Check system requirements:
```
make check
```
This will verify that you have all required tools installed:
- Node.js 22+
- pnpm
- uv (Python package manager)
- nginx

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 and skills
cp extensions_config.example.json extensions_config.json
# Edit extensions_config.json to enable desired MCP servers and skills

Install dependencies:
```
make install
```
Run development server (starts frontend, backend, and nginx):
```
make dev
```
Access the application:
- Web Interface: http://localhost:2026
- All API requests are automatically proxied through nginx

Manual Deployment

If you need to start services individually:

Start backend services:

# Terminal 1: Start LangGraph Server (port 2024)
cd backend
make dev

# Terminal 2: Start Gateway API (port 8001)
cd backend
make gateway

# Terminal 3: Start Frontend (port 3000)
cd frontend
pnpm dev

Start nginx:

make nginx
# or directly: nginx -c $(pwd)/nginx.conf -g 'daemon off;'

Access the application:
- Web Interface: http://localhost:2026

The nginx configuration provides:

Unified entry point on port 2026
Routes /api/langgraph/* to LangGraph Server (2024)
Routes other /api/* endpoints to Gateway API (8001)
Routes non-API requests to Frontend (3000)
Centralized CORS handling
SSE/streaming support for real-time agent responses
Optimized timeouts for long-running operations

Project Structure

deer-flow/
├── config.example.yaml    # Configuration template (copy to config.yaml)
├── nginx.conf            # Nginx reverse proxy configuration
├── backend/              # Backend application
│   ├── src/             # Source code
│   │   ├── gateway/     # Gateway API (port 8001)
│   │   └── agents/      # LangGraph agents (port 2024)
│   └── docs/            # Documentation
├── frontend/            # Frontend application
└── skills/              # Agent skills
    ├── public/          # Public skills
    └── custom/          # Custom skills

Architecture

Browser
  ↓
Nginx (port 2026) ← Unified entry point
  ├→ Frontend (port 3000) ← / (non-API requests)
  ├→ Gateway API (port 8001) ← /api/models, /api/mcp, /api/skills, /api/threads/*/artifacts
  └→ LangGraph Server (port 2024) ← /api/langgraph/* (agent interactions)

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:

Daniel Walnut
Henry Li

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.

Star History

Languages

Python 59.3%

TypeScript 24.3%

HTML 7.3%

CSS 3.2%

JavaScript 2.8%

Other 3.1%