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Josh 394ac1befa
Feat:Openrouter/Anthropic/grok-support (#231)
* Add Anthropic and Grok provider support

* feat: Add crucial GPT-5 and reasoning model support for OpenRouter

- Add requires_max_completion_tokens() function for GPT-5, o1, o3, Grok-3 series
- Add prepare_chat_completion_params() for reasoning model compatibility
- Implement max_tokens → max_completion_tokens conversion for reasoning models
- Add temperature handling for reasoning models (must be 1.0 default)
- Enhanced provider validation and API key security in provider endpoints
- Streamlined retry logic (3→2 attempts) for faster issue detection
- Add failure tracking and circuit breaker analysis for debugging
- Support OpenRouter format detection (openai/gpt-5-nano, openai/o1-mini)
- Improved Grok provider empty response handling with structured fallbacks
- Enhanced contextual embedding with provider-aware model selection

Core provider functionality:
- OpenRouter, Grok, Anthropic provider support with full embedding integration
- Provider-specific model defaults and validation
- Secure API connectivity testing endpoints
- Provider context passing for code generation workflows

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

* fully working model providers, addressing securtiy and code related concerns, throughly hardening our code

* added multiprovider support, embeddings model support, cleaned the pr, need to fix health check, asnyico tasks errors, and contextual embeddings error

* fixed contextual embeddings issue

* - Added inspect-aware shutdown handling so get_llm_client always closes the underlying AsyncOpenAI / httpx.AsyncClient while the loop is   still alive, with defensive logging if shutdown happens late (python/src/server/services/llm_provider_service.py:14, python/src/server/    services/llm_provider_service.py:520).

* - Restructured get_llm_client so client creation and usage live in separate try/finally blocks; fallback clients now close without         logging spurious Error creating LLM client when downstream code raises (python/src/server/services/llm_provider_service.py:335-556).    - Close logic now sanitizes provider names consistently and awaits whichever aclose/close coroutine the SDK exposes, keeping the loop      shut down cleanly (python/src/server/services/llm_provider_service.py:530-559).                                                                                                                                                                                                       Robust JSON Parsing                                                                                                                                                                                                                                                                   - Added _extract_json_payload to strip code fences / extra text returned by Ollama before json.loads runs, averting the markdown-induced   decode errors you saw in logs (python/src/server/services/storage/code_storage_service.py:40-63).                                          - Swapped the direct parse call for the sanitized payload and emit a debug preview when cleanup alters the content (python/src/server/     services/storage/code_storage_service.py:858-864).

* added provider connection support

* added provider api key not being configured warning

* Updated get_llm_client so missing OpenAI keys automatically fall back to Ollama (matching existing tests) and so unsupported providers     still raise the legacy ValueError the suite expects. The fallback now reuses _get_optimal_ollama_instance and rethrows ValueError(OpenAI  API key not found and Ollama fallback failed) when it cant connect.  Adjusted test_code_extraction_source_id.py to accept the new optional argument on the mocked extractor (and confirm its None when         present).

* Resolved a few needed code rabbit suggestion   - Updated the knowledge API key validation to call create_embedding with the provider argument and removed the hard-coded OpenAI fallback  (python/src/server/api_routes/knowledge_api.py).                                                                                           - Broadened embedding provider detection so prefixed OpenRouter/OpenAI model names route through the correct client (python/src/server/    services/embeddings/embedding_service.py, python/src/server/services/llm_provider_service.py).                                             - Removed the duplicate helper definitions from llm_provider_service.py, eliminating the stray docstring that was causing the import-time  syntax error.

* updated via code rabbit PR review, code rabbit in my IDE found no issues and no nitpicks with the updates! what was done:    Credential service now persists the provider under the uppercase key LLM_PROVIDER, matching the read path (no new EMBEDDING_PROVIDER     usage introduced).                                                                                                                          Embedding batch creation stops inserting blank strings, logging failures and skipping invalid items before they ever hit the provider    (python/src/server/services/embeddings/embedding_service.py).                                                                               Contextual embedding prompts use real newline characters everywhereboth when constructing the batch prompt and when parsing the         models response (python/src/server/services/embeddings/contextual_embedding_service.py).                                                   Embedding provider routing already recognizes OpenRouter-prefixed OpenAI models via is_openai_embedding_model; no further change needed  there.                                                                                                                                      Embedding insertion now skips unsupported vector dimensions instead of forcing them into the 1536-column, and the backoff loop uses      await asyncio.sleep so we no longer block the event loop (python/src/server/services/storage/code_storage_service.py).                      RAG settings props were extended to include LLM_INSTANCE_NAME and OLLAMA_EMBEDDING_INSTANCE_NAME, and the debug log no longer prints     API-key prefixes (the rest of the TanStack refactor/EMBEDDING_PROVIDER support remains deferred).

* test fix

* enhanced Openrouters parsing logic to automatically detect reasoning models and parse regardless of json output or not. this commit creates a robust way for archons parsing to work throughly with openrouter automatically, regardless of the model youre using, to ensure proper functionality with out breaking any generation capabilities!

---------

Co-authored-by: Chillbruhhh <joshchesser97@gmail.com>
Co-authored-by: Claude <noreply@anthropic.com>
2025-09-22 10:36:30 +03:00
.claude Removing references to Archon "Alpha" 2025-09-06 14:51:02 -05:00
.github Removing references to Archon "Alpha" 2025-09-06 14:51:02 -05:00
archon-ui-main Feat:Openrouter/Anthropic/grok-support (#231) 2025-09-22 10:36:30 +03:00
docs fix: Change Ollama default URL to host.docker.internal for Docker compatibility 2025-09-20 13:36:33 -07:00
migration fix: Change Ollama default URL to host.docker.internal for Docker compatibility 2025-09-20 13:36:33 -07:00
PRPs docs: Update AI documentation to accurately reflect current codebase (#708) 2025-09-19 13:29:46 +03:00
python Feat:Openrouter/Anthropic/grok-support (#231) 2025-09-22 10:36:30 +03:00
.dockerignore The New Archon (Beta) - The Operating System for AI Coding Assistants! 2025-08-13 07:58:24 -05:00
.env.example feat: Ollama Integration with Separate LLM/Embedding Model Support (#643) 2025-09-15 06:38:02 -07:00
.gitignore refactor: Phase 5 - Remove manual cache invalidations (#707) 2025-09-19 14:26:05 +03:00
AGENTS.md docs: Update AI documentation to accurately reflect current codebase (#708) 2025-09-19 13:29:46 +03:00
check-env.js Improve development environment with Docker Compose profiles (#435) 2025-08-22 17:18:10 +03:00
CLAUDE.md docs: Update AI documentation to accurately reflect current codebase (#708) 2025-09-19 13:29:46 +03:00
CONTRIBUTING.md Adding instructions for stable branch. 2025-09-13 11:03:03 -05:00
docker-compose.docs.yml The New Archon (Beta) - The Operating System for AI Coding Assistants! 2025-08-13 07:58:24 -05:00
docker-compose.yml feat: Universal clipboard utility with improved copy functionality (#663) 2025-09-18 10:04:46 -07:00
LICENSE The New Archon (Beta) - The Operating System for AI Coding Assistants! 2025-08-13 07:58:24 -05:00
Makefile CI fails now when unit tests for backend fail (#536) 2025-08-30 12:52:34 -05:00
README.md Adding instructions for stable branch. 2025-09-13 11:03:03 -05:00

Archon Main Graphic

Power up your AI coding assistants with your own custom knowledge base and task management as an MCP server

Quick StartUpgradingWhat's IncludedArchitectureTroubleshooting


🎯 What is Archon?

Archon is currently in beta! Expect things to not work 100%, and please feel free to share any feedback and contribute with fixes/new features! Thank you to everyone for all the excitement we have for Archon already, as well as the bug reports, PRs, and discussions. It's a lot for our small team to get through but we're committed to addressing everything and making Archon into the best tool it possibly can be!

Archon is the command center for AI coding assistants. For you, it's a sleek interface to manage knowledge, context, and tasks for your projects. For the AI coding assistant(s), it's a Model Context Protocol (MCP) server to collaborate on and leverage the same knowledge, context, and tasks. Connect Claude Code, Kiro, Cursor, Windsurf, etc. to give your AI agents access to:

  • Your documentation (crawled websites, uploaded PDFs/docs)
  • Smart search capabilities with advanced RAG strategies
  • Task management integrated with your knowledge base
  • Real-time updates as you add new content and collaborate with your coding assistant on tasks
  • Much more coming soon to build Archon into an integrated environment for all context engineering

This new vision for Archon replaces the old one (the agenteer). Archon used to be the AI agent that builds other agents, and now you can use Archon to do that and more.

It doesn't matter what you're building or if it's a new/existing codebase - Archon's knowledge and task management capabilities will improve the output of any AI driven coding.

Quick Start

Prerequisites

Setup Instructions

  1. Clone Repository:

    git clone -b stable https://github.com/coleam00/archon.git
    
    cd archon
    

    Note: The stable branch is recommended for using Archon. If you want to contribute or try the latest features, use the main branch with git clone https://github.com/coleam00/archon.git

  2. Environment Configuration:

    cp .env.example .env
    # Edit .env and add your Supabase credentials:
    # SUPABASE_URL=https://your-project.supabase.co
    # SUPABASE_SERVICE_KEY=your-service-key-here
    

    IMPORTANT NOTES:

    • For cloud Supabase: they recently introduced a new type of service role key but use the legacy one (the longer one).
    • For local Supabase: set SUPABASE_URL to http://host.docker.internal:8000 (unless you have an IP address set up).
  3. Database Setup: In your Supabase project SQL Editor, copy, paste, and execute the contents of migration/complete_setup.sql

  4. Start Services (choose one):

    Full Docker Mode (Recommended for Normal Archon Usage)

    docker compose up --build -d
    

    This starts all core microservices in Docker:

    • Server: Core API and business logic (Port: 8181)
    • MCP Server: Protocol interface for AI clients (Port: 8051)
    • UI: Web interface (Port: 3737)

    Ports are configurable in your .env as well!

  5. Configure API Keys:

    • Open http://localhost:3737
    • You'll automatically be brought through an onboarding flow to set your API key (OpenAI is default)

Quick Test

Once everything is running:

  1. Test Web Crawling: Go to http://localhost:3737 → Knowledge Base → "Crawl Website" → Enter a doc URL (such as https://ai.pydantic.dev/llms-full.txt)
  2. Test Document Upload: Knowledge Base → Upload a PDF
  3. Test Projects: Projects → Create a new project and add tasks
  4. Integrate with your AI coding assistant: MCP Dashboard → Copy connection config for your AI coding assistant

Installing Make

🛠️ Make installation (OPTIONAL - For Dev Workflows)

Windows

# Option 1: Using Chocolatey
choco install make

# Option 2: Using Scoop
scoop install make

# Option 3: Using WSL2
wsl --install
# Then in WSL: sudo apt-get install make

macOS

# Make comes pre-installed on macOS
# If needed: brew install make

Linux

# Debian/Ubuntu
sudo apt-get install make

# RHEL/CentOS/Fedora
sudo yum install make
🚀 Quick Command Reference for Make
Command Description
make dev Start hybrid dev (backend in Docker, frontend local)
make dev-docker Everything in Docker
make stop Stop all services
make test Run all tests
make lint Run linters
make install Install dependencies
make check Check environment setup
make clean Remove containers and volumes (with confirmation)

🔄 Database Reset (Start Fresh if Needed)

If you need to completely reset your database and start fresh:

⚠️ Reset Database - This will delete ALL data for Archon!
  1. Run Reset Script: In your Supabase SQL Editor, run the contents of migration/RESET_DB.sql

    ⚠️ WARNING: This will delete all Archon specific tables and data! Nothing else will be touched in your DB though.

  2. Rebuild Database: After reset, run migration/complete_setup.sql to create all the tables again.

  3. Restart Services:

    docker compose --profile full up -d
    
  4. Reconfigure:

    • Select your LLM/embedding provider and set the API key again
    • Re-upload any documents or re-crawl websites

The reset script safely removes all tables, functions, triggers, and policies with proper dependency handling.

📚 Documentation

Core Services

Service Container Name Default URL Purpose
Web Interface archon-ui http://localhost:3737 Main dashboard and controls
API Service archon-server http://localhost:8181 Web crawling, document processing
MCP Server archon-mcp http://localhost:8051 Model Context Protocol interface
Agents Service archon-agents http://localhost:8052 AI/ML operations, reranking

Upgrading

To upgrade Archon to the latest version:

  1. Pull latest changes:

    git pull
    
  2. Check for migrations: Look in the migration/ folder for any SQL files newer than your last update. Check the file created dates to determine if you need to run them. You can run these in the SQL editor just like you did when you first set up Archon. We are also working on a way to make handling these migrations automatic!

  3. Rebuild and restart:

    docker compose up -d --build
    

This is the same command used for initial setup - it rebuilds containers with the latest code and restarts services.

What's Included

🧠 Knowledge Management

  • Smart Web Crawling: Automatically detects and crawls entire documentation sites, sitemaps, and individual pages
  • Document Processing: Upload and process PDFs, Word docs, markdown files, and text documents with intelligent chunking
  • Code Example Extraction: Automatically identifies and indexes code examples from documentation for enhanced search
  • Vector Search: Advanced semantic search with contextual embeddings for precise knowledge retrieval
  • Source Management: Organize knowledge by source, type, and tags for easy filtering

🤖 AI Integration

  • Model Context Protocol (MCP): Connect any MCP-compatible client (Claude Code, Cursor, even non-AI coding assistants like Claude Desktop)
  • MCP Tools: Comprehensive yet simple set of tools for RAG queries, task management, and project operations
  • Multi-LLM Support: Works with OpenAI, Ollama, and Google Gemini models
  • RAG Strategies: Hybrid search, contextual embeddings, and result reranking for optimal AI responses
  • Real-time Streaming: Live responses from AI agents with progress tracking

📋 Project & Task Management

  • Hierarchical Projects: Organize work with projects, features, and tasks in a structured workflow
  • AI-Assisted Creation: Generate project requirements and tasks using integrated AI agents
  • Document Management: Version-controlled documents with collaborative editing capabilities
  • Progress Tracking: Real-time updates and status management across all project activities

🔄 Real-time Collaboration

  • WebSocket Updates: Live progress tracking for crawling, processing, and AI operations
  • Multi-user Support: Collaborative knowledge building and project management
  • Background Processing: Asynchronous operations that don't block the user interface
  • Health Monitoring: Built-in service health checks and automatic reconnection

Architecture

Microservices Structure

Archon uses true microservices architecture with clear separation of concerns:

┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
│   Frontend UI   │    │  Server (API)   │    │   MCP Server    │    │ Agents Service  │
│                 │    │                 │    │                 │    │                 │
│  React + Vite   │◄──►│    FastAPI +    │◄──►│    Lightweight  │◄──►│   PydanticAI    │
│  Port 3737      │    │    SocketIO     │    │    HTTP Wrapper │    │   Port 8052     │
│                 │    │    Port 8181    │    │    Port 8051    │    │                 │
└─────────────────┘    └─────────────────┘    └─────────────────┘    └─────────────────┘
         │                        │                        │                        │
         └────────────────────────┼────────────────────────┼────────────────────────┘
                                  │                        │
                         ┌─────────────────┐               │
                         │    Database     │               │
                         │                 │               │
                         │    Supabase     │◄──────────────┘
                         │    PostgreSQL   │
                         │    PGVector     │
                         └─────────────────┘

Service Responsibilities

Service Location Purpose Key Features
Frontend archon-ui-main/ Web interface and dashboard React, TypeScript, TailwindCSS, Socket.IO client
Server python/src/server/ Core business logic and APIs FastAPI, service layer, Socket.IO broadcasts, all ML/AI operations
MCP Server python/src/mcp/ MCP protocol interface Lightweight HTTP wrapper, MCP tools, session management
Agents python/src/agents/ PydanticAI agent hosting Document and RAG agents, streaming responses

Communication Patterns

  • HTTP-based: All inter-service communication uses HTTP APIs
  • Socket.IO: Real-time updates from Server to Frontend
  • MCP Protocol: AI clients connect to MCP Server via SSE or stdio
  • No Direct Imports: Services are truly independent with no shared code dependencies

Key Architectural Benefits

  • Lightweight Containers: Each service contains only required dependencies
  • Independent Scaling: Services can be scaled independently based on load
  • Development Flexibility: Teams can work on different services without conflicts
  • Technology Diversity: Each service uses the best tools for its specific purpose

🔧 Configuring Custom Ports & Hostname

By default, Archon services run on the following ports:

  • archon-ui: 3737
  • archon-server: 8181
  • archon-mcp: 8051
  • archon-agents: 8052
  • archon-docs: 3838 (optional)

Changing Ports

To use custom ports, add these variables to your .env file:

# Service Ports Configuration
ARCHON_UI_PORT=3737
ARCHON_SERVER_PORT=8181
ARCHON_MCP_PORT=8051
ARCHON_AGENTS_PORT=8052
ARCHON_DOCS_PORT=3838

Example: Running on different ports:

ARCHON_SERVER_PORT=8282
ARCHON_MCP_PORT=8151

Configuring Hostname

By default, Archon uses localhost as the hostname. You can configure a custom hostname or IP address by setting the HOST variable in your .env file:

# Hostname Configuration
HOST=localhost  # Default

# Examples of custom hostnames:
HOST=192.168.1.100     # Use specific IP address
HOST=archon.local      # Use custom domain
HOST=myserver.com      # Use public domain

This is useful when:

  • Running Archon on a different machine and accessing it remotely
  • Using a custom domain name for your installation
  • Deploying in a network environment where localhost isn't accessible

After changing hostname or ports:

  1. Restart Docker containers: docker compose down && docker compose --profile full up -d
  2. Access the UI at: http://${HOST}:${ARCHON_UI_PORT}
  3. Update your AI client configuration with the new hostname and MCP port

🔧 Development

Quick Start

# Install dependencies
make install

# Start development (recommended)
make dev        # Backend in Docker, frontend local with hot reload

# Alternative: Everything in Docker
make dev-docker # All services in Docker

# Stop everything (local FE needs to be stopped manually)
make stop

Development Modes

Best for active development with instant frontend updates:

  • Backend services run in Docker (isolated, consistent)
  • Frontend runs locally with hot module replacement
  • Instant UI updates without Docker rebuilds

Full Docker Mode - make dev-docker

For all services in Docker environment:

  • All services run in Docker containers
  • Better for integration testing
  • Slower frontend updates

Testing & Code Quality

# Run tests
make test       # Run all tests
make test-fe    # Run frontend tests
make test-be    # Run backend tests

# Run linters
make lint       # Lint all code
make lint-fe    # Lint frontend code
make lint-be    # Lint backend code

# Check environment
make check      # Verify environment setup

# Clean up
make clean      # Remove containers and volumes (asks for confirmation)

Viewing Logs

# View logs using Docker Compose directly
docker compose logs -f              # All services
docker compose logs -f archon-server # API server
docker compose logs -f archon-mcp    # MCP server
docker compose logs -f archon-ui     # Frontend

Note: The backend services are configured with --reload flag in their uvicorn commands and have source code mounted as volumes for automatic hot reloading when you make changes.

Troubleshooting

Common Issues and Solutions

Port Conflicts

If you see "Port already in use" errors:

# Check what's using a port (e.g., 3737)
lsof -i :3737

# Stop all containers and local services
make stop

# Change the port in .env

Docker Permission Issues (Linux)

If you encounter permission errors with Docker:

# Add your user to the docker group
sudo usermod -aG docker $USER

# Log out and back in, or run
newgrp docker

Windows-Specific Issues

  • Make not found: Install Make via Chocolatey, Scoop, or WSL2 (see Installing Make)
  • Line ending issues: Configure Git to use LF endings:
    git config --global core.autocrlf false
    

Frontend Can't Connect to Backend

  • Check backend is running: curl http://localhost:8181/health
  • Verify port configuration in .env
  • For custom ports, ensure both ARCHON_SERVER_PORT and VITE_ARCHON_SERVER_PORT are set

Docker Compose Hangs

If docker compose commands hang:

# Reset Docker Compose
docker compose down --remove-orphans
docker system prune -f

# Restart Docker Desktop (if applicable)

Hot Reload Not Working

  • Frontend: Ensure you're running in hybrid mode (make dev) for best HMR experience
  • Backend: Check that volumes are mounted correctly in docker-compose.yml
  • File permissions: On some systems, mounted volumes may have permission issues

📈 Progress

Star History Chart

📄 License

Archon Community License (ACL) v1.2 - see LICENSE file for details.

TL;DR: Archon is free, open, and hackable. Run it, fork it, share it - just don't sell it as-a-service without permission.