mirror of
https://gitee.com/wanwujie/deer-flow
synced 2026-04-03 06:12:14 +08:00
7b7e32f262
* feat: adds docker-based dev environment * docs: updates Docker command help * fix local dev * feat(sandbox): add Kubernetes-based sandbox provider for multi-instance support * fix: skills path in k8s * feat: add example config for k8s sandbox * fix: docker config * fix: load skills on docker dev * feat: support sandbox execution to Kubernetes Deployment model * chore: rename web service name
136 lines
4.6 KiB
Markdown
136 lines
4.6 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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### Option 1: Docker (Recommended)
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The fastest way to get started with a consistent environment:
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1. **Configure the application**:
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```bash
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cp config.example.yaml config.yaml
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# Edit config.yaml and set your API keys
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```
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2. **Initialize and start**:
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```bash
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make docker-start # Start all services
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```
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3. **Access**: http://localhost:2026
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See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed Docker development guide.
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### Option 2: Local Development
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If you prefer running services locally:
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1. **Check prerequisites**:
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```bash
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make check # Verifies Node.js 22+, pnpm, uv, nginx
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```
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2. **Configure and install**:
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```bash
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cp config.example.yaml config.yaml
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make install
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```
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3. **(Optional) Pre-pull sandbox image**:
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```bash
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# Recommended if using Docker/Container-based sandbox
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make setup-sandbox
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```
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4. **Start services**:
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```bash
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make dev
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```
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5. **Access**: http://localhost:2026
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See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed local development guide.
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### Sandbox Configuration
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DeerFlow supports multiple sandbox execution modes. Configure your preferred mode in `config.yaml`:
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**Local Execution** (runs sandbox code directly on the host machine):
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```yaml
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sandbox:
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use: src.sandbox.local:LocalSandboxProvider # Local execution
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```
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**Docker Execution** (runs sandbox code in isolated Docker containers):
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```yaml
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sandbox:
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use: src.community.aio_sandbox:AioSandboxProvider # Docker-based sandbox
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```
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**Docker Execution with Kubernetes** (runs sandbox code in Kubernetes pods):
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Setup Kubernetes sandbox as per [Kubernetes Sandbox Setup](docker/k8s/README.md).
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```bash
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./docker/k8s/setup.sh
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```
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Then configure `config.yaml` with the Kubernetes service URL:
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```yaml
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sandbox:
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use: src.community.k8s_sandbox:AioSandboxProvider # Kubernetes-based sandbox
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base_url: http://deer-flow-sandbox.deer-flow.svc.cluster.local:8080 # Kubernetes service URL
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```
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## Features
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- 🤖 **LangGraph-based Agents** - Multi-agent orchestration with sophisticated workflows
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- 🧠 **Persistent Memory** - LLM-powered context retention across conversations with automatic fact extraction
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- 🔧 **Model Context Protocol (MCP)** - Extensible tool integration
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- 🎯 **Skills System** - Reusable agent capabilities
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- 🛡️ **Sandbox Execution** - Safe code execution environment
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- 🌐 **Unified API Gateway** - Single entry point with nginx reverse proxy
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- 🔄 **Hot Reload** - Fast development iteration
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- 📊 **Real-time Streaming** - Server-Sent Events (SSE) support
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## Documentation
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- [Contributing Guide](CONTRIBUTING.md) - Development environment setup and workflow
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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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- [Backend Architecture](backend/README.md) - Backend architecture and API reference
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## Contributing
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We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for development setup, workflow, and guidelines.
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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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