deer-flow/backend

DeerFlow Backend

DeerFlow is a LangGraph-based AI agent system that provides a powerful "super agent" with sandbox execution capabilities. The backend enables AI agents to execute code, browse the web, manage files, and perform complex multi-step tasks in isolated environments.

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Features

• LangGraph Agent Runtime: Built on LangGraph for robust multi-agent workflow orchestration
• Sandbox Execution: Safe code execution with local or Docker-based isolation
• Multi-Model Support: OpenAI, Anthropic Claude, DeepSeek, Doubao, Kimi, and custom LangChain-compatible models
• MCP Integration: Extensible tool ecosystem via Model Context Protocol
• Skills System: Specialized domain workflows injected into agent prompts
• File Upload & Processing: Multi-format document upload with automatic Markdown conversion
• Context Summarization: Automatic conversation summarization for long conversations
• Plan Mode: TodoList middleware for complex multi-step task tracking

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Architecture

┌─────────────────────────────────────────────────────────────────┐
│                         Nginx (Port 2026)                       │
│              Unified reverse proxy entry point                   │
└─────────────────┬───────────────────────────────┬───────────────┘
                  │                               │
                  ▼                               ▼
┌─────────────────────────────┐   ┌─────────────────────────────┐
│   LangGraph Server (2024)   │   │    Gateway API (8001)       │
│   Agent runtime & workflows │   │   Models, MCP, Skills, etc. │
└─────────────────────────────┘   └─────────────────────────────┘

Request Routing:

• /api/langgraph/* → LangGraph Server (agent interactions, threads, streaming)
• /api/* (other) → Gateway API (models, MCP, skills, artifacts, uploads)
• / (non-API) → Frontend (web interface)

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Quick Start

Prerequisites

• Python 3.12+
• uv package manager
• API keys for your chosen LLM provider

Installation

# Clone the repository (if not already)
cd deer-flow

# Copy configuration files
cp config.example.yaml config.yaml
cp extensions_config.example.json extensions_config.json

# Install backend dependencies
cd backend
make install

Configuration

Edit config.yaml in the project root to configure your models and tools:

models:
  - name: gpt-4
    display_name: GPT-4
    use: langchain_openai:ChatOpenAI
    model: gpt-4
    api_key: $OPENAI_API_KEY  # Set environment variable
    max_tokens: 4096

Set your API keys:

export OPENAI_API_KEY="your-api-key-here"
# Or other provider keys as needed

Running

Full Application (from project root):

make dev  # Starts LangGraph + Gateway + Frontend + Nginx

Access at: http://localhost:2026

Backend Only (from backend directory):

# Terminal 1: LangGraph server
make dev

# Terminal 2: Gateway API
make gateway

Direct access:

• LangGraph: http://localhost:2024
• Gateway: http://localhost:8001

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Project Structure

backend/
├── src/
│   ├── agents/              # LangGraph agents and workflows
│   │   ├── lead_agent/      # Main agent implementation
│   │   └── middlewares/     # Agent middlewares
│   ├── gateway/             # FastAPI Gateway API
│   │   └── routers/         # API route handlers
│   ├── sandbox/             # Sandbox execution system
│   ├── tools/               # Agent tools (builtins)
│   ├── mcp/                 # MCP integration
│   ├── models/              # Model factory
│   ├── skills/              # Skills loader
│   ├── config/              # Configuration system
│   ├── community/           # Community tools (web search, etc.)
│   ├── reflection/          # Dynamic module loading
│   └── utils/               # Utility functions
├── docs/                    # Documentation
├── tests/                   # Test suite
├── langgraph.json           # LangGraph server configuration
├── config.yaml              # Application configuration (optional)
├── pyproject.toml           # Python dependencies
├── Makefile                 # Development commands
└── Dockerfile               # Container build

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API Reference

LangGraph API (via /api/langgraph/*)

• POST /threads - Create new conversation thread
• POST /threads/{thread_id}/runs - Execute agent with input
• GET /threads/{thread_id}/runs - Get run history
• GET /threads/{thread_id}/state - Get current conversation state
• WebSocket support for streaming responses

Gateway API (via /api/*)

Models:

• GET /api/models - List available LLM models
• GET /api/models/{model_name} - Get model details

MCP Configuration:

• GET /api/mcp/config - Get current MCP server configurations
• PUT /api/mcp/config - Update MCP configuration

Skills Management:

• GET /api/skills - List all skills
• GET /api/skills/{skill_name} - Get skill details
• POST /api/skills/{skill_name}/enable - Enable a skill
• POST /api/skills/{skill_name}/disable - Disable a skill
• POST /api/skills/install - Install skill from .skill file

File Uploads:

• POST /api/threads/{thread_id}/uploads - Upload files
• GET /api/threads/{thread_id}/uploads/list - List uploaded files
• DELETE /api/threads/{thread_id}/uploads/{filename} - Delete file

Artifacts:

• GET /api/threads/{thread_id}/artifacts/{path} - Download generated artifacts

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Configuration

Main Configuration (config.yaml)

The application uses a YAML-based configuration file. Place it in the project root directory.

Key sections:

• models: LLM configurations with class paths and API keys
• tool_groups: Logical groupings for tools
• tools: Tool definitions with module paths
• sandbox: Execution environment settings
• skills: Skills directory configuration
• title: Auto-title generation settings
• summarization: Context summarization settings

See docs/CONFIGURATION.md for detailed documentation.

Extensions Configuration (extensions_config.json)

MCP servers and skills are configured in extensions_config.json:

{
  "mcpServers": {
    "github": {
      "enabled": true,
      "type": "stdio",
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-github"],
      "env": {"GITHUB_TOKEN": "$GITHUB_TOKEN"}
    }
  },
  "skills": {
    "pdf-processing": {"enabled": true}
  }
}

Environment Variables

• DEER_FLOW_CONFIG_PATH - Override config.yaml location
• DEER_FLOW_EXTENSIONS_CONFIG_PATH - Override extensions_config.json location
• Model API keys: OPENAI_API_KEY, ANTHROPIC_API_KEY, DEEPSEEK_API_KEY, etc.
• Tool API keys: TAVILY_API_KEY, GITHUB_TOKEN, etc.

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Development

Commands

make install    # Install dependencies
make dev        # Run LangGraph server (port 2024)
make gateway    # Run Gateway API (port 8001)
make lint       # Run linter (ruff)
make format     # Format code (ruff)

Code Style

• Uses ruff for linting and formatting
• Line length: 240 characters
• Python 3.12+ with type hints
• Double quotes, space indentation

Testing

uv run pytest

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Documentation

• Configuration Guide - Detailed configuration options
• Setup Guide - Quick setup instructions
• File Upload - File upload functionality
• Path Examples - Path types and usage
• Summarization - Context summarization feature
• Plan Mode - TodoList middleware usage

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Technology Stack

Core Frameworks

• LangChain (1.2.3+) - LLM orchestration
• LangGraph (1.0.6+) - Multi-agent workflows
• FastAPI (0.115.0+) - REST API
• Uvicorn (0.34.0+) - ASGI server

LLM Integrations

• langchain-openai - OpenAI models
• langchain-anthropic - Claude models
• langchain-deepseek - DeepSeek models

Extensions

• langchain-mcp-adapters - MCP protocol support
• agent-sandbox - Sandboxed code execution

Utilities

• markitdown - Multi-format to Markdown conversion
• tavily-python - Web search
• firecrawl-py - Web scraping
• ddgs - DuckDuckGo image search

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License

See the LICENSE file in the project root.

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Contributing

See CONTRIBUTING.md for contribution guidelines.