Archon: The Intelligent Command Center for AI Coding Assistants

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Empower your AI coding tools with a centralized knowledge base and task management system

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What Exactly Is Archon?

Archon serves as the 「central command hub」 for AI programming assistants. For developers, it provides an intuitive interface to manage project knowledge, context, and tasks. For AI coding tools, it functions as a 「Model Context Protocol (MCP) server」 that enables collaboration through shared knowledge and task management. Whether you use Claude Code, Cursor, or other AI programming tools, Archon gives your AI agents access to:

• 「Your documentation library」 (crawled websites, uploaded PDFs/docs)
• 「Advanced search capabilities」 with sophisticated RAG strategies
• 「Integrated task management」 connected to your knowledge base
• 「Real-time updates」 as you add content and collaborate with AI assistants
• 「Expanding capabilities」 to create a comprehensive environment for context engineering

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Currently in beta! Some features may require refinement – we welcome feedback and contributions!

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How Archon Transforms Development Workflows

Imagine starting a new project where your AI assistant immediately accesses all relevant documentation. When you add requirements, tasks automatically synchronize across all connected AI tools. Archon makes this possible:

• 「Universal compatibility」: Works with both new and existing codebases
• 「Centralized knowledge」: Ensures consistency across all AI assistants
• 「Dynamic context」: Real-time updates to all connected tools
• 「Collaborative efficiency」: Streamlines human-AI task management

Core Functionality Explained

� Intelligent Knowledge Management

graph LR
A[Upload Documents] --> B[Intelligent Chunking]
C[Crawl Websites] --> B
B --> D[Vector Processing]
D --> E[Database Storage]
E --> F[Semantic Search]

🤖 Deep AI Integration

The 「Model Context Protocol (MCP)」 is Archon’s innovative framework for AI-knowledgebase interaction:

1. 「10 Standard Tools」: Covering retrieval, task management, and project operations
2. 「Multi-Model Support」:

   • OpenAI models
   • Google Gemini
   • Local Ollama deployments
3. 「Advanced RAG Strategies」:

   • Hybrid search (keyword + semantic)
   • Context-aware embeddings
   • Result reranking (optional component)

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Enable reranking: Uncomment lines 20-22 in python\requirements.server.txt (note: increases container size significantly)

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📋 Project & Task Management

# Sample AI-generated task structure
project = {
  "name": "E-commerce Platform",
  "features": [
    {
      "title": "User Authentication",
      "tasks": [
        "Implement OAuth login",
        "Design permission system",
        "Create test cases"
      ]
    }
  ]
}

• 「Hierarchical organization」: Project > Feature > Task structure
• 「AI-assisted creation」: Automated task list generation
• 「Version control」: Document change history tracking
• 「Real-time dashboard」: Visual task status monitoring

5-Minute Quick Start

Prerequisites

1. Docker Desktop
2. Supabase account (free tier sufficient)
3. OpenAI API key

Installation Process

# 1. Clone repository
git clone https://github.com/coleam00/archon.git
cd archon

# 2. Configure environment
cp .env.example .env
# Edit .env with your Supabase credentials:
# SUPABASE_URL=https://your-project.supabase.co
# SUPABASE_SERVICE_KEY=your-service-key-here

「Important」: Use Supabase’s legacy service key (the longer version)

「Key Steps」:

1. Execute migration/complete_setup.sql in Supabase dashboard
2. Launch services: docker-compose up --build -d
3. Access web interface: http://localhost:3737
4. Configure: 「Settings → Select LLM provider → Enter API key」

Service Architecture

Technical Architecture Deep Dive

Microservices Design

┌─────────────┐   ┌─────────────┐   ┌─────────────┐   ┌─────────────┐
│   Frontend  │   │  API Service│   │  MCP Service│   │ Agents Serv.│
│ (React+Vite)◄──►(FastAPI)     ◄──►(HTTP Layer) ◄──►(PydanticAI)  │
└─────────────┘   └─────────────┘   └─────────────┘   └─────────────┘
                         │               │               │
                         └───────┬───────┘               │
                                 │                       │
                         ┌───────▼───────┐               │
                         │   Supabase    ◄───────────────┘
                         │ PostgreSQL    │
                         │  PGVector     │
                         └───────────────┘

Component Responsibilities

Architectural Advantages

1. 「Independent scaling」: Services can be scaled based on demand
2. 「Technology flexibility」: Optimal tools for each component
3. 「Development isolation」: Parallel team workflows
4. 「Lightweight containers」: Minimal dependencies for faster deployment

Practical Operation Guide

Initial Functionality Testing

1. 「Website Crawling Test」:

   • Access http://localhost:3737
   • Knowledge Base → “Crawl Website” → Enter documentation URL (e.g., https://ai.pydantic.dev/llms-full.txt)

2. 「Document Upload Test」:

   • Knowledge Base → Upload PDF/Word documents
   • Observe automatic chunking and indexing

3. 「Project Management Test」:

   • Create new project → Add features → Generate tasks
   • Reference knowledge base items using @doc in task descriptions

4. 「AI Tool Integration」:

   • MCP Dashboard → Copy connection configuration
   • Paste into your preferred AI programming tool

Custom Configuration

Modify .env file for service customization:

# Port configuration example
ARCHON_UI_PORT=3737       # Main interface
ARCHON_SERVER_PORT=8181   # API service
ARCHON_MCP_PORT=8051      # MCP protocol
ARCHON_AGENTS_PORT=8052   # Agents service

# Host configuration examples
HOST=192.168.1.100       # LAN access
HOST=archon.yourdomain.com # Domain access

「Restart after changes」: docker-compose down && docker-compose up -d

Database Management Techniques

Safe Database Reset

When you need a fresh start:

1. Execute migration/RESET_DB.sql in Supabase

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   ⚠️ Warning: This erases all Archon data!

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2. Re-run migration/complete_setup.sql

3. Restart services: docker-compose up -d

4. Reconfigure LLM keys and knowledge base

Development Mode Instructions

# Backend hot-reload
docker-compose up archon-server archon-mcp archon-agents --build

# Frontend hot-reload
cd archon-ui-main && npm run dev

# Documentation service
cd docs && npm start

「Development note」: Backend services include --reload configuration for automatic code updates.

Frequently Asked Questions (FAQ)

❓ Does Archon support local models?

✅ Yes! Integrate locally-run LLMs via Ollama – select “Ollama” in settings and specify local API address

❓ How much storage is required?

Base installation ~2GB. Enabling reranking adds ~1.5GB. Document storage depends on knowledge base size

❓ Is team collaboration supported?

✅ Multi-user ready! Supabase database inherently supports team collaboration with real-time synchronization

❓ How to integrate existing projects?

1. Upload project documentation
2. Crawl project-related websites
3. Create corresponding project structure
4. Add MCP configuration to your AI tools

❓ Is codebase indexing supported?

Current version focuses on documentation management – codebase indexing is on the development roadmap

Project Resources

• GitHub Discussions – Join community conversations
• Contribution Guide – Participate in development
• Introduction Video – 15-minute setup demonstration
• Dynamous AI Community – Archon’s origin community

License Information

Archon Community License (ACL) v1.2 – Full details in LICENSE file
「Core principle」: Free to use, modify, and share – commercial SaaS offerings require permission

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In the AI era, knowledge management capability determines development efficiency. Archon creates not just tools, but a new paradigm for human-AI collaboration.

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