deer-flow/README.md

🦌 DeerFlow - v2

Originated from Open Source, give back to Open Source.

A LangGraph-based AI agent backend with sandbox execution capabilities.

Quick Start

Option 1: Docker (Recommended)

The fastest way to get started with a consistent environment:

1. Configure the application:

   cp config.example.yaml config.yaml
   # Edit config.yaml and set your API keys
   

2. Initialize and start:

   make docker-start   # Start all services
   

3. Access: http://localhost:2026

See CONTRIBUTING.md for detailed Docker development guide.

Option 2: Local Development

If you prefer running services locally:

1. Check prerequisites:

   make check  # Verifies Node.js 22+, pnpm, uv, nginx
   

2. Configure and install:

   cp config.example.yaml config.yaml
   make install
   

3. (Optional) Pre-pull sandbox image:

   # Recommended if using Docker/Container-based sandbox
   make setup-sandbox
   

4. Start services:

   make dev
   

5. Access: http://localhost:2026

See CONTRIBUTING.md for detailed local development guide.

Sandbox Configuration

DeerFlow supports multiple sandbox execution modes. Configure your preferred mode in config.yaml:

Local Execution (runs sandbox code directly on the host machine):

sandbox:
   use: src.sandbox.local:LocalSandboxProvider # Local execution

Docker Execution (runs sandbox code in isolated Docker containers):

sandbox:
   use: src.community.aio_sandbox:AioSandboxProvider # Docker-based sandbox

Docker Execution with Kubernetes (runs sandbox code in Kubernetes pods):

Setup Kubernetes sandbox as per Kubernetes Sandbox Setup.

./docker/k8s/setup.sh

Then configure config.yaml with the Kubernetes service URL:

sandbox:
   use: src.community.k8s_sandbox:AioSandboxProvider # Kubernetes-based sandbox
   base_url: http://deer-flow-sandbox.deer-flow.svc.cluster.local:8080 # Kubernetes service URL

Features

• 🤖 LangGraph-based Agents - Multi-agent orchestration with sophisticated workflows
• 🧠 Persistent Memory - LLM-powered context retention across conversations with automatic fact extraction
• 🔧 Model Context Protocol (MCP) - Extensible tool integration
• 🎯 Skills System - Reusable agent capabilities
• 🛡️ Sandbox Execution - Safe code execution environment
• 🌐 Unified API Gateway - Single entry point with nginx reverse proxy
• 🔄 Hot Reload - Fast development iteration
• 📊 Real-time Streaming - Server-Sent Events (SSE) support

Documentation

• Contributing Guide - Development environment setup and workflow
• Configuration Guide - Setup and configuration instructions
• Architecture Overview - Technical architecture details
• Backend Architecture - Backend architecture and API reference

Contributing

We welcome contributions! Please see CONTRIBUTING.md for development setup, workflow, and guidelines.

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.

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