Archon is an AI agent builder designed to autonomously create, refine, and optimize other AI agents. It targets developers and researchers interested in agentic workflows, domain knowledge integration, and scalable AI architectures, offering a framework to understand and build advanced AI systems.

How It Works

Archon employs a multi-agent system orchestrated by LangGraph, featuring a "reasoner" LLM for high-level planning and specialized "refiner" agents for prompt, tool, and configuration optimization. This iterative approach, incorporating user feedback or autonomous refinement, allows for the development of complex agents by breaking down the task into manageable, specialized steps. The integration of a tool library and MCP server support enhances capabilities and reduces hallucinations by providing prebuilt components.

Quick Start & Requirements

• Installation:
  • Docker (Recommended): git clone https://github.com/coleam00/archon.git && cd archon && python run_docker.py
  • Local Python: git clone https://github.com/coleam00/archon.git && cd archon && python -m venv venv && source venv/bin/activate && pip install -r requirements.txt && streamlit run streamlit_ui.py
• Prerequisites: Python 3.11+, Docker (optional), Supabase account, OpenAI/Anthropic/OpenRouter API key or Ollama for local LLMs.
• Setup: Guided process via Streamlit UI for environment configuration, Supabase setup, documentation crawling, and agent service initialization.
• Links: V6 Documentation, Community Forum

Highlighted Details

• "Agenteer" concept: AI agent designed to build other AI agents.
• V6 includes a prebuilt tool library and MCP server integration for enhanced agent creation.
• Supports multiple LLM providers (OpenAI, Anthropic, Ollama) and integrates with AI IDEs via MCP.
• Iterative development roadmap from V1 (Pydantic AI agents) to future V10 (multi-framework support).

Maintenance & Community

The project is actively developed with a clear versioning and roadmap. Community interaction is encouraged via a GitHub Kanban board and a dedicated community forum.

Licensing & Compatibility

MIT License. Permissive for commercial use and closed-source linking.

Limitations & Caveats

The tool/example library is in its early stages. While Docker is recommended, local Python setup requires careful dependency management. Streaming UI support is currently limited to OpenAI.