mirror of
https://gitee.com/wanwujie/deer-flow
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9f755ecc30
Implement a skills framework that enables specialized workflows for specific tasks (e.g., PDF processing, web page generation). Skills are discovered from the skills/ directory and automatically mounted in sandboxes with path mapping support. - Add SkillsConfig for configuring skills path and container mount point - Implement dynamic skill loading from SKILL.md files with YAML frontmatter - Add path mapping in LocalSandbox to translate container paths to local paths - Mount skills directory in AIO Docker sandbox containers - Update lead agent prompt to dynamically inject available skills - Add setup documentation and expand config.example.yaml Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
76 lines
2.7 KiB
Markdown
76 lines
2.7 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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```
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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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## 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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├── backend/ # Backend application
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│ ├── src/ # Source code
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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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## 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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## 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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