# CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

## Project Overview

DeerFlow is a LangGraph-based AI agent system with a full-stack architecture. The backend provides a "super agent" with sandbox execution capabilities that can execute code, browse the web, and manage files in isolated environments.

**Architecture**:
- **LangGraph Server** (port 2024): Agent runtime and workflow execution
- **Gateway API** (port 8001): REST API for models, MCP, skills, and artifacts
- **Frontend** (port 3000): Next.js web interface
- **Nginx** (port 2026): Unified reverse proxy entry point

**Project Structure**:
```
deer-flow/
├── Makefile                    # Root commands (check, install, dev, stop)
├── nginx.conf                  # Nginx reverse proxy configuration
├── config.yaml                 # Main application configuration
├── extensions_config.json      # MCP servers and skills configuration
├── backend/                    # Backend application (this directory)
│   ├── Makefile               # Backend-only commands (dev, gateway, lint)
│   ├── src/
│   │   ├── agents/            # LangGraph agents and workflows
│   │   ├── gateway/           # FastAPI Gateway API
│   │   ├── sandbox/           # Sandbox execution system
│   │   ├── tools/             # Agent tools
│   │   ├── mcp/               # MCP integration
│   │   └── skills/            # Skills loading and management
│   └── langgraph.json         # LangGraph server configuration
├── frontend/                   # Next.js frontend application
└── skills/                     # Agent skills directory
    ├── public/                # Public skills (committed)
    └── custom/                # Custom skills (gitignored)
```

## Commands

**Root directory** (for full application):
```bash
# Check system requirements
make check

# Install all dependencies (frontend + backend)
make install

# Start all services (LangGraph + Gateway + Frontend + Nginx)
make dev

# Stop all services
make stop
```

**Backend directory** (for backend development only):
```bash
# Install backend dependencies
make install

# Run LangGraph server only (port 2024)
make dev

# Run Gateway API only (port 8001)
make gateway

# Lint
make lint

# Format code
make format
```

## Architecture

### Configuration System

The app uses a YAML-based configuration system loaded from `config.yaml`.

**Setup**: Copy `config.example.yaml` to `config.yaml` in the **project root** directory and customize for your environment.

```bash
# From project root (deer-flow/)
cp config.example.yaml config.yaml
```

Configuration priority:
1. Explicit `config_path` argument
2. `DEER_FLOW_CONFIG_PATH` environment variable
3. `config.yaml` in current directory (backend/)
4. `config.yaml` in parent directory (project root - **recommended location**)

Config values starting with `$` are resolved as environment variables (e.g., `$OPENAI_API_KEY`).

### Core Components

**Gateway API** (`src/gateway/`)
- FastAPI application that provides REST endpoints for frontend integration
- Endpoints:
  - `/api/models` - List available LLM models from configuration
  - `/api/mcp` - Manage MCP server configurations (GET, POST)
  - `/api/skills` - Manage skill configurations (GET, POST)
  - `/api/threads/{thread_id}/artifacts/*` - Serve agent-generated artifacts (files, images, etc.)
- Works alongside LangGraph server, handling non-agent HTTP operations
- Proxied through nginx under `/api/*` routes (except `/api/langgraph/*`)

**Agent Graph** (`src/agents/`)
- `lead_agent` is the main entry point registered in `langgraph.json`
- Uses `ThreadState` which extends `AgentState` with sandbox state
- Agent is created via `create_agent()` with model, tools, middleware, and system prompt

**Sandbox System** (`src/sandbox/`)
- Abstract `Sandbox` base class defines interface: `execute_command`, `read_file`, `write_file`, `list_dir`
- `SandboxProvider` manages sandbox lifecycle: `acquire`, `get`, `release`
- `SandboxMiddleware` automatically acquires sandbox on agent start and injects into state
- `LocalSandboxProvider` is a singleton implementation for local execution
- Sandbox tools (`bash`, `ls`, `read_file`, `write_file`, `str_replace`) extract sandbox from tool runtime

**Model Factory** (`src/models/`)
- `create_chat_model()` instantiates LLM from config using reflection
- Supports `thinking_enabled` flag with per-model `when_thinking_enabled` overrides

**Tool System** (`src/tools/`)
- Tools defined in config with `use` path (e.g., `src.sandbox.tools:bash_tool`)
- `get_available_tools()` resolves tool paths via reflection
- Community tools in `src/community/`: Jina AI (web fetch), Tavily (web search)
- Supports MCP (Model Context Protocol) for pluggable external tools

**MCP System** (`src/mcp/`)
- Integrates with MCP servers to provide pluggable external tools using `langchain-mcp-adapters`
- Uses `MultiServerMCPClient` from langchain-mcp-adapters for multi-server management
- **Automatic initialization**: Tools are loaded on first use with lazy initialization
- Supports both eager loading (FastAPI startup) and lazy loading (LangGraph Studio)
- `initialize_mcp_tools()` can be called in FastAPI lifespan handler for eager loading
- `get_cached_mcp_tools()` automatically initializes tools if not already loaded
- Works seamlessly in both FastAPI server and LangGraph Studio environments
- Each server can be enabled/disabled independently via `enabled` flag
- Popular MCP servers: filesystem, postgres, github, brave-search, puppeteer
- Built on top of langchain-ai/langchain-mcp-adapters for seamless integration

**Reflection System** (`src/reflection/`)
- `resolve_variable()` imports module and returns variable (e.g., `module:variable`)
- `resolve_class()` imports and validates class against base class

**Skills System** (`src/skills/`)
- Skills provide specialized workflows for specific tasks (e.g., PDF processing, frontend design)
- Located in `deer-flow/skills/{public,custom}` directory structure
- Each skill has a `SKILL.md` file with YAML front matter (name, description, license)
- Skills are automatically discovered and loaded at runtime
- `load_skills()` scans directories and parses SKILL.md files
- Skills are injected into agent's system prompt with paths (only enabled skills)
- Path mapping system allows seamless access in both local and Docker sandbox:
  - Local sandbox: `/mnt/skills` → `/path/to/deer-flow/skills`
  - Docker sandbox: Automatically mounted as volume
- Each skill can be enabled/disabled independently via `enabled` flag in extensions config

**Middleware System**
- Custom middlewares in `src/agents/middlewares/`: Title generation, thread data, clarification, etc.
- `SummarizationMiddleware` from LangChain automatically condenses conversation history when token limits are approached
- Configured in `config.yaml` under `summarization` key with trigger/keep thresholds
- Middlewares are registered in `src/agents/lead_agent/agent.py` with execution order:
  1. `ThreadDataMiddleware` - Initializes thread context
  2. `SandboxMiddleware` - Manages sandbox lifecycle
  3. `SummarizationMiddleware` - Reduces context when limits are approached (if enabled)
  4. `TitleMiddleware` - Generates conversation titles
  5. `ClarificationMiddleware` - Handles clarification requests (must be last)

### Config Schema

Models, tools, sandbox providers, skills, and middleware settings are configured in `config.yaml`:
- `models[]`: LLM configurations with `use` class path
- `tools[]`: Tool configurations with `use` variable path and `group`
- `sandbox.use`: Sandbox provider class path
- `skills.path`: Host path to skills directory (optional, default: `../skills`)
- `skills.container_path`: Container mount path (default: `/mnt/skills`)
- `title`: Automatic thread title generation configuration
- `summarization`: Automatic conversation summarization configuration

**Extensions Configuration** (`extensions_config.json`)

MCP servers and skills are configured together in `extensions_config.json` in project root:

**Setup**: Copy `extensions_config.example.json` to `extensions_config.json` in the **project root** directory.

```bash
# From project root (deer-flow/)
cp extensions_config.example.json extensions_config.json
```

Configuration priority:
1. Explicit `config_path` argument
2. `DEER_FLOW_EXTENSIONS_CONFIG_PATH` environment variable
3. `extensions_config.json` in current directory (backend/)
4. `extensions_config.json` in parent directory (project root - **recommended location**)
5. For backward compatibility: `mcp_config.json` (will be deprecated)

Structure:
- `mcpServers`: Map of MCP server name to configuration
  - `enabled`: Whether the server is enabled (boolean)
  - `command`: Command to execute to start the server (e.g., "npx", "python")
  - `args`: Arguments to pass to the command (array)
  - `env`: Environment variables (object with `$VAR` support for env variable resolution)
  - `description`: Human-readable description
- `skills`: Map of skill name to state configuration
  - `enabled`: Whether the skill is enabled (boolean, default: true if not specified)

Both MCP servers and skills can be modified at runtime via API endpoints.

## Development Workflow

### Running the Full Application

From the **project root** directory:
```bash
make dev
```

This starts all services and makes the application available at `http://localhost:2026`.

**Nginx routing**:
- `/api/langgraph/*` → LangGraph Server (2024) - Agent interactions, threads, streaming
- `/api/*` (other) → Gateway API (8001) - Models, MCP, skills, artifacts
- `/` (non-API) → Frontend (3000) - Web interface

### Running Backend Services Separately

For backend-only development, from the **backend** directory:

```bash
# Terminal 1: LangGraph server
make dev

# Terminal 2: Gateway API
make gateway
```

Direct access (without nginx):
- LangGraph: `http://localhost:2024`
- Gateway: `http://localhost:8001`

### Frontend Configuration

The frontend uses environment variables to connect to backend services:
- `NEXT_PUBLIC_LANGGRAPH_BASE_URL` - Defaults to `/api/langgraph` (through nginx)
- `NEXT_PUBLIC_BACKEND_BASE_URL` - Defaults to empty string (through nginx)

When using `make dev` from root, the frontend automatically connects through nginx.

## Code Style

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