mirrors/archon
1
0
Fork 0
mirror of https://github.com/coleam00/Archon.git synced 2025-12-24 02:39:17 -05:00
Code Issues Packages Projects Releases Wiki Activity

Archon V3 - MCP Support

Browse Source
This commit is contained in:
Cole Medin
2025-02-17 13:27:24 -06:00
parent e2805b8757
commit 7a9e706581
31 changed files with 1944 additions and 30 deletions
Download Patch File Download Diff File Expand all files Collapse all files

123
README.md
View File

@@ -6,8 +6,8 @@
<h3>🚀 **CURRENT VERSION** 🚀</h3>
**[ V2 - Agentic Workflow ]**
*Using LangGraph + Pydantic AI for multi-agent orchestration and planning*
**[ V3 - MCP Support ]**
*Using LangGraph + Pydantic AI with AI IDE integration*
</div>
@@ -18,7 +18,7 @@ Archon will be developed in iterations, starting with just a simple Pydantic AI
all the way to a full agentic workflow using LangGraph that can build other AI agents with any framework.
Through its iterative development, Archon showcases the power of planning, feedback loops, and domain-specific knowledge in creating robust AI agents.
The current version of Archon is V2 as mentioned above - see [V2 Documentation](iterations/v2-agentic-workflow/README.md) for details.
The current version of Archon is V3 as mentioned above - see [V3 Documentation](iterations/v3-mcp-support/README.md) for details.
## Vision
@@ -36,38 +36,50 @@ Archon demonstrates three key principles in modern AI development:
- Simple code generation without validation
- [Learn more about V1](iterations/v1-single-agent/README.md)
### V2: Current - Agentic Workflow (LangGraph)
### V2: Agentic Workflow (LangGraph)
- Multi-agent system with planning and execution separation
- Reasoning LLM (O3-mini/R1) for architecture planning
- LangGraph for workflow orchestration
- Support for local LLMs via Ollama
- [Learn more about V2](iterations/v2-agentic-workflow/README.md)
### V3: Current - MCP Support
- Integration with AI IDEs like Windsurf and Cursor
- Automated file creation and dependency management
- FastAPI service for agent generation
- Improved project structure and organization
- [Learn more about V3](iterations/v3-mcp-support/README.md)
### Future Iterations
- V3: Self-Feedback Loop - Automated validation and error correction
- V4: Tool Library Integration - Pre-built external tool incorporation
- V5: Multi-Framework Support - Framework-agnostic agent generation
- V6: Autonomous Framework Learning - Self-updating framework adapters
- V4: Self-Feedback Loop - Automated validation and error correction
- V5: Tool Library Integration - Pre-built external tool incorporation
- V6: Multi-Framework Support - Framework-agnostic agent generation
- V7: Autonomous Framework Learning - Self-updating framework adapters
### Future Integrations
- Docker
- LangSmith
- MCP
- Other frameworks besides Pydantic AI
- Other vector databases besides Supabase
- Alternative embedding models besides OpenAI
## Getting Started with V2 (current version)
## Getting Started with V3 (current version)
Since V2 is the current version of Archon, all the code for V2 is in both the `archon` and `archon/iterations/v2-agentic-workflow` directories.
Since V3 is the current version of Archon, all the code for V3 is in both the `archon` and `archon/iterations/v3-mcp-support` directories.
### Prerequisites
- Python 3.11+
- Supabase account and database
- OpenAI/OpenRouter API key or Ollama for local LLMs
- Streamlit (for web interface)
- Windsurf, Cursor, or another MCP-compatible AI IDE (optional)
### Installation
There are two ways to install Archon V3:
#### Option 1: Standard Installation (for Streamlit UI)
1. Clone the repository:
```bash
git clone https://github.com/coleam00/archon.git
@@ -81,7 +93,42 @@ source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
```
3. Configure environment:
#### Option 2: MCP Server Setup (for AI IDE integration)
1. Clone the repository as above
2. Run the MCP setup script:
```bash
python setup_mcp.py
```
For running the crawler and graph service later, activate the virtual environment too:
```bash
source venv/bin/activate # On Windows: venv\Scripts\activate
```
This will:
- Create a virtual environment if it doesn't exist
- Install dependencies from requirements.txt
- Generate an MCP configuration file
3. Configure your AI IDE:
- **In Windsurf**:
- Click on the hammer icon above the chat input
- Click on "Configure"
- Paste the JSON that `setup_mcp.py` gave you as the MCP config
- Click "Refresh" next to "Configure"
- **In Cursor**:
- Go to Cursor Settings > Features > MCP
- Click on "+ Add New MCP Server"
- Name: Archon
- Type: command (equivalent to stdio)
- Command: Paste the command that `setup_mcp.py` gave for Cursor
### Environment Setup
1. Configure environment:
- Rename `.env.example` to `.env`
- Edit `.env` with your settings:
```env
@@ -97,29 +144,58 @@ pip install -r requirements.txt
### Quick Start
1. Set up the database:
- Execute `site_pages.sql` in your Supabase SQL Editor
- Execute `utils/site_pages.sql` in your Supabase SQL Editor
- This creates tables and enables vector similarity search
2. Crawl documentation:
```bash
python crawl_pydantic_ai_docs.py
python archon/crawl_pydantic_ai_docs.py
```
3. Launch the UI:
3. Run Archon either as an MCP Server or with Streamlit:
### Using with AI IDEs (MCP Support)
1. After crawling the documentation, start the graph service:
```bash
python graph_service.py
```
Archon runs as a separate API endpoint for MCP instead of directly in the MCP server because that way Archon can be updated separately without having to restart the MCP server, and the communication protocols for MCP seemed to interfere with LLM calls when done directly within the MCP server.
2. Restart the MCP server in your AI IDE
3. You can now ask your AI IDE to create agents with Archon
4. Be sure to specify when you want to use Archon - not necessary but it helps a lot
### Using the Streamlit UI
For an interactive web interface:
```bash
streamlit run streamlit_ui.py
```
Visit `http://localhost:8501` to start building AI agents!
The interface will be available at `http://localhost:8501`
## Architecture
### Current V2 Components
- `archon_graph.py`: LangGraph workflow and agent coordination
- `pydantic_ai_coder.py`: Main coding agent with RAG capabilities
- `crawl_pydantic_ai_docs.py`: Documentation processor
- `streamlit_ui.py`: Interactive web interface
- `site_pages.sql`: Database schema
### Core Files
- `mcp_server.py`: MCP server script for AI IDE integration
- `graph_service.py`: FastAPI service that handles the agentic workflow
- `setup_mcp.py`: MCP setup script
- `streamlit_ui.py`: Web interface with streaming support
- `requirements.txt`: Project dependencies
### Archon Package
- `archon/`: Core agent and workflow implementation
- `archon_graph.py`: LangGraph workflow definition and agent coordination
- `pydantic_ai_coder.py`: Main coding agent with RAG capabilities
- `crawl_pydantic_ai_docs.py`: Documentation crawler and processor
### Utilities
- `utils/`: Utility functions and database setup
- `utils.py`: Shared utility functions
- `site_pages.sql`: Database setup commands
### Database Schema
```sql
@@ -148,3 +224,4 @@ We welcome contributions! Whether you're fixing bugs, adding features, or improv
For version-specific details:
- [V1 Documentation](iterations/v1-single-agent/README.md)
- [V2 Documentation](iterations/v2-agentic-workflow/README.md)
- [V3 Documentation](iterations/v3-mcp-support/README.md)