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https://gitee.com/wanwujie/deer-flow
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7978e05dc1
* docs: add nginx reverse proxy documentation Add comprehensive nginx configuration documentation to README including: - Production deployment instructions with step-by-step setup - Architecture diagram showing traffic routing between services - Nginx features: unified entry point, CORS handling, SSE support - Updated project structure with nginx.conf and service ports Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com> * feat: implement nginx --------- Co-authored-by: Claude Sonnet 4.5 <noreply@anthropic.com>
125 lines
4.1 KiB
Markdown
125 lines
4.1 KiB
Markdown
# 🦌 DeerFlow - v2
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> Originated from Open Source, give back to Open Source.
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A LangGraph-based AI agent backend with sandbox execution capabilities.
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## Quick Start
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1. **Configure the application**:
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```bash
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# Copy example configuration
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cp config.example.yaml config.yaml
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# Set your API keys
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export OPENAI_API_KEY="your-key-here"
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# or edit config.yaml directly
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# Optional: Enable MCP servers for additional tools
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cp mcp_config.example.json mcp_config.json
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# Edit mcp_config.json to enable desired servers
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```
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2. **Install dependencies**:
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```bash
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cd backend
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make install
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```
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3. **Run development server**:
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```bash
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make dev
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```
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### Production Deployment
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For production environments, use nginx as a reverse proxy to route traffic between the gateway and LangGraph services:
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1. **Start backend services**:
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```bash
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# Terminal 1: Start Gateway API (port 8001)
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cd backend
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python -m src.gateway.app
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# Terminal 2: Start LangGraph Server (port 2024)
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cd backend
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langgraph up
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```
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2. **Start nginx**:
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```bash
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nginx -c $(pwd)/nginx.conf
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```
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3. **Access the application**:
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- Main API: http://localhost:8000
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The nginx configuration provides:
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- Unified entry point on port 8000
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- Routes `/api/models`, `/api/threads/*/artifacts`, and `/health` to Gateway (8001)
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- Routes all other requests to LangGraph (2024)
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- Centralized CORS handling
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- SSE/streaming support for real-time agent responses
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- Optimized timeouts for long-running operations
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## Project Structure
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```
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deer-flow/
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├── config.example.yaml # Configuration template (copy to config.yaml)
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├── nginx.conf # Nginx reverse proxy configuration
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├── backend/ # Backend application
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│ ├── src/ # Source code
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│ │ ├── gateway/ # Gateway API (port 8001)
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│ │ └── agents/ # LangGraph agents (port 2024)
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│ └── docs/ # Documentation
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├── frontend/ # Frontend application
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└── skills/ # Agent skills
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├── public/ # Public skills
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└── custom/ # Custom skills
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```
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### Architecture
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```
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Client
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↓
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Nginx (port 8000) ← Unified entry point
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├→ Gateway API (port 8001) ← /api/models, /api/threads/*/artifacts, /health
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└→ LangGraph Server (port 2024) ← All other requests (agent interactions)
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```
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## Documentation
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- [Configuration Guide](backend/docs/CONFIGURATION.md) - Setup and configuration instructions
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- [Architecture Overview](backend/CLAUDE.md) - Technical architecture details
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- [MCP Setup Guide](MCP_SETUP.md) - Configure Model Context Protocol servers for additional tools
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## License
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This project is open source and available under the [MIT License](./LICENSE).
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## Acknowledgments
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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.
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We would like to extend our sincere appreciation to the following projects for their invaluable contributions:
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- **[LangChain](https://github.com/langchain-ai/langchain)**: Their exceptional framework powers our LLM interactions and chains, enabling seamless integration and functionality.
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- **[LangGraph](https://github.com/langchain-ai/langgraph)**: Their innovative approach to multi-agent orchestration has been instrumental in enabling DeerFlow's sophisticated workflows.
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These projects exemplify the transformative power of open-source collaboration, and we are proud to build upon their foundations.
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### Key Contributors
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A heartfelt thank you goes out to the core authors of `DeerFlow`, whose vision, passion, and dedication have brought this project to life:
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- **[Daniel Walnut](https://github.com/hetaoBackend/)**
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- **[Henry Li](https://github.com/magiccube/)**
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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.
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## Star History
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[](https://star-history.com/#bytedance/deer-flow&Date)
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