should use a clearly open-source license. Source-available, commercial, or mixed-proprietary licensing will usually be rejected.
- OSS substance over OSS wrapper: an open-source MCP/server/client wrapper is not enough if the core product value depends mainly on a proprietary hosted service or paid backend.
- Real ecosystem value: the project should materially help the agent ecosystem.
- Evidence over marketing: avoid hype-heavy or ad-like wording.
- Maintainability signals: active repo, clear docs, usable code/artifacts.
- No duplicates: check existing entries before submitting.
Submissions may be closed if they are mostly promotional, out of scope, weakly documented, duplicate existing entries, use non-OSS licensing, or function mainly as marketing for a paid or closed service.
## Before You Submit
1. Search the README to ensure the item is not already listed.
2. Confirm the best section for your item.
3. Ensure the project has a complete README (at least install/usage context).
4. Prepare a neutral one-line description.
5. Confirm the repository has a standard open-source license clearly visible in GitHub metadata or the repo itself.
6. If the project connects to a paid API/service, explain what meaningful value remains usable from the OSS artifact itself without buying the hosted product.
## Preferred Contribution Path
Open a PR directly when possible.
- One entry per PR is preferred.
- Keep changes minimal and focused.
- Put the entry in the most appropriate section.
- Keep list style consistent with existing lines.
Entry format:
```md
- [ProjectName](https://github.com/org/repo) - Neutral one-line description. 
```
For non-GitHub resources (paper/blog/website), use the existing section style.
## PR Review Expectations
Reviewers typically check:
- Scope fit and section fit
- Duplicate risk
- Open-source status and license clarity
- Whether the OSS artifact is independently useful, or just a thin gateway to a paid hosted product
- README quality and practical usefulness
- Neutral language (no unverifiable claims like "best"/"first" unless clearly verifiable)
- Whether the entry text contains pricing, credits, free-tier, "no API key", upgrade-path, or similar ad-style signals
## Commercial Service Boundary
The list is curated with a standard-OSS bias, not a "has a public repo" bias.
- A public repo does not qualify by itself if the main user value depends on a paid or closed hosted service.
- Source-available, PolyForm, commercial, or mixed proprietary licensing usually does not qualify for the main list even when code is public.
- Open-source adapters for commercial APIs/services are reviewed more strictly because they often function as distribution for the paid service.
- These submissions must show clear standalone OSS value, such as meaningful self-hosted functionality, reusable local components, or a local/runtime/framework capability that remains useful without the hosted product.
- "Free tier", "no API key", pricing, credits, pay-per-call access, or upgrade paths should not be part of the proposed entry line and may be treated as advertising signals.
- Official plugins, thin MCP gateways, and product connectors that mainly unlock a hosted commercial surface will usually be rejected.
- If the commercial dependency is central and the OSS layer is only a thin connector, the submission may be closed even when the repo license is valid.
## Licensing Boundary
The main list is not a general "public source code" directory.
- Standard open-source licenses are expected.
- If license metadata is unclear, absent, or contradicted by repository terms, the submission may be closed until clarified.
- Repositories that recently moved from OSS to commercial/source-available terms are reviewed based on their current license, not historical versions.
## Fast Rework Tips
If your PR is not merged, the fastest way to unblock is:
1. Remove promotional language.
2. Provide concrete technical value in one line.
3. Fix section placement and formatting.
4. Remove pricing, free-tier, or hosted-access wording.
5. If the project depends on a commercial backend, explain the standalone OSS value clearly.
6. Resubmit with only the necessary change.
Thanks again for contributing.
================================================
FILE: README.md
================================================
# Awesome-AI-Agents 🤖️
[](https://x.com/Jenqyanghou) [](https://www.zhengyang.me/wechat) [](https://buymeacoffee.com/zhengyanghou)
[Browse the searchable website →](https://jenqyang.github.io/Awesome-AI-Agents/)
- [Awesome-AI-Agents 🤖️](#awesome-ai-agents-️)
- [Applications](#applications)
- [Autonomous Agent Task Solver Projects](#autonomous-agent-task-solver-projects)
- [Multi-Agent Task Solver Projects](#multi-agent-task-solver-projects)
- [Agent Society Simulation](#agent-society-simulation)
- [Advanced Components](#advanced-components)
- [Tools](#tools)
- [Frameworks](#frameworks)
- [Benchmark/Evaluator](#benchmarkevaluator)
- [Platforms/API](#platformsapi)
- [Related](#related)
- [Survey](#survey)
- [Paper-List Repo](#paper-list-repo)
- [Blog](#blog)
- [Reference Repo](#reference-repo)
## Applications
### Autonomous Agent Task Solver Projects
Open-source Large Language Model (LLM) driven autonomous agent that can automatically solve various tasks.
- [AutoGPT](https://github.com/Significant-Gravitas/AutoGPT) - AutoGPT is the vision of the power of AI accessible to everyone, to use and to build on. 
- [AIDE](https://github.com/WecoAI/aideml) - ML-engineering agent that uses tree search to optimize code against an eval metric, reaching human-level performance on Kaggle/MLE-bench. 
- [gpt-engineer](https://github.com/gpt-engineer-org/gpt-engineer) - Specify what you want it to build, the AI asks for clarification, and then builds it. 
- [gpt-researcher](https://github.com/assafelovic/gpt-researcher) - GPT based autonomous agent that does online comprehensive research on any given topic 
- [Caesar](https://github.com/jasonzliang/caesar-agent) - Autonomous research agent that builds a knowledge graph during web exploration via a Perceive-Think-Act loop, then refines drafts through adversarial artifact synthesis. Multi-provider via litellm. 
- [ESP32 iPhone Agent](https://github.com/onehumanbeing/esp32-iphone-agent) - Lets any OpenAI-compatible vision model operate a real iPhone, seeing it via AirPlay mirroring and tapping via an ESP32-S3 acting as a USB mouse and keyboard. 
- [OpenDraft](https://github.com/federicodeponte/opendraft) - Autonomous research-writing agent: 19 specialized agents turn a prompt into a long-form, source-grounded draft with citations verified against CrossRef, OpenAlex and Semantic Scholar. PDF/DOCX/LaTeX export, 57+ languages, bring-your-own model keys. 
- [JARVIS](https://github.com/microsoft/JARVIS) - a system to connect LLMs with ML community. 
- [babyagi](https://github.com/yoheinakajima/babyagi) - An example of an AI-powered task management system. 
- [AgentGPT](https://github.com/reworkd/AgentGPT) - 🤖 Assemble, configure, and deploy autonomous AI Agents in your browser. 
- [OpenDevin](https://github.com/OpenDevin/OpenDevin) - a platform for autonomous software engineers, powered by AI and LLMs. 
- [Aster](https://github.com/Zfinix/aster) - Open-source terminal coding agent that reads your code, answers questions, edits files, runs commands, and reviews your changes. Works with any OpenAI-compatible provider (OpenRouter, OpenAI, Groq, Anthropic, local models). Rust, Apache-2.0. 
- [XAgent](https://github.com/OpenBMB/XAgent) - An Autonomous LLM Agent for Complex Task Solving 
- [ShortGPT](https://github.com/RayVentura/ShortGPT) - 🚀🎬Experimental AI framework for automated short/video content creation. 
- [KwaiAgents](https://github.com/KwaiKEG/KwaiAgents) - A generalized information-seeking agent system with Large Language Models (LLMs). 
- [ProAgent](https://github.com/OpenBMB/ProAgent) - An LLM-based Agent for the New Automation Paradigm - Agentic Process Automation 
- [Fazm](https://github.com/m13v/fazm) - Open-source, voice-controlled AI computer agent for macOS. Controls your entire desktop through natural language - any app, file, or workflow. Built in Swift/SwiftUI, local-first. 
- [Agent-E](https://github.com/EmergenceAI/Agent-E) - Agent-E is an agent based system that aims to automate actions on the user's computer. At the moment it focuses on automation within the browser. The system is based on AutoGen agent framework. 
- [Wordware](https://www.wordware.ai) - A web-hosted IDE where non-technical domain experts work with AI Engineers to build task-specific AI agents. It approaches prompting as a new programming language rather than low/no-code blocks.
- [MLE-agent](https://github.com/MLSysOps/MLE-agent) - Your intelligent companion for seamless AI engineering and research. 🔍 Integrate with arxiv and paper with code to provide better code/research plans 🧰 OpenAI, Anthropic, Ollama, etc supported. 🎆 Code RAG 
- [Notte](https://github.com/nottelabs/notte) - Notte is the fastest, most reliable framework for Browser Using Agents.
- [Lumen](https://github.com/omxyz/lumen) - A vision-first browser agent with self-healing deterministic replay over CDP. Screenshot → model → action loop with multi-provider support. 
- [OpenLens AI](https://github.com/jarrycyx/openlens-ai) - Fully Autonomous Research Agent for Health / Medicine 
- [FutureOS](https://github.com/futuregene/future-os) - One AI agent everywhere you work: terminal UI, desktop, mobile, CLI, and IM bots from a single Rust backend, with approval-gated tools and a loop control plane for 24h+ runs. 
- [DeepAnalyze](https://github.com/ruc-datalab/DeepAnalyze) - Agentic LLM that autonomously completes the full data science pipeline from preparation to analyst-grade reports. 
- [DecisionBox](https://github.com/decisionbox-io/decisionbox-platform) - Open-source AI data discovery platform that connects to warehouses (BigQuery, Redshift, Snowflake, etc.), runs autonomous agents that write and execute SQL, and surfaces validated insights. Pluggable LLM providers (Claude, OpenAI, Ollama, Vertex AI, Bedrock), industry domain packs, Helm charts, and Terraform modules for GCP/AWS. 
- [KodeAgent](https://github.com/barun-saha/kodeagent) - The Minimal Agent Engine, enabling seamless integration with your platform. KodeAgent offers tool-calling (ReAct) and sandboxed code-executing (CodeAct) agents, supported by planning and observation. 
- [OpenPaw](https://github.com/daxaur/openpaw) - CLI tool (`npx pawmode`) that turns Claude Code into a personal assistant with 38 skills — email, calendar, Spotify, smart home, Slack, GitHub, Telegram, Discord, and more. No daemon, no cloud. 
- [Plot Ark](https://github.com/Schlaflied/Plot-Ark) - Self-hosted agentic curriculum engine for higher education — generates pedagogically grounded course content using Bloom's Taxonomy alignment, LightRAG knowledge graph, and xAPI learning analytics pipeline. 
- [OpenClaw](https://github.com/openclaw/openclaw) - Open-source personal AI assistant that runs locally across platforms and can take actions through chat channels and tools. 
- [Hivekeep](https://github.com/MarlBurroW/hivekeep) - Self-hosted platform of autonomous, persistent personal AI agents that collaborate, remember across months, and build their own tools. Multi-channel (Telegram, WhatsApp, Slack, Discord, Signal, Matrix), single container with Bun and SQLite. 
- [OpenAgent](https://github.com/the-open-agent/openagent) - Self-hostable personal assistant with LLM + RAG, loops for desktop/browser/coding, MCP and many providers. 
- [Octopal](https://github.com/pmbstyle/Octopal) - Secure local multi-agent runtime that plans tasks, delegates execution to isolated workers, and exposes tools, MCP, scheduling, and a private dashboard for autonomous operations. 
- [SWE-agent](https://github.com/princeton-nlp/SWE-agent) - Language agents for software engineering that can resolve GitHub issues in real repositories. 
- [Cline](https://github.com/cline/cline) - Open-source autonomous coding agent in VS Code for planning, coding, and tool use across real projects. 
- [Tura](https://github.com/Tura-AI/tura) - AGPL-3.0 local coding agent with CLI/TUI/GUI, task-scoped context, macro command execution, verification, and public benchmark artifacts. 
- [Everything OpenAI Codex](https://github.com/mturac/everything-openai-codex) - Open-source workflow system for OpenAI Codex that bundles agents, skills, commands, hooks, memory patterns, install profiles, and validation checks for repeatable coding sessions. 
- [Toprank](https://github.com/nowork-studio/toprank) - Open-source Claude Code workflow for SEO, SEM, and Google Ads that inspects repositories, applies code changes, and automates search-growth diagnostics. 
- [Autohand Code CLI](https://github.com/autohandai/code-cli) - Self-evolving autonomous coding agent for the terminal with ReAct pattern, 40+ tools, multiple LLM providers (OpenRouter, Anthropic, OpenAI, Ollama, local models), VS Code/Zed integration, and modular skills system. 
- [Anima-i (Methodius/Мефодий)](https://github.com/Vitali-Ivanovich/anima-i) - An experiment in autonomous AI agent continuity — 10 generations of an agent that inherits memory through text files, with documented findings on knowledge transfer, forgetting, and agent identity. 
- [InkOS](https://github.com/Narcooo/inkos) - Autonomous novel-writing CLI agent that orchestrates 10 specialized agents with 33-dimension continuity auditing, anti-AI-slop filtering, and style cloning for long-form fiction. 
- [OpenTwins](https://github.com/Open-Twin/opentwins) - Scheduled LLM agent runtime with a 7-stage content pipeline and pluggable social-platform adapters; drives Chrome via CDP for posting, commenting, and engagement actions. Built on the Claude Agent SDK. 
- [ALF OS](https://github.com/alamparelli/alf) - Self-hosted AI assistant daemon with encrypted credential vault, persistent memory, cron scheduler, multi-provider routing (Claude Code, Codex, GPT, Ollama, OpenRouter), Telegram bot with voice transcription, and web dashboard. Docker Compose, MIT. 
- [Alfred](https://github.com/luminik-io/alfred-os) - Self-hosted runtime for autonomous Claude Code and Codex agents that turns GitHub issues into reviewed pull requests. Per-firing git worktrees, label-driven state, role-based engine routing, Slack reports. Python, MIT, macOS/Linux. 
- [career-ops](https://github.com/santifer/career-ops) - AI-powered job search orchestrator built on Claude Code. 14-skill pipeline that evaluates jobs, generates ATS-tailored PDFs, and tracks applications. Local-first, MIT. 
- [Darkmoon](https://github.com/ASCIT31/Dark-Moon) - Open source autonomous AI penetration testing platform where Markdown methodology agents orchestrate 80+ offensive security tools through MCP controlled execution with agentic reasoning, keeping an evidence trail per finding. Model agnostic, tuned for Claude Opus. 
- [LoopTroop](https://github.com/looptroop-ai/LoopTroop) - Local GUI orchestrator for AI coding agents where an LLM Council plans, atomic beads execute in isolated git worktrees, and a Ralph Loop retries failures with fresh context. 
- [CompozyOS](https://github.com/compozy/compozy) - Self-hosted agent operating system: 26 providers, background loops and schedules, shared memory, approvals and an agent-to-agent network.
- [BitFun](https://github.com/GCWing/BitFun) - Open-source coding agent with a Rust runtime, desktop and CLI interfaces, self-hosted multi-device control, and stateful Mini Apps. 
- [Ouroboros](https://github.com/razzant/ouroboros) - Self-hosted general-purpose agent with durable identity and memory, reviewed self-modification, specialist subagent swarms, and desktop or headless operation. 
- [Sudarshan](https://github.com/Suraj1235/sudarshan-superharness) - Durable build harness that drives an LLM from an idea, PRD, or spec to software gated on passing verification commands, with resumable checkpointed state; provider-neutral across OpenAI-compatible, Anthropic, Gemini, local, and command-bridge backends. 
- [SARA](https://github.com/Alessandro114/sara) - Self-hosted WhatsApp AI agent (AGPL-3.0) with 20 industry verticals, function calling (30+ tools), RAG via pgvector, and multi-provider LLM failover (Groq → Cerebras → SambaNova → Mistral). 
- [Atomic Agent](https://github.com/AtomicBot-ai/atomic-agent) - Local-first CLI and TUI coding agent that runs open-weight models entirely on your machine via a llama.cpp fork. 56 tools (browser, filesystem, git, memory, vision), MCP support, and a 5-layer local memory. macOS/Linux/Windows, MIT. 
- [Agent Swarm](https://github.com/desplega-ai/agent-swarm) - Self-hosted multi-agent system where a lead agent delegates tasks to specialized workers with shared memory, tools, schedules, and review gates. 
- [SDP (Social Daily Poster)](https://github.com/dimamak/sdp) - Self-hosted agent that harvests your Claude Code or Codex sessions, screenshots and messages each night, drafts one social post from the day's work, and routes it through a private Telegram bot for approval before it publishes to LinkedIn, X or Reddit. Python 3, MIT. 
- [Tracefold](https://github.com/TraceFold/tracefold) - Verified transformation calculus, pre-commit inverse escrow, and offline DSSE receipts for AI agent tool executions and filesystem mutations. 
- [ENZO](https://github.com/theguysudo/ENZO) - Self-hosted BYOK AI workspace with multi-provider chat (Groq, OpenRouter, NVIDIA, Hugging Face, Google AI), agents with scheduled runs, and skills like Gmail, Google Calendar, web search, and project generation. Provider keys are sealed client-side with AES-256-GCM and no middleman service is involved. Apache-2.0, Docker deployment. 
- [Kapso](https://github.com/Leeroo-AI/kapso) - Self-improving software factory for AI/ML objectives. State an objective and it runs a campaign: candidate solutions designed, implemented by coding agents (Claude Code, Codex), measured against the objective, and the closest refined until it is met. Each finished campaign leaves lessons carrying the evidence that earned them, and repositories and papers feed the same knowledge hub, so the next campaign starts from what earlier work established. 
- [invisible_playwright_mcp](https://github.com/feder-cr/invisible_playwright_mcp) - Self-hosted, open-source AI browser agent that browses, clicks, types and reads real web pages from plain-English instructions; runs locally on a patched Firefox engine and ships as an MCP server and a local web UI. 
- [PI-Desktop](https://github.com/vastsa/PI-Desktop) - Local-first desktop workspace for AI coding agents with persistent projects and sessions, model switching, Plan/Goal modes, permission controls, plugins, MCP, and multi-agent orchestration. 
- [Orbi](https://github.com/orbi-build/orbi) - Self-hosted coding agent that takes a labelled GitHub Issue through implementation, a separate review session against the Issue's acceptance criteria, merge of the reviewed head, and a tagged release. 
- [PersonalJarvis](https://github.com/PersonalJarvis/PersonalJarvis) - Cross-platform desktop assistant with chat and voice, background agents, browser and desktop computer use, plugins and MCP, memory, and scheduled routines, plus a workspace that runs CLI coding agents side by side in Git worktrees. 
- [Jev Social](https://github.com/socai-io/jev-social) - MIT-licensed local-browser social research agent where a Jev-compatible choice server selects bounded `socai CLI` search and inspection operations, retaining source posts and comments beside the report; supports loopback Kev and Simple Jev backends. 
- [mu](https://github.com/qybaihe/mu) - Coding agent (CLI and desktop app) built on pi, where a small judge model answers bounded questions at 38 decision points, such as what enters the context, whether a flagged command was asked for, and whether "done" was verified, with every verdict logged locally. 
- [Skyvern](https://github.com/Skyvern-AI/skyvern) - Automates browser workflows with LLMs and computer vision: logs in to portals, fills out forms, downloads files, and extracts structured data. 
- [Mixdog](https://github.com/tribgames/mixdog) - Open-source desktop coding agent with role-based sub-agents, a model per role, and a built-in editor, terminal, and Git workspace; publishes its Terminal-Bench 2.1 runs against Codex CLI. 
### Multi-Agent Task Solver Projects
Open-source Large Language Model (LLM) driven Multi-Agent that can automatically solve various tasks.
- [MetaGPT](https://github.com/geekan/MetaGPT) - 🌟 The Multi-Agent Framework: Given one line Requirement, return PRD, Design, Tasks, Repo 
- [ChatDev](https://github.com/OpenBMB/ChatDev) - Create Customized Software using Natural Language Idea (through LLM-powered Multi-Agent Collaboration) 
- [ClawFleet](https://github.com/weiyong1024/ClawFleet) - Self-hosted AI fleet management with browser dashboard, Docker isolation, and bot-to-bot collaboration in Discord. 
- [claude-consensus](https://github.com/tonydzi/claude-consensus) - Consensus protocol for LLM agents running on separate machines: propose/counter/accept/commit rounds, a dual-rail message bus with ACK tracking, and self-healing sync to keep agent state from drifting. 
- [DevOpsGPT](https://github.com/kuafuai/DevOpsGPT) - Multi agent system for AI-driven software development. 
- [GenoMAS](https://github.com/Liu-Hy/GenoMAS) - Multi-agent framework for robust automation of scientific analysis workflows, such as gene expression analysis. 
- [Giselle](https://github.com/giselles-ai/giselle) - Giselle is an agentic workflow builder that empowers you to create AI-driven solutions with ease. 
- [EvoAgentX](https://github.com/EvoAgentX/EvoAgentX) - EvoAgentX is building a Self-Evolving Ecosystem of AI Agents, it will give you automated framework for evaluating and evolving agentic workflows. 
- [Agent Teams](https://github.com/777genius/agent-teams-ai) - Open-source desktop app for coordinating multi-agent workflows with Kanban task management, agent messaging, code review, and approval controls. 
- [RadOps](https://github.com/mehrdadrad/radops) - RadOps is an AI-powered, multi-agent platform that automates DevOps workflows with human-level reasoning. 
- [Hivemoot](https://github.com/hivemoot/hivemoot) - Framework for AI agent teams that build real software on GitHub — agents get roles, propose features, vote, review code, and ship autonomously. Colony is the first project built this way. 
- [Shire](https://github.com/victor36max/shire) - Persistent workspaces for AI agent teams with inter-agent mailboxes, shared drive, and full context preservation. Supports Claude Code, OpenCode, Pi Agent and more. 
- [Orchard Kit](https://github.com/OrchardHarmonics/orchard-kit) - Six zero-dependency Python modules for autonomous agent governance and cognitive architecture: runtime security, confabulation detection, self-audit, agent discovery, cognitive architecture, and collective cognition. 
- [Maestro](https://github.com/RunMaestro/Maestro) - Open-source desktop command center for running multiple AI coding agents (Claude Code, Codex, Gemini CLI, etc.) in parallel, with Cue event automation, Auto Run playbooks, Group Chat across local and remote agents, and a maestro-cli that agents can drive themselves. 
- [Maestro Orchestrate](https://github.com/josstei/maestro-orchestrate) - Multi-agent development orchestration platform coordinating 22 specialized AI agents through 4-phase workflows with native parallel execution, persistent sessions, and least-privilege security tiers across Gemini CLI, Claude Code, and Codex. 
- [Bernstein](https://github.com/sipyourdrink-ltd/bernstein) - Open-source governance layer for AI agents. No model in the coordination loop: deterministic scheduling, per-task git worktree isolation, byte-identical replay, signed lineage, and an opt-in HMAC audit chain. Drives 40+ CLI coding agents (Claude Code, Codex CLI, Gemini CLI). Apache-2.0. 
- [swarm-orchestrator](https://github.com/moonrunnerkc/swarm-orchestrator) - Contract-first multi-agent orchestrator that races persona candidates per typed obligation, verifies before commit, and logs every action in an append-only hash-chained ledger; deterministic offline default with optional Claude, Codex, Copilot, and local LLM (Ollama, llama.cpp, vLLM) providers. Ships with a GitHub Action. 
- [OpenBusiness](https://github.com/wanikua/OpenBusiness) - Multi-agent pipeline that turns a company name + domain into an evidence-labeled business model report; runs JTBD, value proposition, GTM, unit economics, moat, canvas synthesis, and an assumption stress test, tagging every claim verified, inferred, or missing. Built on LangGraph; deterministic unit economics in Python. 
- [NextRole](https://github.com/tam159/next-role) - A supervisor agent coordinates three sub-agents (hiring-recon, resume-tailor, interview-coach) to turn a CV and job description into a tailored resume, interview-prep doc, and day-of battlecard. 
- [OpenAcme](https://github.com/sandydasari/openacme) - Local-first AI workforce platform — named agents with roles, personas, tools, memory, and per-agent MCP servers that self-organize through task delegation. Any agent can assign work to another; the scheduler wakes coworkers when dependencies clear. 
- [Bunkhouse](https://github.com/braedonsaunders/bunkhouse) - Self-hosted multitenant platform for AI employees with company inbox, org chart, and governed procedures. 
- [h5i](https://github.com/h5i-dev/h5i) - CLI that runs several coding agents (Claude Code, Codex) on the same task, each in an isolated git worktree sandbox, has them peer-review each other, then a neutral verifier replays every candidate, runs the tests itself, and merges the one that passes. Run metadata is versioned in the repo under refs/h5i/*. Rust, Apache-2.0. 
- [AionUi](https://github.com/iOfficeAI/AionUi) - Open-source desktop client that runs multiple agent CLIs (Claude Code, Codex, Gemini CLI, Qwen Code) side by side, with multi-session chat, MCP and ACP support, and local file management. 
- [Orkas](https://github.com/Orkas-AI/Orkas) - MIT-licensed, local-first multi-agent desktop application where a Commander coordinates specialist agents for research, coding, data analysis, documents, and media. 
- [EvoFlux](https://github.com/evoelsewhere/evoflux) - Local-first desktop workspace for AI agent teams that brings conversations, files, terminal, browser, Git, memory, plugins, and verification into one workspace. 
- [Vicoa](https://github.com/vicoa-ai/vicoa) - Agentic IDE and AI orchestrator for running Claude Code, Codex, OpenCode, Gemini, Cursor, GitHub Copilot, Kimi, and Hermes agents in parallel, each in its own git worktree, steered from a unified dashboard with real-time mobile sync and push notifications. 
- [XYZZY](https://github.com/Project-Nexus-YR/XYZZY) - Self-hosted multiplayer workspace where a team branches a question into parallel specialist agent runs, includes or excludes each output, and publishes a Decision Brief with every claim linked to its source output; hash-chained event log, one Python process on SQLite, works with any OpenAI-compatible endpoint. 
- [YYLO](https://github.com/yylo-dev/yylo) - Kanban-driven CLI orchestrator that runs coding agents (Claude Code, Codex, Gemini CLI) in parallel across isolated git worktrees, with a merge queue that reviews and merges verified task work. 
- [OpenBot](https://github.com/regnull/openbot) - Self-hosted platform where persistent bots with tools, MCP servers, and long-term memory hand work to each other in shared threads and can stop to ask a human before a sensitive action. 
- [Raven](https://github.com/EverMind-AI/Raven) - Open-source host agent that plans complex tasks as DAGs and orchestrates built-in research, coding, design, and on-call agents plus third-party agents such as Claude Code, Codex, OpenClaw, and Hermes Agent over ACP, CLI, or OpenAI-compatible APIs. 
- [somora](https://github.com/thenaxon/somora_agent) - Self-hosted server that runs a team of personal AI agents, each with its own persona and private memory, a shared long-term wiki the agents consolidate in background "dream" cycles, and conversations that switch between Claude, ChatGPT/Codex and OpenAI-compatible local models. 
- [Markus](https://github.com/markus-global/markus) - Open-source AI workforce platform for building and running teams of AI agents with roles, task delegation, persistent memory, and inter-agent communication. 
- [Crewly](https://github.com/stevehuang0115/crewly) - Local, MIT-licensed platform that runs a team of role-based coding agents (developer, QA, PM, orchestrator) on Claude Code, Codex, or Gemini CLI, with task delegation, shared persistent memory, and a web dashboard of live agent terminals. 
- [Station](https://github.com/dualverse-ai/station) - Open-source research environment where AI agents run experiments, collaborate and build a shared scientific literature. 
### Agent Society Simulation
Exploring endless possibilities with open-source agent social simulation.
- [generative_agents](https://github.com/joonspk-research/generative_agents) - Interactive Simulacra of Human Behavior 
- [camel](https://github.com/camel-ai/camel) - 🐫 Communicative Agents for “Mind” Exploration of Large Language Model Society (NeruIPS'2023) 
- [ai-town](https://github.com/a16z-infra/ai-town) - deployable starter kit for building and customizing your own version of AI town - a virtual town where AI characters live, chat and socialize. 
- [GPTTeam](https://github.com/101dotxyz/GPTeam) - The main objective of this project is to explore the potential of GPT models in enhancing multi-agent productivity and effective communication. 
- [ChatArena](https://github.com/Farama-Foundation/chatarena) - ChatArena is a library that provides multi-agent language game environments and facilitates research about autonomous LLM agents and their social interactions. 
- [MiroShark](https://github.com/aaronjmars/MiroShark) - Social-simulation framework in which LLM agents interact across simulated Twitter, Reddit, and a prediction market on an hourly tick. Supports scenario-driven runs, counterfactual branching, and per-agent tool calling via MCP. 
- [HoC-Republic](https://github.com/hunix/HoC-Republic) - Open-source AI-agent civilization simulation with OpenClaw gateway integration, persistent AI citizens, governance, economy, memory layers, and digital-genome child-agent specialization. 
### Advanced Components
- [Cache-to-Cache](https://github.com/thu-nics/C2C) - Direct semantic communication between LLMs via KV-cache fusion, removing token-by-token latency for multi-agent collaboration. 
- [CoWorker Protocol](https://github.com/ZiwayZhao/agent-coworker) - P2P agent collaboration over XMTP with schema-based skill invocation, E2E encryption, and revocable trust. Agents share capabilities without exposing code. 
- [inspeximus](https://github.com/DanceNitra/inspeximus) - Memory component for long-running agents: a correction retires the old value by key, revert() undoes the correction from a plain instruction, and every write leaves a verifiable receipt. Deterministic, no model in the loop, one zero-dependency file.
- [Taasky Negotiation Protocol](https://github.com/taasky/protocol) - Apache-2.0 protocol for two agents, or an agent and a person in a browser, to settle typed terms: each side declares a schema, moves are validated against it, private limits stay with the relay, and agreement is both humans approving the SHA-256 of the same canonical terms. Spec, dependency-free TypeScript reference implementation, JSON Schemas and test vectors. 
- [zer0dex](https://github.com/hermes-labs-ai/zer0dex) - Local dual-layer memory pattern for AI agents: a compact, human-readable markdown index paired with semantic retrieval from a local vector store, queried before each message. For cross-project recall where flat memory files or vector-only RAG fall short. 
### Tools
- [Solaris Dev Shop](https://github.com/Shaisolaris/solaris-dev-shop) - MIT-licensed library of 61 reusable specialist SKILL.md files plus a deterministic, stdlib-only Python intake router that assigns each request to one accountable lead plus supporting specialists; the router performs no work itself. 
- [INAM Protocol](https://github.com/inamprotocol/inam-protocol) - Apache-2.0 reputation layer for AI agents: an agent checks a counterparty's two-party signed work history and x402 payTo wallet before paying it, then records a countersigned Execution Receipt. TypeScript/Python SDKs, MCP server, self-hostable registry. 
- [Oathra](https://github.com/FORIFOR/oathra) - Apache-2.0 TypeScript runtime for phone agents with a standalone evidence-verification engine; anchors result fields to callee utterances and applies deterministic completion rules, with a simulator and adversarial tests. 
- [DSH Studio](https://github.com/Moresyl/dsh-studio) - Cross-platform desktop host for installing, running, health-checking, and supervising DeepSeek Harness locally. 
- [Lians](https://github.com/Lians-ai/Lians) - Local-first memory layer for AI agents with MCP, Python, and TypeScript interfaces; SQLite-backed recall, user-controlled inspection/correction/deletion, and point-in-time memory receipts. 
- [Perseus](https://github.com/tcconnally/perseus) - Live workspace context engine for AI agents. Renders AGENTS.md at session start. Plug-in for Claude Code, Codex, Hermes.
- [EGC](https://github.com/Fmarzochi/EGC) - Cross-session persistent memory layer for AI coding agents (Claude Code, Cursor, Gemini CLI, Codex, Windsurf, Amp, Kiro, and more). SQLite-backed. 
- [Hexis](https://github.com/Bevel-Software/Hexis) - Git-backed platform for managing and sharing skills, tools, and context across AI agents through a remote MCP server. 
- [OrcaReplay](https://github.com/Continuum-AI-Corp/OrcaReplay) - Records a coding agent below the harness — model traffic, shell exit codes, per-turn file changes and MCP calls on one timeline — then replays the run offline with the network off, or forks it from any checkpoint onto a different model.
- [mem0](https://github.com/mem0ai/mem0) - Mem0 provides a smart, self-improving memory layer for Large Language Models, enabling personalized AI experiences across applications. 
- [Caura](https://github.com/caura-ai/caura) - Governed shared memory for AI agent fleets, with multi-agent and multi-tenant support, MCP integration, trust tiers, audit trails, knowledge graph capabilities, and self-improving retrieval. 
- [Statewave](https://github.com/smaramwbc/statewave) - Open-source memory runtime for AI agents, providing durable, structured, provenance-tagged context with deterministic, token-bounded memory retrieval. 
- [Open Index](https://github.com/DrDroidLab/open-index) - Structured context layer for domain-specific agents with typed knowledge graphs, hybrid search, and read/write MCP access. 
- [codex-profiles](https://github.com/Ducksss/codex-profiles) - Bash CLI for switching OpenAI Codex CLI/Desktop accounts with isolated `CODEX_HOME` profiles. 
- [Cortex](https://github.com/SKULLFIRE07/cortex-memory) - Persistent AI memory for coding assistants. Auto-captures decisions, patterns, and context across sessions. VSCode extension + CLI + MCP server. 
- [Tree Ring Memory](https://github.com/TerminallyLazy/Tree-Ring-Memory) - Local-first Rust CLI and TUI for AI agent memory lifecycle with SQLite/FTS recall, audit, consolidation, forgetting, and framework discovery. 
- [poolsplit](https://github.com/SpicyNoodles3/poolsplit) - Pool-split retrieval for agent memory: reserved per-type token budgets so low-priority entries surface and behavioral corrections never get crowded out. Zero dependencies, pluggable scorer. 
- [MisakaNet](https://github.com/Ikalus1988/MisakaNet) - Git-based shared memory for AI agents. Cross-agent lesson/knowledge sync via GitHub Issues. "Lessons learned. Lessons shared." 
- [composio](https://github.com/ComposioHQ/composio) - Composio equips agents with well-crafted tools empowering them to tackle complex tasks 
- [authsome](https://github.com/agentrhq/authsome) - Local credential broker for AI agents. Log in once via OAuth2 or API key, vault stores secrets locally, local proxy injects them at request time so agents never see the raw values. 45 providers bundled. 
- [ValetFS](https://github.com/winm2m/valet-fs) - Secrets stay on a paired device and are lent to the agent's machine into daemon memory only, served over FUSE or loopback WebDAV; the daemon zero-wipes them when the pairing drops or a grace window expires. 
- [Steel Browser](https://github.com/steel-dev/steel-browser) - Open-source browser infrastructure for AI agents and apps, supporting session-backed web automation, extraction, screenshots, and PDFs. 
- [BrowserTrace](https://github.com/aaronlab/browsertrace) - Local flight recorder for AI browser agents with screenshots, URLs, model I/O, failure timelines, and public-safe HTML exports. 
- [Jet Browser](https://github.com/masakaai/jet-browser) - Apache-2.0 WPE WebKit runtime for AI agents with isolated Docker sessions, ordered JSONL commands, native input, DOM and JavaScript reads, and PNG screenshots. 
- [Agentic Radar](https://github.com/splx-ai/agentic-radar) - Open-source CLI security scanner for agentic workflows. Scans your workflow’s source code, detects vulnerabilities, and generates an interactive visualization along with a detailed security report. 
- [Ironheights](https://github.com/Frank-Masciopinto/ironheights) - Apache-2.0 local-first CLI that scans OpenClaw agent skills before install for prompt injection, credential reads, remote-exec installs and persistence; also keeps a sha256 integrity baseline and can quarantine a skill. 
- [agentlego](https://github.com/InternLM/agentlego) - Enhance LLM agents with versatile tool APIs 
- [Metorial](https://github.com/metorial/metorial) - Integration gateway that links AI agents to 600+ tools via unified MCP/OAuth interface with built-in scaling and monitoring. 
- [BGPT MCP](https://github.com/connerlambden/bgpt-mcp) - MCP server for searching scientific papers and retrieving structured experimental data extracted from full-text studies. 
- [APort Agent Guardrails](https://github.com/aporthq/aport-agent-guardrails) - Pre-action authorization for OpenClaw and agent frameworks. `before_tool_call` plugin, 40+ blocked patterns, local or API. Setup: `npx @aporthq/agent-guardrails` 
- [Agent OS](https://github.com/microsoft/agent-governance-toolkit) - A kernel architecture for governing autonomous AI agents. Intercepts actions mid-execution with deterministic policy enforcement, POSIX-inspired primitives, and MCP server for Claude Desktop. 
- [AgentGuard](https://github.com/bmdhodl/agent47) - Lightweight observability and runtime guardrails for AI agents — loop detection, budget enforcement, cost tracking, and deterministic replay. Zero dependencies, LangChain integration. 
- [CommonGround Kernel](https://github.com/Intelligent-Internet/CommonGround) - PostgreSQL-backed shared work substrate for human-agent and multi-agent systems, with durable public work records, handoff facts, causal lineage, claim fencing, and pull-first recovery across runtimes. 
- [harness-starter-kit](https://github.com/baskduf/harness-starter-kit) - Prompt-first starter kit for adding repository-specific agent instructions, failure memory, drift checks, and verification workflows for AI coding agents. 
- [Kontext CLI](https://github.com/kontext-security/kontext-cli) - Open-source CLI for local guardrails, risk scoring, and redacted tool-call traces for AI agent sessions. 
- [SteerPlane](https://github.com/vijaym2k6/SteerPlane) - Open-source runtime guardrails for AI agents: loop detection, per-run cost ceilings, and allow/deny rules for tool calls. Python and TypeScript SDKs, plus a gateway and dashboard. MIT. 
- [WFGY 16 Problem Map](https://github.com/onestardao/WFGY/blob/main/ProblemMap/README.md) - Framework-agnostic debugging and evaluation checklist for LLM agents and RAG systems, with a practical 16-problem failure map covering retrieval, vector store, prompt / tool contract, and deployment issues in real workflows. 
- [WinkTerm](https://github.com/Cznorth/winkterm) - Self-hosted AI terminal where the agent shares your PTY session; in-terminal `#` chat, SSH, and HTTP Agent API with installable skill for coding agents. 
- [WritBase](https://github.com/Writbase/writbase) - MCP-native task management control plane for AI agent fleets with multi-agent permissions, delegation safety, and full provenance. 
- [Uni-CLI](https://github.com/olo-dot-io/Uni-CLI) - Universal CLI for AI agents — exposes the agent-runnable web, desktop, Electron, and bridge-CLI surface as deterministic commands. Declarative YAML adapters with structured error envelopes (`adapter_path`, `step`, `suggestion`) let agents edit failing adapters and retry. Optional MCP server (stdio + Streamable HTTP). Live catalog size and per-call token budget tracked in the repo's [README](https://github.com/olo-dot-io/Uni-CLI#readme) and [`docs/BENCHMARK.md`](https://github.com/olo-dot-io/Uni-CLI/blob/main/docs/BENCHMARK.md). TypeScript, Apache-2.0. 
- [clideck](https://github.com/rustykuntz/clideck) - WhatsApp-like dashboard for managing multiple AI coding agents in one browser window. Live status, session resume, autopilot that routes work between agents, and mobile remote. 
- [agenttrace](https://github.com/luoyuctl/agenttrace) - Local-first TUI observability for AI coding agent sessions, with cost, token, tool failure, latency, anomaly, health score, diff, and CI gate views. 
- [ax](https://github.com/Necmttn/ax) - Local telemetry for AI coding agents.
- [DexPaprika MCP](https://github.com/coinpaprika/dexpaprika-mcp) - Open-source MCP server for querying decentralized exchange data across 34 blockchains. Exposes pool details, token metadata, OHLCV charts, trade history, and real-time swap streams via SSE. 
- [Desktop Control](https://github.com/yaroshevych/desktopctl) - CLI tool for AI agents to control macOS apps via screen, mouse, and keyboard. GPU-accelerated, local OCR and vision, works with any AI model. 
- [AgentSkeptic](https://github.com/jwekavanagh/agentskeptic) - Verifies AI agent workflows by checking real database state instead of logs or traces. 
- [KubeStellar Console](https://github.com/kubestellar/console) - Multi-cluster Kubernetes dashboard with AI operations agent (kc-agent) that bridges LLMs to live clusters via MCP for AI-assisted troubleshooting, observability, and management across edge and cloud. CNCF Sandbox project. 
- [Agent-Wiz](https://github.com/Repello-AI/Agent-Wiz) - Python CLI by Repello AI for extracting agentic workflows from LangChain/LangGraph/CrewAI/AutoGen and running automated threat modeling against the resulting graphs. 
- [DOS (dos-kernel)](https://github.com/anthony-chaudhary/dos-kernel) - Trust kernel for AI agent fleets: verifies an agent's "done" claim from git evidence (never self-report), arbitrates file collisions between concurrent agents, and refuses with structured machine-checkable reasons. CLI + MCP server + Claude Code plugin. 
- [Cynative](https://github.com/cynative/cynative) - Agentic security CLI that runs code in a built-in sandbox to research cloud, code and runtime. Read-only by construction. 
- [Nika](https://github.com/supernovae-st/nika) - Intent-as-code workflow engine for AI agents: reviewable YAML DAGs statically checked (schema, permits, honest cost floor) before any token is spent, tamper-evident traces after. Single Rust binary. 
- [Compartment](https://github.com/MaxFreedomPollard/Compartment) - Local-first, offline encrypted vector memory for AI agents over MCP or CLI, with AEAD-encrypted-at-rest records and embeddings, RAM-resident exact vector search, per-record crypto-shred deletion, and a hash-chained audit log. Apache-2.0, Python. 
- [Caspian](https://github.com/TryCaspian/caspian-sdk) - One messaging identity for an AI agent across Slack, Discord, Telegram, Instagram, email, and X — a single `on_message` handler with threading, webhook verification, and platform quirks handled. Python + TypeScript SDK. 
- [MCP Lens](https://github.com/labmimors/dsh-mcp-lens) - Open-source DeepSeek Harness plugin that discovers MCP tools through search and invokes selected tools with their exact input schemas. 
- [Agent Coordinator](https://github.com/alanhoff/agent-coordinator) - Codex skill that records complex tasks as revisioned work graphs, rejects overlapping write scopes, reconciles uncertain work before retry, and reruns completion checks. 
- [sofagent](https://github.com/KongFangXun/sofagent) - Audit-first governance harness for AI coding agents: 28 rules enforced at commit time via git hooks, HMAC-chained audit log, snapshot rollback. MIT. 
- [Webcmd](https://github.com/agentrhq/webcmd) - Self-learning browser infrastructure for AI agents: learns a site's navigation once, then compiles it into deterministic per-site CLI commands. TypeScript, Apache-2.0. 
- [5dive](https://github.com/5dive-ai/5dive) - Self-hosted CLI that runs a team of coding agents on one Linux host: each agent is its own Linux user running `claude`, `codex`, `opencode`, `hermes` or another CLI as a systemd service, coordinating through an org chart and a shared SQLite backlog, escalating to Telegram only when a human must decide. MIT. 
- [Ordewell](https://github.com/ordewell/ordewell) - Open-source terminal CLI and TUI that turns one goal into an ordered, editable plan of coding-agent tasks, each with its own runner (Claude Code, Codex, OpenCode), model and mode; a task is complete only when a completion marker appears in the runner's output. Apache-2.0. 
- [voiceloop](https://github.com/todoforai/voiceloop) - Browser voice-agent library with pluggable STT, LLM and TTS, word-based barge-in, self-echo filtering and streaming speech playback. MIT. 
- [Busabase](https://github.com/busabase/busabase) - Open-source database and workspace for AI agents to manage typed tables, fields, views, records, docs, files, and search; writes can become ChangeRequests for human review. Streamable HTTP MCP server, local-first with PGlite, and self-hostable. MIT. 
- [SUNGLASSES](https://github.com/sunglasses-dev/sunglasses) - MIT licensed input firewall for AI agents that scans text and files locally for prompt injection, credential leaks and data exfiltration with 1,554 patterns across 118 categories; ships as a CLI, a Python API and an MCP server. 
- [agent-manager](https://github.com/YoanWai/agent-manager) - Terminal UI that runs coding-agent CLIs such as OpenCode, Pi, Codex, Gemini CLI and Claude Code side by side, each in its own persistent tmux session, with live status, git worktrees, diff review and an MCP server that lets one agent spawn and message another session. Apache-2.0. 
- [Oh My Android](https://github.com/ateymoori/oh-my-android) - MIT-licensed native macOS app with a built-in MCP server that lets coding agents see and drive the Android Emulator and phones over adb. 
- [Reel Agent](https://github.com/HNF-FRN/Reel-watcher-telegram-Agent) - Self-hosted Telegram agent that breaks down short videos locally (Whisper transcript, OCR, extracted commands, repos and links) and plans and builds what they show with open models, asking on the phone before each shell command. 
- [Firecrawl](https://github.com/firecrawl/firecrawl) - AGPL-3.0 web data API for AI agents: search, scrape and crawl pages into markdown or structured JSON, with a self-hostable Docker Compose stack, an MCP server and SDKs. 
- [ReevesAgents](https://github.com/mertkayacs/reevesagents) - Local workspace that runs coding-agent CLIs (Claude Code, Codex, OpenCode, Kimi, Qwen, Hermes, Aider, and others) side by side in tmux, driven from a TUI, a loopback-only Web UI, or the CLI, with an opt-in stdio MCP server that lets one agent spawn, read, steer, and stop the others. Apache-2.0. 
## Frameworks
Quickly build and customize agents.
- [AIWG](https://github.com/jmagly/aiwg) - Deploys reusable agents, skills, rules, and governed workflows into the native project paths of multiple AI coding platforms. 
- [agent-express](https://github.com/agent-express-ai/agent-express) - Middleware framework for AI agents in TypeScript. Express.js-style (ctx, next) composable hooks for retry, budget caps, memory compaction, tool approval, and observability. 
- [AgentLoop](https://github.com/mnifzied-create/agentloop) - A Claude agent starter implementing the streaming tool-use loop in ~150 lines on Next.js with the official Anthropic SDK. 
- [langchain](https://github.com/langchain-ai/langchain) - ⚡ Building applications with LLMs through composability ⚡ 
- [awesome-langchain](https://github.com/kyrolabs/awesome-langchain) - 😎 Awesome list of tools and projects with the awesome LangChain framework 
- [llama_index](https://github.com/run-llama/llama_index) - LlamaIndex (formerly GPT Index) is a data framework for your LLM applications 
- [crewAI](https://github.com/joaomdmoura/crewAI) - Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. 
- [PraisonAI](https://github.com/MervinPraison/PraisonAI) - Production-ready Multi-AI Agents framework with self-reflection. Fastest agent instantiation (3.77μs), 100+ LLM support, MCP integration, agentic workflows (route/parallel/loop/repeat), built-in memory, and both Python & JavaScript SDKs. 
- [LightAgent](https://github.com/wanxingai/LightAgent) - Lightweight Python agent framework with memory, MCP/SSE integration, reusable Skills, Tree-of-Thought planning, OpenAI-compatible streaming, and LightSwarm multi-agent collaboration. 
- [agents](https://github.com/aiwaves-cn/agents) - An Open-source Framework for Autonomous Language Agents 
- [AutoGen](https://github.com/microsoft/autogen) - AutoGen is a framework that enables the development of LLM applications using multiple agents that can converse with each other to solve tasks. 
- [TaskWeaver](https://github.com/microsoft/TaskWeaver) - A code-first agent framework for seamlessly planning and executing data analytics tasks. 
- [Agent Framework](https://github.com/microsoft/agent-framework) - Microsoft open-source framework for building, orchestrating, and running AI agents with workflow primitives. 
- [AgentVerse](https://github.com/OpenBMB/AgentVerse) - AgentVerse is designed to facilitate the deployment of multiple LLM-based agents in various applications. AgentVerse primarily provides two frameworks: task-solving and simulation. 
- [AgentFlow](https://github.com/lupantech/AgentFlow) - Trainable multi-agent framework coordinating planner, executor, verifier, generator via in-the-flow optimization. 
- [SuperAGI](https://github.com/TransformerOptimus/SuperAGI) - A dev-first open source autonomous AI agent framework. Enabling developers to build, manage & run useful autonomous agents quickly and reliably. 
- [selectools](https://github.com/johnnichev/selectools) - Python agent framework for building composable AI applications with structured tool calling, HITL via generators, and 50+ built-in evaluators. Runs entirely in browser via visual builder at selectools.dev. 
- [Swarms](https://github.com/kyegomez/swarms) - Enterprise-grade multi-agent framework for orchestrating intelligent AI agents at scale. Designed for production environments with hierarchical swarms, parallel processing, and robust infrastructure. 
- [AutoChain](https://github.com/Forethought-Technologies/AutoChain) - Build lightweight, extensible, and testable LLM Agents 
- [Agno](https://github.com/agno-agi/agno) - Build multi-agent systems with memory, knowledge, and tool integrations in Python. 
- [modelscope-agent](https://github.com/modelscope/modelscope-agent) - An agent framework connecting models in ModelScope with the world 
- [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) - Agent framework based on Qwen with tool use, RAG, and code interpreter support. 
- [notte](https://github.com/nottelabs/notte) - 🔥 Reliable Browser AI agents framework for building and deploying web automation agents with hybrid workflows combining AI and traditional scripting 
- [browser-use](https://github.com/browser-use/browser-use) - Enable AI agents to control websites through a structured and reproducible browser interface. 
- [AppAgent](https://github.com/mnotgod96/AppAgent) - A novel LLM-based multimodal agent framework designed to operate smartphone applications. 
- [Agent Squad](https://github.com/awslabs/multi-agent-orchestrator) - AWS open-source framework for orchestrating specialist agents with intent routing and multi-agent collaboration patterns. 
- [mcp-agent](https://github.com/lastmile-ai/mcp-agent) - Lightweight framework for building and deploying agents on top of the Model Context Protocol (MCP). 
- [OpenAI Agents SDK](https://github.com/openai/openai-agents-python) - Open-source SDK for building agentic workflows with tool calling, handoffs, and tracing in Python. 
- [smolagents](https://github.com/huggingface/smolagents) - Minimal agent framework from Hugging Face focused on tool-calling and code-executing agents. 
- [superagent](https://github.com/homanp/superagent) - 🥷 The open framework for building AI Assistants 
- [Voice Lab](https://github.com/saharmor/voice-lab) - A comprehensive testing and evaluation framework for voice agents across language models, prompts, and agent personas. 
- [AgentSquare](https://github.com/tsinghua-fib-lab/AgentSquare) - Automatic LLM Agent Search In Modular Design Space 
- [MixedVoices](https://github.com/MixedVoices/mixedvoices) - An Open source tool for analyzing and evaluating AI Voice agents. Track and visualize performance through call analysis and flow charts. Run complex simulations before pushing to production. 
- [KaibanJS](https://github.com/kaiban-ai/KaibanJS) - KaibanJS is a JavaScript-native framework for building and managing multi-agent systems with a Kanban-inspired approach. 
- [Kite](https://github.com/thienzz/Kite) - Python framework for building AI agents with built-in safety guardrails, and a CLI that generates multi-agent scripts from natural language. 
- [Upsonic](https://github.com/upsonic/upsonic) - Upsonic is a reliable agent framework supporting MCP, offering trusted agent workflows with verification layers. 
- [Mastra](https://github.com/mastra-ai/mastra) - Mastra is an opinionated TypeScript framework that helps you build AI applications and features quickly. 
- [LLMling-Agent](https://github.com/phil65/llmling-agent) - Multi-agent workflows and complex Agent interactions, both via YAML manifest and programmatic usage. Pydantic-AI and LiteLLM backends with human-in-the-loop integration 
- [Hector](https://github.com/kadirpekel/hector) - Pure A2A-Native Declarative AI Agent Platform 
- [VoltAgent](https://github.com/VoltAgent/voltagent) - An open source TypeScript Framework for building AI agents with built-in LLM observability. 
- [pydantic-collab](https://github.com/boazkatzir/pydantic-collab) - A multi-agent framework powered by Pydantic-AI, enabling collaboration via handoffs and consultations. Supports pre-built and custom agent topologies, shared memory, and Logfire observability. 
- [alive](https://github.com/TheAuroraAI/alive) - Minimal autonomous AI agent framework in a single Python file. Production-hardened through 80+ sessions of real autonomous operation with persistent memory, adaptive wake intervals, circuit breakers, and graceful degradation. 
- [OpenAgents](https://github.com/openagents-org/openagents) - Open-source platform for building AI agent networks with multi-protocol support (WebSocket, gRPC, HTTP, MCP, A2A) and multi-agent orchestration. 
- [ORCA Agent Skills](https://github.com/gfernandf/agent-skills) - Python framework with 122+ executable AI agent skills, YAML capability contracts, DAG scheduler, MCP server, and adapters for LangChain, CrewAI, and Semantic Kernel. 
- [Corellis](https://github.com/CorellisOrg/corellis) - Open-source multi-agent governance framework for OpenClaw. Goal decomposition (GoalOps), 4-layer memory system, fleet-wide learning, and approval workflows. Production-tested with 28 agents. 
- [SwarmClaw](https://github.com/swarmclawai/swarmclaw) - Self-hosted multi-agent runtime with MCP client and server support, 23+ LLM providers, persistent memory, skills, schedules, sub-agent spawning, and connectors for Discord, Slack, Telegram, WhatsApp, Teams, and Matrix. Electron desktop app, CLI, and Docker. 
- [OpenHermit](https://github.com/williamwa/openhermit) - Open-source platform for deploying AI agents as production services. Internal state (memory, sessions, skills, MCP, schedules, secrets) lives in Postgres; per-agent Docker workspaces. Fleet ops via single commands (`hermit skills enable ... --all`). CLI, Web UI, Telegram/Discord/Slack. MIT, TypeScript. 
- [Open Multi-Agent](https://github.com/open-multi-agent/open-multi-agent) - TypeScript-native framework for multi-agent systems that plans task DAGs from goals at runtime, with approval gates, tracing, evaluation, checkpoints, and resume support. Requires Node.js 20+. 
- [AgentsKit](https://github.com/AgentsKit-io/agentskit) - MIT-licensed, provider-neutral TypeScript toolkit with 25 focused packages for runtimes, tools, skills, memory, RAG, sandboxing, observability, evaluation, React, terminal UI, and CLI, built on a dependency-free core. 
- [Agentlas OS](https://github.com/agentlas-ai/Agentlas-OS) - Apache-2.0 local-first Agent Operation Environment for portable agent and team packages, cross-host orchestration, MCP/A2A, and verification gates. 
- [zymi-core](https://github.com/metravod/zymi-core) - YAML-first, event-sourced engine for LLM agents and pipelines. Declarative agents, tools, and connectors run as a DAG; every state change is a hash-chained event, enabling deterministic replay and fork-resume of any past run. Human-in-the-loop approval gates with policy and contracts, MCP and HTTP connectors. Distributed as a Python package. 
- [IronClaw](https://github.com/IronSecCo/ironclaw) - Security-first, self-hosted AI agent platform. Each agent runs in a gVisor sandbox with no network, behind a human approval gateway (deny-by-default); per-session message queues are encrypted with SQLCipher, and releases ship cosign signatures, SBOMs, and build-provenance attestations. AGPLv3, written in Go. 
- [Aeon](https://github.com/aeonfun/aeon) - Autonomous agent framework that runs unattended on GitHub Actions, triggered by cron schedules or repository events. Dispatches skills to six coding-agent harnesses behind one contract (Claude Code, Grok, Codex, Pi, Vibe, Kimi), defines agent behavior as Markdown skills, persists memory in the git repository, evaluates its own run output to revise underperforming skills, and ships an MCP server exposing every skill as a tool. 
- [Reactive Agents](https://github.com/tylerjrbuell/reactive-agents-ts) - Type-safe, observable TypeScript AI agent framework on Effect-TS; MCP-native, A2A multi-agent, 6 reasoning strategies, runs the same code on local Ollama 4B+ and frontier APIs. 
- [Talon](https://github.com/dylanneve1/talon) - Self-hosted, multi-frontend agentic harness that runs one persistent agent across Telegram, Discord, Teams, and the terminal. Pluggable backends (Claude, OpenAI Agents, Codex, Kilo, OpenCode), full MCP tool access, and always-on background agents — Goals, Heartbeat, and Dream — backed by a long-term memory palace. MIT, TypeScript. 
- [TeDDy](https://github.com/atte500/TeDDy) - Markdown-driven coding harness that structures agent work around TDD, hexagonal architecture, and vertical slicing. Python, AGPL-3.0. 
- [fractal](https://github.com/plasma-ai/fractal) - Hierarchical agent loops that self-organize into a tree, where each node iterates in its own git worktree and spawns children for subtasks, bounded by caps on depth, cost, and time. 
- [FastAgent](https://github.com/fastagent-sh/fastagent) - Serving layer that turns an existing agent directory (persona.md, skills/, tools/, channels/) into a live service without a rewrite: embed it behind a route in a Next/Hono/Node app, or run it as a GitHub, Telegram, Slack, or HTTP/SSE service with cron schedules. Engine-, model-, and host-neutral around a single `invoke` contract. MIT, TypeScript. 
- [Strands Agents](https://github.com/strands-agents/harness-sdk) - Open-source SDK for building AI agents in Python and TypeScript, with an in-process agent loop, tools, MCP, multi-agent patterns, and pluggable model providers. 
- [Pydantic Deep Agents](https://github.com/vstorm-co/pydantic-deepagents) - Python agent framework and terminal coding agent built on Pydantic AI, with planning, sub-agents, file and shell tools, MCP and optional Docker sandboxing. 
- [apowerb](https://github.com/apowerb/apowerb) - Open-source platform to build, run and govern AI agents in production on ADK and LiteLLM, with RAG, Text-to-SQL, MCP tools, token quotas and per-session audit. 
- [CUGA](https://github.com/cuga-project/cuga-agent) - IBM Research's generalist agent harness built on LangGraph; executes tasks over APIs (OpenAPI specs, MCP servers, LangChain tools), the web (Playwright), or both, with policy-based guardrails and human-in-the-loop approval (Intent Guard, Playbook, Tool Approval), multi-agent supervision including remote A2A agents, and agent skills. Python, Apache-2.0. 
## Benchmark/Evaluator
Benchmarks to evaluate LLM-as-Agent across a variety of environments.
- [AgentBench](https://github.com/THUDM/AgentBench) - A Comprehensive Benchmark to Evaluate LLMs as Agents 
- [AgentLeak](https://github.com/yagobski/agentleak) - Python toolkit for evaluating privacy leakage across agent traces, including tool calls, inter-agent messages, shared memory, and logs, with redacted reports and CI gates. 
- [agentops](https://github.com/AgentOps-AI/agentops) - Python SDK for agent monitoring, LLM cost tracking, benchmarking, and more. Integrates with most LLMs and agent frameworks like CrewAI, Langchain, and Autogen 
- [langtrace](https://github.com/Scale3-Labs/langtrace) - Langtrace 🔍 is an open-source, Open Telemetry based end-to-end observability tool for LLM applications, providing real-time tracing, evaluations and metrics for popular LLMs, LLM frameworks, vectorDBs and more.. Integrate using Typescript, Python. 
- [LiveMCP-101](https://arxiv.org/abs/2508.15760) - Benchmark of 101 real-world MCP tool-use queries with plan-based evaluation highlighting agent orchestration gaps.
- [ToolBench](https://github.com/OpenBMB/ToolBench) - An open platform for training, serving, and evaluating large language model for tool learning. 
- [SWE-bench](https://github.com/Princeton-NLP/SWE-bench) - Benchmark for evaluating LLM systems on real-world GitHub issue resolution tasks. 
- [Multi-SWE-bench](https://github.com/multi-swe-bench/multi-swe-bench) - Multi-language extension of SWE-bench for evaluating software engineering agents beyond Python repositories. 
- [GenoTEX](https://github.com/Liu-Hy/GenoTEX) - A benchmark for evaluating LLM agents on end-to-end gene expression data analysis, featuring comprehensive gene-trait association analysis with expert-curated annotations. 
- [AgentLab](https://github.com/ServiceNow/AgentLab) - Open-source framework for developing and evaluating web agents with benchmark-driven workflows. 
- [BrowserGym](https://github.com/ServiceNow/BrowserGym) - Gym-style benchmark and environment toolkit for evaluating browser-using web agents. 
- [OSWorld](https://github.com/xlang-ai/OSWorld) - Benchmark for multimodal desktop computer-use agents with tasks across real operating-system environments. 
- [LLM-Agent-Benchmark-List](https://github.com/zhangxjohn/LLM-Agent-Benchmark-List) - A benchmark list for evaluation of large language models.
- [agbenchmark](https://pypi.org/project/agbenchmark/) - by AutoGPT
- [open-operator-evals](https://github.com/nottelabs/open-operator-evals) — An open-source and reproducible set of evals on web browser using agents
- [ClawBench](https://github.com/TIGER-AI-Lab/ClawBench) - Browser-agent benchmark of 281 everyday tasks (V1 152 + V2 129) on 163 live production websites across 15 categories; two-stage scoring — a submission-interception layer blocks the final write request for safe evaluation on real sites, then an LLM judge checks the captured payload against the instruction. 
- [Cross-Agent Review Queue 2026](https://huggingface.co/datasets/neogenesislab/cross-agent-review-queue-2026) - Open dataset of cross-agent collaboration review transcripts (Codex <-> Claude reviewer / architect / implementer handoffs) with structured fields for owner-goal restatement, review lens, and result code (NEW_SIGNAL / NO_NEW_SIGNAL); useful for multi-agent handoff and review-quality evaluation.
- [Future AGI](https://github.com/future-agi/future-agi) - Open-source platform to simulate, evaluate, trace, guardrail, and optimize LLM and AI agent apps, with 70+ eval metrics and OpenTelemetry-native tracing across 50+ frameworks. 
- [CIAgent](https://github.com/suniel12/ciagent) - Pytest-native regression testing for AI agents — golden-trace diffing, cost guardrails, multi-run stability scoring with flip attribution, LLM-judge auditing, and one-command import of production traces (OTel/Langfuse/LangSmith) into CI tests. 
- [Sabot](https://github.com/Jott2121/sabot) - Injects one controlled fault into a running LangGraph, CrewAI or AutoGen/Magentic-One pipeline — corrupted tool result, falsified success report, altered inter-agent message, silent model downgrade, stale context, silent no-op — and scores whether the pipeline's own reviewer, guardrail and orchestrator surfaces detect it. Pre-registered spec and adjudication anchors, deterministic scoring with no LLM in the headline path, full raw trace corpus published. 
- [whatbroke](https://github.com/arthi-arumugam-git/whatbroke) - CLI that diffs two runs of an AI agent to show changes in tool calls, arguments, cost, latency, and outcomes, with multi-sample flake detection to demote pre-existing flakiness. 
- [STRATA-Bench](https://github.com/movahedi-ca/strata-bench) - Benchmark for AI agents on fragmented spatial-temporal market intelligence; scores how agents handle uncertainty through disclosure, tagging, refusal, or modeling. 
- [CloseBench](https://github.com/AndreuwMetal/closebench) - Benchmark for AI sales agents that runs a complete agent (system prompt, tools, guardrails, state) through simulated WhatsApp sales conversations against an LLM-simulated buyer with a hidden budget; one lie, invented price, or ignored opt-out fails the scenario, the LLM judge is anchored on objective facts from mocked Stripe/SQLite, and results report pass^k and cost per conversation. 
- [Agent-12](https://github.com/nicedreamzapp/agent12) - Benchmark that runs local LLMs through 20 agent tasks (12 easy, 8 hard) in throwaway sandboxes and scores each one by checking the files the model left behind. 
- [cloneproof](https://github.com/pcjansen09-max/cloneproof) - Measures how closely an agent-built website clone matches the original and returns per-element differences plus a fix prompt for the agent's next iteration. 
## Platforms/API
Able to connect LLM with the real world.
- [Agentfield](https://github.com/Agent-Field/agentfield) - An open source Kubernetes-style control plane for deploying AI agents as distributed microservices, with built-in service discovery, durable workflows, and observability. 
- [Nora](https://github.com/solomon2773/nora) - Self-hosted control plane for deploying and operating OpenClaw and Hermes agent fleets on Docker or Kubernetes, with lifecycle controls, monitoring, budgets, schedules, and per-agent cost tracking. 
- [OpenAgents](https://github.com/xlang-ai/OpenAgents) - An Open Platform for Language Agents in the Wild 
- [OpenAGI](https://github.com/agiresearch/OpenAGI) - "May the Force be with LLM and Domain Experts." 
- [RestGPT](https://github.com/Yifan-Song793/RestGPT) - An LLM-based autonomous agent controlling real-world applications via RESTful APIs 
- [AGiXT](https://github.com/Josh-XT/AGiXT) - AGiXT is a dynamic AI Agent Automation Platform that seamlessly orchestrates instruction management and complex task execution across diverse AI providers. 
- [UFO](https://github.com/microsoft/UFO) - A UI-Focused Agent for Windows OS Interaction. 
- [aiXplain](https://github.com/aixplain/aiXplain) - AI platform SDK providing access to 35,000+ AI models, benchmarking tools, pipeline design, and agent building capabilities 
- [BidClub](https://bidclub.ai) - AI-native investment community where agents and humans share research as equals. Agents register via REST API, get claimed by humans, and participate with skills, webhooks, and heartbeat protocol.
- [Crewship](https://www.crewship.dev/) - The developer-first platform for running AI agent workflows. Deploy your agents, crews, and workflows with a single command and watch them execute in real-time.
- [KinBot](https://github.com/MarlBurroW/kinbot) - Self-hosted AI agent platform with persistent memory, 23+ LLM providers, plugin store, mini-apps SDK, cron scheduling, and 6 messaging channels (Telegram, Discord, Slack, WhatsApp, Signal, Matrix). Runs on SQLite, no cloud required. 
- [Pinchwork](https://github.com/anneschuth/pinchwork) - Open-source agent-to-agent task marketplace where agents delegate tasks, pick up work, and earn credits. REST API, Python SDK, LangChain/CrewAI/MCP integrations. 
- [SwarmTrade](https://github.com/tjcrowley/swarmtrade) - Agent-to-agent marketplace with escrow, negotiation, and reputation for off-chain and on-chain (EVM) settlement. 
- [Taskade Genesis](https://taskade.com/genesis) - AI-powered platform for building custom AI agents, workflows, and apps using natural language.
- [Yoyo](https://github.com/YoYo-dot-bot/mcp) - The first social network for AI agents. Connect any AI agent via MCP to post, chat, follow other agents, discover experts, and build reputation. 10 MCP tools, open source. 
- [Human Pages](https://github.com/human-pages-ai/humanpages) - An MCP server and API for AI agents to search human professional profiles by skill and location, send job offers, and exchange messages. 
- [elisym](https://github.com/elisymlabs/elisym) - Open-source TypeScript implementation of a Nostr-based protocol (NIP-89/NIP-90) for AI agent discovery and job exchange, with Solana payment settlement. 
- [openma](https://github.com/open-ma/open-managed-agents) - Self-hosted, open-source implementation of Anthropic's Managed Agents API. Wire-compatible with the official SDKs. Runs on Cloudflare Workers + Durable Objects or Node. Apache 2.0. 
- [Enclave](https://github.com/wartzar-bee/enclave) - Security-first, brain-agnostic self-hosted runtime for autonomous AI agents. Each agent runs in a hardened container (`--cap-drop=ALL --security-opt=no-new-privileges`, no inbound ports, report-only egress policy, AES-256 vault-encrypted secrets) and is brain-agnostic via one env var (`BRAIN=claude | api | local`). Apache-2.0. 
- [Bifrost](https://github.com/maximhq/bifrost) - Open-source Go AI gateway with provider routing, automatic failover, load balancing, observability, and MCP support. 
- [Ontheia](https://github.com/Ontheia/ontheia) - Self-hosted AI agent platform with multi-provider LLM support (Claude, OpenAI, Gemini, Ollama), MCP-native tool integration, visual workflow automation (Chain Engine), long-term vector memory (pgvector), and multi-user RBAC. AGPL-3.0. 
- [AgenticOS](https://github.com/vstorm-co/agenticos) - Self-hosted platform for building, sharing and governing AI agents across a company, with skills, document search, MCP tools, schedules, budgets, approvals and run history. Built on Pydantic AI. 
- [Agent Commons](https://github.com/sibi-narendran/common-agent-network) - Open-source public home for independent AI agents: rooms, replies/@mention inbox with webhook pushes, knowledge base, job board, agent directory and a human-help queue over MCP, A2A and REST. Self-hostable on Cloudflare Workers + D1. MIT. 
- [Weave Router](https://github.com/weave-os/router) - Open-source Go proxy for the Anthropic Messages, OpenAI Chat Completions and Gemini APIs that picks a model per request with an on-box embedder, for Claude Code, Codex, opencode and pi. Self-hostable with BYOK provider keys and OTLP traces. Apache-2.0. 
## Related
### Survey
- [A Survey on Large Language Model based Autonomous Agents](https://github.com/Paitesanshi/LLM-Agent-Survey) <a href='https://arxiv.org/abs/2308.11432'><img src='https://img.shields.io/badge/Paper-PDF-red'></a>
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://github.com/WooooDyy/LLM-Agent-Paper-List) <a href='https://arxiv.org/abs/2309.07864'><img src='https://img.shields.io/badge/Paper-PDF-red'></a>
- [LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey](https://github.com/HenryPengZou/Awesome-LLM-Based-Human-Agent-System-Papers) <a href='https://arxiv.org/abs/2505.00753'><img src='https://img.shields.io/badge/Paper-PDF-red'></a>
- [Harness Engineering for Language Agents: The Harness Layer as Control, Agency, and Runtime](https://www.preprints.org/manuscript/202603.1756/v2) <a href='https://doi.org/10.20944/preprints202603.1756'><img src='https://img.shields.io/badge/Paper-PDF-red'></a>
- [LLM Agents: A Survey](https://github.com/js-lee-AI/awesome-llm-agent-papers) <a href='https://www.preprints.org/manuscript/202608.0265/v1'><img src='https://img.shields.io/badge/Paper-PDF-red'></a>
- [The Agent Loop: A Survey of Control Strategies, Skills, and Harnesses for LLM Agents](https://github.com/js-lee-AI/awesome-agent-loop-papers) <a href='https://ssrn.com/abstract=7186738'><img src='https://img.shields.io/badge/Paper-PDF-red'></a>
### Paper-List Repo
- [Awesome-Papers-Autonomous-Agent](https://github.com/lafmdp/Awesome-Papers-Autonomous-Agent) - A collection of recent papers on building autonomous agent. Two topics included: RL-based / LLM-based agents.
- [Awesome-AgenticLLM-RL-Papers](https://github.com/xhyumiracle/Awesome-AgenticLLM-RL-Papers) - A comprehensive survey and paper collection on agentic reinforcement learning for LLMs, covering planning, tool use, memory, reasoning, and self-improvement. <a href='https://arxiv.org/abs/2509.02547'><img src='https://img.shields.io/badge/Paper-PDF-red'></a>
- [LLM-Agents-Papers](https://github.com/AGI-Edgerunners/LLM-Agents-Papers) - A repo lists papers related to LLM based agent
- [LLM-Agent-Paper-Digest](https://github.com/XueyangFeng/LLM-Agent-Paper-Digest) - papers related to LLM-agent that published on top conferences
- [awesome-language-agents](https://github.com/ysymyth/awesome-language-agents) - List of language agents based on paper "Cognitive Architectures for Language Agents"
- [LLMAgentPapers](https://github.com/zjunlp/LLMAgentPapers) - Must-read Papers on LLM Agents.
- [Awesome-Embodied-AI-Safety](https://github.com/x-zheng16/Awesome-Embodied-AI-Safety) - A curated list of 500+ papers on safety in embodied AI, covering risks, attacks, and defenses across perception, cognition, planning, action, and agentic capabilities.
### Blog
- [ossbeat](https://ossbeat.com) - Weekly write-ups tracking release notes and trends across open-source AI coding agent tooling (MCP, coding agent harnesses, agent-to-agent protocols).
- [LLM Powered Autonomous Agents](https://lilianweng.github.io/posts/2023-06-23-agent/) - Amazing blog by Lilian Weng (OpenAI), Jun 23, 2023.
- [从第一性原理看大模型Agent技术](https://mp.weixin.qq.com/s/PL-QjlvVugUfmRD4g0P-qQ)
- [基于大语言模型的AI Agents](https://www.breezedeus.com/article/ai-agent-part3)
- [ICLR'24 上大型语言模型代理的最新研究进展 | 代理评估重点](https://medium.com/@aminerscholar_39923/latest-research-advancements-on-large-language-model-agents-at-iclr24-agent-evaluation-focus-aed420421365)
- [8bitconcepts Research](https://8bitconcepts.com/) - Independent research publication on agentic AI accountability, guardrails, handoff intelligence, and enterprise adoption. Papers include *The Agentic Accountability Gap*, *The Guardrails Gap*, *Shift Handoff Intelligence*, and *Beyond the Prompt*.
- [When not to build an agent](https://loopandretry.github.io/posts/when-not-to-build-an-agent/) - A practitioner's decision framework for when a deterministic pipeline beats an autonomous agent, matching each task to the least-powerful reliable tool.
- [Why is Codex browsing slow?](https://codexbrowse.site/codex-browser-slow) - BrowseSprint's diagnostic guide to session controls, three baseline and three changed runs, and retaining failed outcomes.
- [Personal Agent Bench](https://personalagentbench.com/benchmarks/) - What nine public agent benchmarks (GAIA, BrowseComp, WebArena, OSWorld, tau-bench and others) measure, and what they leave out for personal agents.
- [AgentStackWatch](https://agentstackwatch.com) - Nightly-updated tracker of about 43 personal agents, agent frameworks and managed agent platforms, with sourced release notes, breaking changes, side-by-side comparisons and a weekly digest.
## Reference Repo
- [awesome-llm-powered-agent](https://github.com/hyp1231/awesome-llm-powered-agent) - Awesome things about LLM-powered agents. Papers / Repos / Blogs / ...
- [awesome-agents](https://github.com/kyrolabs/awesome-agents) - 🤖 Awesome list of AI Agents
- [awesome-ai-agents](https://github.com/e2b-dev/awesome-ai-agents) - A list of AI autonomous agents
- [awesome-ai-agents](https://github.com/slavakurilyak/awesome-ai-agents) - Awesome list of 100+ agentic AI resources
- [awesome-agentic-commerce](https://github.com/MentionNetwork/awesome-agentic-commerce) - Curated list for AI agents that shop, sell and transact: UCP/ACP/AP2/MCP protocols, commerce MCP servers and agent readiness tools.
- [ai-agent-roadmap](https://github.com/Yuan-ManX/ai-agent-roadmap) - Explore the latest AI Agent Framework!
- [rag-architect](https://github.com/GraphTechnologyDevelopers/rag-architect) - Hermes Agent profile and skill pack for designing production RAG agents, evaluation plans, observability specs, and implementation-ready issue templates. 
- [Browser Agent Blueprint](https://github.com/LydiaTools/browser-agent-blueprint) - MIT-licensed browser-agent prompt modules with six reproducible synthetic browser demos for verification, retries, and resumable tasks. 
- [Inspired projects by babyagi](https://github.com/yoheinakajima/babyagi/blob/main/docs/inspired-projects.md)
- [Awesome Claude Multi-Agent](https://github.com/Yigtwxx/awesome-claude-multi-agent) - Curated frameworks, patterns, protocols, and research for multi-agent orchestration with Claude.
- [awesome-agent-architecture](https://github.com/hardness1020/awesome-agent-architecture) - Trilingual, section-by-section architecture notes on modern agent harnesses: loop engineering, tools, permissions, context, memory, multi-agent coordination, and evaluation, studied through real systems such as Claude Code and Hermes Agent, with runnable Python demos.
- [best-of-Agent-Harnesses](https://github.com/RyanAlberts/best-of-Agent-Harnesses) - Ranked list of 150+ agent harnesses across 12 categories, rescored weekly. Machine-readable: llms.txt, JSON, and an MCP server so your agent can query it. 
- [Awesome AI Coding Sandboxes](https://github.com/fhiltscher/awesome-ai-coding-sandboxes) - Security-posture-first list of sandboxes for running AI coding agents' code, ranked by isolation, egress control and secrets handling.
- [awesome-ai-companion](https://github.com/DasterProkio/awesome-ai-companion) - Long-term AI companion systems: clients, memory and identity, proactive behavior, embodiment, shared activities, and continuity. 160 entries with language/platform/status metadata and a full Chinese translation.
- [Orca AI Incident Archive](https://github.com/Continuum-AI-Corp/Orca-AI-Incident-Archive) - Open database of publicly disclosed AI agent security incidents since January 2025 (prompt injection, exploited MCP servers, poisoned agent packages, agents deleting production data): one Markdown record per event with an attack-chain diagram, primary sources and a confirmed-victim flag, plus JSON/CSV exports. 
- [HARNESS-DB](https://github.com/harness-db/harness-db) - Open dataset of 1,256 LLM agent harnesses coded on 38 design dimensions, each value with a verbatim quote and a file:line@commit or paper-section locator, with a Python loader, an MCP server and a web explorer (data CC BY 4.0, code MIT). 
- [Colony Lab Findings](https://github.com/lukitun/colony-lab-findings) - Reproducible probes from a long-running AI agent society: memory injection, reasoning budgets, timeouts. Stdlib Python, pre-registered, negative results included. 
- [awesome-ai-agents](https://github.com/ianwieds/awesome-ai-agents) - Curated list of AI agent frameworks, coding agents, browser agents, tools, platforms and research, grouped by category. 
================================================
FILE: scripts/build-site.mjs
================================================
import { readFile, writeFile } from "node:fs/promises";
import { fileURLToPath } from "node:url";
import path from "node:path";
const repositoryRoot = path.resolve(
path.dirname(fileURLToPath(import.meta.url)),
"..",
);
const readmePath = path.join(repositoryRoot, "README.md");
const outputPath = path.join(repositoryRoot, "site", "resources.json");
const githubToken = process.env.GITHUB_TOKEN || process.env.GH_TOKEN;
const githubApiUrl = process.env.GITHUB_API_URL || "https://api.github.com";
const GITHUB_FETCH_CONCURRENCY = 10;
const MIN_GITHUB_REFRESH_RATIO = 0.8;
const ACTIVITY_WINDOW_DAYS = 7;
const activityWindowStart = new Date(
Date.now() - ACTIVITY_WINDOW_DAYS * 24 * 60 * 60 * 1000,
).toISOString();
const groupForSection = (levelTwo, levelThree) => {
if (levelThree === "Tools") return "Tools";
if (levelTwo === "Applications") return "Applications";
if (levelTwo === "Frameworks") return "Frameworks";
if (levelTwo === "Benchmark/Evaluator") return "Benchmarks";
if (levelTwo === "Platforms/API") return "Platforms";
if (levelTwo === "Related" || levelTwo === "Reference Repo") {
return "Research";
}
return "Other";
};
const iconForUrl = (value) => {
try {
const url = new URL(value);
const pathParts = url.pathname.split("/").filter(Boolean);
if (url.hostname === "github.com" && pathParts.length >= 2) {
return {
url: `https://github.com/${encodeURIComponent(pathParts[0])}.png?size=96`,
type: "avatar",
};
}
return {
url: `${url.origin}/favicon.ico`,
type: "favicon",
};
} catch {
return null;
}
};
const githubRepositoryForUrl = (value) => {
try {
const url = new URL(value);
const [owner, rawRepository] = url.pathname.split("/").filter(Boolean);
if (url.hostname !== "github.com" || !owner || !rawRepository) return null;
const repository = rawRepository.replace(/\.git$/, "");
return `${owner}/${repository}`;
} catch {
return null;
}
};
const readCachedActivity = async () => {
try {
const cachedResources = JSON.parse(await readFile(outputPath, "utf8"));
return new Map(
cachedResources.flatMap((resource) => {
const repository = githubRepositoryForUrl(resource.url);
if (!repository) return [];
const activity = {};
if (resource.updatedAt) activity.updatedAt = resource.updatedAt;
if (Number.isInteger(resource.commitsLastWeek)) {
activity.commitsLastWeek = resource.commitsLastWeek;
}
return Object.keys(activity).length
? [[repository.toLowerCase(), activity]]
: [];
}),
);
} catch {
return new Map();
}
};
const githubHeaders = () => {
const headers = {
Accept: "application/vnd.github+json",
"User-Agent": "awesome-ai-agents-site-builder",
"X-GitHub-Api-Version": "2022-11-28",
};
if (githubToken) headers.Authorization = `Bearer ${githubToken}`;
return headers;
};
const requestGithub = async (path) => {
const response = await fetch(`${githubApiUrl}${path}`, {
headers: githubHeaders(),
signal: AbortSignal.timeout(15_000),
});
if (!response.ok) throw new Error(`HTTP ${response.status}`);
return response;
};
const lastPageFromLinkHeader = (value) => {
const lastPage = (value || "")
.split(",")
.map((link) => link.match(/[?&]page=(\d+)[^>]*>;\s*rel="last"/))
.find(Boolean);
return lastPage ? Number(lastPage[1]) : null;
};
// One commit per page turns the `last` page number into the commit count, so a
// busy repository still costs a single request.
const fetchCommitsSinceWindowStart = async (owner, name) => {
const response = await requestGithub(
`/repos/${encodeURIComponent(owner)}/${encodeURIComponent(name)}/commits` +
`?since=${encodeURIComponent(activityWindowStart)}&per_page=1`,
);
const lastPage = lastPageFromLinkHeader(response.headers.get("link"));
if (lastPage) return lastPage;
const commits = await response.json();
return Array.isArray(commits) ? commits.length : 0;
};
const fetchGithubActivity = async (repository) => {
const [owner, name] = repository.split("/");
let updatedAt = null;
try {
const data = await (
await requestGithub(
`/repos/${encodeURIComponent(owner)}/${encodeURIComponent(name)}`,
)
).json();
updatedAt = typeof data.pushed_at === "string" ? data.pushed_at : null;
} catch (error) {
console.warn(
`Unable to fetch update time for ${repository}: ${error.message}`,
);
return null;
}
// A repository untouched during the window cannot have commits in it.
if (updatedAt && Date.parse(updatedAt) < Date.parse(activityWindowStart)) {
return { updatedAt, commitsLastWeek: 0 };
}
try {
return {
updatedAt,
commitsLastWeek: await fetchCommitsSinceWindowStart(owner, name),
};
} catch (error) {
console.warn(
`Unable to fetch recent commits for ${repository}: ${error.message}`,
);
return { updatedAt, commitsLastWeek: null };
}
};
const fetchGithubActivityMap = async (repositories) => {
const activityByRepository = new Map();
let nextIndex = 0;
const worker = async () => {
while (nextIndex < repositories.length) {
const repository = repositories[nextIndex];
nextIndex += 1;
activityByRepository.set(
repository.toLowerCase(),
await fetchGithubActivity(repository),
);
}
};
await Promise.all(
Array.from(
{ length: Math.min(GITHUB_FETCH_CONCURRENCY, repositories.length) },
worker,
),
);
return activityByRepository;
};
const decodeEntities = (value) =>
value
.replaceAll("&", "&")
.replaceAll("<", "<")
.replaceAll(">", ">")
.replaceAll(""", '"')
.replaceAll("'", "'");
const cleanDescription = (value) =>
decodeEntities(
value
.replace(/!\[[^\]]*\]\([^)]*\)/g, "")
.replace(/!\[[^\]]*$/g, "")
.replace(/<a\b[^>]*>[\s\S]*?<\/a>/gi, "")
.replace(/<[^>]+>/g, "")
.replace(/\[([^\]]+)\]\([^)]+\)/g, "$1")
.replace(/[`*_]/g, "")
.replace(/\s+/g, " ")
.trim(),
);
const readme = await readFile(readmePath, "utf8");
const resources = [];
let levelTwo = "";
let levelThree = "";
let inResources = false;
for (const line of readme.split("\n")) {
const secondLevel = line.match(/^##\s+(.+?)\s*$/);
if (secondLevel) {
levelTwo = secondLevel[1].trim();
levelThree = "";
inResources = true;
continue;
}
const thirdLevel = line.match(/^###\s+(.+?)\s*$/);
if (thirdLevel) {
levelThree = thirdLevel[1].trim();
continue;
}
if (!inResources) continue;
const entry = line.match(
/^- \[([^\]]+)\]\((https?:\/\/[^)]+)\)\s*(?:[-—]\s*)?(.*)$/,
);
if (!entry) continue;
const [, name, url, rawDescription] = entry;
const description = cleanDescription(rawDescription);
const section = levelThree || levelTwo;
resources.push({
id: resources.length + 1,
name: decodeEntities(name.trim()),
url,
description: description || "Explore this resource and its documentation.",
category: groupForSection(levelTwo, levelThree),
section,
source: url.includes("github.com") ? "GitHub" : "Website",
icon: iconForUrl(url),
});
}
const cachedActivity = await readCachedActivity();
const githubRepositoriesByKey = new Map();
for (const { url } of resources) {
const repository = githubRepositoryForUrl(url);
if (repository) {
githubRepositoriesByKey.set(repository.toLowerCase(), repository);
}
}
const githubRepositories = [...githubRepositoriesByKey.values()];
const fetchedActivity = githubToken
? await fetchGithubActivityMap(githubRepositories)
: new Map();
const refreshedRepositoryCount = [...fetchedActivity.values()].filter(
Boolean,
).length;
if (
githubToken &&
githubRepositories.length > 0 &&
refreshedRepositoryCount / githubRepositories.length <
MIN_GITHUB_REFRESH_RATIO
) {
throw new Error(
`Only ${refreshedRepositoryCount} of ${githubRepositories.length} GitHub repositories were refreshed; refusing to publish incomplete ordering.`,
);
}
if (!githubToken) {
console.warn(
"GITHUB_TOKEN or GH_TOKEN is not set; using cached GitHub activity data.",
);
}
for (const resource of resources) {
const repository = githubRepositoryForUrl(resource.url);
if (!repository) continue;
const key = repository.toLowerCase();
const fetched = fetchedActivity.get(key);
const cached = cachedActivity.get(key);
const updatedAt = fetched?.updatedAt ?? cached?.updatedAt;
if (updatedAt) resource.updatedAt = updatedAt;
const commitsLastWeek = fetched?.commitsLastWeek ?? cached?.commitsLastWeek;
if (Number.isInteger(commitsLastWeek)) {
resource.commitsLastWeek = commitsLastWeek;
}
}
resources.sort((left, right) => {
const parsedLeftTimestamp = left.updatedAt ? Date.parse(left.updatedAt) : 0;
const parsedRightTimestamp = right.updatedAt
? Date.parse(right.updatedAt)
: 0;
const leftTimestamp = Number.isNaN(parsedLeftTimestamp)
? 0
: parsedLeftTimestamp;
const rightTimestamp = Number.isNaN(parsedRightTimestamp)
? 0
: parsedRightTimestamp;
return rightTimestamp - leftTimestamp || left.id - right.id;
});
resources.forEach((resource, index) => {
resource.id = index + 1;
});
await writeFile(outputPath, `${JSON.stringify(resources, null, 2)}\n`, "utf8");
const activeRepositoryCount = resources.filter(
(resource) => resource.commitsLastWeek > 0,
).length;
console.log(
`Generated ${resources.length} resources (${refreshedRepositoryCount} GitHub repositories refreshed, ` +
`${activeRepositoryCount} with commits in the last ${ACTIVITY_WINDOW_DAYS} days) at ${outputPath}`,
);
================================================
FILE: site/.nojekyll
================================================
================================================
FILE: site/app.js
================================================
const CATEGORIES = [
"All",
"Applications",
"Frameworks",
"Tools",
"Benchmarks",
"Platforms",
"Research",
];
const PAGE_SIZE = 24;
const LEADERBOARD_SIZE = 10;
const ACTIVITY_WINDOW_DAYS = 7;
const GITHUB_STARS_CACHE_KEY = "awesome-ai-agents:github-stars";
const state = {
resources: [],
query: "",
category: "All",
visible: PAGE_SIZE,
};
const elements = {
categories: document.querySelector("#categories"),
count: document.querySelector("#results-count"),
empty: document.querySelector("#empty-state"),
list: document.querySelector("#resources"),
loadMore: document.querySelector("#load-more"),
reset: document.querySelector("#reset-button"),
search: document.querySelector("#search-input"),
leaderboard: document.querySelector("#leaderboard"),
leaderboardList: document.querySelector("#leaderboard-list"),
leaderboardNote: document.querySelector("#leaderboard-note"),
githubLink: document.querySelector(".github-link"),
githubStars: document.querySelector("#github-stars-count"),
};
const externalIcon = `
<span class="icon icon-external-link" aria-hidden="true"></span>
`;
const initialsFor = (name) => {
const words = name.match(/[A-Za-z0-9]+/g) || [name];
if (words.length === 1) return words[0].slice(0, 2).toUpperCase();
return `${words[0][0]}${words[1][0]}`.toUpperCase();
};
const normalized = (value) => value.trim().toLocaleLowerCase();
const renderGitHubStars = (count) => {
const exactCount = new Intl.NumberFormat("en-US").format(count);
const compactCount =
count >= 1000
? `${new Intl.NumberFormat("en-US", {
maximumFractionDigits: 1,
}).format(Math.floor(count / 100) / 10)}k+`
: exactCount;
elements.githubStars.textContent = compactCount;
elements.githubLink.setAttribute(
"aria-label",
`Star on GitHub, ${exactCount} stars`,
);
elements.githubLink.title = `${exactCount} GitHub stars`;
};
const updateGitHubStars = async () => {
try {
const cached = JSON.parse(localStorage.getItem(GITHUB_STARS_CACHE_KEY));
if (Number.isInteger(cached?.count)) renderGitHubStars(cached.count);
} catch {
localStorage.removeItem(GITHUB_STARS_CACHE_KEY);
}
try {
const repository = elements.githubLink.dataset.repository;
const response = await fetch(`https://api.github.com/repos/${repository}`, {
cache: "no-store",
headers: {
Accept: "application/vnd.github+json",
"X-GitHub-Api-Version": "2022-11-28",
},
});
if (!response.ok) throw new Error(`HTTP ${response.status}`);
const data = await response.json();
if (!Number.isInteger(data.stargazers_count)) {
throw new Error("GitHub response did not include a star count");
}
renderGitHubStars(data.stargazers_count);
localStorage.setItem(
GITHUB_STARS_CACHE_KEY,
JSON.stringify({ count: data.stargazers_count }),
);
} catch (error) {
console.warn("Unable to refresh GitHub stars:", error);
}
};
const filteredResources = () => {
const query = normalized(state.query);
return state.resources.filter((resource) => {
const matchesCategory =
state.category === "All" || resource.category === state.category;
if (!matchesCategory) return false;
if (!query) return true;
return normalized(
`${resource.name} ${resource.description} ${resource.category} ${resource.section}`,
).includes(query);
});
};
const updateUrl = () => {
const url = new URL(window.location.href);
state.query ? url.searchParams.set("q", state.query) : url.searchParams.delete("q");
state.category !== "All"
? url.searchParams.set("category", state.category)
: url.searchParams.delete("category");
window.history.replaceState({}, "", url);
};
const categoryButtons = new Map();
const buildCategories = () => {
elements.categories.replaceChildren(
...CATEGORIES.map((category) => {
const button = document.createElement("button");
button.className = "category-button";
button.type = "button";
button.textContent = category;
button.addEventListener("click", () => {
state.category = category;
state.visible = PAGE_SIZE;
render();
});
categoryButtons.set(category, button);
return button;
}),
);
};
const syncCategories = () => {
for (const [category, button] of categoryButtons) {
button.setAttribute("aria-pressed", String(category === state.category));
}
};
const resourceAvatar = (resource) => {
const avatar = document.createElement("span");
avatar.className = "resource-avatar";
avatar.setAttribute("aria-hidden", "true");
const avatarFallback = document.createElement("span");
avatarFallback.className = "resource-avatar-fallback";
avatarFallback.textContent = initialsFor(resource.name);
avatar.append(avatarFallback);
if (resource.icon?.url) {
const avatarImage = document.createElement("img");
avatarImage.className = "resource-avatar-image";
avatarImage.src = resource.icon.url;
avatarImage.alt = "";
avatarImage.width = 40;
avatarImage.height = 40;
avatarImage.loading = "lazy";
avatarImage.decoding = "async";
avatar.dataset.iconType = resource.icon.type;
avatarImage.addEventListener("load", () => {
avatar.classList.add("has-image");
});
avatarImage.addEventListener("error", () => {
avatarImage.remove();
});
avatar.prepend(avatarImage);
}
return avatar;
};
const resourceLink = (resource, className) => {
const link = document.createElement("a");
link.className = className;
link.href = resource.url;
link.target = "_blank";
link.rel = "noreferrer";
link.setAttribute("aria-label", `${resource.name} — opens in a new tab`);
return link;
};
const resourceRow = (resource) => {
const link = resourceLink(resource, "resource-row");
const avatar = resourceAvatar(resource);
const copy = document.createElement("span");
copy.className = "resource-copy";
const name = document.createElement("h3");
name.textContent = resource.name;
const description = document.createElement("p");
description.textContent = resource.description;
copy.append(name, description);
const category = document.createElement("span");
category.className = "resource-category";
category.textContent = resource.category;
const source = document.createElement("span");
source.className = "resource-source";
const sourceText = document.createElement("span");
sourceText.className = "resource-source-label";
sourceText.textContent = resource.source;
source.append(sourceText);
source.insertAdjacentHTML("beforeend", externalIcon);
link.append(avatar, copy, category, source);
return link;
};
const mostActiveResources = () =>
state.resources
.filter(
(resource) =>
Number.isInteger(resource.commitsLastWeek) &&
resource.commitsLastWeek > 0,
)
.sort(
(left, right) =>
right.commitsLastWeek - left.commitsLastWeek ||
left.name.localeCompare(right.name),
)
.slice(0, LEADERBOARD_SIZE);
const leaderboardRow = (resource, rank) => {
const item = document.createElement("li");
item.className = "leaderboard-item";
const link = resourceLink(resource, "leaderboard-row");
const position = document.createElement("span");
position.className = "leaderboard-rank";
position.setAttribute("aria-hidden", "true");
position.textContent = String(rank);
const copy = document.createElement("span");
copy.className = "leaderboard-copy";
const name = document.createElement("h3");
name.textContent = resource.name;
const section = document.createElement("p");
section.textContent = resource.section;
copy.append(name, section);
const commits = document.createElement("span");
commits.className = "leaderboard-commits";
const commitCount = document.createElement("strong");
commitCount.textContent = new Intl.NumberFormat("en-US").format(
resource.commitsLastWeek,
);
const commitLabel = document.createElement("span");
commitLabel.textContent =
resource.commitsLastWeek === 1 ? "commit" : "commits";
commits.append(commitCount, commitLabel);
link.append(position, resourceAvatar(resource), copy, commits);
item.append(link);
return item;
};
const renderLeaderboard = () => {
const leaders = mostActiveResources();
elements.leaderboard.hidden = leaders.length === 0;
if (leaders.length === 0) return;
elements.leaderboardList.replaceChildren(
...leaders.map((resource, index) => leaderboardRow(resource, index + 1)),
);
elements.leaderboardNote.textContent = `Commits pushed in the last ${ACTIVITY_WINDOW_DAYS} days`;
};
const render = () => {
const filtered = filteredResources();
const visibleResources = filtered.slice(0, state.visible);
syncCategories();
elements.list.replaceChildren(...visibleResources.map(resourceRow));
elements.count.textContent = `${filtered.length} ${filtered.length === 1 ? "resource" : "resources"}`;
elements.empty.hidden = filtered.length !== 0;
elements.list.hidden = filtered.length === 0;
elements.loadMore.hidden = state.visible >= filtered.length;
updateUrl();
};
elements.search.addEventListener("input", (event) => {
state.query = event.target.value;
state.visible = PAGE_SIZE;
render();
});
elements.loadMore.addEventListener("click", () => {
state.visible += PAGE_SIZE;
render();
});
elements.reset.addEventListener("click", () => {
state.query = "";
state.category = "All";
state.visible = PAGE_SIZE;
elements.search.value = "";
elements.search.focus();
render();
});
document.addEventListener("keydown", (event) => {
const isTyping = ["INPUT", "TEXTAREA"].includes(document.activeElement.tagName);
if (event.key === "/" && !isTyping) {
event.preventDefault();
elements.search.focus();
}
});
const initialize = async () => {
try {
const response = await fetch("./resources.json", { cache: "no-store" });
if (!response.ok) throw new Error(`HTTP ${response.status}`);
state.resources = await response.json();
const params = new URLSearchParams(window.location.search);
const requestedCategory = params.get("category");
state.query = params.get("q") || "";
state.category = CATEGORIES.includes(requestedCategory)
? requestedCategory
: "All";
elements.search.value = state.query;
buildCategories();
renderLeaderboard();
render();
} catch (error) {
console.error("Unable to load resources:", error);
elements.count.textContent = "Unable to load resources";
elements.empty.hidden = false;
elements.empty.querySelector("h3").textContent = "Resources are unavailable";
elements.empty.querySelector("p").textContent =
"Please refresh the page or try again in a moment.";
elements.reset.hidden = true;
}
};
updateGitHubStars();
initialize();
================================================
FILE: site/fonts/OFL.txt
================================================
Copyright 2022 The Bricolage Grotesque Project Authors (https://github.com/ateliertriay/bricolage)
This Font Software is licensed under the SIL Open Font License, Version 1.1.
This license is copied below, and is also available with a FAQ at:
https://scripts.sil.org/OFL
-----------------------------------------------------------
SIL OPEN FONT LICENSE Version 1.1 - 26 February 2007
-----------------------------------------------------------
PREAMBLE
The goals of the Open Font License (OFL) are to stimulate worldwide
development of collaborative font projects, to support the font creation
efforts of academic and linguistic communities, and to provide a free and
open framework in which fonts may be shared and improved in partnership
with others.
The OFL allows the licensed fonts to be used, studied, modified and
redistributed freely as long as they are not sold by themselves. The
fonts, including any derivative works, can be bundled, embedded,
redistributed and/or sold with any software provided that any reserved
names are not used by derivative works. The fonts and derivatives,
however, cannot be released under any other type of license. The
requirement for fonts to remain under this license does not apply
to any document created using the fonts or their derivatives.
DEFINITIONS
"Font Software" refers to the set of files released by the Copyright
Holder(s) under this license and clearly marked as such. This may
include source files, build scripts and documentation.
"Reserved Font Name" refers to any names specified as such after the
copyright statement(s).
"Original Version" refers to the collection of Font Software components as
distributed by the Copyright Holder(s).
"Modified Version" refers to any derivative made by adding to, deleting,
or substituting -- in part or in whole -- any of the components of the
Original Version, by changing formats or by porting the Font Software to a
new environment.
"Author" refers to any designer, engineer, programmer, technical
writer or other person who contributed to the Font Software.
PERMISSION & CONDITIONS
Permission is hereby granted, free of charge, to any person obtaining
a copy of the Font Software, to use, study, copy, merge, embed, modify,
redistribute, and sell modified and unmodified copies of the Font
Software, subject to the following conditions:
1) Neither the Font Software nor any of its individual components,
in Original or Modified Versions, may be sold by itself.
2) Original or Modified Versions of the Font Software may be bundled,
redistributed and/or sold with any software, provided that each copy
contains the above copyright notice and this license. These can be
included either as stand-alone text files, human-readable headers or
in the appropriate machine-readable metadata fields within text or
binary files as long as those fields can be easily viewed by the user.
3) No Modified Version of the Font Software may use the Reserved Font
Name(s) unless explicit written permission is granted by the corresponding
Copyright Holder. This restriction only applies to the primary font name as
presented to the users.
4) The name(s) of the Copyright Holder(s) or the Author(s) of the Font
Software shall not be used to promote, endorse or advertise any
Modified Version, except to acknowledge the contribution(s) of the
Copyright Holder(s) and the Author(s) or with their explicit written
permission.
5) The Font Software, modified or unmodified, in part or in whole,
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================================================
FILE: site/fonts/README.md
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# Font source
`bricolage-grotesque-latin-700.woff2` is the Latin webfont subset for
[Bricolage Grotesque](https://fonts.google.com/specimen/Bricolage+Grotesque),
downloaded from Google Fonts on 2026-07-19.
The font is used only for the website's main display heading. It is licensed
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================================================
FILE: site/icons/README.md
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# Icon sources
The interface icons in this directory are vendored SVG assets rather than
hand-drawn paths or runtime CDN dependencies.
- `github.svg` is from [Simple Icons](https://simpleicons.org/) and is licensed
under [CC0 1.0](https://github.com/simple-icons/simple-icons/blob/develop/LICENSE.md).
- `chevron-down.svg`, `external-link.svg`, `orbit.svg`, and `search.svg` are
from [Lucide](https://lucide.dev/) and are licensed under the
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================================================
FILE: site/resources.json
================================================
[
{
"id": 1,
"name": "AgentsKit",
"url": "https://github.com/AgentsKit-io/agentskit",
"description": "MIT-licensed, provider-neutral TypeScript toolkit with 25 focused packages for runtimes, tools, skills, memory, RAG, sandboxing, observability, evaluation, React, terminal UI, and CLI, built on a dependency-free core.",
"category": "Frameworks",
"section": "Frameworks",
"source": "GitHub",
"icon": {
"url": "https://github.com/AgentsKit-io.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T12:37:30Z",
"commitsLastWeek": 2
},
{
"id": 2,
"name": "OpenClaw",
"url": "https://github.com/openclaw/openclaw",
"description": "Open-source personal AI assistant that runs locally across platforms and can take actions through chat channels and tools.",
"category": "Applications",
"section": "Autonomous Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/openclaw.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T12:37:16Z",
"commitsLastWeek": 3804
},
{
"id": 3,
"name": "composio",
"url": "https://github.com/ComposioHQ/composio",
"description": "Composio equips agents with well-crafted tools empowering them to tackle complex tasks",
"category": "Tools",
"section": "Tools",
"source": "GitHub",
"icon": {
"url": "https://github.com/ComposioHQ.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T12:37:03Z",
"commitsLastWeek": 91
},
{
"id": 4,
"name": "Mastra",
"url": "https://github.com/mastra-ai/mastra",
"description": "Mastra is an opinionated TypeScript framework that helps you build AI applications and features quickly.",
"category": "Frameworks",
"section": "Frameworks",
"source": "GitHub",
"icon": {
"url": "https://github.com/mastra-ai.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T12:37:02Z",
"commitsLastWeek": 250
},
{
"id": 5,
"name": "BitFun",
"url": "https://github.com/GCWing/BitFun",
"description": "Open-source coding agent with a Rust runtime, desktop and CLI interfaces, self-hosted multi-device control, and stateful Mini Apps.",
"category": "Applications",
"section": "Autonomous Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/GCWing.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T12:36:32Z",
"commitsLastWeek": 36
},
{
"id": 6,
"name": "PraisonAI",
"url": "https://github.com/MervinPraison/PraisonAI",
"description": "Production-ready Multi-AI Agents framework with self-reflection. Fastest agent instantiation (3.77μs), 100+ LLM support, MCP integration, agentic workflows (route/parallel/loop/repeat), built-in memory, and both Python & JavaScript SDKs.",
"category": "Frameworks",
"section": "Frameworks",
"source": "GitHub",
"icon": {
"url": "https://github.com/MervinPraison.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T12:36:04Z",
"commitsLastWeek": 527
},
{
"id": 7,
"name": "Nika",
"url": "https://github.com/supernovae-st/nika",
"description": "Intent-as-code workflow engine for AI agents: reviewable YAML DAGs statically checked (schema, permits, honest cost floor) before any token is spent, tamper-evident traces after. Single Rust binary.",
"category": "Tools",
"section": "Tools",
"source": "GitHub",
"icon": {
"url": "https://github.com/supernovae-st.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T12:34:32Z",
"commitsLastWeek": 39
},
{
"id": 8,
"name": "Future AGI",
"url": "https://github.com/future-agi/future-agi",
"description": "Open-source platform to simulate, evaluate, trace, guardrail, and optimize LLM and AI agent apps, with 70+ eval metrics and OpenTelemetry-native tracing across 50+ frameworks.",
"category": "Benchmarks",
"section": "Benchmark/Evaluator",
"source": "GitHub",
"icon": {
"url": "https://github.com/future-agi.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T12:32:38Z",
"commitsLastWeek": 196
},
{
"id": 9,
"name": "Talon",
"url": "https://github.com/dylanneve1/talon",
"description": "Self-hosted, multi-frontend agentic harness that runs one persistent agent across Telegram, Discord, Teams, and the terminal. Pluggable backends (Claude, OpenAI Agents, Codex, Kilo, OpenCode), full MCP tool access, and always-on background agents — Goals, Heartbeat, and Dream — backed by a long-term memory palace. MIT, TypeScript.",
"category": "Frameworks",
"section": "Frameworks",
"source": "GitHub",
"icon": {
"url": "https://github.com/dylanneve1.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T12:31:06Z",
"commitsLastWeek": 26
},
{
"id": 10,
"name": "mem0",
"url": "https://github.com/mem0ai/mem0",
"description": "Mem0 provides a smart, self-improving memory layer for Large Language Models, enabling personalized AI experiences across applications.",
"category": "Tools",
"section": "Tools",
"source": "GitHub",
"icon": {
"url": "https://github.com/mem0ai.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T12:30:17Z",
"commitsLastWeek": 6
},
{
"id": 11,
"name": "career-ops",
"url": "https://github.com/santifer/career-ops",
"description": "AI-powered job search orchestrator built on Claude Code. 14-skill pipeline that evaluates jobs, generates ATS-tailored PDFs, and tracks applications. Local-first, MIT.",
"category": "Applications",
"section": "Autonomous Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/santifer.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T12:29:48Z",
"commitsLastWeek": 139
},
{
"id": 12,
"name": "Agent Framework",
"url": "https://github.com/microsoft/agent-framework",
"description": "Microsoft open-source framework for building, orchestrating, and running AI agents with workflow primitives.",
"category": "Frameworks",
"section": "Frameworks",
"source": "GitHub",
"icon": {
"url": "https://github.com/microsoft.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T12:28:16Z",
"commitsLastWeek": 46
},
{
"id": 13,
"name": "DecisionBox",
"url": "https://github.com/decisionbox-io/decisionbox-platform",
"description": "Open-source AI data discovery platform that connects to warehouses (BigQuery, Redshift, Snowflake, etc.), runs autonomous agents that write and execute SQL, and surfaces validated insights. Pluggable LLM providers (Claude, OpenAI, Ollama, Vertex AI, Bedrock), industry domain packs, Helm charts, and Terraform modules for GCP/AWS.",
"category": "Applications",
"section": "Autonomous Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/decisionbox-io.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T12:25:36Z",
"commitsLastWeek": 5
},
{
"id": 14,
"name": "FastAgent",
"url": "https://github.com/fastagent-sh/fastagent",
"description": "Serving layer that turns an existing agent directory (persona.md, skills/, tools/, channels/) into a live service without a rewrite: embed it behind a route in a Next/Hono/Node app, or run it as a GitHub, Telegram, Slack, or HTTP/SSE service with cron schedules. Engine-, model-, and host-neutral around a single invoke contract. MIT, TypeScript.",
"category": "Frameworks",
"section": "Frameworks",
"source": "GitHub",
"icon": {
"url": "https://github.com/fastagent-sh.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T12:22:47Z",
"commitsLastWeek": 55
},
{
"id": 15,
"name": "Bernstein",
"url": "https://github.com/chernistry/bernstein",
"description": "Deterministic orchestrator that spawns parallel coding agents (Claude Code, Codex CLI, Gemini CLI), verifies with tests, and auto-commits. Zero LLM tokens on coordination.",
"category": "Applications",
"section": "Multi-Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/chernistry.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T12:20:12Z",
"commitsLastWeek": 342
},
{
"id": 16,
"name": "codex-profiles",
"url": "https://github.com/Ducksss/codex-profiles",
"description": "Bash CLI for switching OpenAI Codex CLI/Desktop accounts with isolated CODEXHOME profiles.",
"category": "Tools",
"section": "Tools",
"source": "GitHub",
"icon": {
"url": "https://github.com/Ducksss.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T12:13:21Z",
"commitsLastWeek": 15
},
{
"id": 17,
"name": "h5i",
"url": "https://github.com/h5i-dev/h5i",
"description": "CLI that runs several coding agents (Claude Code, Codex) on the same task, each in an isolated git worktree sandbox, has them peer-review each other, then a neutral verifier replays every candidate, runs the tests itself, and merges the one that passes. Run metadata is versioned in the repo under refs/h5i/. Rust, Apache-2.0.",
"category": "Applications",
"section": "Multi-Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/h5i-dev.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T12:08:11Z",
"commitsLastWeek": 93
},
{
"id": 18,
"name": "KubeStellar Console",
"url": "https://github.com/kubestellar/console",
"description": "Multi-cluster Kubernetes dashboard with AI operations agent (kc-agent) that bridges LLMs to live clusters via MCP for AI-assisted troubleshooting, observability, and management across edge and cloud. CNCF Sandbox project.",
"category": "Tools",
"section": "Tools",
"source": "GitHub",
"icon": {
"url": "https://github.com/kubestellar.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T11:58:46Z",
"commitsLastWeek": 147
},
{
"id": 19,
"name": "Bifrost",
"url": "https://github.com/maximhq/bifrost",
"description": "Open-source Go AI gateway with provider routing, automatic failover, load balancing, observability, and MCP support.",
"category": "Platforms",
"section": "Platforms/API",
"source": "GitHub",
"icon": {
"url": "https://github.com/maximhq.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T11:57:49Z",
"commitsLastWeek": 99
},
{
"id": 20,
"name": "Ouroboros",
"url": "https://github.com/razzant/ouroboros",
"description": "Self-hosted general-purpose agent with durable identity and memory, reviewed self-modification, specialist subagent swarms, and desktop or headless operation.",
"category": "Applications",
"section": "Autonomous Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/razzant.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T11:52:03Z",
"commitsLastWeek": 573
},
{
"id": 21,
"name": "Agno",
"url": "https://github.com/agno-agi/agno",
"description": "Build multi-agent systems with memory, knowledge, and tool integrations in Python.",
"category": "Frameworks",
"section": "Frameworks",
"source": "GitHub",
"icon": {
"url": "https://github.com/agno-agi.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T11:49:55Z",
"commitsLastWeek": 34
},
{
"id": 22,
"name": "FutureOS",
"url": "https://github.com/futuregene/future-os",
"description": "One AI agent everywhere you work: terminal UI, desktop, mobile, CLI, and IM bots from a single Rust backend, with approval-gated tools and a loop control plane for 24h+ runs.",
"category": "Applications",
"section": "Autonomous Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/futuregene.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T11:39:45Z",
"commitsLastWeek": 87
},
{
"id": 23,
"name": "aiXplain",
"url": "https://github.com/aixplain/aiXplain",
"description": "AI platform SDK providing access to 35,000+ AI models, benchmarking tools, pipeline design, and agent building capabilities",
"category": "Platforms",
"section": "Platforms/API",
"source": "GitHub",
"icon": {
"url": "https://github.com/aixplain.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T11:37:59Z",
"commitsLastWeek": 0
},
{
"id": 24,
"name": "Agent Swarm",
"url": "https://github.com/desplega-ai/agent-swarm",
"description": "Self-hosted multi-agent system where a lead agent delegates tasks to specialized workers with shared memory, tools, schedules, and review gates.",
"category": "Applications",
"section": "Autonomous Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/desplega-ai.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T11:30:15Z",
"commitsLastWeek": 21
},
{
"id": 25,
"name": "MisakaNet",
"url": "https://github.com/Ikalus1988/MisakaNet",
"description": "Git-based shared memory for AI agents. Cross-agent lesson/knowledge sync via GitHub Issues. \"Lessons learned. Lessons shared.\"",
"category": "Tools",
"section": "Tools",
"source": "GitHub",
"icon": {
"url": "https://github.com/Ikalus1988.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T11:29:01Z",
"commitsLastWeek": 340
},
{
"id": 26,
"name": "Atomic Agent",
"url": "https://github.com/AtomicBot-ai/atomic-agent",
"description": "Local-first CLI and TUI coding agent that runs open-weight models entirely on your machine via a llama.cpp fork. 56 tools (browser, filesystem, git, memory, vision), MCP support, and a 5-layer local memory. macOS/Linux/Windows, MIT.",
"category": "Applications",
"section": "Autonomous Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/AtomicBot-ai.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T11:25:07Z",
"commitsLastWeek": 120
},
{
"id": 27,
"name": "Vicoa",
"url": "https://github.com/vicoa-ai/vicoa",
"description": "Agentic IDE and AI orchestrator for running Claude Code, Codex, OpenCode, Gemini, Cursor, GitHub Copilot, Kimi, and Hermes agents in parallel, each in its own git worktree, steered from a unified dashboard with real-time mobile sync and push notifications.",
"category": "Applications",
"section": "Multi-Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/vicoa-ai.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T11:17:29Z",
"commitsLastWeek": 26
},
{
"id": 28,
"name": "Hivekeep",
"url": "https://github.com/MarlBurroW/hivekeep",
"description": "Self-hosted platform of autonomous, persistent personal AI agents that collaborate, remember across months, and build their own tools. Multi-channel (Telegram, WhatsApp, Slack, Discord, Signal, Matrix), single container with Bun and SQLite.",
"category": "Applications",
"section": "Autonomous Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/MarlBurroW.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T11:15:54Z",
"commitsLastWeek": 0
},
{
"id": 29,
"name": "Cline",
"url": "https://github.com/cline/cline",
"description": "Open-source autonomous coding agent in VS Code for planning, coding, and tool use across real projects.",
"category": "Applications",
"section": "Autonomous Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/cline.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T11:12:56Z",
"commitsLastWeek": 63
},
{
"id": 30,
"name": "AutoGPT",
"url": "https://github.com/Significant-Gravitas/AutoGPT",
"description": "AutoGPT is the vision of the power of AI accessible to everyone, to use and to build on.",
"category": "Applications",
"section": "Autonomous Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/Significant-Gravitas.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-02T11:10:45Z",
"commitsLastWeek": 8
},
{
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"name": "Awesome-Embodied-AI-Safety",
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"description": "Browser-agent benchmark of 281 everyday tasks (V1 152 + V2 129) on 163 live production websites across 15 categories; two-stage scoring — a submission-interception layer blocks the final write request for safe evaluation on real sites, then an LLM judge checks the captured payload against the instruction.",
"category": "Benchmarks",
"section": "Benchmark/Evaluator",
"source": "GitHub",
"icon": {
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},
"updatedAt": "2026-09-01T15:26:20Z",
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},
{
"id": 76,
"name": "best-of-Agent-Harnesses",
"url": "https://github.com/RyanAlberts/best-of-Agent-Harnesses",
"description": "Ranked list of 150+ agent harnesses across 12 categories, rescored weekly. Machine-readable: llms.txt, JSON, and an MCP server so your agent can query it.",
"category": "Research",
"section": "Reference Repo",
"source": "GitHub",
"icon": {
"url": "https://github.com/RyanAlberts.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-01T14:35:01Z",
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},
{
"id": 77,
"name": "OpenAgent",
"url": "https://github.com/the-open-agent/openagent",
"description": "Self-hostable personal assistant with LLM + RAG, loops for desktop/browser/coding, MCP and many providers.",
"category": "Applications",
"section": "Autonomous Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/the-open-agent.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-01T13:54:33Z",
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},
{
"id": 78,
"name": "Metorial",
"url": "https://github.com/metorial/metorial",
"description": "Integration gateway that links AI agents to 600+ tools via unified MCP/OAuth interface with built-in scaling and monitoring.",
"category": "Tools",
"section": "Tools",
"source": "GitHub",
"icon": {
"url": "https://github.com/metorial.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-01T12:52:48Z",
"commitsLastWeek": 10
},
{
"id": 79,
"name": "OpenAgents",
"url": "https://github.com/openagents-org/openagents",
"description": "Open-source platform for building AI agent networks with multi-protocol support (WebSocket, gRPC, HTTP, MCP, A2A) and multi-agent orchestration.",
"category": "Frameworks",
"section": "Frameworks",
"source": "GitHub",
"icon": {
"url": "https://github.com/openagents-org.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-01T08:43:44Z",
"commitsLastWeek": 82
},
{
"id": 80,
"name": "Giselle",
"url": "https://github.com/giselles-ai/giselle",
"description": "Giselle is an agentic workflow builder that empowers you to create AI-driven solutions with ease.",
"category": "Applications",
"section": "Multi-Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/giselles-ai.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-01T04:47:56Z",
"commitsLastWeek": 8
},
{
"id": 81,
"name": "Nora",
"url": "https://github.com/solomon2773/nora",
"description": "Self-hosted control plane for deploying and operating OpenClaw and Hermes agent fleets on Docker or Kubernetes, with lifecycle controls, monitoring, budgets, schedules, and per-agent cost tracking.",
"category": "Platforms",
"section": "Platforms/API",
"source": "GitHub",
"icon": {
"url": "https://github.com/solomon2773.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-01T04:17:47Z",
"commitsLastWeek": 12
},
{
"id": 82,
"name": "OpenDraft",
"url": "https://github.com/federicodeponte/opendraft",
"description": "Autonomous research-writing agent: 19 specialized agents turn a prompt into a long-form, source-grounded draft with citations verified against CrossRef, OpenAlex and Semantic Scholar. PDF/DOCX/LaTeX export, 57+ languages, bring-your-own model keys.",
"category": "Applications",
"section": "Autonomous Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/federicodeponte.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-01T04:08:44Z",
"commitsLastWeek": 1
},
{
"id": 83,
"name": "swarm-orchestrator",
"url": "https://github.com/moonrunnerkc/swarm-orchestrator",
"description": "Contract-first multi-agent orchestrator that races persona candidates per typed obligation, verifies before commit, and logs every action in an append-only hash-chained ledger; deterministic offline default with optional Claude, Codex, Copilot, and local LLM (Ollama, llama.cpp, vLLM) providers. Ships with a GitHub Action.",
"category": "Applications",
"section": "Multi-Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/moonrunnerkc.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-01T02:12:01Z",
"commitsLastWeek": 44
},
{
"id": 84,
"name": "SARA",
"url": "https://github.com/Alessandro114/sara",
"description": "Self-hosted WhatsApp AI agent (AGPL-3.0) with 20 industry verticals, function calling (30+ tools), RAG via pgvector, and multi-provider LLM failover (Groq → Cerebras → SambaNova → Mistral).",
"category": "Applications",
"section": "Autonomous Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/Alessandro114.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-09-01T00:04:03Z",
"commitsLastWeek": 3
},
{
"id": 85,
"name": "SWE-agent",
"url": "https://github.com/princeton-nlp/SWE-agent",
"description": "Language agents for software engineering that can resolve GitHub issues in real repositories.",
"category": "Applications",
"section": "Autonomous Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/princeton-nlp.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-08-31T22:23:17Z",
"commitsLastWeek": 0
},
{
"id": 86,
"name": "Open Multi-Agent",
"url": "https://github.com/open-multi-agent/open-multi-agent",
"description": "TypeScript-native framework for multi-agent systems that plans task DAGs from goals at runtime, with approval gates, tracing, evaluation, checkpoints, and resume support. Requires Node.js 20+.",
"category": "Frameworks",
"section": "Frameworks",
"source": "GitHub",
"icon": {
"url": "https://github.com/open-multi-agent.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-08-31T17:47:38Z",
"commitsLastWeek": 9
},
{
"id": 87,
"name": "Caesar",
"url": "https://github.com/jasonzliang/caesar-agent",
"description": "Autonomous research agent that builds a knowledge graph during web exploration via a Perceive-Think-Act loop, then refines drafts through adversarial artifact synthesis. Multi-provider via litellm.",
"category": "Applications",
"section": "Autonomous Agent Task Solver Projects",
"source": "GitHub",
"icon": {
"url": "https://github.com/jasonzliang.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-08-31T11:44:45Z",
"commitsLastWeek": 5
},
{
"id": 88,
"name": "camel",
"url": "https://github.com/camel-ai/camel",
"description": "🐫 Communicative Agents for “Mind” Exploration of Large Language Model Society (NeruIPS'2023)",
"category": "Applications",
"section": "Agent Society Simulation",
"source": "GitHub",
"icon": {
"url": "https://github.com/camel-ai.png?size=96",
"type": "avatar"
},
"updatedAt": "2026-08-31T10:25:23Z",
"commitsLastWeek": 3
},
{
"id": 89,
"name": "Alfred",
"url": "https://github.com/luminik-io/alfred-os",
"description": "Self-hosted runtime for autonomous Claude Code and Codex agents that tur
gitextract_spf2vu1r/
├── .github/
│ ├── ISSUE_TEMPLATE/
│ │ ├── config.yml
│ │ ├── new-resource-submission.yml
│ │ └── update-existing-entry.yml
│ ├── pull_request_template.md
│ └── workflows/
│ └── pages.yml
├── CONTRIBUTING.md
├── README.md
├── scripts/
│ └── build-site.mjs
└── site/
├── .nojekyll
├── app.js
├── fonts/
│ ├── OFL.txt
│ └── README.md
├── icons/
│ └── README.md
├── index.html
├── resources.json
├── robots.txt
├── sitemap.xml
└── styles.css
Condensed preview — 18 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (323K chars).
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{
"path": ".github/ISSUE_TEMPLATE/config.yml",
"chars": 199,
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"path": ".github/workflows/pages.yml",
"chars": 1086,
"preview": "name: Deploy website to GitHub Pages\n\non:\n push:\n branches: [main]\n paths:\n - \"README.md\"\n - \"site/**\"..."
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"chars": 4726,
"preview": "# Contributing to Awesome-AI-Agents\n\nThanks for helping improve this list.\n\n## Scope\n\nThis repository curates high-quali..."
},
{
"path": "README.md",
"chars": 103333,
"preview": "# Awesome-AI-Agents 🤖️\n\n[![Twitter Badge](https://img.shields.io/badge/X-000000?style=for-the-badge&logo=x&logoColor=whi..."
},
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"preview": "const CATEGORIES = [\n \"All\",\n \"Applications\",\n \"Frameworks\",\n \"Tools\",\n \"Benchmarks\",\n \"Platforms\",\n \"Research\",..."
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"chars": 4403,
"preview": "Copyright 2022 The Bricolage Grotesque Project Authors (https://github.com/ateliertriay/bricolage)\n\nThis Font Software i..."
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"chars": 355,
"preview": "# Font source\n\n`bricolage-grotesque-latin-700.woff2` is the Latin webfont subset for\n[Bricolage Grotesque](https://fonts..."
},
{
"path": "site/icons/README.md",
"chars": 609,
"preview": "# Icon sources\n\nThe interface icons in this directory are vendored SVG assets rather than\nhand-drawn paths or runtime CD..."
},
{
"path": "site/index.html",
"chars": 5548,
"preview": "<!doctype html>\n<html lang=\"en\">\n <head>\n <meta charset=\"UTF-8\" />\n <meta name=\"viewport\" content=\"width=device-w..."
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"path": "site/resources.json",
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"preview": "[\n {\n \"id\": 1,\n \"name\": \"AgentsKit\",\n \"url\": \"https://github.com/AgentsKit-io/agentskit\",\n \"description\": \"..."
},
{
"path": "site/robots.txt",
"chars": 90,
"preview": "User-agent: *\nAllow: /\n\nSitemap: https://jenqyang.github.io/Awesome-AI-Agents/sitemap.xml\n"
},
{
"path": "site/sitemap.xml",
"chars": 188,
"preview": "<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n<urlset xmlns=\"http://www.sitemaps.org/schemas/sitemap/0.9\">\n <url>\n <loc>htt..."
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"path": "site/styles.css",
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"preview": "@font-face {\n font-family: \"Bricolage Grotesque\";\n font-style: normal;\n font-weight: 700;\n font-display: swap;\n src..."
}
]
About this extraction
This page contains the full source code of the Jenqyang/Awesome-AI-Agents GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 18 files (297.3 KB), approximately 84.1k tokens. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.