mirror of
https://gitee.com/wanwujie/deer-flow
synced 2026-04-17 19:44:45 +08:00
b2abfecf67
- Add AioSandboxProvider for Docker-based sandbox execution with configurable container lifecycle, volume mounts, and port management - Add TitleMiddleware to auto-generate thread titles after first user-assistant exchange using LLM - Add Claude Code documentation (CLAUDE.md, AGENTS.md) - Extend SandboxConfig with Docker-specific options (image, port, mounts) - Fix hardcoded mount path to use expanduser - Add agent-sandbox and dotenv dependencies Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>
77 lines
2.7 KiB
Markdown
77 lines
2.7 KiB
Markdown
# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Project Overview
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DeerFlow is a LangGraph-based AI agent backend that provides a "super agent" with sandbox execution capabilities. The agent can execute code, browse the web, and manage files in isolated sandbox environments.
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## Commands
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```bash
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# Install dependencies
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make install
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# Run development server (LangGraph Studio)
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make dev
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# Lint
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make lint
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# Format code
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make format
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```
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## Architecture
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### Configuration System
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The app uses a YAML-based configuration system loaded from `config.yaml`. Configuration priority:
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1. Explicit `config_path` argument
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2. `DEER_FLOW_CONFIG_PATH` environment variable
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3. `config.yaml` in current directory
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4. `config.yaml` in parent directory
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Config values starting with `$` are resolved as environment variables (e.g., `$OPENAI_API_KEY`).
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### Core Components
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**Agent Graph** (`src/agents/`)
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- `lead_agent` is the main entry point registered in `langgraph.json`
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- Uses `ThreadState` which extends `AgentState` with sandbox state
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- Agent is created via `create_agent()` with model, tools, middleware, and system prompt
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**Sandbox System** (`src/sandbox/`)
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- Abstract `Sandbox` base class defines interface: `execute_command`, `read_file`, `write_file`, `list_dir`
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- `SandboxProvider` manages sandbox lifecycle: `acquire`, `get`, `release`
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- `SandboxMiddleware` automatically acquires sandbox on agent start and injects into state
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- `LocalSandboxProvider` is a singleton implementation for local execution
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- Sandbox tools (`bash`, `ls`, `read_file`, `write_file`, `str_replace`) extract sandbox from tool runtime
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**Model Factory** (`src/models/`)
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- `create_chat_model()` instantiates LLM from config using reflection
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- Supports `thinking_enabled` flag with per-model `when_thinking_enabled` overrides
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**Tool System** (`src/tools/`)
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- Tools defined in config with `use` path (e.g., `src.sandbox.tools:bash_tool`)
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- `get_available_tools()` resolves tool paths via reflection
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- Community tools in `src/community/`: Jina AI (web fetch), Tavily (web search)
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**Reflection System** (`src/reflection/`)
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- `resolve_variable()` imports module and returns variable (e.g., `module:variable`)
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- `resolve_class()` imports and validates class against base class
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### Config Schema
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Models, tools, and sandbox providers are configured in `config.yaml`:
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- `models[]`: LLM configurations with `use` class path
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- `tools[]`: Tool configurations with `use` variable path and `group`
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- `sandbox.use`: Sandbox provider class path
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## Code Style
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- Uses `ruff` for linting and formatting
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- Line length: 240 characters
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- Python 3.12+ with type hints
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- Double quotes, space indentation
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