74d4a16492
Add comprehensive MCP integration using langchain-mcp-adapters to enable pluggable external tools from MCP servers. Features: - MCP server configuration via mcp_config.json - Automatic lazy initialization for seamless use in both FastAPI and LangGraph Studio - Support for multiple MCP servers (filesystem, postgres, github, brave-search, etc.) - Environment variable resolution in configuration - Tool caching mechanism for optimal performance - Complete documentation and setup guide Implementation: - Add src/mcp module with client, tools, and cache components - Integrate MCP config loading in AppConfig - Update tool system to include MCP tools automatically - Add eager initialization in FastAPI lifespan handler - Add lazy initialization fallback for LangGraph Studio Dependencies: - Add langchain-mcp-adapters>=0.1.0 Documentation: - Add MCP_SETUP.md with comprehensive setup guide - Update CLAUDE.md with MCP system architecture - Update config.example.yaml with MCP configuration notes - Update README.md with MCP setup instructions Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
2.9 KiB
🦌 DeerFlow - v2
Originated from Open Source, give back to Open Source.
A LangGraph-based AI agent backend with sandbox execution capabilities.
Quick Start
-
Configure the application:
# Copy example configuration cp config.example.yaml config.yaml # Set your API keys export OPENAI_API_KEY="your-key-here" # or edit config.yaml directly # Optional: Enable MCP servers for additional tools cp mcp_config.example.json mcp_config.json # Edit mcp_config.json to enable desired servers -
Install dependencies:
cd backend make install -
Run development server:
make dev
Project Structure
deer-flow/
├── config.example.yaml # Configuration template (copy to config.yaml)
├── backend/ # Backend application
│ ├── src/ # Source code
│ └── docs/ # Documentation
├── frontend/ # Frontend application
└── skills/ # Agent skills
├── public/ # Public skills
└── custom/ # Custom skills
Documentation
- Configuration Guide - Setup and configuration instructions
- Architecture Overview - Technical architecture details
- MCP Setup Guide - Configure Model Context Protocol servers for additional tools
License
This project is open source and available under the MIT License.
Acknowledgments
DeerFlow is built upon the incredible work of the open-source community. We are deeply grateful to all the projects and contributors whose efforts have made DeerFlow possible. Truly, we stand on the shoulders of giants.
We would like to extend our sincere appreciation to the following projects for their invaluable contributions:
- LangChain: Their exceptional framework powers our LLM interactions and chains, enabling seamless integration and functionality.
- LangGraph: Their innovative approach to multi-agent orchestration has been instrumental in enabling DeerFlow's sophisticated workflows.
These projects exemplify the transformative power of open-source collaboration, and we are proud to build upon their foundations.
Key Contributors
A heartfelt thank you goes out to the core authors of DeerFlow, whose vision, passion, and dedication have brought this project to life:
Your unwavering commitment and expertise have been the driving force behind DeerFlow's success. We are honored to have you at the helm of this journey.