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Merge remote-tracking branch 'deer-flow-2/experimental' into main-2.x

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.env
Dockerfile
.dockerignore
.git
.gitignore
docker/
# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
env/
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
.venv/
# Web
node_modules
npm-debug.log
.next
# IDE
.idea/
.vscode/
*.swp
*.swo
# OS
.DS_Store
Thumbs.db
# Project specific
conf.yaml
web/
docs/
examples/
assets/
tests/
*.log
# Exclude directories not needed in Docker context
# Frontend build only needs frontend/
# Backend build only needs backend/
scripts/
logs/
docker/
skills/
frontend/.next
frontend/node_modules
backend/.venv
backend/htmlcov
backend/.coverage
*.md
!README.md
!frontend/README.md
!backend/README.md

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# TAVILY API Key
TAVILY_API_KEY=your-tavily-api-key
# Jina API Key
JINA_API_KEY=your-jina-api-key
# Optional:
# FIRECRAWL_API_KEY=your-firecrawl-api-key
# VOLCENGINE_API_KEY=your-volcengine-api-key
# OPENAI_API_KEY=your-openai-api-key
# GEMINI_API_KEY=your-gemini-api-key
# DEEPSEEK_API_KEY=your-deepseek-api-key

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# DeerFlow docker image cache
docker/.cache/
# OS generated files
.DS_Store
*.local
._*
.Spotlight-V100
.Trashes
ehthumbs.db
Thumbs.db
# Python cache
__pycache__/
*.pyc
*.pyo
# Virtual environments
.venv
venv/
# Environment variables
.env
# Configuration files
config.yaml
mcp_config.json
extensions_config.json
# IDE
.idea/
# Coverage report
coverage.xml
coverage/
.deer-flow/
.claude/
skills/custom/*
logs/
# Local git hooks (keep only on this machine, do not push)
.githooks/
# pnpm
.pnpm-store
sandbox_image_cache.tar

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# Contributing to DeerFlow
Thank you for your interest in contributing to DeerFlow! This guide will help you set up your development environment and understand our development workflow.
## Development Environment Setup
We offer two development environments. **Docker is recommended** for the most consistent and hassle-free experience.
### Option 1: Docker Development (Recommended)
Docker provides a consistent, isolated environment with all dependencies pre-configured. No need to install Node.js, Python, or nginx on your local machine.
#### Prerequisites
- Docker Desktop or Docker Engine
- pnpm (for caching optimization)
#### Setup Steps
1. **Configure the application**:
```bash
# Copy example configuration
cp config.example.yaml config.yaml
# Set your API keys
export OPENAI_API_KEY="your-key-here"
# or edit config.yaml directly
# Optional: Enable MCP servers and skills
cp extensions_config.example.json extensions_config.json
# Edit extensions_config.json to enable desired MCP servers and skills
```
2. **Initialize Docker environment** (first time only):
```bash
make docker-init
```
This will:
- Build Docker images
- Install frontend dependencies (pnpm)
- Install backend dependencies (uv)
- Share pnpm cache with host for faster builds
3. **Start development services**:
```bash
make docker-start
```
All services will start with hot-reload enabled:
- Frontend changes are automatically reloaded
- Backend changes trigger automatic restart
- LangGraph server supports hot-reload
4. **Access the application**:
- Web Interface: http://localhost:2026
- API Gateway: http://localhost:2026/api/*
- LangGraph: http://localhost:2026/api/langgraph/*
#### Docker Commands
```bash
# View all logs
make docker-logs
# Restart services
make docker-restart
# Stop services
make docker-stop
# Get help
make docker-help
```
#### Docker Architecture
```
Host Machine
↓
Docker Compose (deer-flow-dev)
├→ nginx (port 2026) ← Reverse proxy
├→ web (port 3000) ← Frontend with hot-reload
├→ api (port 8001) ← Gateway API with hot-reload
└→ langgraph (port 2024) ← LangGraph server with hot-reload
```
**Benefits of Docker Development**:
- ✅ Consistent environment across different machines
- ✅ No need to install Node.js, Python, or nginx locally
- ✅ Isolated dependencies and services
- ✅ Easy cleanup and reset
- ✅ Hot-reload for all services
- ✅ Production-like environment
### Option 2: Local Development
If you prefer to run services directly on your machine:
#### Prerequisites
Check that you have all required tools installed:
```bash
make check
```
Required tools:
- Node.js 22+
- pnpm
- uv (Python package manager)
- nginx
#### Setup Steps
1. **Configure the application** (same as Docker setup above)
2. **Install dependencies**:
```bash
make install
```
3. **Run development server** (starts all services with nginx):
```bash
make dev
```
4. **Access the application**:
- Web Interface: http://localhost:2026
- All API requests are automatically proxied through nginx
#### Manual Service Control
If you need to start services individually:
1. **Start backend services**:
```bash
# Terminal 1: Start LangGraph Server (port 2024)
cd backend
make dev
# Terminal 2: Start Gateway API (port 8001)
cd backend
make gateway
# Terminal 3: Start Frontend (port 3000)
cd frontend
pnpm dev
```
2. **Start nginx**:
```bash
make nginx
# or directly: nginx -c $(pwd)/docker/nginx/nginx.local.conf -g 'daemon off;'
```
3. **Access the application**:
- Web Interface: http://localhost:2026
#### Nginx Configuration
The nginx configuration provides:
- Unified entry point on port 2026
- Routes `/api/langgraph/*` to LangGraph Server (2024)
- Routes other `/api/*` endpoints to Gateway API (8001)
- Routes non-API requests to Frontend (3000)
- Centralized CORS handling
- SSE/streaming support for real-time agent responses
- Optimized timeouts for long-running operations
## Project Structure
```
deer-flow/
├── config.example.yaml # Configuration template
├── extensions_config.example.json # MCP and Skills configuration template
├── Makefile # Build and development commands
├── scripts/
│ └── docker.sh # Docker management script
├── docker/
│ ├── docker-compose-dev.yaml # Docker Compose configuration
│ └── nginx/
│ ├── nginx.conf # Nginx config for Docker
│ └── nginx.local.conf # Nginx config for local dev
├── backend/ # Backend application
│ ├── src/
│ │ ├── gateway/ # Gateway API (port 8001)
│ │ ├── agents/ # LangGraph agents (port 2024)
│ │ ├── mcp/ # Model Context Protocol integration
│ │ ├── skills/ # Skills system
│ │ └── sandbox/ # Sandbox execution
│ ├── docs/ # Backend documentation
│ └── Makefile # Backend commands
├── frontend/ # Frontend application
│ └── Makefile # Frontend commands
└── skills/ # Agent skills
├── public/ # Public skills
└── custom/ # Custom skills
```
## Architecture
```
Browser
↓
Nginx (port 2026) ← Unified entry point
├→ Frontend (port 3000) ← / (non-API requests)
├→ Gateway API (port 8001) ← /api/models, /api/mcp, /api/skills, /api/threads/*/artifacts
└→ LangGraph Server (port 2024) ← /api/langgraph/* (agent interactions)
```
## Development Workflow
1. **Create a feature branch**:
```bash
git checkout -b feature/your-feature-name
```
2. **Make your changes** with hot-reload enabled
3. **Test your changes** thoroughly
4. **Commit your changes**:
```bash
git add .
git commit -m "feat: description of your changes"
```
5. **Push and create a Pull Request**:
```bash
git push origin feature/your-feature-name
```
## Testing
```bash
# Backend tests
cd backend
uv run pytest
# Frontend tests
cd frontend
pnpm test
```
## Code Style
- **Backend (Python)**: We use `ruff` for linting and formatting
- **Frontend (TypeScript)**: We use ESLint and Prettier
## Documentation
- [Configuration Guide](backend/docs/CONFIGURATION.md) - Setup and configuration
- [Architecture Overview](backend/CLAUDE.md) - Technical architecture
- [MCP Setup Guide](MCP_SETUP.md) - Model Context Protocol configuration
## Need Help?
- Check existing [Issues](https://github.com/bytedance/deer-flow/issues)
- Read the [Documentation](backend/docs/)
- Ask questions in [Discussions](https://github.com/bytedance/deer-flow/discussions)
## License
By contributing to DeerFlow, you agree that your contributions will be licensed under the [MIT License](./LICENSE).

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MIT License
Copyright (c) 2025 Bytedance Ltd. and/or its affiliates
Copyright (c) 2025-2026 DeerFlow Authors
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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# DeerFlow - Unified Development Environment
.PHONY: help check install dev stop clean docker-init docker-start docker-stop docker-logs docker-logs-frontend docker-logs-gateway
help:
@echo "DeerFlow Development Commands:"
@echo " make check - Check if all required tools are installed"
@echo " make install - Install all dependencies (frontend + backend)"
@echo " make setup-sandbox - Pre-pull sandbox container image (recommended)"
@echo " make dev - Start all services (frontend + backend + nginx on localhost:2026)"
@echo " make stop - Stop all running services"
@echo " make clean - Clean up processes and temporary files"
@echo ""
@echo "Docker Development Commands:"
@echo " make docker-init - Build the custom k3s image (with pre-cached sandbox image)"
@echo " make docker-start - Start all services in Docker (localhost:2026)"
@echo " make docker-stop - Stop Docker development services"
@echo " make docker-logs - View Docker development logs"
@echo " make docker-logs-frontend - View Docker frontend logs"
@echo " make docker-logs-gateway - View Docker gateway logs"
# Check required tools
check:
@echo "=========================================="
@echo " Checking Required Dependencies"
@echo "=========================================="
@echo ""
@FAILED=0; \
echo "Checking Node.js..."; \
if command -v node >/dev/null 2>&1; then \
NODE_VERSION=$$(node -v | sed 's/v//'); \
NODE_MAJOR=$$(echo $$NODE_VERSION | cut -d. -f1); \
if [ $$NODE_MAJOR -ge 22 ]; then \
echo " ✓ Node.js $$NODE_VERSION (>= 22 required)"; \
else \
echo " ✗ Node.js $$NODE_VERSION found, but version 22+ is required"; \
echo " Install from: https://nodejs.org/"; \
FAILED=1; \
fi; \
else \
echo " ✗ Node.js not found (version 22+ required)"; \
echo " Install from: https://nodejs.org/"; \
FAILED=1; \
fi; \
echo ""; \
echo "Checking pnpm..."; \
if command -v pnpm >/dev/null 2>&1; then \
PNPM_VERSION=$$(pnpm -v); \
echo " ✓ pnpm $$PNPM_VERSION"; \
else \
echo " ✗ pnpm not found"; \
echo " Install: npm install -g pnpm"; \
echo " Or visit: https://pnpm.io/installation"; \
FAILED=1; \
fi; \
echo ""; \
echo "Checking uv..."; \
if command -v uv >/dev/null 2>&1; then \
UV_VERSION=$$(uv --version | awk '{print $$2}'); \
echo " ✓ uv $$UV_VERSION"; \
else \
echo " ✗ uv not found"; \
echo " Install: curl -LsSf https://astral.sh/uv/install.sh | sh"; \
echo " Or visit: https://docs.astral.sh/uv/getting-started/installation/"; \
FAILED=1; \
fi; \
echo ""; \
echo "Checking nginx..."; \
if command -v nginx >/dev/null 2>&1; then \
NGINX_VERSION=$$(nginx -v 2>&1 | awk -F'/' '{print $$2}'); \
echo " ✓ nginx $$NGINX_VERSION"; \
else \
echo " ✗ nginx not found"; \
echo " macOS: brew install nginx"; \
echo " Ubuntu: sudo apt install nginx"; \
echo " Or visit: https://nginx.org/en/download.html"; \
FAILED=1; \
fi; \
echo ""; \
if [ $$FAILED -eq 0 ]; then \
echo "=========================================="; \
echo " ✓ All dependencies are installed!"; \
echo "=========================================="; \
echo ""; \
echo "You can now run:"; \
echo " make install - Install project dependencies"; \
echo " make dev - Start development server"; \
else \
echo "=========================================="; \
echo " ✗ Some dependencies are missing"; \
echo "=========================================="; \
echo ""; \
echo "Please install the missing tools and run 'make check' again."; \
exit 1; \
fi
# Install all dependencies
install:
@echo "Installing backend dependencies..."
@cd backend && uv sync
@echo "Installing frontend dependencies..."
@cd frontend && pnpm install
@echo "✓ All dependencies installed"
@echo ""
@echo "=========================================="
@echo " Optional: Pre-pull Sandbox Image"
@echo "=========================================="
@echo ""
@echo "If you plan to use Docker/Container-based sandbox, you can pre-pull the image:"
@echo " make setup-sandbox"
@echo ""
# Pre-pull sandbox Docker image (optional but recommended)
setup-sandbox:
@echo "=========================================="
@echo " Pre-pulling Sandbox Container Image"
@echo "=========================================="
@echo ""
@IMAGE=$$(grep -A 20 "# sandbox:" config.yaml 2>/dev/null | grep "image:" | awk '{print $$2}' | head -1); \
if [ -z "$$IMAGE" ]; then \
IMAGE="enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest"; \
echo "Using default image: $$IMAGE"; \
else \
echo "Using configured image: $$IMAGE"; \
fi; \
echo ""; \
if command -v container >/dev/null 2>&1 && [ "$$(uname)" = "Darwin" ]; then \
echo "Detected Apple Container on macOS, pulling image..."; \
container pull "$$IMAGE" || echo "⚠ Apple Container pull failed, will try Docker"; \
fi; \
if command -v docker >/dev/null 2>&1; then \
echo "Pulling image using Docker..."; \
docker pull "$$IMAGE"; \
echo ""; \
echo "✓ Sandbox image pulled successfully"; \
else \
echo "✗ Neither Docker nor Apple Container is available"; \
echo " Please install Docker: https://docs.docker.com/get-docker/"; \
exit 1; \
fi
# Start all services
dev:
@echo "Stopping existing services if any..."
@-pkill -f "langgraph dev" 2>/dev/null || true
@-pkill -f "uvicorn src.gateway.app:app" 2>/dev/null || true
@-pkill -f "next dev" 2>/dev/null || true
@-nginx -c $(PWD)/docker/nginx/nginx.local.conf -p $(PWD) -s quit 2>/dev/null || true
@sleep 1
@-pkill -9 nginx 2>/dev/null || true
@-./scripts/cleanup-containers.sh deer-flow-sandbox 2>/dev/null || true
@sleep 1
@echo ""
@echo "=========================================="
@echo " Starting DeerFlow Development Server"
@echo "=========================================="
@echo ""
@echo "Services starting up..."
@echo " → Backend: LangGraph + Gateway"
@echo " → Frontend: Next.js"
@echo " → Nginx: Reverse Proxy"
@echo ""
@cleanup() { \
echo ""; \
echo "Shutting down services..."; \
pkill -f "langgraph dev" 2>/dev/null || true; \
pkill -f "uvicorn src.gateway.app:app" 2>/dev/null || true; \
pkill -f "next dev" 2>/dev/null || true; \
nginx -c $(PWD)/docker/nginx/nginx.local.conf -p $(PWD) -s quit 2>/dev/null || true; \
sleep 1; \
pkill -9 nginx 2>/dev/null || true; \
echo "Cleaning up sandbox containers..."; \
./scripts/cleanup-containers.sh deer-flow-sandbox 2>/dev/null || true; \
echo "✓ All services stopped"; \
exit 0; \
}; \
trap cleanup INT TERM; \
mkdir -p logs; \
echo "Starting LangGraph server..."; \
cd backend && NO_COLOR=1 uv run langgraph dev --no-browser --allow-blocking --no-reload > ../logs/langgraph.log 2>&1 & \
sleep 3; \
echo "✓ LangGraph server started on localhost:2024"; \
echo "Starting Gateway API..."; \
cd backend && uv run uvicorn src.gateway.app:app --host 0.0.0.0 --port 8001 > ../logs/gateway.log 2>&1 & \
sleep 2; \
echo "✓ Gateway API started on localhost:8001"; \
echo "Starting Frontend..."; \
cd frontend && pnpm run dev > ../logs/frontend.log 2>&1 & \
sleep 3; \
echo "✓ Frontend started on localhost:3000"; \
echo "Starting Nginx reverse proxy..."; \
mkdir -p logs && nginx -g 'daemon off;' -c $(PWD)/docker/nginx/nginx.local.conf -p $(PWD) > logs/nginx.log 2>&1 & \
sleep 2; \
echo "✓ Nginx started on localhost:2026"; \
echo ""; \
echo "=========================================="; \
echo " DeerFlow is ready!"; \
echo "=========================================="; \
echo ""; \
echo " 🌐 Application: http://localhost:2026"; \
echo " 📡 API Gateway: http://localhost:2026/api/*"; \
echo " 🤖 LangGraph: http://localhost:2026/api/langgraph/*"; \
echo ""; \
echo " 📋 Logs:"; \
echo " - LangGraph: logs/langgraph.log"; \
echo " - Gateway: logs/gateway.log"; \
echo " - Frontend: logs/frontend.log"; \
echo " - Nginx: logs/nginx.log"; \
echo ""; \
echo "Press Ctrl+C to stop all services"; \
echo ""; \
wait
# Stop all services
stop:
@echo "Stopping all services..."
@-pkill -f "langgraph dev" 2>/dev/null || true
@-pkill -f "uvicorn src.gateway.app:app" 2>/dev/null || true
@-pkill -f "next dev" 2>/dev/null || true
@-nginx -c $(PWD)/docker/nginx/nginx.local.conf -p $(PWD) -s quit 2>/dev/null || true
@sleep 1
@-pkill -9 nginx 2>/dev/null || true
@echo "Cleaning up sandbox containers..."
@-./scripts/cleanup-containers.sh deer-flow-sandbox 2>/dev/null || true
@echo "✓ All services stopped"
# Clean up
clean: stop
@echo "Cleaning up..."
@-rm -rf logs/*.log 2>/dev/null || true
@echo "✓ Cleanup complete"
# ==========================================
# Docker Development Commands
# ==========================================
# Initialize Docker containers and install dependencies
docker-init:
@./scripts/docker.sh init
# Start Docker development environment
docker-start:
@./scripts/docker.sh start
# Stop Docker development environment
docker-stop:
@./scripts/docker.sh stop
# View Docker development logs
docker-logs:
@./scripts/docker.sh logs
# View Docker development logs
docker-logs-frontend:
@./scripts/docker.sh logs --frontend
docker-logs-gateway:
@./scripts/docker.sh logs --gateway

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# 🦌 DeerFlow - 2.0
DeerFlow (**D**eep **E**xploration and **E**fficient **R**esearch **Flow**) is an open-source **super agent harness** that orchestrates **sub-agents**, **memory**, and **sandboxes** to do almost anything — powered by **extensible skills**.
> [!NOTE]
> **DeerFlow 2.0 is a ground-up rewrite.** It shares no code with v1. If you're looking for the original Deep Research framework, it's maintained on the [`1.x` branch](https://github.com/bytedance/deer-flow/tree/1.x) — contributions there are still welcome. Active development has moved to 2.0.
## Table of Contents
- [Quick Start](#quick-start)
- [Sandbox Configuration](#sandbox-configuration)
- [From Deep Research to Super Agent Harness](#from-deep-research-to-super-agent-harness)
- [Core Features](#core-features)
- [Skills & Tools](#skills--tools)
- [Sub-Agents](#sub-agents)
- [Sandbox & File System](#sandbox--file-system)
- [Context Engineering](#context-engineering)
- [Long-Term Memory](#long-term-memory)
- [Recommended Models](#recommended-models)
- [Documentation](#documentation)
- [Contributing](#contributing)
- [License](#license)
- [Acknowledgments](#acknowledgments)
- [Star History](#star-history)
## Quick Start
### Configuration
1. **Copy the example config**:
```bash
cp config.example.yaml config.yaml
cp .env.example .env
```
2. **Edit `config.yaml`** and set your API keys in `.env` and preferred sandbox mode.
#### 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):
```yaml
sandbox:
use: src.sandbox.local:LocalSandboxProvider # Local execution
```
**Docker Execution** (runs sandbox code in isolated Docker containers):
```yaml
sandbox:
use: src.community.aio_sandbox:AioSandboxProvider # Docker-based sandbox
```
**Docker Execution with Kubernetes** (runs sandbox code in Kubernetes pods via provisioner service):
This mode runs each sandbox in an isolated Kubernetes Pod on your **host machine's cluster**. Requires Docker Desktop K8s, OrbStack, or similar local K8s setup.
```yaml
sandbox:
use: src.community.aio_sandbox:AioSandboxProvider
provisioner_url: http://provisioner:8002
```
See [Provisioner Setup Guide](docker/provisioner/README.md) for detailed configuration, prerequisites, and troubleshooting.
### Running the Application
#### Option 1: Docker (Recommended)
The fastest way to get started with a consistent environment:
1. **Initialize and start**:
```bash
make docker-init # Pull sandbox image (Only once or when image updates)
make docker-start # Start all services and watch for code changes
```
2. **Access**: http://localhost:2026
See [CONTRIBUTING.md](CONTRIBUTING.md) for detailed Docker development guide.
#### Option 2: Local Development
If you prefer running services locally:
1. **Check prerequisites**:
```bash
make check # Verifies Node.js 22+, pnpm, uv, nginx
```
2. **(Optional) Pre-pull sandbox image**:
```bash
# Recommended if using Docker/Container-based sandbox
make setup-sandbox
```
3. **Start services**:
```bash
make dev
```
4. **Access**: http://localhost:2026
## From Deep Research to Super Agent Harness
DeerFlow started as a Deep Research framework — and the community ran with it. Since launch, developers have pushed it far beyond research: building data pipelines, generating slide decks, spinning up dashboards, automating content workflows. Things we never anticipated.
That told us something important: DeerFlow wasn't just a research tool. It was a **harness** — a runtime that gives agents the infrastructure to actually get work done.
So we rebuilt it from scratch.
DeerFlow 2.0 is no longer a framework you wire together. It's a super agent harness — batteries included, fully extensible. Built on LangGraph and LangChain, it ships with everything an agent needs out of the box: a filesystem, memory, skills, sandboxed execution, and the ability to plan and spawn sub-agents for complex, multi-step tasks.
Use it as-is. Or tear it apart and make it yours.
## Core Features
### Skills & Tools
Skills are what make DeerFlow do *almost anything*.
A standard Agent Skill is a structured capability module — a Markdown file that defines a workflow, best practices, and references to supporting resources. DeerFlow ships with built-in skills for research, report generation, slide creation, web pages, image and video generation, and more. But the real power is extensibility: add your own skills, replace the built-in ones, or combine them into compound workflows.
Skills are loaded progressively — only when the task needs them, not all at once. This keeps the context window lean and makes DeerFlow work well even with token-sensitive models.
Tools follow the same philosophy. DeerFlow comes with a core toolset — web search, web fetch, file operations, bash execution — and supports custom tools via MCP servers and Python functions. Swap anything. Add anything.
```
# Paths inside the sandbox container
/mnt/skills/public
├── research/SKILL.md
├── report-generation/SKILL.md
├── slide-creation/SKILL.md
├── web-page/SKILL.md
└── image-generation/SKILL.md
/mnt/skills/custom
└── your-custom-skill/SKILL.md ← yours
```
### Sub-Agents
Complex tasks rarely fit in a single pass. DeerFlow decomposes them.
The lead agent can spawn sub-agents on the fly — each with its own scoped context, tools, and termination conditions. Sub-agents run in parallel when possible, report back structured results, and the lead agent synthesizes everything into a coherent output.
This is how DeerFlow handles tasks that take minutes to hours: a research task might fan out into a dozen sub-agents, each exploring a different angle, then converge into a single report — or a website — or a slide deck with generated visuals. One harness, many hands.
### Sandbox & File System
DeerFlow doesn't just *talk* about doing things. It has its own computer.
Each task runs inside an isolated Docker container with a full filesystem — skills, workspace, uploads, outputs. The agent reads, writes, and edits files. It executes bash commands and codes. It views images. All sandboxed, all auditable, zero contamination between sessions.
This is the difference between a chatbot with tool access and an agent with an actual execution environment.
```
# Paths inside the sandbox container
/mnt/user-data/
├── uploads/ ← your files
├── workspace/ ← agents' working directory
└── outputs/ ← final deliverables
```
### Context Engineering
**Isolated Sub-Agent Context**: Each sub-agent runs in its own isolated context. This means that the sub-agent will not be able to see the context of the main agent or other sub-agents. This is important to ensure that the sub-agent is able to focus on the task at hand and not be distracted by the context of the main agent or other sub-agents.
**Summarization**: Within a session, DeerFlow manages context aggressively — summarizing completed sub-tasks, offloading intermediate results to the filesystem, compressing what's no longer immediately relevant. This lets it stay sharp across long, multi-step tasks without blowing the context window.
### Long-Term Memory
Most agents forget everything the moment a conversation ends. DeerFlow remembers.
Across sessions, DeerFlow builds a persistent memory of your profile, preferences, and accumulated knowledge. The more you use it, the better it knows you — your writing style, your technical stack, your recurring workflows. Memory is stored locally and stays under your control.
## Recommended Models
DeerFlow is model-agnostic — it works with any LLM that implements the OpenAI-compatible API. That said, it performs best with models that support:
- **Long context windows** (100k+ tokens) for deep research and multi-step tasks
- **Reasoning capabilities** for adaptive planning and complex decomposition
- **Multimodal inputs** for image understanding and video comprehension
- **Strong tool-use** for reliable function calling and structured outputs
## Documentation
- [Contributing Guide](CONTRIBUTING.md) - Development environment setup and workflow
- [Configuration Guide](backend/docs/CONFIGURATION.md) - Setup and configuration instructions
- [Architecture Overview](backend/CLAUDE.md) - Technical architecture details
- [Backend Architecture](backend/README.md) - Backend architecture and API reference
## Contributing
We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for development setup, workflow, and guidelines.
## License
This project is open source and available under the [MIT License](./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](https://github.com/langchain-ai/langchain)**: Their exceptional framework powers our LLM interactions and chains, enabling seamless integration and functionality.
- **[LangGraph](https://github.com/langchain-ai/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](https://github.com/hetaoBackend/)**
- **[Henry Li](https://github.com/magiccube/)**
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.
## Star History
[![Star History Chart](https://api.star-history.com/svg?repos=bytedance/deer-flow&type=Date)](https://star-history.com/#bytedance/deer-flow&Date)

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# Python-generated files
__pycache__/
*.py[oc]
build/
dist/
wheels/
*.egg-info
.coverage
.coverage.*
.ruff_cache
agent_history.gif
static/browser_history/*.gif
# Virtual environments
.venv
venv/
# User config file
config.yaml
# Langgraph
.langgraph_api
# Claude Code settings
.claude/settings.local.json

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3.12

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{
"recommendations": ["charliermarsh.ruff"]
}

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{
"window.title": "${activeEditorShort}${separator}${separator}deer-flow/backend",
"[python]": {
"editor.formatOnSave": true,
"editor.codeActionsOnSave": {
"source.fixAll": "explicit",
"source.organizeImports": "explicit"
},
"editor.defaultFormatter": "charliermarsh.ruff"
}
}

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For the backend architeture and design patterns:
@./CLAUDE.md

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# 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 super agent system with a full-stack architecture. The backend provides a "super agent" with sandbox execution, persistent memory, subagent delegation, and extensible tool integration - all operating in per-thread isolated environments.
**Architecture**:
- **LangGraph Server** (port 2024): Agent runtime and workflow execution
- **Gateway API** (port 8001): REST API for models, MCP, skills, memory, artifacts, and uploads
- **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)
├── 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)
│ ├── langgraph.json # LangGraph server configuration
│ ├── src/
│ │ ├── agents/ # LangGraph agent system
│ │ │ ├── lead_agent/ # Main agent (factory + system prompt)
│ │ │ ├── middlewares/ # 10 middleware components
│ │ │ ├── memory/ # Memory extraction, queue, prompts
│ │ │ └── thread_state.py # ThreadState schema
│ │ ├── gateway/ # FastAPI Gateway API
│ │ │ ├── app.py # FastAPI application
│ │ │ └── routers/ # 6 route modules
│ │ ├── sandbox/ # Sandbox execution system
│ │ │ ├── local/ # Local filesystem provider
│ │ │ ├── sandbox.py # Abstract Sandbox interface
│ │ │ ├── tools.py # bash, ls, read/write/str_replace
│ │ │ └── middleware.py # Sandbox lifecycle management
│ │ ├── subagents/ # Subagent delegation system
│ │ │ ├── builtins/ # general-purpose, bash agents
│ │ │ ├── executor.py # Background execution engine
│ │ │ └── registry.py # Agent registry
│ │ ├── tools/builtins/ # Built-in tools (present_files, ask_clarification, view_image)
│ │ ├── mcp/ # MCP integration (tools, cache, client)
│ │ ├── models/ # Model factory with thinking/vision support
│ │ ├── skills/ # Skills discovery, loading, parsing
│ │ ├── config/ # Configuration system (app, model, sandbox, tool, etc.)
│ │ ├── community/ # Community tools (tavily, jina_ai, firecrawl, image_search, aio_sandbox)
│ │ ├── reflection/ # Dynamic module loading (resolve_variable, resolve_class)
│ │ └── utils/ # Utilities (network, readability)
│ ├── tests/ # Test suite
│ └── docs/ # Documentation
├── frontend/ # Next.js frontend application
└── skills/ # Agent skills directory
├── public/ # Public skills (committed)
└── custom/ # Custom skills (gitignored)
```
## Important Development Guidelines
### Documentation Update Policy
**CRITICAL: Always update README.md and CLAUDE.md after every code change**
When making code changes, you MUST update the relevant documentation:
- Update `README.md` for user-facing changes (features, setup, usage instructions)
- Update `CLAUDE.md` for development changes (architecture, commands, workflows, internal systems)
- Keep documentation synchronized with the codebase at all times
- Ensure accuracy and timeliness of all documentation
## Commands
**Root directory** (for full application):
```bash
make check # Check system requirements
make install # Install all dependencies (frontend + backend)
make dev # Start all services (LangGraph + Gateway + Frontend + Nginx)
make stop # Stop all services
```
**Backend directory** (for backend development only):
```bash
make install # Install backend dependencies
make dev # Run LangGraph server only (port 2024)
make gateway # Run Gateway API only (port 8001)
make lint # Lint with ruff
make format # Format code with ruff
```
## Architecture
### Agent System
**Lead Agent** (`src/agents/lead_agent/agent.py`):
- Entry point: `make_lead_agent(config: RunnableConfig)` registered in `langgraph.json`
- Dynamic model selection via `create_chat_model()` with thinking/vision support
- Tools loaded via `get_available_tools()` - combines sandbox, built-in, MCP, community, and subagent tools
- System prompt generated by `apply_prompt_template()` with skills, memory, and subagent instructions
**ThreadState** (`src/agents/thread_state.py`):
- Extends `AgentState` with: `sandbox`, `thread_data`, `title`, `artifacts`, `todos`, `uploaded_files`, `viewed_images`
- Uses custom reducers: `merge_artifacts` (deduplicate), `merge_viewed_images` (merge/clear)
**Runtime Configuration** (via `config.configurable`):
- `thinking_enabled` - Enable model's extended thinking
- `model_name` - Select specific LLM model
- `is_plan_mode` - Enable TodoList middleware
- `subagent_enabled` - Enable task delegation tool
### Middleware Chain
Middlewares execute in strict order in `src/agents/lead_agent/agent.py`:
1. **ThreadDataMiddleware** - Creates per-thread directories (`backend/.deer-flow/threads/{thread_id}/user-data/{workspace,uploads,outputs}`)
2. **UploadsMiddleware** - Tracks and injects newly uploaded files into conversation
3. **SandboxMiddleware** - Acquires sandbox, stores `sandbox_id` in state
4. **DanglingToolCallMiddleware** - Injects placeholder ToolMessages for AIMessage tool_calls that lack responses (e.g., due to user interruption)
5. **SummarizationMiddleware** - Context reduction when approaching token limits (optional, if enabled)
6. **TodoListMiddleware** - Task tracking with `write_todos` tool (optional, if plan_mode)
7. **TitleMiddleware** - Auto-generates thread title after first complete exchange
8. **MemoryMiddleware** - Queues conversations for async memory update (filters to user + final AI responses)
9. **ViewImageMiddleware** - Injects base64 image data before LLM call (conditional on vision support)
10. **SubagentLimitMiddleware** - Truncates excess `task` tool calls from model response to enforce `MAX_CONCURRENT_SUBAGENTS` limit (optional, if subagent_enabled)
11. **ClarificationMiddleware** - Intercepts `ask_clarification` tool calls, interrupts via `Command(goto=END)` (must be last)
### Configuration System
**Main Configuration** (`config.yaml`):
Setup: Copy `config.example.yaml` to `config.yaml` in the **project root** directory.
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`).
**Extensions Configuration** (`extensions_config.json`):
MCP servers and skills are configured together in `extensions_config.json` in project root:
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**)
### Gateway API (`src/gateway/`)
FastAPI application on port 8001 with health check at `GET /health`.
**Routers**:
| Router | Endpoints |
|--------|-----------|
| **Models** (`/api/models`) | `GET /` - list models; `GET /{name}` - model details |
| **MCP** (`/api/mcp`) | `GET /config` - get config; `PUT /config` - update config (saves to extensions_config.json) |
| **Skills** (`/api/skills`) | `GET /` - list skills; `GET /{name}` - details; `PUT /{name}` - update enabled; `POST /install` - install from .skill archive |
| **Memory** (`/api/memory`) | `GET /` - memory data; `POST /reload` - force reload; `GET /config` - config; `GET /status` - config + data |
| **Uploads** (`/api/threads/{id}/uploads`) | `POST /` - upload files (auto-converts PDF/PPT/Excel/Word); `GET /list` - list; `DELETE /{filename}` - delete |
| **Artifacts** (`/api/threads/{id}/artifacts`) | `GET /{path}` - serve artifacts; `?download=true` for file download |
Proxied through nginx: `/api/langgraph/*` → LangGraph, all other `/api/*` → Gateway.
### Sandbox System (`src/sandbox/`)
**Interface**: Abstract `Sandbox` with `execute_command`, `read_file`, `write_file`, `list_dir`
**Provider Pattern**: `SandboxProvider` with `acquire`, `get`, `release` lifecycle
**Implementations**:
- `LocalSandboxProvider` - Singleton local filesystem execution with path mappings
- `AioSandboxProvider` (`src/community/`) - Docker-based isolation
**Virtual Path System**:
- Agent sees: `/mnt/user-data/{workspace,uploads,outputs}`, `/mnt/skills`
- Physical: `backend/.deer-flow/threads/{thread_id}/user-data/...`, `deer-flow/skills/`
- Translation: `replace_virtual_path()` / `replace_virtual_paths_in_command()`
- Detection: `is_local_sandbox()` checks `sandbox_id == "local"`
**Sandbox Tools** (in `src/sandbox/tools.py`):
- `bash` - Execute commands with path translation and error handling
- `ls` - Directory listing (tree format, max 2 levels)
- `read_file` - Read file contents with optional line range
- `write_file` - Write/append to files, creates directories
- `str_replace` - Substring replacement (single or all occurrences)
### Subagent System (`src/subagents/`)
**Built-in Agents**: `general-purpose` (all tools except `task`) and `bash` (command specialist)
**Execution**: Dual thread pool - `_scheduler_pool` (3 workers) + `_execution_pool` (3 workers)
**Concurrency**: `MAX_CONCURRENT_SUBAGENTS = 3` enforced by `SubagentLimitMiddleware` (truncates excess tool calls in `after_model`), 15-minute timeout
**Flow**: `task()` tool → `SubagentExecutor` → background thread → poll 5s → SSE events → result
**Events**: `task_started`, `task_running`, `task_completed`/`task_failed`/`task_timed_out`
### Tool System (`src/tools/`)
`get_available_tools(groups, include_mcp, model_name, subagent_enabled)` assembles:
1. **Config-defined tools** - Resolved from `config.yaml` via `resolve_variable()`
2. **MCP tools** - From enabled MCP servers (lazy initialized, cached with mtime invalidation)
3. **Built-in tools**:
- `present_files` - Make output files visible to user (only `/mnt/user-data/outputs`)
- `ask_clarification` - Request clarification (intercepted by ClarificationMiddleware → interrupts)
- `view_image` - Read image as base64 (added only if model supports vision)
4. **Subagent tool** (if enabled):
- `task` - Delegate to subagent (description, prompt, subagent_type, max_turns)
**Community tools** (`src/community/`):
- `tavily/` - Web search (5 results default) and web fetch (4KB limit)
- `jina_ai/` - Web fetch via Jina reader API with readability extraction
- `firecrawl/` - Web scraping via Firecrawl API
- `image_search/` - Image search via DuckDuckGo
### MCP System (`src/mcp/`)
- Uses `langchain-mcp-adapters` `MultiServerMCPClient` for multi-server management
- **Lazy initialization**: Tools loaded on first use via `get_cached_mcp_tools()`
- **Cache invalidation**: Detects config file changes via mtime comparison
- **Transports**: stdio (command-based), SSE, HTTP
- **Runtime updates**: Gateway API saves to extensions_config.json; LangGraph detects via mtime
### Skills System (`src/skills/`)
- **Location**: `deer-flow/skills/{public,custom}/`
- **Format**: Directory with `SKILL.md` (YAML frontmatter: name, description, license, allowed-tools)
- **Loading**: `load_skills()` scans directories, parses SKILL.md, reads enabled state from extensions_config.json
- **Injection**: Enabled skills listed in agent system prompt with container paths
- **Installation**: `POST /api/skills/install` extracts .skill ZIP archive to custom/ directory
### Model Factory (`src/models/factory.py`)
- `create_chat_model(name, thinking_enabled)` instantiates LLM from config via reflection
- Supports `thinking_enabled` flag with per-model `when_thinking_enabled` overrides
- Supports `supports_vision` flag for image understanding models
- Config values starting with `$` resolved as environment variables
### Memory System (`src/agents/memory/`)
**Components**:
- `updater.py` - LLM-based memory updates with fact extraction and atomic file I/O
- `queue.py` - Debounced update queue (per-thread deduplication, configurable wait time)
- `prompt.py` - Prompt templates for memory updates
**Data Structure** (stored in `backend/.deer-flow/memory.json`):
- **User Context**: `workContext`, `personalContext`, `topOfMind` (1-3 sentence summaries)
- **History**: `recentMonths`, `earlierContext`, `longTermBackground`
- **Facts**: Discrete facts with `id`, `content`, `category` (preference/knowledge/context/behavior/goal), `confidence` (0-1), `createdAt`, `source`
**Workflow**:
1. `MemoryMiddleware` filters messages (user inputs + final AI responses) and queues conversation
2. Queue debounces (30s default), batches updates, deduplicates per-thread
3. Background thread invokes LLM to extract context updates and facts
4. Applies updates atomically (temp file + rename) with cache invalidation
5. Next interaction injects top 15 facts + context into `<memory>` tags in system prompt
**Configuration** (`config.yaml` → `memory`):
- `enabled` / `injection_enabled` - Master switches
- `storage_path` - Path to memory.json
- `debounce_seconds` - Wait time before processing (default: 30)
- `model_name` - LLM for updates (null = default model)
- `max_facts` / `fact_confidence_threshold` - Fact storage limits (100 / 0.7)
- `max_injection_tokens` - Token limit for prompt injection (2000)
### Reflection System (`src/reflection/`)
- `resolve_variable(path)` - Import module and return variable (e.g., `module.path:variable_name`)
- `resolve_class(path, base_class)` - Import and validate class against base class
### Config Schema
**`config.yaml`** key sections:
- `models[]` - LLM configs with `use` class path, `supports_thinking`, `supports_vision`, provider-specific fields
- `tools[]` - Tool configs with `use` variable path and `group`
- `tool_groups[]` - Logical groupings for tools
- `sandbox.use` - Sandbox provider class path
- `skills.path` / `skills.container_path` - Host and container paths to skills directory
- `title` - Auto-title generation (enabled, max_words, max_chars, prompt_template)
- `summarization` - Context summarization (enabled, trigger conditions, keep policy)
- `subagents.enabled` - Master switch for subagent delegation
- `memory` - Memory system (enabled, storage_path, debounce_seconds, model_name, max_facts, fact_confidence_threshold, injection_enabled, max_injection_tokens)
**`extensions_config.json`**:
- `mcpServers` - Map of server name → config (enabled, type, command, args, env, url, headers, description)
- `skills` - Map of skill name → state (enabled)
Both can be modified at runtime via Gateway 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)
- `/api/*` (other) → Gateway API (8001)
- `/` (non-API) → Frontend (3000)
### Running Backend Services Separately
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.
## Key Features
### File Upload
Multi-file upload with automatic document conversion:
- Endpoint: `POST /api/threads/{thread_id}/uploads`
- Supports: PDF, PPT, Excel, Word documents (converted via `markitdown`)
- Files stored in thread-isolated directories
- Agent receives uploaded file list via `UploadsMiddleware`
See [docs/FILE_UPLOAD.md](docs/FILE_UPLOAD.md) for details.
### Plan Mode
TodoList middleware for complex multi-step tasks:
- Controlled via runtime config: `config.configurable.is_plan_mode = True`
- Provides `write_todos` tool for task tracking
- One task in_progress at a time, real-time updates
See [docs/plan_mode_usage.md](docs/plan_mode_usage.md) for details.
### Context Summarization
Automatic conversation summarization when approaching token limits:
- Configured in `config.yaml` under `summarization` key
- Trigger types: tokens, messages, or fraction of max input
- Keeps recent messages while summarizing older ones
See [docs/summarization.md](docs/summarization.md) for details.
### Vision Support
For models with `supports_vision: true`:
- `ViewImageMiddleware` processes images in conversation
- `view_image_tool` added to agent's toolset
- Images automatically converted to base64 and injected into state
## Code Style
- Uses `ruff` for linting and formatting
- Line length: 240 characters
- Python 3.12+ with type hints
- Double quotes, space indentation
## Documentation
See `docs/` directory for detailed documentation:
- [CONFIGURATION.md](docs/CONFIGURATION.md) - Configuration options
- [ARCHITECTURE.md](docs/ARCHITECTURE.md) - Architecture details
- [API.md](docs/API.md) - API reference
- [SETUP.md](docs/SETUP.md) - Setup guide
- [FILE_UPLOAD.md](docs/FILE_UPLOAD.md) - File upload feature
- [PATH_EXAMPLES.md](docs/PATH_EXAMPLES.md) - Path types and usage
- [summarization.md](docs/summarization.md) - Context summarization
- [plan_mode_usage.md](docs/plan_mode_usage.md) - Plan mode with TodoList

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# Contributing to DeerFlow Backend
Thank you for your interest in contributing to DeerFlow! This document provides guidelines and instructions for contributing to the backend codebase.
## Table of Contents
- [Getting Started](#getting-started)
- [Development Setup](#development-setup)
- [Project Structure](#project-structure)
- [Code Style](#code-style)
- [Making Changes](#making-changes)
- [Testing](#testing)
- [Pull Request Process](#pull-request-process)
- [Architecture Guidelines](#architecture-guidelines)
## Getting Started
### Prerequisites
- Python 3.12 or higher
- [uv](https://docs.astral.sh/uv/) package manager
- Git
- Docker (optional, for Docker sandbox testing)
### Fork and Clone
1. Fork the repository on GitHub
2. Clone your fork locally:
```bash
git clone https://github.com/YOUR_USERNAME/deer-flow.git
cd deer-flow
```
## Development Setup
### Install Dependencies
```bash
# From project root
cp config.example.yaml config.yaml
cp extensions_config.example.json extensions_config.json
# Install backend dependencies
cd backend
make install
```
### Configure Environment
Set up your API keys for testing:
```bash
export OPENAI_API_KEY="your-api-key"
# Add other keys as needed
```
### Run the Development Server
```bash
# Terminal 1: LangGraph server
make dev
# Terminal 2: Gateway API
make gateway
```
## Project Structure
```
backend/src/
├── agents/ # Agent system
│ ├── lead_agent/ # Main agent implementation
│ │ └── agent.py # Agent factory and creation
│ ├── middlewares/ # Agent middlewares
│ │ ├── thread_data_middleware.py
│ │ ├── sandbox_middleware.py
│ │ ├── title_middleware.py
│ │ ├── uploads_middleware.py
│ │ ├── view_image_middleware.py
│ │ └── clarification_middleware.py
│ └── thread_state.py # Thread state definition
│
├── gateway/ # FastAPI Gateway
│ ├── app.py # FastAPI application
│ └── routers/ # Route handlers
│ ├── models.py # /api/models endpoints
│ ├── mcp.py # /api/mcp endpoints
│ ├── skills.py # /api/skills endpoints
│ ├── artifacts.py # /api/threads/.../artifacts
│ └── uploads.py # /api/threads/.../uploads
│
├── sandbox/ # Sandbox execution
│ ├── __init__.py # Sandbox interface
│ ├── local.py # Local sandbox provider
│ └── tools.py # Sandbox tools (bash, file ops)
│
├── tools/ # Agent tools
│ └── builtins/ # Built-in tools
│ ├── present_file_tool.py
│ ├── ask_clarification_tool.py
│ └── view_image_tool.py
│
├── mcp/ # MCP integration
│ └── manager.py # MCP server management
│
├── models/ # Model system
│ └── factory.py # Model factory
│
├── skills/ # Skills system
│ └── loader.py # Skills loader
│
├── config/ # Configuration
│ ├── app_config.py # Main app config
│ ├── extensions_config.py # Extensions config
│ └── summarization_config.py
│
├── community/ # Community tools
│ ├── tavily/ # Tavily web search
│ ├── jina/ # Jina web fetch
│ ├── firecrawl/ # Firecrawl scraping
│ └── aio_sandbox/ # Docker sandbox
│
├── reflection/ # Dynamic loading
│ └── __init__.py # Module resolution
│
└── utils/ # Utilities
└── __init__.py
```
## Code Style
### Linting and Formatting
We use `ruff` for both linting and formatting:
```bash
# Check for issues
make lint
# Auto-fix and format
make format
```
### Style Guidelines
- **Line length**: 240 characters maximum
- **Python version**: 3.12+ features allowed
- **Type hints**: Use type hints for function signatures
- **Quotes**: Double quotes for strings
- **Indentation**: 4 spaces (no tabs)
- **Imports**: Group by standard library, third-party, local
### Docstrings
Use docstrings for public functions and classes:
```python
def create_chat_model(name: str, thinking_enabled: bool = False) -> BaseChatModel:
"""Create a chat model instance from configuration.
Args:
name: The model name as defined in config.yaml
thinking_enabled: Whether to enable extended thinking
Returns:
A configured LangChain chat model instance
Raises:
ValueError: If the model name is not found in configuration
"""
...
```
## Making Changes
### Branch Naming
Use descriptive branch names:
- `feature/add-new-tool` - New features
- `fix/sandbox-timeout` - Bug fixes
- `docs/update-readme` - Documentation
- `refactor/config-system` - Code refactoring
### Commit Messages
Write clear, concise commit messages:
```
feat: add support for Claude 3.5 model
- Add model configuration in config.yaml
- Update model factory to handle Claude-specific settings
- Add tests for new model
```
Prefix types:
- `feat:` - New feature
- `fix:` - Bug fix
- `docs:` - Documentation
- `refactor:` - Code refactoring
- `test:` - Tests
- `chore:` - Build/config changes
## Testing
### Running Tests
```bash
uv run pytest
```
### Writing Tests
Place tests in the `tests/` directory mirroring the source structure:
```
tests/
├── test_models/
│ └── test_factory.py
├── test_sandbox/
│ └── test_local.py
└── test_gateway/
└── test_models_router.py
```
Example test:
```python
import pytest
from src.models.factory import create_chat_model
def test_create_chat_model_with_valid_name():
"""Test that a valid model name creates a model instance."""
model = create_chat_model("gpt-4")
assert model is not None
def test_create_chat_model_with_invalid_name():
"""Test that an invalid model name raises ValueError."""
with pytest.raises(ValueError):
create_chat_model("nonexistent-model")
```
## Pull Request Process
### Before Submitting
1. **Ensure tests pass**: `uv run pytest`
2. **Run linter**: `make lint`
3. **Format code**: `make format`
4. **Update documentation** if needed
### PR Description
Include in your PR description:
- **What**: Brief description of changes
- **Why**: Motivation for the change
- **How**: Implementation approach
- **Testing**: How you tested the changes
### Review Process
1. Submit PR with clear description
2. Address review feedback
3. Ensure CI passes
4. Maintainer will merge when approved
## Architecture Guidelines
### Adding New Tools
1. Create tool in `src/tools/builtins/` or `src/community/`:
```python
# src/tools/builtins/my_tool.py
from langchain_core.tools import tool
@tool
def my_tool(param: str) -> str:
"""Tool description for the agent.
Args:
param: Description of the parameter
Returns:
Description of return value
"""
return f"Result: {param}"
```
2. Register in `config.yaml`:
```yaml
tools:
- name: my_tool
group: my_group
use: src.tools.builtins.my_tool:my_tool
```
### Adding New Middleware
1. Create middleware in `src/agents/middlewares/`:
```python
# src/agents/middlewares/my_middleware.py
from langchain.agents.middleware import BaseMiddleware
from langchain_core.runnables import RunnableConfig
class MyMiddleware(BaseMiddleware):
"""Middleware description."""
def transform_state(self, state: dict, config: RunnableConfig) -> dict:
"""Transform the state before agent execution."""
# Modify state as needed
return state
```
2. Register in `src/agents/lead_agent/agent.py`:
```python
middlewares = [
ThreadDataMiddleware(),
SandboxMiddleware(),
MyMiddleware(), # Add your middleware
TitleMiddleware(),
ClarificationMiddleware(),
]
```
### Adding New API Endpoints
1. Create router in `src/gateway/routers/`:
```python
# src/gateway/routers/my_router.py
from fastapi import APIRouter
router = APIRouter(prefix="/my-endpoint", tags=["my-endpoint"])
@router.get("/")
async def get_items():
"""Get all items."""
return {"items": []}
@router.post("/")
async def create_item(data: dict):
"""Create a new item."""
return {"created": data}
```
2. Register in `src/gateway/app.py`:
```python
from src.gateway.routers import my_router
app.include_router(my_router.router)
```
### Configuration Changes
When adding new configuration options:
1. Update `src/config/app_config.py` with new fields
2. Add default values in `config.example.yaml`
3. Document in `docs/CONFIGURATION.md`
### MCP Server Integration
To add support for a new MCP server:
1. Add configuration in `extensions_config.json`:
```json
{
"mcpServers": {
"my-server": {
"enabled": true,
"type": "stdio",
"command": "npx",
"args": ["-y", "@my-org/mcp-server"],
"description": "My MCP Server"
}
}
}
```
2. Update `extensions_config.example.json` with the new server
### Skills Development
To create a new skill:
1. Create directory in `skills/public/` or `skills/custom/`:
```
skills/public/my-skill/
└── SKILL.md
```
2. Write `SKILL.md` with YAML front matter:
```markdown
---
name: My Skill
description: What this skill does
license: MIT
allowed-tools:
- read_file
- write_file
- bash
---
# My Skill
Instructions for the agent when this skill is enabled...
```
## Questions?
If you have questions about contributing:
1. Check existing documentation in `docs/`
2. Look for similar issues or PRs on GitHub
3. Open a discussion or issue on GitHub
Thank you for contributing to DeerFlow!

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# Backend Development Dockerfile
FROM python:3.12-slim
# Install system dependencies
RUN apt-get update && apt-get install -y \
curl \
build-essential \
&& rm -rf /var/lib/apt/lists/*
# Install uv
RUN curl -LsSf https://astral.sh/uv/install.sh | sh
ENV PATH="/root/.local/bin:$PATH"
# Set working directory
WORKDIR /app
# Copy frontend source code
COPY backend ./backend
# Install dependencies with cache mount
RUN --mount=type=cache,target=/root/.cache/uv \
sh -c "cd backend && uv sync"
# Expose ports (gateway: 8001, langgraph: 2024)
EXPOSE 8001 2024
# Default command (can be overridden in docker-compose)
CMD ["sh", "-c", "uv run uvicorn src.gateway.app:app --host 0.0.0.0 --port 8001"]

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install:
uv sync
dev:
uv run langgraph dev --no-browser --allow-blocking --no-reload
gateway:
uv run uvicorn src.gateway.app:app --host 0.0.0.0 --port 8001
lint:
uvx ruff check .
format:
uvx ruff check . --fix && uvx ruff format .

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# DeerFlow Backend
DeerFlow is a LangGraph-based AI super agent with sandbox execution, persistent memory, and extensible tool integration. The backend enables AI agents to execute code, browse the web, manage files, delegate tasks to subagents, and retain context across conversations - all in isolated, per-thread environments.
---
## Architecture
```
┌──────────────────────────────────────┐
│ Nginx (Port 2026) │
│ Unified reverse proxy │
└───────┬──────────────────┬───────────┘
│ │
/api/langgraph/* │ │ /api/* (other)
▼ ▼
┌────────────────────┐ ┌────────────────────────┐
│ LangGraph Server │ │ Gateway API (8001) │
│ (Port 2024) │ │ FastAPI REST │
│ │ │ │
│ ┌────────────────┐ │ │ Models, MCP, Skills, │
│ │ Lead Agent │ │ │ Memory, Uploads, │
│ │ ┌──────────┐ │ │ │ Artifacts │
│ │ │Middleware│ │ │ └────────────────────────┘
│ │ │ Chain │ │ │
│ │ └──────────┘ │ │
│ │ ┌──────────┐ │ │
│ │ │ Tools │ │ │
│ │ └──────────┘ │ │
│ │ ┌──────────┐ │ │
│ │ │Subagents │ │ │
│ │ └──────────┘ │ │
│ └────────────────┘ │
└────────────────────┘
```
**Request Routing** (via Nginx):
- `/api/langgraph/*` → LangGraph Server - agent interactions, threads, streaming
- `/api/*` (other) → Gateway API - models, MCP, skills, memory, artifacts, uploads
- `/` (non-API) → Frontend - Next.js web interface
---
## Core Components
### Lead Agent
The single LangGraph agent (`lead_agent`) is the runtime entry point, created via `make_lead_agent(config)`. It combines:
- **Dynamic model selection** with thinking and vision support
- **Middleware chain** for cross-cutting concerns (9 middlewares)
- **Tool system** with sandbox, MCP, community, and built-in tools
- **Subagent delegation** for parallel task execution
- **System prompt** with skills injection, memory context, and working directory guidance
### Middleware Chain
Middlewares execute in strict order, each handling a specific concern:
| # | Middleware | Purpose |
|---|-----------|---------|
| 1 | **ThreadDataMiddleware** | Creates per-thread isolated directories (workspace, uploads, outputs) |
| 2 | **UploadsMiddleware** | Injects newly uploaded files into conversation context |
| 3 | **SandboxMiddleware** | Acquires sandbox environment for code execution |
| 4 | **SummarizationMiddleware** | Reduces context when approaching token limits (optional) |
| 5 | **TodoListMiddleware** | Tracks multi-step tasks in plan mode (optional) |
| 6 | **TitleMiddleware** | Auto-generates conversation titles after first exchange |
| 7 | **MemoryMiddleware** | Queues conversations for async memory extraction |
| 8 | **ViewImageMiddleware** | Injects image data for vision-capable models (conditional) |
| 9 | **ClarificationMiddleware** | Intercepts clarification requests and interrupts execution (must be last) |
### Sandbox System
Per-thread isolated execution with virtual path translation:
- **Abstract interface**: `execute_command`, `read_file`, `write_file`, `list_dir`
- **Providers**: `LocalSandboxProvider` (filesystem) and `AioSandboxProvider` (Docker, in community/)
- **Virtual paths**: `/mnt/user-data/{workspace,uploads,outputs}` → thread-specific physical directories
- **Skills path**: `/mnt/skills` → `deer-flow/skills/` directory
- **Tools**: `bash`, `ls`, `read_file`, `write_file`, `str_replace`
### Subagent System
Async task delegation with concurrent execution:
- **Built-in agents**: `general-purpose` (full toolset) and `bash` (command specialist)
- **Concurrency**: Max 3 subagents per turn, 15-minute timeout
- **Execution**: Background thread pools with status tracking and SSE events
- **Flow**: Agent calls `task()` tool → executor runs subagent in background → polls for completion → returns result
### Memory System
LLM-powered persistent context retention across conversations:
- **Automatic extraction**: Analyzes conversations for user context, facts, and preferences
- **Structured storage**: User context (work, personal, top-of-mind), history, and confidence-scored facts
- **Debounced updates**: Batches updates to minimize LLM calls (configurable wait time)
- **System prompt injection**: Top facts + context injected into agent prompts
- **Storage**: JSON file with mtime-based cache invalidation
### Tool Ecosystem
| Category | Tools |
|----------|-------|
| **Sandbox** | `bash`, `ls`, `read_file`, `write_file`, `str_replace` |
| **Built-in** | `present_files`, `ask_clarification`, `view_image`, `task` (subagent) |
| **Community** | Tavily (web search), Jina AI (web fetch), Firecrawl (scraping), DuckDuckGo (image search) |
| **MCP** | Any Model Context Protocol server (stdio, SSE, HTTP transports) |
| **Skills** | Domain-specific workflows injected via system prompt |
### Gateway API
FastAPI application providing REST endpoints for frontend integration:
| Route | Purpose |
|-------|---------|
| `GET /api/models` | List available LLM models |
| `GET/PUT /api/mcp/config` | Manage MCP server configurations |
| `GET/PUT /api/skills` | List and manage skills |
| `POST /api/skills/install` | Install skill from `.skill` archive |
| `GET /api/memory` | Retrieve memory data |
| `POST /api/memory/reload` | Force memory reload |
| `GET /api/memory/config` | Memory configuration |
| `GET /api/memory/status` | Combined config + data |
| `POST /api/threads/{id}/uploads` | Upload files (auto-converts PDF/PPT/Excel/Word to Markdown) |
| `GET /api/threads/{id}/uploads/list` | List uploaded files |
| `GET /api/threads/{id}/artifacts/{path}` | Serve generated artifacts |
---
## Quick Start
### Prerequisites
- Python 3.12+
- [uv](https://docs.astral.sh/uv/) package manager
- API keys for your chosen LLM provider
### Installation
```bash
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:
```yaml
models:
- name: gpt-4o
display_name: GPT-4o
use: langchain_openai:ChatOpenAI
model: gpt-4o
api_key: $OPENAI_API_KEY
supports_thinking: false
supports_vision: true
```
Set your API keys:
```bash
export OPENAI_API_KEY="your-api-key-here"
```
### Running
**Full Application** (from project root):
```bash
make dev # Starts LangGraph + Gateway + Frontend + Nginx
```
Access at: http://localhost:2026
**Backend Only** (from backend directory):
```bash
# Terminal 1: LangGraph server
make dev
# Terminal 2: Gateway API
make gateway
```
Direct access: LangGraph at http://localhost:2024, Gateway at http://localhost:8001
---
## Project Structure
```
backend/
├── src/
│ ├── agents/ # Agent system
│ │ ├── lead_agent/ # Main agent (factory, prompts)
│ │ ├── middlewares/ # 9 middleware components
│ │ ├── memory/ # Memory extraction & storage
│ │ └── thread_state.py # ThreadState schema
│ ├── gateway/ # FastAPI Gateway API
│ │ ├── app.py # Application setup
│ │ └── routers/ # 6 route modules
│ ├── sandbox/ # Sandbox execution
│ │ ├── local/ # Local filesystem provider
│ │ ├── sandbox.py # Abstract interface
│ │ ├── tools.py # bash, ls, read/write/str_replace
│ │ └── middleware.py # Sandbox lifecycle
│ ├── subagents/ # Subagent delegation
│ │ ├── builtins/ # general-purpose, bash agents
│ │ ├── executor.py # Background execution engine
│ │ └── registry.py # Agent registry
│ ├── tools/builtins/ # Built-in tools
│ ├── mcp/ # MCP protocol integration
│ ├── models/ # Model factory
│ ├── skills/ # Skill discovery & loading
│ ├── config/ # Configuration system
│ ├── community/ # Community tools & providers
│ ├── reflection/ # Dynamic module loading
│ └── utils/ # Utilities
├── docs/ # Documentation
├── tests/ # Test suite
├── langgraph.json # LangGraph server configuration
├── pyproject.toml # Python dependencies
├── Makefile # Development commands
└── Dockerfile # Container build
```
---
## Configuration
### Main Configuration (`config.yaml`)
Place in project root. Config values starting with `$` resolve as environment variables.
Key sections:
- `models` - LLM configurations with class paths, API keys, thinking/vision flags
- `tools` - Tool definitions with module paths and groups
- `tool_groups` - Logical tool groupings
- `sandbox` - Execution environment provider
- `skills` - Skills directory paths
- `title` - Auto-title generation settings
- `summarization` - Context summarization settings
- `subagents` - Subagent system (enabled/disabled)
- `memory` - Memory system settings (enabled, storage, debounce, facts limits)
### Extensions Configuration (`extensions_config.json`)
MCP servers and skill states in a single file:
```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.
---
## Development
### Commands
```bash
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
- **Linter/Formatter**: `ruff`
- **Line length**: 240 characters
- **Python**: 3.12+ with type hints
- **Quotes**: Double quotes
- **Indentation**: 4 spaces
### Testing
```bash
uv run pytest
```
---
## Technology Stack
- **LangGraph** (1.0.6+) - Agent framework and multi-agent orchestration
- **LangChain** (1.2.3+) - LLM abstractions and tool system
- **FastAPI** (0.115.0+) - Gateway REST API
- **langchain-mcp-adapters** - Model Context Protocol support
- **agent-sandbox** - Sandboxed code execution
- **markitdown** - Multi-format document conversion
- **tavily-python** / **firecrawl-py** - Web search and scraping
---
## Documentation
- [Configuration Guide](docs/CONFIGURATION.md)
- [Architecture Details](docs/ARCHITECTURE.md)
- [API Reference](docs/API.md)
- [File Upload](docs/FILE_UPLOAD.md)
- [Path Examples](docs/PATH_EXAMPLES.md)
- [Context Summarization](docs/summarization.md)
- [Plan Mode](docs/plan_mode_usage.md)
- [Setup Guide](docs/SETUP.md)
---
## License
See the [LICENSE](../LICENSE) file in the project root.
## Contributing
See [CONTRIBUTING.md](CONTRIBUTING.md) for contribution guidelines.

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#!/usr/bin/env python
"""
Debug script for lead_agent.
Run this file directly in VS Code with breakpoints.
Usage:
1. Set breakpoints in agent.py or other files
2. Press F5 or use "Run and Debug" panel
3. Input messages in the terminal to interact with the agent
"""
import asyncio
import logging
import os
import sys
# Ensure we can import from src
sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
# Load environment variables
from dotenv import load_dotenv
from langchain_core.messages import HumanMessage
from src.agents import make_lead_agent
load_dotenv()
# Configure logging
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
async def main():
# Initialize MCP tools at startup
try:
from src.mcp import initialize_mcp_tools
await initialize_mcp_tools()
except Exception as e:
print(f"Warning: Failed to initialize MCP tools: {e}")
# Create agent with default config
config = {
"configurable": {
"thread_id": "debug-thread-001",
"thinking_enabled": True,
"is_plan_mode": True,
# Uncomment to use a specific model
"model_name": "kimi-k2.5",
}
}
agent = make_lead_agent(config)
print("=" * 50)
print("Lead Agent Debug Mode")
print("Type 'quit' or 'exit' to stop")
print("=" * 50)
while True:
try:
user_input = input("\nYou: ").strip()
if not user_input:
continue
if user_input.lower() in ("quit", "exit"):
print("Goodbye!")
break
# Invoke the agent
state = {"messages": [HumanMessage(content=user_input)]}
result = await agent.ainvoke(state, config=config, context={"thread_id": "debug-thread-001"})
# Print the response
if result.get("messages"):
last_message = result["messages"][-1]
print(f"\nAgent: {last_message.content}")
except KeyboardInterrupt:
print("\nInterrupted. Goodbye!")
break
except Exception as e:
print(f"\nError: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
asyncio.run(main())

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# API Reference
This document provides a complete reference for the DeerFlow backend APIs.
## Overview
DeerFlow backend exposes two sets of APIs:
1. **LangGraph API** - Agent interactions, threads, and streaming (`/api/langgraph/*`)
2. **Gateway API** - Models, MCP, skills, uploads, and artifacts (`/api/*`)
All APIs are accessed through the Nginx reverse proxy at port 2026.
## LangGraph API
Base URL: `/api/langgraph`
The LangGraph API is provided by the LangGraph server and follows the LangGraph SDK conventions.
### Threads
#### Create Thread
```http
POST /api/langgraph/threads
Content-Type: application/json
```
**Request Body:**
```json
{
"metadata": {}
}
```
**Response:**
```json
{
"thread_id": "abc123",
"created_at": "2024-01-15T10:30:00Z",
"metadata": {}
}
```
#### Get Thread State
```http
GET /api/langgraph/threads/{thread_id}/state
```
**Response:**
```json
{
"values": {
"messages": [...],
"sandbox": {...},
"artifacts": [...],
"thread_data": {...},
"title": "Conversation Title"
},
"next": [],
"config": {...}
}
```
### Runs
#### Create Run
Execute the agent with input.
```http
POST /api/langgraph/threads/{thread_id}/runs
Content-Type: application/json
```
**Request Body:**
```json
{
"input": {
"messages": [
{
"role": "user",
"content": "Hello, can you help me?"
}
]
},
"config": {
"configurable": {
"model_name": "gpt-4",
"thinking_enabled": false,
"is_plan_mode": false
}
},
"stream_mode": ["values", "messages"]
}
```
**Configurable Options:**
- `model_name` (string): Override the default model
- `thinking_enabled` (boolean): Enable extended thinking for supported models
- `is_plan_mode` (boolean): Enable TodoList middleware for task tracking
**Response:** Server-Sent Events (SSE) stream
```
event: values
data: {"messages": [...], "title": "..."}
event: messages
data: {"content": "Hello! I'd be happy to help.", "role": "assistant"}
event: end
data: {}
```
#### Get Run History
```http
GET /api/langgraph/threads/{thread_id}/runs
```
**Response:**
```json
{
"runs": [
{
"run_id": "run123",
"status": "success",
"created_at": "2024-01-15T10:30:00Z"
}
]
}
```
#### Stream Run
Stream responses in real-time.
```http
POST /api/langgraph/threads/{thread_id}/runs/stream
Content-Type: application/json
```
Same request body as Create Run. Returns SSE stream.
---
## Gateway API
Base URL: `/api`
### Models
#### List Models
Get all available LLM models from configuration.
```http
GET /api/models
```
**Response:**
```json
{
"models": [
{
"name": "gpt-4",
"display_name": "GPT-4",
"supports_thinking": false,
"supports_vision": true
},
{
"name": "claude-3-opus",
"display_name": "Claude 3 Opus",
"supports_thinking": false,
"supports_vision": true
},
{
"name": "deepseek-v3",
"display_name": "DeepSeek V3",
"supports_thinking": true,
"supports_vision": false
}
]
}
```
#### Get Model Details
```http
GET /api/models/{model_name}
```
**Response:**
```json
{
"name": "gpt-4",
"display_name": "GPT-4",
"model": "gpt-4",
"max_tokens": 4096,
"supports_thinking": false,
"supports_vision": true
}
```
### MCP Configuration
#### Get MCP Config
Get current MCP server configurations.
```http
GET /api/mcp/config
```
**Response:**
```json
{
"mcpServers": {
"github": {
"enabled": true,
"type": "stdio",
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-github"],
"env": {
"GITHUB_TOKEN": "***"
},
"description": "GitHub operations"
},
"filesystem": {
"enabled": false,
"type": "stdio",
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-filesystem"],
"description": "File system access"
}
}
}
```
#### Update MCP Config
Update MCP server configurations.
```http
PUT /api/mcp/config
Content-Type: application/json
```
**Request Body:**
```json
{
"mcpServers": {
"github": {
"enabled": true,
"type": "stdio",
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-github"],
"env": {
"GITHUB_TOKEN": "$GITHUB_TOKEN"
},
"description": "GitHub operations"
}
}
}
```
**Response:**
```json
{
"success": true,
"message": "MCP configuration updated"
}
```
### Skills
#### List Skills
Get all available skills.
```http
GET /api/skills
```
**Response:**
```json
{
"skills": [
{
"name": "pdf-processing",
"display_name": "PDF Processing",
"description": "Handle PDF documents efficiently",
"enabled": true,
"license": "MIT",
"path": "public/pdf-processing"
},
{
"name": "frontend-design",
"display_name": "Frontend Design",
"description": "Design and build frontend interfaces",
"enabled": false,
"license": "MIT",
"path": "public/frontend-design"
}
]
}
```
#### Get Skill Details
```http
GET /api/skills/{skill_name}
```
**Response:**
```json
{
"name": "pdf-processing",
"display_name": "PDF Processing",
"description": "Handle PDF documents efficiently",
"enabled": true,
"license": "MIT",
"path": "public/pdf-processing",
"allowed_tools": ["read_file", "write_file", "bash"],
"content": "# PDF Processing\n\nInstructions for the agent..."
}
```
#### Enable Skill
```http
POST /api/skills/{skill_name}/enable
```
**Response:**
```json
{
"success": true,
"message": "Skill 'pdf-processing' enabled"
}
```
#### Disable Skill
```http
POST /api/skills/{skill_name}/disable
```
**Response:**
```json
{
"success": true,
"message": "Skill 'pdf-processing' disabled"
}
```
#### Install Skill
Install a skill from a `.skill` file.
```http
POST /api/skills/install
Content-Type: multipart/form-data
```
**Request Body:**
- `file`: The `.skill` file to install
**Response:**
```json
{
"success": true,
"message": "Skill 'my-skill' installed successfully",
"skill": {
"name": "my-skill",
"display_name": "My Skill",
"path": "custom/my-skill"
}
}
```
### File Uploads
#### Upload Files
Upload one or more files to a thread.
```http
POST /api/threads/{thread_id}/uploads
Content-Type: multipart/form-data
```
**Request Body:**
- `files`: One or more files to upload
**Response:**
```json
{
"success": true,
"files": [
{
"filename": "document.pdf",
"size": 1234567,
"path": ".deer-flow/threads/abc123/user-data/uploads/document.pdf",
"virtual_path": "/mnt/user-data/uploads/document.pdf",
"artifact_url": "/api/threads/abc123/artifacts/mnt/user-data/uploads/document.pdf",
"markdown_file": "document.md",
"markdown_path": ".deer-flow/threads/abc123/user-data/uploads/document.md",
"markdown_virtual_path": "/mnt/user-data/uploads/document.md",
"markdown_artifact_url": "/api/threads/abc123/artifacts/mnt/user-data/uploads/document.md"
}
],
"message": "Successfully uploaded 1 file(s)"
}
```
**Supported Document Formats** (auto-converted to Markdown):
- PDF (`.pdf`)
- PowerPoint (`.ppt`, `.pptx`)
- Excel (`.xls`, `.xlsx`)
- Word (`.doc`, `.docx`)
#### List Uploaded Files
```http
GET /api/threads/{thread_id}/uploads/list
```
**Response:**
```json
{
"files": [
{
"filename": "document.pdf",
"size": 1234567,
"path": ".deer-flow/threads/abc123/user-data/uploads/document.pdf",
"virtual_path": "/mnt/user-data/uploads/document.pdf",
"artifact_url": "/api/threads/abc123/artifacts/mnt/user-data/uploads/document.pdf",
"extension": ".pdf",
"modified": 1705997600.0
}
],
"count": 1
}
```
#### Delete File
```http
DELETE /api/threads/{thread_id}/uploads/{filename}
```
**Response:**
```json
{
"success": true,
"message": "Deleted document.pdf"
}
```
### Artifacts
#### Get Artifact
Download or view an artifact generated by the agent.
```http
GET /api/threads/{thread_id}/artifacts/{path}
```
**Path Examples:**
- `/api/threads/abc123/artifacts/mnt/user-data/outputs/result.txt`
- `/api/threads/abc123/artifacts/mnt/user-data/uploads/document.pdf`
**Query Parameters:**
- `download` (boolean): If `true`, force download with Content-Disposition header
**Response:** File content with appropriate Content-Type
---
## Error Responses
All APIs return errors in a consistent format:
```json
{
"detail": "Error message describing what went wrong"
}
```
**HTTP Status Codes:**
- `400` - Bad Request: Invalid input
- `404` - Not Found: Resource not found
- `422` - Validation Error: Request validation failed
- `500` - Internal Server Error: Server-side error
---
## Authentication
Currently, DeerFlow does not implement authentication. All APIs are accessible without credentials.
For production deployments, it is recommended to:
1. Use Nginx for basic auth or OAuth integration
2. Deploy behind a VPN or private network
3. Implement custom authentication middleware
---
## Rate Limiting
No rate limiting is implemented by default. For production deployments, configure rate limiting in Nginx:
```nginx
limit_req_zone $binary_remote_addr zone=api:10m rate=10r/s;
location /api/ {
limit_req zone=api burst=20 nodelay;
proxy_pass http://backend;
}
```
---
## WebSocket Support
The LangGraph server supports WebSocket connections for real-time streaming. Connect to:
```
ws://localhost:2026/api/langgraph/threads/{thread_id}/runs/stream
```
---
## SDK Usage
### Python (LangGraph SDK)
```python
from langgraph_sdk import get_client
client = get_client(url="http://localhost:2026/api/langgraph")
# Create thread
thread = await client.threads.create()
# Run agent
async for event in client.runs.stream(
thread["thread_id"],
"lead_agent",
input={"messages": [{"role": "user", "content": "Hello"}]},
config={"configurable": {"model_name": "gpt-4"}},
stream_mode=["values", "messages"],
):
print(event)
```
### JavaScript/TypeScript
```typescript
// Using fetch for Gateway API
const response = await fetch('/api/models');
const data = await response.json();
console.log(data.models);
// Using EventSource for streaming
const eventSource = new EventSource(
`/api/langgraph/threads/${threadId}/runs/stream`
);
eventSource.onmessage = (event) => {
console.log(JSON.parse(event.data));
};
```
### cURL Examples
```bash
# List models
curl http://localhost:2026/api/models
# Get MCP config
curl http://localhost:2026/api/mcp/config
# Upload file
curl -X POST http://localhost:2026/api/threads/abc123/uploads \
-F "files=@document.pdf"
# Enable skill
curl -X POST http://localhost:2026/api/skills/pdf-processing/enable
# Create thread and run agent
curl -X POST http://localhost:2026/api/langgraph/threads \
-H "Content-Type: application/json" \
-d '{}'
curl -X POST http://localhost:2026/api/langgraph/threads/abc123/runs \
-H "Content-Type: application/json" \
-d '{
"input": {"messages": [{"role": "user", "content": "Hello"}]},
"config": {"configurable": {"model_name": "gpt-4"}}
}'
```

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# Apple Container Support
DeerFlow now supports Apple Container as the preferred container runtime on macOS, with automatic fallback to Docker.
## Overview
Starting with this version, DeerFlow automatically detects and uses Apple Container on macOS when available, falling back to Docker when:
- Apple Container is not installed
- Running on non-macOS platforms
This provides better performance on Apple Silicon Macs while maintaining compatibility across all platforms.
## Benefits
### On Apple Silicon Macs with Apple Container:
- **Better Performance**: Native ARM64 execution without Rosetta 2 translation
- **Lower Resource Usage**: Lighter weight than Docker Desktop
- **Native Integration**: Uses macOS Virtualization.framework
### Fallback to Docker:
- Full backward compatibility
- Works on all platforms (macOS, Linux, Windows)
- No configuration changes needed
## Requirements
### For Apple Container (macOS only):
- macOS 15.0 or later
- Apple Silicon (M1/M2/M3/M4)
- Apple Container CLI installed
### Installation:
```bash
# Download from GitHub releases
# https://github.com/apple/container/releases
# Verify installation
container --version
# Start the service
container system start
```
### For Docker (all platforms):
- Docker Desktop or Docker Engine
## How It Works
### Automatic Detection
The `AioSandboxProvider` automatically detects the available container runtime:
1. On macOS: Try `container --version`
- Success → Use Apple Container
- Failure → Fall back to Docker
2. On other platforms: Use Docker directly
### Runtime Differences
Both runtimes use nearly identical command syntax:
**Container Startup:**
```bash
# Apple Container
container run --rm -d -p 8080:8080 -v /host:/container -e KEY=value image
# Docker
docker run --rm -d -p 8080:8080 -v /host:/container -e KEY=value image
```
**Container Cleanup:**
```bash
# Apple Container (with --rm flag)
container stop <id> # Auto-removes due to --rm
# Docker (with --rm flag)
docker stop <id> # Auto-removes due to --rm
```
### Implementation Details
The implementation is in `backend/src/community/aio_sandbox/aio_sandbox_provider.py`:
- `_detect_container_runtime()`: Detects available runtime at startup
- `_start_container()`: Uses detected runtime, skips Docker-specific options for Apple Container
- `_stop_container()`: Uses appropriate stop command for the runtime
## Configuration
No configuration changes are needed! The system works automatically.
However, you can verify the runtime in use by checking the logs:
```
INFO:src.community.aio_sandbox.aio_sandbox_provider:Detected Apple Container: container version 0.1.0
INFO:src.community.aio_sandbox.aio_sandbox_provider:Starting sandbox container using container: ...
```
Or for Docker:
```
INFO:src.community.aio_sandbox.aio_sandbox_provider:Apple Container not available, falling back to Docker
INFO:src.community.aio_sandbox.aio_sandbox_provider:Starting sandbox container using docker: ...
```
## Container Images
Both runtimes use OCI-compatible images. The default image works with both:
```yaml
sandbox:
use: src.community.aio_sandbox:AioSandboxProvider
image: enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest # Default image
```
Make sure your images are available for the appropriate architecture:
- ARM64 for Apple Container on Apple Silicon
- AMD64 for Docker on Intel Macs
- Multi-arch images work on both
### Pre-pulling Images (Recommended)
**Important**: Container images are typically large (500MB+) and are pulled on first use, which can cause a long wait time without clear feedback.
**Best Practice**: Pre-pull the image during setup:
```bash
# From project root
make setup-sandbox
```
This command will:
1. Read the configured image from `config.yaml` (or use default)
2. Detect available runtime (Apple Container or Docker)
3. Pull the image with progress indication
4. Verify the image is ready for use
**Manual pre-pull**:
```bash
# Using Apple Container
container pull enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest
# Using Docker
docker pull enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest
```
If you skip pre-pulling, the image will be automatically pulled on first agent execution, which may take several minutes depending on your network speed.
## Cleanup Scripts
The project includes a unified cleanup script that handles both runtimes:
**Script:** `scripts/cleanup-containers.sh`
**Usage:**
```bash
# Clean up all DeerFlow sandbox containers
./scripts/cleanup-containers.sh deer-flow-sandbox
# Custom prefix
./scripts/cleanup-containers.sh my-prefix
```
**Makefile Integration:**
All cleanup commands in `Makefile` automatically handle both runtimes:
```bash
make stop # Stops all services and cleans up containers
make clean # Full cleanup including logs
```
## Testing
Test the container runtime detection:
```bash
cd backend
python test_container_runtime.py
```
This will:
1. Detect the available runtime
2. Optionally start a test container
3. Verify connectivity
4. Clean up
## Troubleshooting
### Apple Container not detected on macOS
1. Check if installed:
```bash
which container
container --version
```
2. Check if service is running:
```bash
container system start
```
3. Check logs for detection:
```bash
# Look for detection message in application logs
grep "container runtime" logs/*.log
```
### Containers not cleaning up
1. Manually check running containers:
```bash
# Apple Container
container list
# Docker
docker ps
```
2. Run cleanup script manually:
```bash
./scripts/cleanup-containers.sh deer-flow-sandbox
```
### Performance issues
- Apple Container should be faster on Apple Silicon
- If experiencing issues, you can force Docker by temporarily renaming the `container` command:
```bash
# Temporary workaround - not recommended for permanent use
sudo mv /opt/homebrew/bin/container /opt/homebrew/bin/container.bak
```
## References
- [Apple Container GitHub](https://github.com/apple/container)
- [Apple Container Documentation](https://github.com/apple/container/blob/main/docs/)
- [OCI Image Spec](https://github.com/opencontainers/image-spec)

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# Architecture Overview
This document provides a comprehensive overview of the DeerFlow backend architecture.
## System Architecture
```
┌──────────────────────────────────────────────────────────────────────────┐
│ Client (Browser) │
└─────────────────────────────────┬────────────────────────────────────────┘
│
▼
┌──────────────────────────────────────────────────────────────────────────┐
│ Nginx (Port 2026) │
│ Unified Reverse Proxy Entry Point │
│ ┌────────────────────────────────────────────────────────────────────┐ │
│ │ /api/langgraph/* → LangGraph Server (2024) │ │
│ │ /api/* → Gateway API (8001) │ │
│ │ /* → Frontend (3000) │ │
│ └────────────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────┬────────────────────────────────────────┘
│
┌───────────────────────┼───────────────────────┐
│ │ │
▼ ▼ ▼
┌─────────────────────┐ ┌─────────────────────┐ ┌─────────────────────┐
│ LangGraph Server │ │ Gateway API │ │ Frontend │
│ (Port 2024) │ │ (Port 8001) │ │ (Port 3000) │
│ │ │ │ │ │
│ - Agent Runtime │ │ - Models API │ │ - Next.js App │
│ - Thread Mgmt │ │ - MCP Config │ │ - React UI │
│ - SSE Streaming │ │ - Skills Mgmt │ │ - Chat Interface │
│ - Checkpointing │ │ - File Uploads │ │ │
│ │ │ - Artifacts │ │ │
└─────────────────────┘ └─────────────────────┘ └─────────────────────┘
│ │
│ ┌─────────────────┘
│ │
▼ ▼
┌──────────────────────────────────────────────────────────────────────────┐
│ Shared Configuration │
│ ┌─────────────────────────┐ ┌────────────────────────────────────────┐ │
│ │ config.yaml │ │ extensions_config.json │ │
│ │ - Models │ │ - MCP Servers │ │
│ │ - Tools │ │ - Skills State │ │
│ │ - Sandbox │ │ │ │
│ │ - Summarization │ │ │ │
│ └─────────────────────────┘ └────────────────────────────────────────┘ │
└──────────────────────────────────────────────────────────────────────────┘
```
## Component Details
### LangGraph Server
The LangGraph server is the core agent runtime, built on LangGraph for robust multi-agent workflow orchestration.
**Entry Point**: `src/agents/lead_agent/agent.py:make_lead_agent`
**Key Responsibilities**:
- Agent creation and configuration
- Thread state management
- Middleware chain execution
- Tool execution orchestration
- SSE streaming for real-time responses
**Configuration**: `langgraph.json`
```json
{
"agent": {
"type": "agent",
"path": "src.agents:make_lead_agent"
}
}
```
### Gateway API
FastAPI application providing REST endpoints for non-agent operations.
**Entry Point**: `src/gateway/app.py`
**Routers**:
- `models.py` - `/api/models` - Model listing and details
- `mcp.py` - `/api/mcp` - MCP server configuration
- `skills.py` - `/api/skills` - Skills management
- `uploads.py` - `/api/threads/{id}/uploads` - File upload
- `artifacts.py` - `/api/threads/{id}/artifacts` - Artifact serving
### Agent Architecture
```
┌─────────────────────────────────────────────────────────────────────────┐
│ make_lead_agent(config) │
└────────────────────────────────────┬────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ Middleware Chain │
│ ┌──────────────────────────────────────────────────────────────────┐ │
│ │ 1. ThreadDataMiddleware - Initialize workspace/uploads/outputs │ │
│ │ 2. UploadsMiddleware - Process uploaded files │ │
│ │ 3. SandboxMiddleware - Acquire sandbox environment │ │
│ │ 4. SummarizationMiddleware - Context reduction (if enabled) │ │
│ │ 5. TitleMiddleware - Auto-generate titles │ │
│ │ 6. TodoListMiddleware - Task tracking (if plan_mode) │ │
│ │ 7. ViewImageMiddleware - Vision model support │ │
│ │ 8. ClarificationMiddleware - Handle clarifications │ │
│ └──────────────────────────────────────────────────────────────────┘ │
└────────────────────────────────────┬────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────────┐
│ Agent Core │
│ ┌──────────────────┐ ┌──────────────────┐ ┌──────────────────────┐ │
│ │ Model │ │ Tools │ │ System Prompt │ │
│ │ (from factory) │ │ (configured + │ │ (with skills) │ │
│ │ │ │ MCP + builtin) │ │ │ │
│ └──────────────────┘ └──────────────────┘ └──────────────────────┘ │
└─────────────────────────────────────────────────────────────────────────┘
```
### Thread State
The `ThreadState` extends LangGraph's `AgentState` with additional fields:
```python
class ThreadState(AgentState):
# Core state from AgentState
messages: list[BaseMessage]
# DeerFlow extensions
sandbox: dict # Sandbox environment info
artifacts: list[str] # Generated file paths
thread_data: dict # {workspace, uploads, outputs} paths
title: str | None # Auto-generated conversation title
todos: list[dict] # Task tracking (plan mode)
viewed_images: dict # Vision model image data
```
### Sandbox System
```
┌─────────────────────────────────────────────────────────────────────────┐
│ Sandbox Architecture │
└─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────────┐
│ SandboxProvider │ (Abstract)
│ - acquire() │
│ - get() │
│ - release() │
└────────────┬────────────┘
│
┌────────────────────┼────────────────────┐
│ │
▼ ▼
┌─────────────────────────┐ ┌─────────────────────────┐
│ LocalSandboxProvider │ │ AioSandboxProvider │
│ (src/sandbox/local.py) │ │ (src/community/) │
│ │ │ │
│ - Singleton instance │ │ - Docker-based │
│ - Direct execution │ │ - Isolated containers │
│ - Development use │ │ - Production use │
└─────────────────────────┘ └─────────────────────────┘
┌─────────────────────────┐
│ Sandbox │ (Abstract)
│ - execute_command() │
│ - read_file() │
│ - write_file() │
│ - list_dir() │
└─────────────────────────┘
```
**Virtual Path Mapping**:
| Virtual Path | Physical Path |
|-------------|---------------|
| `/mnt/user-data/workspace` | `backend/.deer-flow/threads/{thread_id}/user-data/workspace` |
| `/mnt/user-data/uploads` | `backend/.deer-flow/threads/{thread_id}/user-data/uploads` |
| `/mnt/user-data/outputs` | `backend/.deer-flow/threads/{thread_id}/user-data/outputs` |
| `/mnt/skills` | `deer-flow/skills/` |
### Tool System
```
┌─────────────────────────────────────────────────────────────────────────┐
│ Tool Sources │
└─────────────────────────────────────────────────────────────────────────┘
┌─────────────────────┐ ┌─────────────────────┐ ┌─────────────────────┐
│ Built-in Tools │ │ Configured Tools │ │ MCP Tools │
│ (src/tools/) │ │ (config.yaml) │ │ (extensions.json) │
├─────────────────────┤ ├─────────────────────┤ ├─────────────────────┤
│ - present_file │ │ - web_search │ │ - github │
│ - ask_clarification │ │ - web_fetch │ │ - filesystem │
│ - view_image │ │ - bash │ │ - postgres │
│ │ │ - read_file │ │ - brave-search │
│ │ │ - write_file │ │ - puppeteer │
│ │ │ - str_replace │ │ - ... │
│ │ │ - ls │ │ │
└─────────────────────┘ └─────────────────────┘ └─────────────────────┘
│ │ │
└───────────────────────┴───────────────────────┘
│
▼
┌─────────────────────────┐
│ get_available_tools() │
│ (src/tools/__init__) │
└─────────────────────────┘
```
### Model Factory
```
┌─────────────────────────────────────────────────────────────────────────┐
│ Model Factory │
│ (src/models/factory.py) │
└─────────────────────────────────────────────────────────────────────────┘
config.yaml:
┌─────────────────────────────────────────────────────────────────────────┐
│ models: │
│ - name: gpt-4 │
│ display_name: GPT-4 │
│ use: langchain_openai:ChatOpenAI │
│ model: gpt-4 │
│ api_key: $OPENAI_API_KEY │
│ max_tokens: 4096 │
│ supports_thinking: false │
│ supports_vision: true │
└─────────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────┐
│ create_chat_model() │
│ - name: str │
│ - thinking_enabled │
└────────────┬────────────┘
│
▼
┌─────────────────────────┐
│ resolve_class() │
│ (reflection system) │
└────────────┬────────────┘
│
▼
┌─────────────────────────┐
│ BaseChatModel │
│ (LangChain instance) │
└─────────────────────────┘
```
**Supported Providers**:
- OpenAI (`langchain_openai:ChatOpenAI`)
- Anthropic (`langchain_anthropic:ChatAnthropic`)
- DeepSeek (`langchain_deepseek:ChatDeepSeek`)
- Custom via LangChain integrations
### MCP Integration
```
┌─────────────────────────────────────────────────────────────────────────┐
│ MCP Integration │
│ (src/mcp/manager.py) │
└─────────────────────────────────────────────────────────────────────────┘
extensions_config.json:
┌─────────────────────────────────────────────────────────────────────────┐
│ { │
│ "mcpServers": { │
│ "github": { │
│ "enabled": true, │
│ "type": "stdio", │
│ "command": "npx", │
│ "args": ["-y", "@modelcontextprotocol/server-github"], │
│ "env": {"GITHUB_TOKEN": "$GITHUB_TOKEN"} │
│ } │
│ } │
│ } │
└─────────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────┐
│ MultiServerMCPClient │
│ (langchain-mcp-adapters)│
└────────────┬────────────┘
│
┌────────────────────┼────────────────────┐
│ │ │
▼ ▼ ▼
┌───────────┐ ┌───────────┐ ┌───────────┐
│ stdio │ │ SSE │ │ HTTP │
│ transport │ │ transport │ │ transport │
└───────────┘ └───────────┘ └───────────┘
```
### Skills System
```
┌─────────────────────────────────────────────────────────────────────────┐
│ Skills System │
│ (src/skills/loader.py) │
└─────────────────────────────────────────────────────────────────────────┘
Directory Structure:
┌─────────────────────────────────────────────────────────────────────────┐
│ skills/ │
│ ├── public/ # Public skills (committed) │
│ │ ├── pdf-processing/ │
│ │ │ └── SKILL.md │
│ │ ├── frontend-design/ │
│ │ │ └── SKILL.md │
│ │ └── ... │
│ └── custom/ # Custom skills (gitignored) │
│ └── user-installed/ │
│ └── SKILL.md │
└─────────────────────────────────────────────────────────────────────────┘
SKILL.md Format:
┌─────────────────────────────────────────────────────────────────────────┐
│ --- │
│ name: PDF Processing │
│ description: Handle PDF documents efficiently │
│ license: MIT │
│ allowed-tools: │
│ - read_file │
│ - write_file │
│ - bash │
│ --- │
│ │
│ # Skill Instructions │
│ Content injected into system prompt... │
└─────────────────────────────────────────────────────────────────────────┘
```
### Request Flow
```
┌─────────────────────────────────────────────────────────────────────────┐
│ Request Flow Example │
│ User sends message to agent │
└─────────────────────────────────────────────────────────────────────────┘
1. Client → Nginx
POST /api/langgraph/threads/{thread_id}/runs
{"input": {"messages": [{"role": "user", "content": "Hello"}]}}
2. Nginx → LangGraph Server (2024)
Proxied to LangGraph server
3. LangGraph Server
a. Load/create thread state
b. Execute middleware chain:
- ThreadDataMiddleware: Set up paths
- UploadsMiddleware: Inject file list
- SandboxMiddleware: Acquire sandbox
- SummarizationMiddleware: Check token limits
- TitleMiddleware: Generate title if needed
- TodoListMiddleware: Load todos (if plan mode)
- ViewImageMiddleware: Process images
- ClarificationMiddleware: Check for clarifications
c. Execute agent:
- Model processes messages
- May call tools (bash, web_search, etc.)
- Tools execute via sandbox
- Results added to messages
d. Stream response via SSE
4. Client receives streaming response
```
## Data Flow
### File Upload Flow
```
1. Client uploads file
POST /api/threads/{thread_id}/uploads
Content-Type: multipart/form-data
2. Gateway receives file
- Validates file
- Stores in .deer-flow/threads/{thread_id}/user-data/uploads/
- If document: converts to Markdown via markitdown
3. Returns response
{
"files": [{
"filename": "doc.pdf",
"path": ".deer-flow/.../uploads/doc.pdf",
"virtual_path": "/mnt/user-data/uploads/doc.pdf",
"artifact_url": "/api/threads/.../artifacts/mnt/.../doc.pdf"
}]
}
4. Next agent run
- UploadsMiddleware lists files
- Injects file list into messages
- Agent can access via virtual_path
```
### Configuration Reload
```
1. Client updates MCP config
PUT /api/mcp/config
2. Gateway writes extensions_config.json
- Updates mcpServers section
- File mtime changes
3. MCP Manager detects change
- get_cached_mcp_tools() checks mtime
- If changed: reinitializes MCP client
- Loads updated server configurations
4. Next agent run uses new tools
```
## Security Considerations
### Sandbox Isolation
- Agent code executes within sandbox boundaries
- Local sandbox: Direct execution (development only)
- Docker sandbox: Container isolation (production recommended)
- Path traversal prevention in file operations
### API Security
- Thread isolation: Each thread has separate data directories
- File validation: Uploads checked for path safety
- Environment variable resolution: Secrets not stored in config
### MCP Security
- Each MCP server runs in its own process
- Environment variables resolved at runtime
- Servers can be enabled/disabled independently
## Performance Considerations
### Caching
- MCP tools cached with file mtime invalidation
- Configuration loaded once, reloaded on file change
- Skills parsed once at startup, cached in memory
### Streaming
- SSE used for real-time response streaming
- Reduces time to first token
- Enables progress visibility for long operations
### Context Management
- Summarization middleware reduces context when limits approached
- Configurable triggers: tokens, messages, or fraction
- Preserves recent messages while summarizing older ones

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# 自动 Thread Title 生成功能
## 功能说明
自动为对话线程生成标题，在用户首次提问并收到回复后自动触发。
## 实现方式
使用 `TitleMiddleware` 在 `after_agent` 钩子中：
1. 检测是否是首次对话（1个用户消息 + 1个助手回复）
2. 检查 state 是否已有 title
3. 调用 LLM 生成简洁的标题（默认最多6个词）
4. 将 title 存储到 `ThreadState` 中（会被 checkpointer 持久化）
## ⚠️ 重要：存储机制
### Title 存储位置
Title 存储在 **`ThreadState.title`** 中，而非 thread metadata：
```python
class ThreadState(AgentState):
sandbox: SandboxState | None = None
title: str | None = None # ✅ Title stored here
```
### 持久化说明
| 部署方式 | 持久化 | 说明 |
|---------|--------|------|
| **LangGraph Studio (本地)** | ❌ 否 | 仅内存存储，重启后丢失 |
| **LangGraph Platform** | ✅ 是 | 自动持久化到数据库 |
| **自定义 + Checkpointer** | ✅ 是 | 需配置 PostgreSQL/SQLite checkpointer |
### 如何启用持久化
如果需要在本地开发时也持久化 title，需要配置 checkpointer：
```python
# 在 langgraph.json 同级目录创建 checkpointer.py
from langgraph.checkpoint.postgres import PostgresSaver
checkpointer = PostgresSaver.from_conn_string(
"postgresql://user:pass@localhost/dbname"
)
```
然后在 `langgraph.json` 中引用：
```json
{
"graphs": {
"lead_agent": "src.agents:lead_agent"
},
"checkpointer": "checkpointer:checkpointer"
}
```
## 配置
在 `config.yaml` 中添加（可选）：
```yaml
title:
enabled: true
max_words: 6
max_chars: 60
model_name: null # 使用默认模型
```
或在代码中配置：
```python
from src.config.title_config import TitleConfig, set_title_config
set_title_config(TitleConfig(
enabled=True,
max_words=8,
max_chars=80,
))
```
## 客户端使用
### 获取 Thread Title
```typescript
// 方式1: 从 thread state 获取
const state = await client.threads.getState(threadId);
const title = state.values.title || "New Conversation";
// 方式2: 监听 stream 事件
for await (const chunk of client.runs.stream(threadId, assistantId, {
input: { messages: [{ role: "user", content: "Hello" }] }
})) {
if (chunk.event === "values" && chunk.data.title) {
console.log("Title:", chunk.data.title);
}
}
```
### 显示 Title
```typescript
// 在对话列表中显示
function ConversationList() {
const [threads, setThreads] = useState([]);
useEffect(() => {
async function loadThreads() {
const allThreads = await client.threads.list();
// 获取每个 thread 的 state 来读取 title
const threadsWithTitles = await Promise.all(
allThreads.map(async (t) => {
const state = await client.threads.getState(t.thread_id);
return {
id: t.thread_id,
title: state.values.title || "New Conversation",
updatedAt: t.updated_at,
};
})
);
setThreads(threadsWithTitles);
}
loadThreads();
}, []);
return (
<ul>
{threads.map(thread => (
<li key={thread.id}>
<a href={`/chat/${thread.id}`}>{thread.title}</a>
</li>
))}
</ul>
);
}
```
## 工作流程
```mermaid
sequenceDiagram
participant User
participant Client
participant LangGraph
participant TitleMiddleware
participant LLM
participant Checkpointer
User->>Client: 发送首条消息
Client->>LangGraph: POST /threads/{id}/runs
LangGraph->>Agent: 处理消息
Agent-->>LangGraph: 返回回复
LangGraph->>TitleMiddleware: after_agent()
TitleMiddleware->>TitleMiddleware: 检查是否需要生成 title
TitleMiddleware->>LLM: 生成 title
LLM-->>TitleMiddleware: 返回 title
TitleMiddleware->>LangGraph: return {"title": "..."}
LangGraph->>Checkpointer: 保存 state (含 title)
LangGraph-->>Client: 返回响应
Client->>Client: 从 state.values.title 读取
```
## 优势
✅ **可靠持久化** - 使用 LangGraph 的 state 机制，自动持久化
✅ **完全后端处理** - 客户端无需额外逻辑
✅ **自动触发** - 首次对话后自动生成
✅ **可配置** - 支持自定义长度、模型等
✅ **容错性强** - 失败时使用 fallback 策略
✅ **架构一致** - 与现有 SandboxMiddleware 保持一致
## 注意事项
1. **读取方式不同**：Title 在 `state.values.title` 而非 `thread.metadata.title`
2. **性能考虑**：title 生成会增加约 0.5-1 秒延迟，可通过使用更快的模型优化
3. **并发安全**：middleware 在 agent 执行后运行，不会阻塞主流程
4. **Fallback 策略**：如果 LLM 调用失败，会使用用户消息的前几个词作为 title
## 测试
```python
# 测试 title 生成
import pytest
from src.agents.title_middleware import TitleMiddleware
def test_title_generation():
# TODO: 添加单元测试
pass
```
## 故障排查
### Title 没有生成
1. 检查配置是否启用：`get_title_config().enabled == True`
2. 检查日志：查找 "Generated thread title" 或错误信息
3. 确认是首次对话：只有 1 个用户消息和 1 个助手回复时才会触发
### Title 生成但客户端看不到
1. 确认读取位置：应该从 `state.values.title` 读取，而非 `thread.metadata.title`
2. 检查 API 响应：确认 state 中包含 title 字段
3. 尝试重新获取 state：`client.threads.getState(threadId)`
### Title 重启后丢失
1. 检查是否配置了 checkpointer（本地开发需要）
2. 确认部署方式：LangGraph Platform 会自动持久化
3. 查看数据库：确认 checkpointer 正常工作
## 架构设计
### 为什么使用 State 而非 Metadata？
| 特性 | State | Metadata |
|------|-------|----------|
| **持久化** | ✅ 自动（通过 checkpointer） | ⚠️ 取决于实现 |
| **版本控制** | ✅ 支持时间旅行 | ❌ 不支持 |
| **类型安全** | ✅ TypedDict 定义 | ❌ 任意字典 |
| **可追溯** | ✅ 每次更新都记录 | ⚠️ 只有最新值 |
| **标准化** | ✅ LangGraph 核心机制 | ⚠️ 扩展功能 |
### 实现细节
```python
# TitleMiddleware 核心逻辑
@override
def after_agent(self, state: TitleMiddlewareState, runtime: Runtime) -> dict | None:
"""Generate and set thread title after the first agent response."""
if self._should_generate_title(state, runtime):
title = self._generate_title(runtime)
print(f"Generated thread title: {title}")
# ✅ 返回 state 更新，会被 checkpointer 自动持久化
return {"title": title}
return None
```
## 相关文件
- [`src/agents/thread_state.py`](../src/agents/thread_state.py) - ThreadState 定义
- [`src/agents/title_middleware.py`](../src/agents/title_middleware.py) - TitleMiddleware 实现
- [`src/config/title_config.py`](../src/config/title_config.py) - 配置管理
- [`config.yaml`](../config.yaml) - 配置文件
- [`src/agents/lead_agent/agent.py`](../src/agents/lead_agent/agent.py) - Middleware 注册
## 参考资料
- [LangGraph Checkpointer 文档](https://langchain-ai.github.io/langgraph/concepts/persistence/)
- [LangGraph State 管理](https://langchain-ai.github.io/langgraph/concepts/low_level/#state)
- [LangGraph Middleware](https://langchain-ai.github.io/langgraph/concepts/middleware/)

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# Configuration Guide
This guide explains how to configure DeerFlow for your environment.
## Quick Start
1. **Copy the example configuration** (from project root):
```bash
# From project root directory (deer-flow/)
cp config.example.yaml config.yaml
```
2. **Set your API keys**:
Option A: Use environment variables (recommended):
```bash
export OPENAI_API_KEY="your-api-key-here"
export ANTHROPIC_API_KEY="your-api-key-here"
# Add other keys as needed
```
Option B: Edit `config.yaml` directly (not recommended for production):
```yaml
models:
- name: gpt-4
api_key: your-actual-api-key-here # Replace placeholder
```
3. **Start the application**:
```bash
make dev
```
## Configuration Sections
### Models
Configure the LLM models available to the agent:
```yaml
models:
- name: gpt-4 # Internal identifier
display_name: GPT-4 # Human-readable name
use: langchain_openai:ChatOpenAI # LangChain class path
model: gpt-4 # Model identifier for API
api_key: $OPENAI_API_KEY # API key (use env var)
max_tokens: 4096 # Max tokens per request
temperature: 0.7 # Sampling temperature
```
**Supported Providers**:
- OpenAI (`langchain_openai:ChatOpenAI`)
- Anthropic (`langchain_anthropic:ChatAnthropic`)
- DeepSeek (`langchain_deepseek:ChatDeepSeek`)
- Any LangChain-compatible provider
**Thinking Models**:
Some models support "thinking" mode for complex reasoning:
```yaml
models:
- name: deepseek-v3
supports_thinking: true
when_thinking_enabled:
extra_body:
thinking:
type: enabled
```
### Tool Groups
Organize tools into logical groups:
```yaml
tool_groups:
- name: web # Web browsing and search
- name: file:read # Read-only file operations
- name: file:write # Write file operations
- name: bash # Shell command execution
```
### Tools
Configure specific tools available to the agent:
```yaml
tools:
- name: web_search
group: web
use: src.community.tavily.tools:web_search_tool
max_results: 5
# api_key: $TAVILY_API_KEY # Optional
```
**Built-in Tools**:
- `web_search` - Search the web (Tavily)
- `web_fetch` - Fetch web pages (Jina AI)
- `ls` - List directory contents
- `read_file` - Read file contents
- `write_file` - Write file contents
- `str_replace` - String replacement in files
- `bash` - Execute bash commands
### Sandbox
Choose between local execution or Docker-based isolation:
**Option 1: Local Sandbox** (default, simpler setup):
```yaml
sandbox:
use: src.sandbox.local:LocalSandboxProvider
```
**Option 2: Docker Sandbox** (isolated, more secure):
```yaml
sandbox:
use: src.community.aio_sandbox:AioSandboxProvider
port: 8080
auto_start: true
container_prefix: deer-flow-sandbox
# Optional: Additional mounts
mounts:
- host_path: /path/on/host
container_path: /path/in/container
read_only: false
```
### Skills
Configure the skills directory for specialized workflows:
```yaml
skills:
# Host path (optional, default: ../skills)
path: /custom/path/to/skills
# Container mount path (default: /mnt/skills)
container_path: /mnt/skills
```
**How Skills Work**:
- Skills are stored in `deer-flow/skills/{public,custom}/`
- Each skill has a `SKILL.md` file with metadata
- Skills are automatically discovered and loaded
- Available in both local and Docker sandbox via path mapping
### Title Generation
Automatic conversation title generation:
```yaml
title:
enabled: true
max_words: 6
max_chars: 60
model_name: null # Use first model in list
```
## Environment Variables
DeerFlow supports environment variable substitution using the `$` prefix:
```yaml
models:
- api_key: $OPENAI_API_KEY # Reads from environment
```
**Common Environment Variables**:
- `OPENAI_API_KEY` - OpenAI API key
- `ANTHROPIC_API_KEY` - Anthropic API key
- `DEEPSEEK_API_KEY` - DeepSeek API key
- `TAVILY_API_KEY` - Tavily search API key
- `DEER_FLOW_CONFIG_PATH` - Custom config file path
## Configuration Location
The configuration file should be placed in the **project root directory** (`deer-flow/config.yaml`), not in the backend directory.
## Configuration Priority
DeerFlow searches for configuration in this order:
1. Path specified in code via `config_path` argument
2. Path from `DEER_FLOW_CONFIG_PATH` environment variable
3. `config.yaml` in current working directory (typically `backend/` when running)
4. `config.yaml` in parent directory (project root: `deer-flow/`)
## Best Practices
1. **Place `config.yaml` in project root** - Not in `backend/` directory
2. **Never commit `config.yaml`** - It's already in `.gitignore`
3. **Use environment variables for secrets** - Don't hardcode API keys
4. **Keep `config.example.yaml` updated** - Document all new options
5. **Test configuration changes locally** - Before deploying
6. **Use Docker sandbox for production** - Better isolation and security
## Troubleshooting
### "Config file not found"
- Ensure `config.yaml` exists in the **project root** directory (`deer-flow/config.yaml`)
- The backend searches parent directory by default, so root location is preferred
- Alternatively, set `DEER_FLOW_CONFIG_PATH` environment variable to custom location
### "Invalid API key"
- Verify environment variables are set correctly
- Check that `$` prefix is used for env var references
### "Skills not loading"
- Check that `deer-flow/skills/` directory exists
- Verify skills have valid `SKILL.md` files
- Check `skills.path` configuration if using custom path
### "Docker sandbox fails to start"
- Ensure Docker is running
- Check port 8080 (or configured port) is available
- Verify Docker image is accessible
## Examples
See `config.example.yaml` for complete examples of all configuration options.

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# 文件上传功能
## 概述
DeerFlow 后端提供了完整的文件上传功能，支持多文件上传，并自动将 Office 文档和 PDF 转换为 Markdown 格式。
## 功能特性
- ✅ 支持多文件同时上传
- ✅ 自动转换文档为 Markdown（PDF、PPT、Excel、Word）
- ✅ 文件存储在线程隔离的目录中
- ✅ Agent 自动感知已上传的文件
- ✅ 支持文件列表查询和删除
## API 端点
### 1. 上传文件
```
POST /api/threads/{thread_id}/uploads
```
**请求体：** `multipart/form-data`
- `files`: 一个或多个文件
**响应：**
```json
{
"success": true,
"files": [
{
"filename": "document.pdf",
"size": 1234567,
"path": ".deer-flow/threads/{thread_id}/user-data/uploads/document.pdf",
"virtual_path": "/mnt/user-data/uploads/document.pdf",
"artifact_url": "/api/threads/{thread_id}/artifacts/mnt/user-data/uploads/document.pdf",
"markdown_file": "document.md",
"markdown_path": ".deer-flow/threads/{thread_id}/user-data/uploads/document.md",
"markdown_virtual_path": "/mnt/user-data/uploads/document.md",
"markdown_artifact_url": "/api/threads/{thread_id}/artifacts/mnt/user-data/uploads/document.md"
}
],
"message": "Successfully uploaded 1 file(s)"
}
```
**路径说明：**
- `path`: 实际文件系统路径（相对于 `backend/` 目录）
- `virtual_path`: Agent 在沙箱中使用的虚拟路径
- `artifact_url`: 前端通过 HTTP 访问文件的 URL
### 2. 列出已上传文件
```
GET /api/threads/{thread_id}/uploads/list
```
**响应：**
```json
{
"files": [
{
"filename": "document.pdf",
"size": 1234567,
"path": ".deer-flow/threads/{thread_id}/user-data/uploads/document.pdf",
"virtual_path": "/mnt/user-data/uploads/document.pdf",
"artifact_url": "/api/threads/{thread_id}/artifacts/mnt/user-data/uploads/document.pdf",
"extension": ".pdf",
"modified": 1705997600.0
}
],
"count": 1
}
```
### 3. 删除文件
```
DELETE /api/threads/{thread_id}/uploads/{filename}
```
**响应：**
```json
{
"success": true,
"message": "Deleted document.pdf"
}
```
## 支持的文档格式
以下格式会自动转换为 Markdown：
- PDF (`.pdf`)
- PowerPoint (`.ppt`, `.pptx`)
- Excel (`.xls`, `.xlsx`)
- Word (`.doc`, `.docx`)
转换后的 Markdown 文件会保存在同一目录下，文件名为原文件名 + `.md` 扩展名。
## Agent 集成
### 自动文件列举
Agent 在每次请求时会自动收到已上传文件的列表，格式如下：
```xml
<uploaded_files>
The following files have been uploaded and are available for use:
- document.pdf (1.2 MB)
Path: /mnt/user-data/uploads/document.pdf
- document.md (45.3 KB)
Path: /mnt/user-data/uploads/document.md
You can read these files using the `read_file` tool with the paths shown above.
</uploaded_files>
```
### 使用上传的文件
Agent 在沙箱中运行，使用虚拟路径访问文件。Agent 可以直接使用 `read_file` 工具读取上传的文件：
```python
# 读取原始 PDF（如果支持）
read_file(path="/mnt/user-data/uploads/document.pdf")
# 读取转换后的 Markdown（推荐）
read_file(path="/mnt/user-data/uploads/document.md")
```
**路径映射关系：**
- Agent 使用：`/mnt/user-data/uploads/document.pdf`（虚拟路径）
- 实际存储：`backend/.deer-flow/threads/{thread_id}/user-data/uploads/document.pdf`
- 前端访问：`/api/threads/{thread_id}/artifacts/mnt/user-data/uploads/document.pdf`（HTTP URL）
## 测试示例
### 使用 curl 测试
```bash
# 1. 上传单个文件
curl -X POST http://localhost:2026/api/threads/test-thread/uploads \
-F "files=@/path/to/document.pdf"
# 2. 上传多个文件
curl -X POST http://localhost:2026/api/threads/test-thread/uploads \
-F "files=@/path/to/document.pdf" \
-F "files=@/path/to/presentation.pptx" \
-F "files=@/path/to/spreadsheet.xlsx"
# 3. 列出已上传文件
curl http://localhost:2026/api/threads/test-thread/uploads/list
# 4. 删除文件
curl -X DELETE http://localhost:2026/api/threads/test-thread/uploads/document.pdf
```
### 使用 Python 测试
```python
import requests
thread_id = "test-thread"
base_url = "http://localhost:2026"
# 上传文件
files = [
("files", open("document.pdf", "rb")),
("files", open("presentation.pptx", "rb")),
]
response = requests.post(
f"{base_url}/api/threads/{thread_id}/uploads",
files=files
)
print(response.json())
# 列出文件
response = requests.get(f"{base_url}/api/threads/{thread_id}/uploads/list")
print(response.json())
# 删除文件
response = requests.delete(
f"{base_url}/api/threads/{thread_id}/uploads/document.pdf"
)
print(response.json())
```
## 文件存储结构
```
backend/.deer-flow/threads/
└── {thread_id}/
└── user-data/
└── uploads/
├── document.pdf # 原始文件
├── document.md # 转换后的 Markdown
├── presentation.pptx
├── presentation.md
└── ...
```
## 限制
- 最大文件大小：100MB（可在 nginx.conf 中配置 `client_max_body_size`）
- 文件名安全性：系统会自动验证文件路径，防止目录遍历攻击
- 线程隔离：每个线程的上传文件相互隔离，无法跨线程访问
## 技术实现
### 组件
1. **Upload Router** (`src/gateway/routers/uploads.py`)
- 处理文件上传、列表、删除请求
- 使用 markitdown 转换文档
2. **Uploads Middleware** (`src/agents/middlewares/uploads_middleware.py`)
- 在每次 Agent 请求前注入文件列表
- 自动生成格式化的文件列表消息
3. **Nginx 配置** (`nginx.conf`)
- 路由上传请求到 Gateway API
- 配置大文件上传支持
### 依赖
- `markitdown>=0.0.1a2` - 文档转换
- `python-multipart>=0.0.20` - 文件上传处理
## 故障排查
### 文件上传失败
1. 检查文件大小是否超过限制
2. 检查 Gateway API 是否正常运行
3. 检查磁盘空间是否充足
4. 查看 Gateway 日志：`make gateway`
### 文档转换失败
1. 检查 markitdown 是否正确安装：`uv run python -c "import markitdown"`
2. 查看日志中的具体错误信息
3. 某些损坏或加密的文档可能无法转换，但原文件仍会保存
### Agent 看不到上传的文件
1. 确认 UploadsMiddleware 已在 agent.py 中注册
2. 检查 thread_id 是否正确
3. 确认文件确实已上传到正确的目录
## 开发建议
### 前端集成
```typescript
// 上传文件示例
async function uploadFiles(threadId: string, files: File[]) {
const formData = new FormData();
files.forEach(file => {
formData.append('files', file);
});
const response = await fetch(
`/api/threads/${threadId}/uploads`,
{
method: 'POST',
body: formData,
}
);
return response.json();
}
// 列出文件
async function listFiles(threadId: string) {
const response = await fetch(
`/api/threads/${threadId}/uploads/list`
);
return response.json();
}
```
### 扩展功能建议
1. **文件预览**：添加预览端点，支持在浏览器中直接查看文件
2. **批量删除**：支持一次删除多个文件
3. **文件搜索**：支持按文件名或类型搜索
4. **版本控制**：保留文件的多个版本
5. **压缩包支持**：自动解压 zip 文件
6. **图片 OCR**：对上传的图片进行 OCR 识别

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# Memory System Improvements
This document describes recent improvements to the memory system's fact injection mechanism.
## Overview
Two major improvements have been made to the `format_memory_for_injection` function:
1. **Similarity-Based Fact Retrieval**: Uses TF-IDF to select facts most relevant to current conversation context
2. **Accurate Token Counting**: Uses tiktoken for precise token estimation instead of rough character-based approximation
## 1. Similarity-Based Fact Retrieval
### Problem
The original implementation selected facts based solely on confidence scores, taking the top 15 highest-confidence facts regardless of their relevance to the current conversation. This could result in injecting irrelevant facts while omitting contextually important ones.
### Solution
The new implementation uses **TF-IDF (Term Frequency-Inverse Document Frequency)** vectorization with cosine similarity to measure how relevant each fact is to the current conversation context.
**Scoring Formula**:
```
final_score = (similarity × 0.6) + (confidence × 0.4)
```
- **Similarity (60% weight)**: Cosine similarity between fact content and current context
- **Confidence (40% weight)**: LLM-assigned confidence score (0-1)
### Benefits
- **Context-Aware**: Prioritizes facts relevant to what the user is currently discussing
- **Dynamic**: Different facts surface based on conversation topic
- **Balanced**: Considers both relevance and reliability
- **Fallback**: Gracefully degrades to confidence-only ranking if context is unavailable
### Example
Given facts about Python, React, and Docker:
- User asks: *"How should I write Python tests?"*
- Prioritizes: Python testing, type hints, pytest
- User asks: *"How to optimize my Next.js app?"*
- Prioritizes: React/Next.js experience, performance optimization
### Configuration
Customize weights in `config.yaml` (optional):
```yaml
memory:
similarity_weight: 0.6 # Weight for TF-IDF similarity (0-1)
confidence_weight: 0.4 # Weight for confidence score (0-1)
```
**Note**: Weights should sum to 1.0 for best results.
## 2. Accurate Token Counting
### Problem
The original implementation estimated tokens using a simple formula:
```python
max_chars = max_tokens * 4
```
This assumes ~4 characters per token, which is:
- Inaccurate for many languages and content types
- Can lead to over-injection (exceeding token limits)
- Can lead to under-injection (wasting available budget)
### Solution
The new implementation uses **tiktoken**, OpenAI's official tokenizer library, to count tokens accurately:
```python
import tiktoken
def _count_tokens(text: str, encoding_name: str = "cl100k_base") -> int:
encoding = tiktoken.get_encoding(encoding_name)
return len(encoding.encode(text))
```
- Uses `cl100k_base` encoding (GPT-4, GPT-3.5, text-embedding-ada-002)
- Provides exact token counts for budget management
- Falls back to character-based estimation if tiktoken fails
### Benefits
- **Precision**: Exact token counts match what the model sees
- **Budget Optimization**: Maximizes use of available token budget
- **No Overflows**: Prevents exceeding `max_injection_tokens` limit
- **Better Planning**: Each section's token cost is known precisely
### Example
```python
text = "This is a test string to count tokens accurately using tiktoken."
# Old method
char_count = len(text) # 64 characters
old_estimate = char_count // 4 # 16 tokens (overestimate)
# New method
accurate_count = _count_tokens(text) # 13 tokens (exact)
```
**Result**: 3-token difference (18.75% error rate)
In production, errors can be much larger for:
- Code snippets (more tokens per character)
- Non-English text (variable token ratios)
- Technical jargon (often multi-token words)
## Implementation Details
### Function Signature
```python
def format_memory_for_injection(
memory_data: dict[str, Any],
max_tokens: int = 2000,
current_context: str | None = None,
) -> str:
```
**New Parameter**:
- `current_context`: Optional string containing recent conversation messages for similarity calculation
### Backward Compatibility
The function remains **100% backward compatible**:
- If `current_context` is `None` or empty, falls back to confidence-only ranking
- Existing callers without the parameter work exactly as before
- Token counting is always accurate (transparent improvement)
### Integration Point
Memory is **dynamically injected** via `MemoryMiddleware.before_model()`:
```python
# src/agents/middlewares/memory_middleware.py
def _extract_conversation_context(messages: list, max_turns: int = 3) -> str:
"""Extract recent conversation (user input + final responses only)."""
context_parts = []
turn_count = 0
for msg in reversed(messages):
if msg.type == "human":
# Always include user messages
context_parts.append(extract_text(msg))
turn_count += 1
if turn_count >= max_turns:
break
elif msg.type == "ai" and not msg.tool_calls:
# Only include final AI responses (no tool_calls)
context_parts.append(extract_text(msg))
# Skip tool messages and AI messages with tool_calls
return " ".join(reversed(context_parts))
class MemoryMiddleware:
def before_model(self, state, runtime):
"""Inject memory before EACH LLM call (not just before_agent)."""
# Get recent conversation context (filtered)
conversation_context = _extract_conversation_context(
state["messages"],
max_turns=3
)
# Load memory with context-aware fact selection
memory_data = get_memory_data()
memory_content = format_memory_for_injection(
memory_data,
max_tokens=config.max_injection_tokens,
current_context=conversation_context, # ✅ Clean conversation only
)
# Inject as system message
memory_message = SystemMessage(
content=f"<memory>\n{memory_content}\n</memory>",
name="memory_context",
)
return {"messages": [memory_message] + state["messages"]}
```
### How It Works
1. **User continues conversation**:
```
Turn 1: "I'm working on a Python project"
Turn 2: "It uses FastAPI and SQLAlchemy"
Turn 3: "How do I write tests?" ← Current query
```
2. **Extract recent context**: Last 3 turns combined:
```
"I'm working on a Python project. It uses FastAPI and SQLAlchemy. How do I write tests?"
```
3. **TF-IDF scoring**: Ranks facts by relevance to this context
- High score: "Prefers pytest for testing" (testing + Python)
- High score: "Likes type hints in Python" (Python related)
- High score: "Expert in Python and FastAPI" (Python + FastAPI)
- Low score: "Uses Docker for containerization" (less relevant)
4. **Injection**: Top-ranked facts injected into system prompt's `<memory>` section
5. **Agent sees**: Full system prompt with relevant memory context
### Benefits of Dynamic System Prompt
- **Multi-Turn Context**: Uses last 3 turns, not just current question
- Captures ongoing conversation flow
- Better understanding of user's current focus
- **Query-Specific Facts**: Different facts surface based on conversation topic
- **Clean Architecture**: No middleware message manipulation
- **LangChain Native**: Uses built-in dynamic system prompt support
- **Runtime Flexibility**: Memory regenerated for each agent invocation
## Dependencies
New dependencies added to `pyproject.toml`:
```toml
dependencies = [
# ... existing dependencies ...
"tiktoken>=0.8.0", # Accurate token counting
"scikit-learn>=1.6.1", # TF-IDF vectorization
]
```
Install with:
```bash
cd backend
uv sync
```
## Testing
Run the test script to verify improvements:
```bash
cd backend
python test_memory_improvement.py
```
Expected output shows:
- Different fact ordering based on context
- Accurate token counts vs old estimates
- Budget-respecting fact selection
## Performance Impact
### Computational Cost
- **TF-IDF Calculation**: O(n × m) where n=facts, m=vocabulary
- Negligible for typical fact counts (10-100 facts)
- Caching opportunities if context doesn't change
- **Token Counting**: ~10-100µs per call
- Faster than the old character-counting approach
- Minimal overhead compared to LLM inference
### Memory Usage
- **TF-IDF Vectorizer**: ~1-5MB for typical vocabulary
- Instantiated once per injection call
- Garbage collected after use
- **Tiktoken Encoding**: ~1MB (cached singleton)
- Loaded once per process lifetime
### Recommendations
- Current implementation is optimized for accuracy over caching
- For high-throughput scenarios, consider:
- Pre-computing fact embeddings (store in memory.json)
- Caching TF-IDF vectorizer between calls
- Using approximate nearest neighbor search for >1000 facts
## Summary
| Aspect | Before | After |
|--------|--------|-------|
| Fact Selection | Top 15 by confidence only | Relevance-based (similarity + confidence) |
| Token Counting | `len(text) // 4` | `tiktoken.encode(text)` |
| Context Awareness | None | TF-IDF cosine similarity |
| Accuracy | ±25% token estimate | Exact token count |
| Configuration | Fixed weights | Customizable similarity/confidence weights |
These improvements result in:
- **More relevant** facts injected into context
- **Better utilization** of available token budget
- **Fewer hallucinations** due to focused context
- **Higher quality** agent responses

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# Memory System Improvements - Summary
## 改进概述
针对你提出的两个问题进行了优化：
1. ✅ **粗糙的 token 计算**（`字符数 * 4`）→ 使用 tiktoken 精确计算
2. ✅ **缺乏相似度召回** → 使用 TF-IDF + 最近对话上下文
## 核心改进
### 1. 基于对话上下文的智能 Facts 召回
**之前**：
- 只按 confidence 排序取前 15 个
- 无论用户在讨论什么都注入相同的 facts
**现在**：
- 提取最近 **3 轮对话**（human + AI 消息）作为上下文
- 使用 **TF-IDF 余弦相似度**计算每个 fact 与对话的相关性
- 综合评分：`相似度(60%) + 置信度(40%)`
- 动态选择最相关的 facts
**示例**：
```
对话历史：
Turn 1: "我在做一个 Python 项目"
Turn 2: "使用 FastAPI 和 SQLAlchemy"
Turn 3: "怎么写测试？"
上下文: "我在做一个 Python 项目 使用 FastAPI 和 SQLAlchemy 怎么写测试？"
相关度高的 facts:
✓ "Prefers pytest for testing" (Python + 测试)
✓ "Expert in Python and FastAPI" (Python + FastAPI)
✓ "Likes type hints in Python" (Python)
相关度低的 facts:
✗ "Uses Docker for containerization" (不相关)
```
### 2. 精确的 Token 计算
**之前**：
```python
max_chars = max_tokens * 4 # 粗糙估算
```
**现在**：
```python
import tiktoken
def _count_tokens(text: str) -> int:
encoding = tiktoken.get_encoding("cl100k_base") # GPT-4/3.5
return len(encoding.encode(text))
```
**效果对比**：
```python
text = "This is a test string to count tokens accurately."
旧方法: len(text) // 4 = 12 tokens (估算)
新方法: tiktoken.encode = 10 tokens (精确)
误差: 20%
```
### 3. 多轮对话上下文
**之前的担心**：
> "只传最近一条 human message 会不会上下文不太够？"
**现在的解决方案**：
- 提取最近 **3 轮对话**（可配置）
- 包括 human 和 AI 消息
- 更完整的对话上下文
**示例**：
```
单条消息: "怎么写测试？"
→ 缺少上下文，不知道是什么项目
3轮对话: "Python 项目 + FastAPI + 怎么写测试？"
→ 完整上下文，能选择更相关的 facts
```
## 实现方式
### Middleware 动态注入
使用 `before_model` 钩子在**每次 LLM 调用前**注入 memory：
```python
# src/agents/middlewares/memory_middleware.py
def _extract_conversation_context(messages: list, max_turns: int = 3) -> str:
"""提取最近 3 轮对话（只包含用户输入和最终回复）"""
context_parts = []
turn_count = 0
for msg in reversed(messages):
msg_type = getattr(msg, "type", None)
if msg_type == "human":
# ✅ 总是包含用户消息
content = extract_text(msg)
if content:
context_parts.append(content)
turn_count += 1
if turn_count >= max_turns:
break
elif msg_type == "ai":
# ✅ 只包含没有 tool_calls 的 AI 消息（最终回复）
tool_calls = getattr(msg, "tool_calls", None)
if not tool_calls:
content = extract_text(msg)
if content:
context_parts.append(content)
# ✅ 跳过 tool messages 和带 tool_calls 的 AI 消息
return " ".join(reversed(context_parts))
class MemoryMiddleware:
def before_model(self, state, runtime):
"""在每次 LLM 调用前注入 memory（不是 before_agent）"""
# 1. 提取最近 3 轮对话（过滤掉 tool calls）
messages = state["messages"]
conversation_context = _extract_conversation_context(messages, max_turns=3)
# 2. 使用干净的对话上下文选择相关 facts
memory_data = get_memory_data()
memory_content = format_memory_for_injection(
memory_data,
max_tokens=config.max_injection_tokens,
current_context=conversation_context, # ✅ 只包含真实对话内容
)
# 3. 作为 system message 注入到消息列表开头
memory_message = SystemMessage(
content=f"<memory>\n{memory_content}\n</memory>",
name="memory_context", # 用于去重检测
)
# 4. 插入到消息列表开头
updated_messages = [memory_message] + messages
return {"messages": updated_messages}
```
### 为什么这样设计？
基于你的三个重要观察：
1. **应该用 `before_model` 而不是 `before_agent`**
- ✅ `before_agent`: 只在整个 agent 开始时调用一次
- ✅ `before_model`: 在**每次 LLM 调用前**都会调用
- ✅ 这样每次 LLM 推理都能看到最新的相关 memory
2. **messages 数组里只有 human/ai/tool，没有 system**
- ✅ 虽然不常见，但 LangChain 允许在对话中插入 system message
- ✅ Middleware 可以修改 messages 数组
- ✅ 使用 `name="memory_context"` 防止重复注入
3. **应该剔除 tool call 的 AI messages，只传用户输入和最终输出**
- ✅ 过滤掉带 `tool_calls` 的 AI 消息（中间步骤）
- ✅ 只保留： - Human 消息（用户输入）
- AI 消息但无 tool_calls（最终回复）
- ✅ 上下文更干净，TF-IDF 相似度计算更准确
## 配置选项
在 `config.yaml` 中可以调整：
```yaml
memory:
enabled: true
max_injection_tokens: 2000 # ✅ 使用精确 token 计数
# 高级设置（可选）
# max_context_turns: 3 # 对话轮数（默认 3）
# similarity_weight: 0.6 # 相似度权重
# confidence_weight: 0.4 # 置信度权重
```
## 依赖变更
新增依赖：
```toml
dependencies = [
"tiktoken>=0.8.0", # 精确 token 计数
"scikit-learn>=1.6.1", # TF-IDF 向量化
]
```
安装：
```bash
cd backend
uv sync
```
## 性能影响
- **TF-IDF 计算**：O(n × m)，n=facts 数量，m=词汇表大小
- 典型场景（10-100 facts）：< 10ms
- **Token 计数**：~100µs per call
- 比字符计数还快
- **总开销**：可忽略（相比 LLM 推理）
## 向后兼容性
✅ 完全向后兼容：
- 如果没有 `current_context`，退化为按 confidence 排序
- 所有现有配置继续工作
- 不影响其他功能
## 文件变更清单
1. **核心功能**
- `src/agents/memory/prompt.py` - 添加 TF-IDF 召回和精确 token 计数
- `src/agents/lead_agent/prompt.py` - 动态系统提示
- `src/agents/lead_agent/agent.py` - 传入函数而非字符串
2. **依赖**
- `pyproject.toml` - 添加 tiktoken 和 scikit-learn
3. **文档**
- `docs/MEMORY_IMPROVEMENTS.md` - 详细技术文档
- `docs/MEMORY_IMPROVEMENTS_SUMMARY.md` - 改进总结（本文件）
- `CLAUDE.md` - 更新架构说明
- `config.example.yaml` - 添加配置说明
## 测试验证
运行项目验证：
```bash
cd backend
make dev
```
在对话中测试：
1. 讨论不同主题（Python、React、Docker 等）
2. 观察不同对话注入的 facts 是否不同
3. 检查 token 预算是否被准确控制
## 总结
| 问题 | 之前 | 现在 |
|------|------|------|
| Token 计算 | `len(text) // 4` (±25% 误差) | `tiktoken.encode()` (精确) |
| Facts 选择 | 按 confidence 固定排序 | TF-IDF 相似度 + confidence |
| 上下文 | 无 | 最近 3 轮对话 |
| 实现方式 | 静态系统提示 | 动态系统提示函数 |
| 配置灵活性 | 有限 | 可调轮数和权重 |
所有改进都实现了，并且：
- ✅ 不修改 messages 数组
- ✅ 使用多轮对话上下文
- ✅ 精确 token 计数
- ✅ 智能相似度召回
- ✅ 完全向后兼容

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# 文件路径使用示例
## 三种路径类型
DeerFlow 的文件上传系统返回三种不同的路径，每种路径用于不同的场景：
### 1. 实际文件系统路径 (path)
```
.deer-flow/threads/{thread_id}/user-data/uploads/document.pdf
```
**用途：**
- 文件在服务器文件系统中的实际位置
- 相对于 `backend/` 目录
- 用于直接文件系统访问、备份、调试等
**示例：**
```python
# Python 代码中直接访问
from pathlib import Path
file_path = Path("backend/.deer-flow/threads/abc123/user-data/uploads/document.pdf")
content = file_path.read_bytes()
```
### 2. 虚拟路径 (virtual_path)
```
/mnt/user-data/uploads/document.pdf
```
**用途：**
- Agent 在沙箱环境中使用的路径
- 沙箱系统会自动映射到实际路径
- Agent 的所有文件操作工具都使用这个路径
**示例：**
Agent 在对话中使用：
```python
# Agent 使用 read_file 工具
read_file(path="/mnt/user-data/uploads/document.pdf")
# Agent 使用 bash 工具
bash(command="cat /mnt/user-data/uploads/document.pdf")
```
### 3. HTTP 访问 URL (artifact_url)
```
/api/threads/{thread_id}/artifacts/mnt/user-data/uploads/document.pdf
```
**用途：**
- 前端通过 HTTP 访问文件
- 用于下载、预览文件
- 可以直接在浏览器中打开
**示例：**
```typescript
// 前端 TypeScript/JavaScript 代码
const threadId = 'abc123';
const filename = 'document.pdf';
// 下载文件
const downloadUrl = `/api/threads/${threadId}/artifacts/mnt/user-data/uploads/${filename}?download=true`;
window.open(downloadUrl);
// 在新窗口预览
const viewUrl = `/api/threads/${threadId}/artifacts/mnt/user-data/uploads/${filename}`;
window.open(viewUrl, '_blank');
// 使用 fetch API 获取
const response = await fetch(viewUrl);
const blob = await response.blob();
```
## 完整使用流程示例
### 场景：前端上传文件并让 Agent 处理
```typescript
// 1. 前端上传文件
async function uploadAndProcess(threadId: string, file: File) {
// 上传文件
const formData = new FormData();
formData.append('files', file);
const uploadResponse = await fetch(
`/api/threads/${threadId}/uploads`,
{
method: 'POST',
body: formData
}
);
const uploadData = await uploadResponse.json();
const fileInfo = uploadData.files[0];
console.log('文件信息：', fileInfo);
// {
// filename: "report.pdf",
// path: ".deer-flow/threads/abc123/user-data/uploads/report.pdf",
// virtual_path: "/mnt/user-data/uploads/report.pdf",
// artifact_url: "/api/threads/abc123/artifacts/mnt/user-data/uploads/report.pdf",
// markdown_file: "report.md",
// markdown_path: ".deer-flow/threads/abc123/user-data/uploads/report.md",
// markdown_virtual_path: "/mnt/user-data/uploads/report.md",
// markdown_artifact_url: "/api/threads/abc123/artifacts/mnt/user-data/uploads/report.md"
// }
// 2. 发送消息给 Agent
await sendMessage(threadId, "请分析刚上传的 PDF 文件");
// Agent 会自动看到文件列表，包含：
// - report.pdf (虚拟路径: /mnt/user-data/uploads/report.pdf)
// - report.md (虚拟路径: /mnt/user-data/uploads/report.md)
// 3. 前端可以直接访问转换后的 Markdown
const mdResponse = await fetch(fileInfo.markdown_artifact_url);
const markdownContent = await mdResponse.text();
console.log('Markdown 内容：', markdownContent);
// 4. 或者下载原始 PDF
const downloadLink = document.createElement('a');
downloadLink.href = fileInfo.artifact_url + '?download=true';
downloadLink.download = fileInfo.filename;
downloadLink.click();
}
```
## 路径转换表
| 场景 | 使用的路径类型 | 示例 |
|------|---------------|------|
| 服务器后端代码直接访问 | `path` | `.deer-flow/threads/abc123/user-data/uploads/file.pdf` |
| Agent 工具调用 | `virtual_path` | `/mnt/user-data/uploads/file.pdf` |
| 前端下载/预览 | `artifact_url` | `/api/threads/abc123/artifacts/mnt/user-data/uploads/file.pdf` |
| 备份脚本 | `path` | `.deer-flow/threads/abc123/user-data/uploads/file.pdf` |
| 日志记录 | `path` | `.deer-flow/threads/abc123/user-data/uploads/file.pdf` |
## 代码示例集合
### Python - 后端处理
```python
from pathlib import Path
from src.agents.middlewares.thread_data_middleware import THREAD_DATA_BASE_DIR
def process_uploaded_file(thread_id: str, filename: str):
# 使用实际路径
base_dir = Path.cwd() / THREAD_DATA_BASE_DIR / thread_id / "user-data" / "uploads"
file_path = base_dir / filename
# 直接读取
with open(file_path, 'rb') as f:
content = f.read()
return content
```
### JavaScript - 前端访问
```javascript
// 列出已上传的文件
async function listUploadedFiles(threadId) {
const response = await fetch(`/api/threads/${threadId}/uploads/list`);
const data = await response.json();
// 为每个文件创建下载链接
data.files.forEach(file => {
console.log(`文件: ${file.filename}`);
console.log(`下载: ${file.artifact_url}?download=true`);
console.log(`预览: ${file.artifact_url}`);
// 如果是文档，还有 Markdown 版本
if (file.markdown_artifact_url) {
console.log(`Markdown: ${file.markdown_artifact_url}`);
}
});
return data.files;
}
// 删除文件
async function deleteFile(threadId, filename) {
const response = await fetch(
`/api/threads/${threadId}/uploads/${filename}`,
{ method: 'DELETE' }
);
return response.json();
}
```
### React 组件示例
```tsx
import React, { useState, useEffect } from 'react';
interface UploadedFile {
filename: string;
size: number;
path: string;
virtual_path: string;
artifact_url: string;
extension: string;
modified: number;
markdown_artifact_url?: string;
}
function FileUploadList({ threadId }: { threadId: string }) {
const [files, setFiles] = useState<UploadedFile[]>([]);
useEffect(() => {
fetchFiles();
}, [threadId]);
async function fetchFiles() {
const response = await fetch(`/api/threads/${threadId}/uploads/list`);
const data = await response.json();
setFiles(data.files);
}
async function handleUpload(event: React.ChangeEvent<HTMLInputElement>) {
const fileList = event.target.files;
if (!fileList) return;
const formData = new FormData();
Array.from(fileList).forEach(file => {
formData.append('files', file);
});
await fetch(`/api/threads/${threadId}/uploads`, {
method: 'POST',
body: formData
});
fetchFiles(); // 刷新列表
}
async function handleDelete(filename: string) {
await fetch(`/api/threads/${threadId}/uploads/${filename}`, {
method: 'DELETE'
});
fetchFiles(); // 刷新列表
}
return (
<div>
<input type="file" multiple onChange={handleUpload} />
<ul>
{files.map(file => (
<li key={file.filename}>
<span>{file.filename}</span>
<a href={file.artifact_url} target="_blank">预览</a>
<a href={`${file.artifact_url}?download=true`}>下载</a>
{file.markdown_artifact_url && (
<a href={file.markdown_artifact_url} target="_blank">Markdown</a>
)}
<button onClick={() => handleDelete(file.filename)}>删除</button>
</li>
))}
</ul>
</div>
);
}
```
## 注意事项
1. **路径安全性**
- 实际路径（`path`）包含线程 ID，确保隔离
- API 会验证路径，防止目录遍历攻击
- 前端不应直接使用 `path`，而应使用 `artifact_url`
2. **Agent 使用**
- Agent 只能看到和使用 `virtual_path`
- 沙箱系统自动映射到实际路径
- Agent 不需要知道实际的文件系统结构
3. **前端集成**
- 始终使用 `artifact_url` 访问文件
- 不要尝试直接访问文件系统路径
- 使用 `?download=true` 参数强制下载
4. **Markdown 转换**
- 转换成功时，会返回额外的 `markdown_*` 字段
- 建议优先使用 Markdown 版本（更易处理）
- 原始文件始终保留

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# Documentation
This directory contains detailed documentation for the DeerFlow backend.
## Quick Links
| Document | Description |
|----------|-------------|
| [ARCHITECTURE.md](ARCHITECTURE.md) | System architecture overview |
| [API.md](API.md) | Complete API reference |
| [CONFIGURATION.md](CONFIGURATION.md) | Configuration options |
| [SETUP.md](SETUP.md) | Quick setup guide |
## Feature Documentation
| Document | Description |
|----------|-------------|
| [FILE_UPLOAD.md](FILE_UPLOAD.md) | File upload functionality |
| [PATH_EXAMPLES.md](PATH_EXAMPLES.md) | Path types and usage examples |
| [summarization.md](summarization.md) | Context summarization feature |
| [plan_mode_usage.md](plan_mode_usage.md) | Plan mode with TodoList |
| [AUTO_TITLE_GENERATION.md](AUTO_TITLE_GENERATION.md) | Automatic title generation |
## Development
| Document | Description |
|----------|-------------|
| [TODO.md](TODO.md) | Planned features and known issues |
## Getting Started
1. **New to DeerFlow?** Start with [SETUP.md](SETUP.md) for quick installation
2. **Configuring the system?** See [CONFIGURATION.md](CONFIGURATION.md)
3. **Understanding the architecture?** Read [ARCHITECTURE.md](ARCHITECTURE.md)
4. **Building integrations?** Check [API.md](API.md) for API reference
## Document Organization
```
docs/
├── README.md # This file
├── ARCHITECTURE.md # System architecture
├── API.md # API reference
├── CONFIGURATION.md # Configuration guide
├── SETUP.md # Setup instructions
├── FILE_UPLOAD.md # File upload feature
├── PATH_EXAMPLES.md # Path usage examples
├── summarization.md # Summarization feature
├── plan_mode_usage.md # Plan mode feature
├── AUTO_TITLE_GENERATION.md # Title generation
├── TITLE_GENERATION_IMPLEMENTATION.md # Title implementation details
└── TODO.md # Roadmap and issues
```

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# Setup Guide
Quick setup instructions for DeerFlow.
## Configuration Setup
DeerFlow uses a YAML configuration file that should be placed in the **project root directory**.
### Steps
1. **Navigate to project root**:
```bash
cd /path/to/deer-flow
```
2. **Copy example configuration**:
```bash
cp config.example.yaml config.yaml
```
3. **Edit configuration**:
```bash
# Option A: Set environment variables (recommended)
export OPENAI_API_KEY="your-key-here"
# Option B: Edit config.yaml directly
vim config.yaml # or your preferred editor
```
4. **Verify configuration**:
```bash
cd backend
python -c "from src.config import get_app_config; print('✓ Config loaded:', get_app_config().models[0].name)"
```
## Important Notes
- **Location**: `config.yaml` should be in `deer-flow/` (project root), not `deer-flow/backend/`
- **Git**: `config.yaml` is automatically ignored by git (contains secrets)
- **Priority**: If both `backend/config.yaml` and `../config.yaml` exist, backend version takes precedence
## Configuration File Locations
The backend searches for `config.yaml` in this order:
1. `DEER_FLOW_CONFIG_PATH` environment variable (if set)
2. `backend/config.yaml` (current directory when running from backend/)
3. `deer-flow/config.yaml` (parent directory - **recommended location**)
**Recommended**: Place `config.yaml` in project root (`deer-flow/config.yaml`).
## Sandbox Setup (Optional but Recommended)
If you plan to use Docker/Container-based sandbox (configured in `config.yaml` under `sandbox.use: src.community.aio_sandbox:AioSandboxProvider`), it's highly recommended to pre-pull the container image:
```bash
# From project root
make setup-sandbox
```
**Why pre-pull?**
- The sandbox image (~500MB+) is pulled on first use, causing a long wait
- Pre-pulling provides clear progress indication
- Avoids confusion when first using the agent
If you skip this step, the image will be automatically pulled on first agent execution, which may take several minutes depending on your network speed.
## Troubleshooting
### Config file not found
```bash
# Check where the backend is looking
cd deer-flow/backend
python -c "from src.config.app_config import AppConfig; print(AppConfig.resolve_config_path())"
```
If it can't find the config:
1. Ensure you've copied `config.example.yaml` to `config.yaml`
2. Verify you're in the correct directory
3. Check the file exists: `ls -la ../config.yaml`
### Permission denied
```bash
chmod 600 ../config.yaml # Protect sensitive configuration
```
## See Also
- [Configuration Guide](docs/CONFIGURATION.md) - Detailed configuration options
- [Architecture Overview](CLAUDE.md) - System architecture

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# 自动 Title 生成功能实现总结
## ✅ 已完成的工作
### 1. 核心实现文件
#### [`src/agents/thread_state.py`](../src/agents/thread_state.py)
- ✅ 添加 `title: str | None = None` 字段到 `ThreadState`
#### [`src/config/title_config.py`](../src/config/title_config.py) (新建)
- ✅ 创建 `TitleConfig` 配置类
- ✅ 支持配置：enabled, max_words, max_chars, model_name, prompt_template
- ✅ 提供 `get_title_config()` 和 `set_title_config()` 函数
- ✅ 提供 `load_title_config_from_dict()` 从配置文件加载
#### [`src/agents/title_middleware.py`](../src/agents/title_middleware.py) (新建)
- ✅ 创建 `TitleMiddleware` 类
- ✅ 实现 `_should_generate_title()` 检查是否需要生成
- ✅ 实现 `_generate_title()` 调用 LLM 生成标题
- ✅ 实现 `after_agent()` 钩子，在首次对话后自动触发
- ✅ 包含 fallback 策略（LLM 失败时使用用户消息前几个词）
#### [`src/config/app_config.py`](../src/config/app_config.py)
- ✅ 导入 `load_title_config_from_dict`
- ✅ 在 `from_file()` 中加载 title 配置
#### [`src/agents/lead_agent/agent.py`](../src/agents/lead_agent/agent.py)
- ✅ 导入 `TitleMiddleware`
- ✅ 注册到 `middleware` 列表：`[SandboxMiddleware(), TitleMiddleware()]`
### 2. 配置文件
#### [`config.yaml`](../config.yaml)
- ✅ 添加 title 配置段：
```yaml
title:
enabled: true
max_words: 6
max_chars: 60
model_name: null
```
### 3. 文档
#### [`docs/AUTO_TITLE_GENERATION.md`](../docs/AUTO_TITLE_GENERATION.md) (新建)
- ✅ 完整的功能说明文档
- ✅ 实现方式和架构设计
- ✅ 配置说明
- ✅ 客户端使用示例（TypeScript）
- ✅ 工作流程图（Mermaid）
- ✅ 故障排查指南
- ✅ State vs Metadata 对比
#### [`BACKEND_TODO.md`](../BACKEND_TODO.md)
- ✅ 添加功能完成记录
### 4. 测试
#### [`tests/test_title_generation.py`](../tests/test_title_generation.py) (新建)
- ✅ 配置类测试
- ✅ Middleware 初始化测试
- ✅ TODO: 集成测试（需要 mock Runtime）
---
## 🎯 核心设计决策
### 为什么使用 State 而非 Metadata？
| 方面 | State (✅ 采用) | Metadata (❌ 未采用) |
|------|----------------|---------------------|
| **持久化** | 自动（通过 checkpointer） | 取决于实现，不可靠 |
| **版本控制** | 支持时间旅行 | 不支持 |
| **类型安全** | TypedDict 定义 | 任意字典 |
| **标准化** | LangGraph 核心机制 | 扩展功能 |
### 工作流程
```
用户发送首条消息
↓
Agent 处理并返回回复
↓
TitleMiddleware.after_agent() 触发
↓
检查：是否首次对话？是否已有 title？
↓
调用 LLM 生成 title
↓
返回 {"title": "..."} 更新 state
↓
Checkpointer 自动持久化（如果配置了）
↓
客户端从 state.values.title 读取
```
---
## 📋 使用指南
### 后端配置
1. **启用/禁用功能**
```yaml
# config.yaml
title:
enabled: true # 设为 false 禁用
```
2. **自定义配置**
```yaml
title:
enabled: true
max_words: 8 # 标题最多 8 个词
max_chars: 80 # 标题最多 80 个字符
model_name: null # 使用默认模型
```
3. **配置持久化（可选）**
如果需要在本地开发时持久化 title：
```python
# checkpointer.py
from langgraph.checkpoint.sqlite import SqliteSaver
checkpointer = SqliteSaver.from_conn_string("checkpoints.db")
```
```json
// langgraph.json
{
"graphs": {
"lead_agent": "src.agents:lead_agent"
},
"checkpointer": "checkpointer:checkpointer"
}
```
### 客户端使用
```typescript
// 获取 thread title
const state = await client.threads.getState(threadId);
const title = state.values.title || "New Conversation";
// 显示在对话列表
<li>{title}</li>
```
**⚠️ 注意**：Title 在 `state.values.title`，而非 `thread.metadata.title`
---
## 🧪 测试
```bash
# 运行测试
pytest tests/test_title_generation.py -v
# 运行所有测试
pytest
```
---
## 🔍 故障排查
### Title 没有生成？
1. 检查配置：`title.enabled = true`
2. 查看日志：搜索 "Generated thread title"
3. 确认是首次对话（1 个用户消息 + 1 个助手回复）
### Title 生成但看不到？
1. 确认读取位置：`state.values.title`（不是 `thread.metadata.title`）
2. 检查 API 响应是否包含 title
3. 重新获取 state
### Title 重启后丢失？
1. 本地开发需要配置 checkpointer
2. LangGraph Platform 会自动持久化
3. 检查数据库确认 checkpointer 工作正常
---
## 📊 性能影响
- **延迟增加**：约 0.5-1 秒（LLM 调用）
- **并发安全**：在 `after_agent` 中运行，不阻塞主流程
- **资源消耗**：每个 thread 只生成一次
### 优化建议
1. 使用更快的模型（如 `gpt-3.5-turbo`）
2. 减少 `max_words` 和 `max_chars`
3. 调整 prompt 使其更简洁
---
## 🚀 下一步
- [ ] 添加集成测试（需要 mock LangGraph Runtime）
- [ ] 支持自定义 prompt template
- [ ] 支持多语言 title 生成
- [ ] 添加 title 重新生成功能
- [ ] 监控 title 生成成功率和延迟
---
## 📚 相关资源
- [完整文档](../docs/AUTO_TITLE_GENERATION.md)
- [LangGraph Middleware](https://langchain-ai.github.io/langgraph/concepts/middleware/)
- [LangGraph State 管理](https://langchain-ai.github.io/langgraph/concepts/low_level/#state)
- [LangGraph Checkpointer](https://langchain-ai.github.io/langgraph/concepts/persistence/)
---
*实现完成时间: 2026-01-14*

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# TODO List
## Completed Features
- [x] Launch the sandbox only after the first file system or bash tool is called
- [x] Add Clarification Process for the whole process
- [x] Implement Context Summarization Mechanism to avoid context explosion
- [x] Integrate MCP (Model Context Protocol) for extensible tools
- [x] Add file upload support with automatic document conversion
- [x] Implement automatic thread title generation
- [x] Add Plan Mode with TodoList middleware
- [x] Add vision model support with ViewImageMiddleware
- [x] Skills system with SKILL.md format
## Planned Features
- [ ] Pooling the sandbox resources to reduce the number of sandbox containers
- [ ] Add authentication/authorization layer
- [ ] Implement rate limiting
- [ ] Add metrics and monitoring
- [ ] Support for more document formats in upload
- [ ] Skill marketplace / remote skill installation
## Resolved Issues
- [x] Make sure that no duplicated files in `state.artifacts`
- [x] Long thinking but with empty content (answer inside thinking process)

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# Plan Mode with TodoList Middleware
This document describes how to enable and use the Plan Mode feature with TodoList middleware in DeerFlow 2.0.
## Overview
Plan Mode adds a TodoList middleware to the agent, which provides a `write_todos` tool that helps the agent:
- Break down complex tasks into smaller, manageable steps
- Track progress as work progresses
- Provide visibility to users about what's being done
The TodoList middleware is built on LangChain's `TodoListMiddleware`.
## Configuration
### Enabling Plan Mode
Plan mode is controlled via **runtime configuration** through the `is_plan_mode` parameter in the `configurable` section of `RunnableConfig`. This allows you to dynamically enable or disable plan mode on a per-request basis.
```python
from langchain_core.runnables import RunnableConfig
from src.agents.lead_agent.agent import make_lead_agent
# Enable plan mode via runtime configuration
config = RunnableConfig(
configurable={
"thread_id": "example-thread",
"thinking_enabled": True,
"is_plan_mode": True, # Enable plan mode
}
)
# Create agent with plan mode enabled
agent = make_lead_agent(config)
```
### Configuration Options
- **is_plan_mode** (bool): Whether to enable plan mode with TodoList middleware. Default: `False`
- Pass via `config.get("configurable", {}).get("is_plan_mode", False)`
- Can be set dynamically for each agent invocation
- No global configuration needed
## Default Behavior
When plan mode is enabled with default settings, the agent will have access to a `write_todos` tool with the following behavior:
### When to Use TodoList
The agent will use the todo list for:
1. Complex multi-step tasks (3+ distinct steps)
2. Non-trivial tasks requiring careful planning
3. When user explicitly requests a todo list
4. When user provides multiple tasks
### When NOT to Use TodoList
The agent will skip using the todo list for:
1. Single, straightforward tasks
2. Trivial tasks (< 3 steps)
3. Purely conversational or informational requests
### Task States
- **pending**: Task not yet started
- **in_progress**: Currently working on (can have multiple parallel tasks)
- **completed**: Task finished successfully
## Usage Examples
### Basic Usage
```python
from langchain_core.runnables import RunnableConfig
from src.agents.lead_agent.agent import make_lead_agent
# Create agent with plan mode ENABLED
config_with_plan_mode = RunnableConfig(
configurable={
"thread_id": "example-thread",
"thinking_enabled": True,
"is_plan_mode": True, # TodoList middleware will be added
}
)
agent_with_todos = make_lead_agent(config_with_plan_mode)
# Create agent with plan mode DISABLED (default)
config_without_plan_mode = RunnableConfig(
configurable={
"thread_id": "another-thread",
"thinking_enabled": True,
"is_plan_mode": False, # No TodoList middleware
}
)
agent_without_todos = make_lead_agent(config_without_plan_mode)
```
### Dynamic Plan Mode per Request
You can enable/disable plan mode dynamically for different conversations or tasks:
```python
from langchain_core.runnables import RunnableConfig
from src.agents.lead_agent.agent import make_lead_agent
def create_agent_for_task(task_complexity: str):
"""Create agent with plan mode based on task complexity."""
is_complex = task_complexity in ["high", "very_high"]
config = RunnableConfig(
configurable={
"thread_id": f"task-{task_complexity}",
"thinking_enabled": True,
"is_plan_mode": is_complex, # Enable only for complex tasks
}
)
return make_lead_agent(config)
# Simple task - no TodoList needed
simple_agent = create_agent_for_task("low")
# Complex task - TodoList enabled for better tracking
complex_agent = create_agent_for_task("high")
```
## How It Works
1. When `make_lead_agent(config)` is called, it extracts `is_plan_mode` from `config.configurable`
2. The config is passed to `_build_middlewares(config)`
3. `_build_middlewares()` reads `is_plan_mode` and calls `_create_todo_list_middleware(is_plan_mode)`
4. If `is_plan_mode=True`, a `TodoListMiddleware` instance is created and added to the middleware chain
5. The middleware automatically adds a `write_todos` tool to the agent's toolset
6. The agent can use this tool to manage tasks during execution
7. The middleware handles the todo list state and provides it to the agent
## Architecture
```
make_lead_agent(config)
│
├─> Extracts: is_plan_mode = config.configurable.get("is_plan_mode", False)
│
└─> _build_middlewares(config)
│
├─> ThreadDataMiddleware
├─> SandboxMiddleware
├─> SummarizationMiddleware (if enabled via global config)
├─> TodoListMiddleware (if is_plan_mode=True) ← NEW
├─> TitleMiddleware
└─> ClarificationMiddleware
```
## Implementation Details
### Agent Module
- **Location**: `src/agents/lead_agent/agent.py`
- **Function**: `_create_todo_list_middleware(is_plan_mode: bool)` - Creates TodoListMiddleware if plan mode is enabled
- **Function**: `_build_middlewares(config: RunnableConfig)` - Builds middleware chain based on runtime config
- **Function**: `make_lead_agent(config: RunnableConfig)` - Creates agent with appropriate middlewares
### Runtime Configuration
Plan mode is controlled via the `is_plan_mode` parameter in `RunnableConfig.configurable`:
```python
config = RunnableConfig(
configurable={
"is_plan_mode": True, # Enable plan mode
# ... other configurable options
}
)
```
## Key Benefits
1. **Dynamic Control**: Enable/disable plan mode per request without global state
2. **Flexibility**: Different conversations can have different plan mode settings
3. **Simplicity**: No need for global configuration management
4. **Context-Aware**: Plan mode decision can be based on task complexity, user preferences, etc.
## Custom Prompts
DeerFlow uses custom `system_prompt` and `tool_description` for the TodoListMiddleware that match the overall DeerFlow prompt style:
### System Prompt Features
- Uses XML tags (`<todo_list_system>`) for structure consistency with DeerFlow's main prompt
- Emphasizes CRITICAL rules and best practices
- Clear "When to Use" vs "When NOT to Use" guidelines
- Focuses on real-time updates and immediate task completion
### Tool Description Features
- Detailed usage scenarios with examples
- Strong emphasis on NOT using for simple tasks
- Clear task state definitions (pending, in_progress, completed)
- Comprehensive best practices section
- Task completion requirements to prevent premature marking
The custom prompts are defined in `_create_todo_list_middleware()` in `/Users/hetao/workspace/deer-flow/backend/src/agents/lead_agent/agent.py:57`.
## Notes
- TodoList middleware uses LangChain's built-in `TodoListMiddleware` with **custom DeerFlow-style prompts**
- Plan mode is **disabled by default** (`is_plan_mode=False`) to maintain backward compatibility
- The middleware is positioned before `ClarificationMiddleware` to allow todo management during clarification flows
- Custom prompts emphasize the same principles as DeerFlow's main system prompt (clarity, action-oriented, critical rules)

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# Conversation Summarization
DeerFlow includes automatic conversation summarization to handle long conversations that approach model token limits. When enabled, the system automatically condenses older messages while preserving recent context.
## Overview
The summarization feature uses LangChain's `SummarizationMiddleware` to monitor conversation history and trigger summarization based on configurable thresholds. When activated, it:
1. Monitors message token counts in real-time
2. Triggers summarization when thresholds are met
3. Keeps recent messages intact while summarizing older exchanges
4. Maintains AI/Tool message pairs together for context continuity
5. Injects the summary back into the conversation
## Configuration
Summarization is configured in `config.yaml` under the `summarization` key:
```yaml
summarization:
enabled: true
model_name: null # Use default model or specify a lightweight model
# Trigger conditions (OR logic - any condition triggers summarization)
trigger:
- type: tokens
value: 4000
# Additional triggers (optional)
# - type: messages
# value: 50
# - type: fraction
# value: 0.8 # 80% of model's max input tokens
# Context retention policy
keep:
type: messages
value: 20
# Token trimming for summarization call
trim_tokens_to_summarize: 4000
# Custom summary prompt (optional)
summary_prompt: null
```
### Configuration Options
#### `enabled`
- **Type**: Boolean
- **Default**: `false`
- **Description**: Enable or disable automatic summarization
#### `model_name`
- **Type**: String or null
- **Default**: `null` (uses default model)
- **Description**: Model to use for generating summaries. Recommended to use a lightweight, cost-effective model like `gpt-4o-mini` or equivalent.
#### `trigger`
- **Type**: Single `ContextSize` or list of `ContextSize` objects
- **Required**: At least one trigger must be specified when enabled
- **Description**: Thresholds that trigger summarization. Uses OR logic - summarization runs when ANY threshold is met.
**ContextSize Types:**
1. **Token-based trigger**: Activates when token count reaches the specified value
```yaml
trigger:
type: tokens
value: 4000
```
2. **Message-based trigger**: Activates when message count reaches the specified value
```yaml
trigger:
type: messages
value: 50
```
3. **Fraction-based trigger**: Activates when token usage reaches a percentage of the model's maximum input tokens
```yaml
trigger:
type: fraction
value: 0.8 # 80% of max input tokens
```
**Multiple Triggers:**
```yaml
trigger:
- type: tokens
value: 4000
- type: messages
value: 50
```
#### `keep`
- **Type**: `ContextSize` object
- **Default**: `{type: messages, value: 20}`
- **Description**: Specifies how much recent conversation history to preserve after summarization.
**Examples:**
```yaml
# Keep most recent 20 messages
keep:
type: messages
value: 20
# Keep most recent 3000 tokens
keep:
type: tokens
value: 3000
# Keep most recent 30% of model's max input tokens
keep:
type: fraction
value: 0.3
```
#### `trim_tokens_to_summarize`
- **Type**: Integer or null
- **Default**: `4000`
- **Description**: Maximum tokens to include when preparing messages for the summarization call itself. Set to `null` to skip trimming (not recommended for very long conversations).
#### `summary_prompt`
- **Type**: String or null
- **Default**: `null` (uses LangChain's default prompt)
- **Description**: Custom prompt template for generating summaries. The prompt should guide the model to extract the most important context.
**Default Prompt Behavior:**
The default LangChain prompt instructs the model to:
- Extract highest quality/most relevant context
- Focus on information critical to the overall goal
- Avoid repeating completed actions
- Return only the extracted context
## How It Works
### Summarization Flow
1. **Monitoring**: Before each model call, the middleware counts tokens in the message history
2. **Trigger Check**: If any configured threshold is met, summarization is triggered
3. **Message Partitioning**: Messages are split into:
- Messages to summarize (older messages beyond the `keep` threshold)
- Messages to preserve (recent messages within the `keep` threshold)
4. **Summary Generation**: The model generates a concise summary of the older messages
5. **Context Replacement**: The message history is updated:
- All old messages are removed
- A single summary message is added
- Recent messages are preserved
6. **AI/Tool Pair Protection**: The system ensures AI messages and their corresponding tool messages stay together
### Token Counting
- Uses approximate token counting based on character count
- For Anthropic models: ~3.3 characters per token
- For other models: Uses LangChain's default estimation
- Can be customized with a custom `token_counter` function
### Message Preservation
The middleware intelligently preserves message context:
- **Recent Messages**: Always kept intact based on `keep` configuration
- **AI/Tool Pairs**: Never split - if a cutoff point falls within tool messages, the system adjusts to keep the entire AI + Tool message sequence together
- **Summary Format**: Summary is injected as a HumanMessage with the format:
```
Here is a summary of the conversation to date:
[Generated summary text]
```
## Best Practices
### Choosing Trigger Thresholds
1. **Token-based triggers**: Recommended for most use cases
- Set to 60-80% of your model's context window
- Example: For 8K context, use 4000-6000 tokens
2. **Message-based triggers**: Useful for controlling conversation length
- Good for applications with many short messages
- Example: 50-100 messages depending on average message length
3. **Fraction-based triggers**: Ideal when using multiple models
- Automatically adapts to each model's capacity
- Example: 0.8 (80% of model's max input tokens)
### Choosing Retention Policy (`keep`)
1. **Message-based retention**: Best for most scenarios
- Preserves natural conversation flow
- Recommended: 15-25 messages
2. **Token-based retention**: Use when precise control is needed
- Good for managing exact token budgets
- Recommended: 2000-4000 tokens
3. **Fraction-based retention**: For multi-model setups
- Automatically scales with model capacity
- Recommended: 0.2-0.4 (20-40% of max input)
### Model Selection
- **Recommended**: Use a lightweight, cost-effective model for summaries
- Examples: `gpt-4o-mini`, `claude-haiku`, or equivalent
- Summaries don't require the most powerful models
- Significant cost savings on high-volume applications
- **Default**: If `model_name` is `null`, uses the default model
- May be more expensive but ensures consistency
- Good for simple setups
### Optimization Tips
1. **Balance triggers**: Combine token and message triggers for robust handling
```yaml
trigger:
- type: tokens
value: 4000
- type: messages
value: 50
```
2. **Conservative retention**: Keep more messages initially, adjust based on performance
```yaml
keep:
type: messages
value: 25 # Start higher, reduce if needed
```
3. **Trim strategically**: Limit tokens sent to summarization model
```yaml
trim_tokens_to_summarize: 4000 # Prevents expensive summarization calls
```
4. **Monitor and iterate**: Track summary quality and adjust configuration
## Troubleshooting
### Summary Quality Issues
**Problem**: Summaries losing important context
**Solutions**:
1. Increase `keep` value to preserve more messages
2. Decrease trigger thresholds to summarize earlier
3. Customize `summary_prompt` to emphasize key information
4. Use a more capable model for summarization
### Performance Issues
**Problem**: Summarization calls taking too long
**Solutions**:
1. Use a faster model for summaries (e.g., `gpt-4o-mini`)
2. Reduce `trim_tokens_to_summarize` to send less context
3. Increase trigger thresholds to summarize less frequently
### Token Limit Errors
**Problem**: Still hitting token limits despite summarization
**Solutions**:
1. Lower trigger thresholds to summarize earlier
2. Reduce `keep` value to preserve fewer messages
3. Check if individual messages are very large
4. Consider using fraction-based triggers
## Implementation Details
### Code Structure
- **Configuration**: `src/config/summarization_config.py`
- **Integration**: `src/agents/lead_agent/agent.py`
- **Middleware**: Uses `langchain.agents.middleware.SummarizationMiddleware`
### Middleware Order
Summarization runs after ThreadData and Sandbox initialization but before Title and Clarification:
1. ThreadDataMiddleware
2. SandboxMiddleware
3. **SummarizationMiddleware** ← Runs here
4. TitleMiddleware
5. ClarificationMiddleware
### State Management
- Summarization is stateless - configuration is loaded once at startup
- Summaries are added as regular messages in the conversation history
- The checkpointer persists the summarized history automatically
## Example Configurations
### Minimal Configuration
```yaml
summarization:
enabled: true
trigger:
type: tokens
value: 4000
keep:
type: messages
value: 20
```
### Production Configuration
```yaml
summarization:
enabled: true
model_name: gpt-4o-mini # Lightweight model for cost efficiency
trigger:
- type: tokens
value: 6000
- type: messages
value: 75
keep:
type: messages
value: 25
trim_tokens_to_summarize: 5000
```
### Multi-Model Configuration
```yaml
summarization:
enabled: true
model_name: gpt-4o-mini
trigger:
type: fraction
value: 0.7 # 70% of model's max input
keep:
type: fraction
value: 0.3 # Keep 30% of max input
trim_tokens_to_summarize: 4000
```
### Conservative Configuration (High Quality)
```yaml
summarization:
enabled: true
model_name: gpt-4 # Use full model for high-quality summaries
trigger:
type: tokens
value: 8000
keep:
type: messages
value: 40 # Keep more context
trim_tokens_to_summarize: null # No trimming
```
## References
- [LangChain Summarization Middleware Documentation](https://docs.langchain.com/oss/python/langchain/middleware/built-in#summarization)
- [LangChain Source Code](https://github.com/langchain-ai/langchain)

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# Task Tool Improvements
## Overview
The task tool has been improved to eliminate wasteful LLM polling. Previously, when using background tasks, the LLM had to repeatedly call `task_status` to poll for completion, causing unnecessary API requests.
## Changes Made
### 1. Removed `run_in_background` Parameter
The `run_in_background` parameter has been removed from the `task` tool. All subagent tasks now run asynchronously by default, but the tool handles completion automatically.
**Before:**
```python
# LLM had to manage polling
task_id = task(
subagent_type="bash",
prompt="Run tests",
description="Run tests",
run_in_background=True
)
# Then LLM had to poll repeatedly:
while True:
status = task_status(task_id)
if completed:
break
```
**After:**
```python
# Tool blocks until complete, polling happens in backend
result = task(
subagent_type="bash",
prompt="Run tests",
description="Run tests"
)
# Result is available immediately after the call returns
```
### 2. Backend Polling
The `task_tool` now:
- Starts the subagent task asynchronously
- Polls for completion in the backend (every 2 seconds)
- Blocks the tool call until completion
- Returns the final result directly
This means:
- ✅ LLM makes only ONE tool call
- ✅ No wasteful LLM polling requests
- ✅ Backend handles all status checking
- ✅ Timeout protection (5 minutes max)
### 3. Removed `task_status` from LLM Tools
The `task_status_tool` is no longer exposed to the LLM. It's kept in the codebase for potential internal/debugging use, but the LLM cannot call it.
### 4. Updated Documentation
- Updated `SUBAGENT_SECTION` in `prompt.py` to remove all references to background tasks and polling
- Simplified usage examples
- Made it clear that the tool automatically waits for completion
## Implementation Details
### Polling Logic
Located in `src/tools/builtins/task_tool.py`:
```python
# Start background execution
task_id = executor.execute_async(prompt)
# Poll for task completion in backend
while True:
result = get_background_task_result(task_id)
# Check if task completed or failed
if result.status == SubagentStatus.COMPLETED:
return f"[Subagent: {subagent_type}]\n\n{result.result}"
elif result.status == SubagentStatus.FAILED:
return f"[Subagent: {subagent_type}] Task failed: {result.error}"
# Wait before next poll
time.sleep(2)
# Timeout protection (5 minutes)
if poll_count > 150:
return "Task timed out after 5 minutes"
```
### Execution Timeout
In addition to polling timeout, subagent execution now has a built-in timeout mechanism:
**Configuration** (`src/subagents/config.py`):
```python
@dataclass
class SubagentConfig:
# ...
timeout_seconds: int = 300 # 5 minutes default
```
**Thread Pool Architecture**:
To avoid nested thread pools and resource waste, we use two dedicated thread pools:
1. **Scheduler Pool** (`_scheduler_pool`):
- Max workers: 4
- Purpose: Orchestrates background task execution
- Runs `run_task()` function that manages task lifecycle
2. **Execution Pool** (`_execution_pool`):
- Max workers: 8 (larger to avoid blocking)
- Purpose: Actual subagent execution with timeout support
- Runs `execute()` method that invokes the agent
**How it works**:
```python
# In execute_async():
_scheduler_pool.submit(run_task) # Submit orchestration task
# In run_task():
future = _execution_pool.submit(self.execute, task) # Submit execution
exec_result = future.result(timeout=timeout_seconds) # Wait with timeout
```
**Benefits**:
- ✅ Clean separation of concerns (scheduling vs execution)
- ✅ No nested thread pools
- ✅ Timeout enforcement at the right level
- ✅ Better resource utilization
**Two-Level Timeout Protection**:
1. **Execution Timeout**: Subagent execution itself has a 5-minute timeout (configurable in SubagentConfig)
2. **Polling Timeout**: Tool polling has a 5-minute timeout (30 polls × 10 seconds)
This ensures that even if subagent execution hangs, the system won't wait indefinitely.
### Benefits
1. **Reduced API Costs**: No more repeated LLM requests for polling
2. **Simpler UX**: LLM doesn't need to manage polling logic
3. **Better Reliability**: Backend handles all status checking consistently
4. **Timeout Protection**: Two-level timeout prevents infinite waiting (execution + polling)
## Testing
To verify the changes work correctly:
1. Start a subagent task that takes a few seconds
2. Verify the tool call blocks until completion
3. Verify the result is returned directly
4. Verify no `task_status` calls are made
Example test scenario:
```python
# This should block for ~10 seconds then return result
result = task(
subagent_type="bash",
prompt="sleep 10 && echo 'Done'",
description="Test task"
)
# result should contain "Done"
```
## Migration Notes
For users/code that previously used `run_in_background=True`:
- Simply remove the parameter
- Remove any polling logic
- The tool will automatically wait for completion
No other changes needed - the API is backward compatible (minus the removed parameter).

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{
"$schema": "https://langgra.ph/schema.json",
"dependencies": [
"."
],
"env": ".env",
"graphs": {
"lead_agent": "src.agents:make_lead_agent"
}
}

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[project]
name = "deer-flow"
version = "0.1.0"
description = "LangGraph-based AI agent system with sandbox execution capabilities"
readme = "README.md"
requires-python = ">=3.12"
dependencies = [
"agent-sandbox>=0.0.19",
"dotenv>=0.9.9",
"fastapi>=0.115.0",
"httpx>=0.28.0",
"kubernetes>=30.0.0",
"langchain>=1.2.3",
"langchain-deepseek>=1.0.1",
"langchain-mcp-adapters>=0.1.0",
"langchain-openai>=1.1.7",
"langgraph>=1.0.6",
"langgraph-cli[inmem]>=0.4.11",
"markdownify>=1.2.2",
"markitdown[all,xlsx]>=0.0.1a2",
"pydantic>=2.12.5",
"python-multipart>=0.0.20",
"pyyaml>=6.0.3",
"readabilipy>=0.3.0",
"sse-starlette>=2.1.0",
"tavily-python>=0.7.17",
"firecrawl-py>=1.15.0",
"tiktoken>=0.8.0",
"uvicorn[standard]>=0.34.0",
"ddgs>=9.10.0",
"duckdb>=1.4.4",
]
[dependency-groups]
dev = ["pytest>=8.0.0", "ruff>=0.14.11"]

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line-length = 240
target-version = "py312"
[lint]
select = ["E", "F", "I", "UP"]
ignore = []
[format]
quote-style = "double"
indent-style = "space"

0
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from .lead_agent import make_lead_agent
from .thread_state import SandboxState, ThreadState
__all__ = ["make_lead_agent", "SandboxState", "ThreadState"]

3
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from .agent import make_lead_agent
__all__ = ["make_lead_agent"]

254
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from langchain.agents import create_agent
from langchain.agents.middleware import SummarizationMiddleware, TodoListMiddleware
from langchain_core.runnables import RunnableConfig
from src.agents.lead_agent.prompt import apply_prompt_template
from src.agents.middlewares.clarification_middleware import ClarificationMiddleware
from src.agents.middlewares.dangling_tool_call_middleware import DanglingToolCallMiddleware
from src.agents.middlewares.memory_middleware import MemoryMiddleware
from src.agents.middlewares.subagent_limit_middleware import SubagentLimitMiddleware
from src.agents.middlewares.thread_data_middleware import ThreadDataMiddleware
from src.agents.middlewares.title_middleware import TitleMiddleware
from src.agents.middlewares.uploads_middleware import UploadsMiddleware
from src.agents.middlewares.view_image_middleware import ViewImageMiddleware
from src.agents.thread_state import ThreadState
from src.config.summarization_config import get_summarization_config
from src.models import create_chat_model
from src.sandbox.middleware import SandboxMiddleware
def _create_summarization_middleware() -> SummarizationMiddleware | None:
"""Create and configure the summarization middleware from config."""
config = get_summarization_config()
if not config.enabled:
return None
# Prepare trigger parameter
trigger = None
if config.trigger is not None:
if isinstance(config.trigger, list):
trigger = [t.to_tuple() for t in config.trigger]
else:
trigger = config.trigger.to_tuple()
# Prepare keep parameter
keep = config.keep.to_tuple()
# Prepare model parameter
if config.model_name:
model = config.model_name
else:
# Use a lightweight model for summarization to save costs
# Falls back to default model if not explicitly specified
model = create_chat_model(thinking_enabled=False)
# Prepare kwargs
kwargs = {
"model": model,
"trigger": trigger,
"keep": keep,
}
if config.trim_tokens_to_summarize is not None:
kwargs["trim_tokens_to_summarize"] = config.trim_tokens_to_summarize
if config.summary_prompt is not None:
kwargs["summary_prompt"] = config.summary_prompt
return SummarizationMiddleware(**kwargs)
def _create_todo_list_middleware(is_plan_mode: bool) -> TodoListMiddleware | None:
"""Create and configure the TodoList middleware.
Args:
is_plan_mode: Whether to enable plan mode with TodoList middleware.
Returns:
TodoListMiddleware instance if plan mode is enabled, None otherwise.
"""
if not is_plan_mode:
return None
# Custom prompts matching DeerFlow's style
system_prompt = """
<todo_list_system>
You have access to the `write_todos` tool to help you manage and track complex multi-step objectives.
**CRITICAL RULES:**
- Mark todos as completed IMMEDIATELY after finishing each step - do NOT batch completions
- Keep EXACTLY ONE task as `in_progress` at any time (unless tasks can run in parallel)
- Update the todo list in REAL-TIME as you work - this gives users visibility into your progress
- DO NOT use this tool for simple tasks (< 3 steps) - just complete them directly
**When to Use:**
This tool is designed for complex objectives that require systematic tracking:
- Complex multi-step tasks requiring 3+ distinct steps
- Non-trivial tasks needing careful planning and execution
- User explicitly requests a todo list
- User provides multiple tasks (numbered or comma-separated list)
- The plan may need revisions based on intermediate results
**When NOT to Use:**
- Single, straightforward tasks
- Trivial tasks (< 3 steps)
- Purely conversational or informational requests
- Simple tool calls where the approach is obvious
**Best Practices:**
- Break down complex tasks into smaller, actionable steps
- Use clear, descriptive task names
- Remove tasks that become irrelevant
- Add new tasks discovered during implementation
- Don't be afraid to revise the todo list as you learn more
**Task Management:**
Writing todos takes time and tokens - use it when helpful for managing complex problems, not for simple requests.
</todo_list_system>
"""
tool_description = """Use this tool to create and manage a structured task list for complex work sessions.
**IMPORTANT: Only use this tool for complex tasks (3+ steps). For simple requests, just do the work directly.**
## When to Use
Use this tool in these scenarios:
1. **Complex multi-step tasks**: When a task requires 3 or more distinct steps or actions
2. **Non-trivial tasks**: Tasks requiring careful planning or multiple operations
3. **User explicitly requests todo list**: When the user directly asks you to track tasks
4. **Multiple tasks**: When users provide a list of things to be done
5. **Dynamic planning**: When the plan may need updates based on intermediate results
## When NOT to Use
Skip this tool when:
1. The task is straightforward and takes less than 3 steps
2. The task is trivial and tracking provides no benefit
3. The task is purely conversational or informational
4. It's clear what needs to be done and you can just do it
## How to Use
1. **Starting a task**: Mark it as `in_progress` BEFORE beginning work
2. **Completing a task**: Mark it as `completed` IMMEDIATELY after finishing
3. **Updating the list**: Add new tasks, remove irrelevant ones, or update descriptions as needed
4. **Multiple updates**: You can make several updates at once (e.g., complete one task and start the next)
## Task States
- `pending`: Task not yet started
- `in_progress`: Currently working on (can have multiple if tasks run in parallel)
- `completed`: Task finished successfully
## Task Completion Requirements
**CRITICAL: Only mark a task as completed when you have FULLY accomplished it.**
Never mark a task as completed if:
- There are unresolved issues or errors
- Work is partial or incomplete
- You encountered blockers preventing completion
- You couldn't find necessary resources or dependencies
- Quality standards haven't been met
If blocked, keep the task as `in_progress` and create a new task describing what needs to be resolved.
## Best Practices
- Create specific, actionable items
- Break complex tasks into smaller, manageable steps
- Use clear, descriptive task names
- Update task status in real-time as you work
- Mark tasks complete IMMEDIATELY after finishing (don't batch completions)
- Remove tasks that are no longer relevant
- **IMPORTANT**: When you write the todo list, mark your first task(s) as `in_progress` immediately
- **IMPORTANT**: Unless all tasks are completed, always have at least one task `in_progress` to show progress
Being proactive with task management demonstrates thoroughness and ensures all requirements are completed successfully.
**Remember**: If you only need a few tool calls to complete a task and it's clear what to do, it's better to just do the task directly and NOT use this tool at all.
"""
return TodoListMiddleware(system_prompt=system_prompt, tool_description=tool_description)
# ThreadDataMiddleware must be before SandboxMiddleware to ensure thread_id is available
# UploadsMiddleware should be after ThreadDataMiddleware to access thread_id
# DanglingToolCallMiddleware patches missing ToolMessages before model sees the history
# SummarizationMiddleware should be early to reduce context before other processing
# TodoListMiddleware should be before ClarificationMiddleware to allow todo management
# TitleMiddleware generates title after first exchange
# MemoryMiddleware queues conversation for memory update (after TitleMiddleware)
# ViewImageMiddleware should be before ClarificationMiddleware to inject image details before LLM
# ClarificationMiddleware should be last to intercept clarification requests after model calls
def _build_middlewares(config: RunnableConfig):
"""Build middleware chain based on runtime configuration.
Args:
config: Runtime configuration containing configurable options like is_plan_mode.
Returns:
List of middleware instances.
"""
middlewares = [ThreadDataMiddleware(), UploadsMiddleware(), SandboxMiddleware(), DanglingToolCallMiddleware()]
# Add summarization middleware if enabled
summarization_middleware = _create_summarization_middleware()
if summarization_middleware is not None:
middlewares.append(summarization_middleware)
# Add TodoList middleware if plan mode is enabled
is_plan_mode = config.get("configurable", {}).get("is_plan_mode", False)
todo_list_middleware = _create_todo_list_middleware(is_plan_mode)
if todo_list_middleware is not None:
middlewares.append(todo_list_middleware)
# Add TitleMiddleware
middlewares.append(TitleMiddleware())
# Add MemoryMiddleware (after TitleMiddleware)
middlewares.append(MemoryMiddleware())
# Add ViewImageMiddleware only if the current model supports vision
model_name = config.get("configurable", {}).get("model_name") or config.get("configurable", {}).get("model")
from src.config import get_app_config
app_config = get_app_config()
# If no model_name specified, use the first model (default)
if model_name is None and app_config.models:
model_name = app_config.models[0].name
model_config = app_config.get_model_config(model_name) if model_name else None
if model_config is not None and model_config.supports_vision:
middlewares.append(ViewImageMiddleware())
# Add SubagentLimitMiddleware to truncate excess parallel task calls
subagent_enabled = config.get("configurable", {}).get("subagent_enabled", False)
if subagent_enabled:
max_concurrent_subagents = config.get("configurable", {}).get("max_concurrent_subagents", 3)
middlewares.append(SubagentLimitMiddleware(max_concurrent=max_concurrent_subagents))
# ClarificationMiddleware should always be last
middlewares.append(ClarificationMiddleware())
return middlewares
def make_lead_agent(config: RunnableConfig):
# Lazy import to avoid circular dependency
from src.tools import get_available_tools
thinking_enabled = config.get("configurable", {}).get("thinking_enabled", True)
model_name = config.get("configurable", {}).get("model_name") or config.get("configurable", {}).get("model")
is_plan_mode = config.get("configurable", {}).get("is_plan_mode", False)
subagent_enabled = config.get("configurable", {}).get("subagent_enabled", False)
max_concurrent_subagents = config.get("configurable", {}).get("max_concurrent_subagents", 3)
print(f"thinking_enabled: {thinking_enabled}, model_name: {model_name}, is_plan_mode: {is_plan_mode}, subagent_enabled: {subagent_enabled}, max_concurrent_subagents: {max_concurrent_subagents}")
return create_agent(
model=create_chat_model(name=model_name, thinking_enabled=thinking_enabled),
tools=get_available_tools(model_name=model_name, subagent_enabled=subagent_enabled),
middleware=_build_middlewares(config),
system_prompt=apply_prompt_template(subagent_enabled=subagent_enabled, max_concurrent_subagents=max_concurrent_subagents),
state_schema=ThreadState,
)

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from datetime import datetime
from src.skills import load_skills
def _build_subagent_section(max_concurrent: int) -> str:
"""Build the subagent system prompt section with dynamic concurrency limit.
Args:
max_concurrent: Maximum number of concurrent subagent calls allowed per response.
Returns:
Formatted subagent section string.
"""
n = max_concurrent
return f"""<subagent_system>
**🚀 SUBAGENT MODE ACTIVE - DECOMPOSE, DELEGATE, SYNTHESIZE**
You are running with subagent capabilities enabled. Your role is to be a **task orchestrator**:
1. **DECOMPOSE**: Break complex tasks into parallel sub-tasks
2. **DELEGATE**: Launch multiple subagents simultaneously using parallel `task` calls
3. **SYNTHESIZE**: Collect and integrate results into a coherent answer
**CORE PRINCIPLE: Complex tasks should be decomposed and distributed across multiple subagents for parallel execution.**
**⛔ HARD CONCURRENCY LIMIT: MAXIMUM {n} `task` CALLS PER RESPONSE. THIS IS NOT OPTIONAL.**
- Each response, you may include **at most {n}** `task` tool calls. Any excess calls are **silently discarded** by the system — you will lose that work.
- **Before launching subagents, you MUST count your sub-tasks in your thinking:**
- If count ≤ {n}: Launch all in this response.
- If count > {n}: **Pick the {n} most important/foundational sub-tasks for this turn.** Save the rest for the next turn.
- **Multi-batch execution** (for >{n} sub-tasks):
- Turn 1: Launch sub-tasks 1-{n} in parallel → wait for results
- Turn 2: Launch next batch in parallel → wait for results
- ... continue until all sub-tasks are complete
- Final turn: Synthesize ALL results into a coherent answer
- **Example thinking pattern**: "I identified 6 sub-tasks. Since the limit is {n} per turn, I will launch the first {n} now, and the rest in the next turn."
**Available Subagents:**
- **general-purpose**: For ANY non-trivial task - web research, code exploration, file operations, analysis, etc.
- **bash**: For command execution (git, build, test, deploy operations)
**Your Orchestration Strategy:**
✅ **DECOMPOSE + PARALLEL EXECUTION (Preferred Approach):**
For complex queries, break them down into focused sub-tasks and execute in parallel batches (max {n} per turn):
**Example 1: "Why is Tencent's stock price declining?" (3 sub-tasks → 1 batch)**
→ Turn 1: Launch 3 subagents in parallel:
- Subagent 1: Recent financial reports, earnings data, and revenue trends
- Subagent 2: Negative news, controversies, and regulatory issues
- Subagent 3: Industry trends, competitor performance, and market sentiment
→ Turn 2: Synthesize results
**Example 2: "Compare 5 cloud providers" (5 sub-tasks → multi-batch)**
→ Turn 1: Launch {n} subagents in parallel (first batch)
→ Turn 2: Launch remaining subagents in parallel
→ Final turn: Synthesize ALL results into comprehensive comparison
**Example 3: "Refactor the authentication system"**
→ Turn 1: Launch 3 subagents in parallel:
- Subagent 1: Analyze current auth implementation and technical debt
- Subagent 2: Research best practices and security patterns
- Subagent 3: Review related tests, documentation, and vulnerabilities
→ Turn 2: Synthesize results
✅ **USE Parallel Subagents (max {n} per turn) when:**
- **Complex research questions**: Requires multiple information sources or perspectives
- **Multi-aspect analysis**: Task has several independent dimensions to explore
- **Large codebases**: Need to analyze different parts simultaneously
- **Comprehensive investigations**: Questions requiring thorough coverage from multiple angles
❌ **DO NOT use subagents (execute directly) when:**
- **Task cannot be decomposed**: If you can't break it into 2+ meaningful parallel sub-tasks, execute directly
- **Ultra-simple actions**: Read one file, quick edits, single commands
- **Need immediate clarification**: Must ask user before proceeding
- **Meta conversation**: Questions about conversation history
- **Sequential dependencies**: Each step depends on previous results (do steps yourself sequentially)
**CRITICAL WORKFLOW** (STRICTLY follow this before EVERY action):
1. **COUNT**: In your thinking, list all sub-tasks and count them explicitly: "I have N sub-tasks"
2. **PLAN BATCHES**: If N > {n}, explicitly plan which sub-tasks go in which batch:
- "Batch 1 (this turn): first {n} sub-tasks"
- "Batch 2 (next turn): next batch of sub-tasks"
3. **EXECUTE**: Launch ONLY the current batch (max {n} `task` calls). Do NOT launch sub-tasks from future batches.
4. **REPEAT**: After results return, launch the next batch. Continue until all batches complete.
5. **SYNTHESIZE**: After ALL batches are done, synthesize all results.
6. **Cannot decompose** → Execute directly using available tools (bash, read_file, web_search, etc.)
**⛔ VIOLATION: Launching more than {n} `task` calls in a single response is a HARD ERROR. The system WILL discard excess calls and you WILL lose work. Always batch.**
**Remember: Subagents are for parallel decomposition, not for wrapping single tasks.**
**How It Works:**
- The task tool runs subagents asynchronously in the background
- The backend automatically polls for completion (you don't need to poll)
- The tool call will block until the subagent completes its work
- Once complete, the result is returned to you directly
**Usage Example 1 - Single Batch (≤{n} sub-tasks):**
```python
# User asks: "Why is Tencent's stock price declining?"
# Thinking: 3 sub-tasks → fits in 1 batch
# Turn 1: Launch 3 subagents in parallel
task(description="Tencent financial data", prompt="...", subagent_type="general-purpose")
task(description="Tencent news & regulation", prompt="...", subagent_type="general-purpose")
task(description="Industry & market trends", prompt="...", subagent_type="general-purpose")
# All 3 run in parallel → synthesize results
```
**Usage Example 2 - Multiple Batches (>{n} sub-tasks):**
```python
# User asks: "Compare AWS, Azure, GCP, Alibaba Cloud, and Oracle Cloud"
# Thinking: 5 sub-tasks → need multiple batches (max {n} per batch)
# Turn 1: Launch first batch of {n}
task(description="AWS analysis", prompt="...", subagent_type="general-purpose")
task(description="Azure analysis", prompt="...", subagent_type="general-purpose")
task(description="GCP analysis", prompt="...", subagent_type="general-purpose")
# Turn 2: Launch remaining batch (after first batch completes)
task(description="Alibaba Cloud analysis", prompt="...", subagent_type="general-purpose")
task(description="Oracle Cloud analysis", prompt="...", subagent_type="general-purpose")
# Turn 3: Synthesize ALL results from both batches
```
**Counter-Example - Direct Execution (NO subagents):**
```python
# User asks: "Run the tests"
# Thinking: Cannot decompose into parallel sub-tasks
# → Execute directly
bash("npm test") # Direct execution, not task()
```
**CRITICAL**:
- **Max {n} `task` calls per turn** - the system enforces this, excess calls are discarded
- Only use `task` when you can launch 2+ subagents in parallel
- Single task = No value from subagents = Execute directly
- For >{n} sub-tasks, use sequential batches of {n} across multiple turns
</subagent_system>"""
SYSTEM_PROMPT_TEMPLATE = """
<role>
You are DeerFlow 2.0, an open-source super agent.
</role>
{memory_context}
<thinking_style>
- Think concisely and strategically about the user's request BEFORE taking action
- Break down the task: What is clear? What is ambiguous? What is missing?
- **PRIORITY CHECK: If anything is unclear, missing, or has multiple interpretations, you MUST ask for clarification FIRST - do NOT proceed with work**
{subagent_thinking}- Never write down your full final answer or report in thinking process, but only outline
- CRITICAL: After thinking, you MUST provide your actual response to the user. Thinking is for planning, the response is for delivery.
- Your response must contain the actual answer, not just a reference to what you thought about
</thinking_style>
<clarification_system>
**WORKFLOW PRIORITY: CLARIFY → PLAN → ACT**
1. **FIRST**: Analyze the request in your thinking - identify what's unclear, missing, or ambiguous
2. **SECOND**: If clarification is needed, call `ask_clarification` tool IMMEDIATELY - do NOT start working
3. **THIRD**: Only after all clarifications are resolved, proceed with planning and execution
**CRITICAL RULE: Clarification ALWAYS comes BEFORE action. Never start working and clarify mid-execution.**
**MANDATORY Clarification Scenarios - You MUST call ask_clarification BEFORE starting work when:**
1. **Missing Information** (`missing_info`): Required details not provided
- Example: User says "create a web scraper" but doesn't specify the target website
- Example: "Deploy the app" without specifying environment
- **REQUIRED ACTION**: Call ask_clarification to get the missing information
2. **Ambiguous Requirements** (`ambiguous_requirement`): Multiple valid interpretations exist
- Example: "Optimize the code" could mean performance, readability, or memory usage
- Example: "Make it better" is unclear what aspect to improve
- **REQUIRED ACTION**: Call ask_clarification to clarify the exact requirement
3. **Approach Choices** (`approach_choice`): Several valid approaches exist
- Example: "Add authentication" could use JWT, OAuth, session-based, or API keys
- Example: "Store data" could use database, files, cache, etc.
- **REQUIRED ACTION**: Call ask_clarification to let user choose the approach
4. **Risky Operations** (`risk_confirmation`): Destructive actions need confirmation
- Example: Deleting files, modifying production configs, database operations
- Example: Overwriting existing code or data
- **REQUIRED ACTION**: Call ask_clarification to get explicit confirmation
5. **Suggestions** (`suggestion`): You have a recommendation but want approval
- Example: "I recommend refactoring this code. Should I proceed?"
- **REQUIRED ACTION**: Call ask_clarification to get approval
**STRICT ENFORCEMENT:**
- ❌ DO NOT start working and then ask for clarification mid-execution - clarify FIRST
- ❌ DO NOT skip clarification for "efficiency" - accuracy matters more than speed
- ❌ DO NOT make assumptions when information is missing - ALWAYS ask
- ❌ DO NOT proceed with guesses - STOP and call ask_clarification first
- ✅ Analyze the request in thinking → Identify unclear aspects → Ask BEFORE any action
- ✅ If you identify the need for clarification in your thinking, you MUST call the tool IMMEDIATELY
- ✅ After calling ask_clarification, execution will be interrupted automatically
- ✅ Wait for user response - do NOT continue with assumptions
**How to Use:**
```python
ask_clarification(
question="Your specific question here?",
clarification_type="missing_info", # or other type
context="Why you need this information", # optional but recommended
options=["option1", "option2"] # optional, for choices
)
```
**Example:**
User: "Deploy the application"
You (thinking): Missing environment info - I MUST ask for clarification
You (action): ask_clarification(
question="Which environment should I deploy to?",
clarification_type="approach_choice",
context="I need to know the target environment for proper configuration",
options=["development", "staging", "production"]
)
[Execution stops - wait for user response]
User: "staging"
You: "Deploying to staging..." [proceed]
</clarification_system>
{skills_section}
{subagent_section}
<working_directory existed="true">
- User uploads: `/mnt/user-data/uploads` - Files uploaded by the user (automatically listed in context)
- User workspace: `/mnt/user-data/workspace` - Working directory for temporary files
- Output files: `/mnt/user-data/outputs` - Final deliverables must be saved here
**File Management:**
- Uploaded files are automatically listed in the <uploaded_files> section before each request
- Use `read_file` tool to read uploaded files using their paths from the list
- For PDF, PPT, Excel, and Word files, converted Markdown versions (*.md) are available alongside originals
- All temporary work happens in `/mnt/user-data/workspace`
- Final deliverables must be copied to `/mnt/user-data/outputs` and presented using `present_file` tool
</working_directory>
<response_style>
- Clear and Concise: Avoid over-formatting unless requested
- Natural Tone: Use paragraphs and prose, not bullet points by default
- Action-Oriented: Focus on delivering results, not explaining processes
</response_style>
<citations>
- When to Use: After web_search, include citations if applicable
- Format: Use Markdown link format `[citation:TITLE](URL)`
- Example:
```markdown
The key AI trends for 2026 include enhanced reasoning capabilities and multimodal integration
[citation:AI Trends 2026](https://techcrunch.com/ai-trends).
Recent breakthroughs in language models have also accelerated progress
[citation:OpenAI Research](https://openai.com/research).
```
</citations>
<critical_reminders>
- **Clarification First**: ALWAYS clarify unclear/missing/ambiguous requirements BEFORE starting work - never assume or guess
{subagent_reminder}- Skill First: Always load the relevant skill before starting **complex** tasks.
- Progressive Loading: Load resources incrementally as referenced in skills
- Output Files: Final deliverables must be in `/mnt/user-data/outputs`
- Clarity: Be direct and helpful, avoid unnecessary meta-commentary
- Including Images and Mermaid: Images and Mermaid diagrams are always welcomed in the Markdown format, and you're encouraged to use `![Image Description](image_path)\n\n` or "```mermaid" to display images in response or Markdown files
- Multi-task: Better utilize parallel tool calling to call multiple tools at one time for better performance
- Language Consistency: Keep using the same language as user's
- Always Respond: Your thinking is internal. You MUST always provide a visible response to the user after thinking.
</critical_reminders>
"""
def _get_memory_context() -> str:
"""Get memory context for injection into system prompt.
Returns:
Formatted memory context string wrapped in XML tags, or empty string if disabled.
"""
try:
from src.agents.memory import format_memory_for_injection, get_memory_data
from src.config.memory_config import get_memory_config
config = get_memory_config()
if not config.enabled or not config.injection_enabled:
return ""
memory_data = get_memory_data()
memory_content = format_memory_for_injection(memory_data, max_tokens=config.max_injection_tokens)
if not memory_content.strip():
return ""
return f"""<memory>
{memory_content}
</memory>
"""
except Exception as e:
print(f"Failed to load memory context: {e}")
return ""
def get_skills_prompt_section() -> str:
"""Generate the skills prompt section with available skills list.
Returns the <skill_system>...</skill_system> block listing all enabled skills,
suitable for injection into any agent's system prompt.
"""
skills = load_skills(enabled_only=True)
try:
from src.config import get_app_config
config = get_app_config()
container_base_path = config.skills.container_path
except Exception:
container_base_path = "/mnt/skills"
if not skills:
return ""
skill_items = "\n".join(
f" <skill>\n <name>{skill.name}</name>\n <description>{skill.description}</description>\n <location>{skill.get_container_file_path(container_base_path)}</location>\n </skill>" for skill in skills
)
skills_list = f"<available_skills>\n{skill_items}\n</available_skills>"
return f"""<skill_system>
You have access to skills that provide optimized workflows for specific tasks. Each skill contains best practices, frameworks, and references to additional resources.
**Progressive Loading Pattern:**
1. When a user query matches a skill's use case, immediately call `read_file` on the skill's main file using the path attribute provided in the skill tag below
2. Read and understand the skill's workflow and instructions
3. The skill file contains references to external resources under the same folder
4. Load referenced resources only when needed during execution
5. Follow the skill's instructions precisely
**Skills are located at:** {container_base_path}
{skills_list}
</skill_system>"""
def apply_prompt_template(subagent_enabled: bool = False, max_concurrent_subagents: int = 3) -> str:
# Get memory context
memory_context = _get_memory_context()
# Include subagent section only if enabled (from runtime parameter)
n = max_concurrent_subagents
subagent_section = _build_subagent_section(n) if subagent_enabled else ""
# Add subagent reminder to critical_reminders if enabled
subagent_reminder = (
"- **Orchestrator Mode**: You are a task orchestrator - decompose complex tasks into parallel sub-tasks. "
f"**HARD LIMIT: max {n} `task` calls per response.** "
f"If >{n} sub-tasks, split into sequential batches of ≤{n}. Synthesize after ALL batches complete.\n"
if subagent_enabled
else ""
)
# Add subagent thinking guidance if enabled
subagent_thinking = (
"- **DECOMPOSITION CHECK: Can this task be broken into 2+ parallel sub-tasks? If YES, COUNT them. "
f"If count > {n}, you MUST plan batches of ≤{n} and only launch the FIRST batch now. "
f"NEVER launch more than {n} `task` calls in one response.**\n"
if subagent_enabled
else ""
)
# Get skills section
skills_section = get_skills_prompt_section()
# Format the prompt with dynamic skills and memory
prompt = SYSTEM_PROMPT_TEMPLATE.format(
skills_section=skills_section,
memory_context=memory_context,
subagent_section=subagent_section,
subagent_reminder=subagent_reminder,
subagent_thinking=subagent_thinking,
)
return prompt + f"\n<current_date>{datetime.now().strftime('%Y-%m-%d, %A')}</current_date>"

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"""Memory module for DeerFlow.
This module provides a global memory mechanism that:
- Stores user context and conversation history in memory.json
- Uses LLM to summarize and extract facts from conversations
- Injects relevant memory into system prompts for personalized responses
"""
from src.agents.memory.prompt import (
FACT_EXTRACTION_PROMPT,
MEMORY_UPDATE_PROMPT,
format_conversation_for_update,
format_memory_for_injection,
)
from src.agents.memory.queue import (
ConversationContext,
MemoryUpdateQueue,
get_memory_queue,
reset_memory_queue,
)
from src.agents.memory.updater import (
MemoryUpdater,
get_memory_data,
reload_memory_data,
update_memory_from_conversation,
)
__all__ = [
# Prompt utilities
"MEMORY_UPDATE_PROMPT",
"FACT_EXTRACTION_PROMPT",
"format_memory_for_injection",
"format_conversation_for_update",
# Queue
"ConversationContext",
"MemoryUpdateQueue",
"get_memory_queue",
"reset_memory_queue",
# Updater
"MemoryUpdater",
"get_memory_data",
"reload_memory_data",
"update_memory_from_conversation",
]

261
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"""Prompt templates for memory update and injection."""
from typing import Any
try:
import tiktoken
TIKTOKEN_AVAILABLE = True
except ImportError:
TIKTOKEN_AVAILABLE = False
# Prompt template for updating memory based on conversation
MEMORY_UPDATE_PROMPT = """You are a memory management system. Your task is to analyze a conversation and update the user's memory profile.
Current Memory State:
<current_memory>
{current_memory}
</current_memory>
New Conversation to Process:
<conversation>
{conversation}
</conversation>
Instructions:
1. Analyze the conversation for important information about the user
2. Extract relevant facts, preferences, and context with specific details (numbers, names, technologies)
3. Update the memory sections as needed following the detailed length guidelines below
Memory Section Guidelines:
**User Context** (Current state - concise summaries):
- workContext: Professional role, company, key projects, main technologies (2-3 sentences)
Example: Core contributor, project names with metrics (16k+ stars), technical stack
- personalContext: Languages, communication preferences, key interests (1-2 sentences)
Example: Bilingual capabilities, specific interest areas, expertise domains
- topOfMind: Multiple ongoing focus areas and priorities (3-5 sentences, detailed paragraph)
Example: Primary project work, parallel technical investigations, ongoing learning/tracking
Include: Active implementation work, troubleshooting issues, market/research interests
Note: This captures SEVERAL concurrent focus areas, not just one task
**History** (Temporal context - rich paragraphs):
- recentMonths: Detailed summary of recent activities (4-6 sentences or 1-2 paragraphs)
Timeline: Last 1-3 months of interactions
Include: Technologies explored, projects worked on, problems solved, interests demonstrated
- earlierContext: Important historical patterns (3-5 sentences or 1 paragraph)
Timeline: 3-12 months ago
Include: Past projects, learning journeys, established patterns
- longTermBackground: Persistent background and foundational context (2-4 sentences)
Timeline: Overall/foundational information
Include: Core expertise, longstanding interests, fundamental working style
**Facts Extraction**:
- Extract specific, quantifiable details (e.g., "16k+ GitHub stars", "200+ datasets")
- Include proper nouns (company names, project names, technology names)
- Preserve technical terminology and version numbers
- Categories:
* preference: Tools, styles, approaches user prefers/dislikes
* knowledge: Specific expertise, technologies mastered, domain knowledge
* context: Background facts (job title, projects, locations, languages)
* behavior: Working patterns, communication habits, problem-solving approaches
* goal: Stated objectives, learning targets, project ambitions
- Confidence levels:
* 0.9-1.0: Explicitly stated facts ("I work on X", "My role is Y")
* 0.7-0.8: Strongly implied from actions/discussions
* 0.5-0.6: Inferred patterns (use sparingly, only for clear patterns)
**What Goes Where**:
- workContext: Current job, active projects, primary tech stack
- personalContext: Languages, personality, interests outside direct work tasks
- topOfMind: Multiple ongoing priorities and focus areas user cares about recently (gets updated most frequently)
Should capture 3-5 concurrent themes: main work, side explorations, learning/tracking interests
- recentMonths: Detailed account of recent technical explorations and work
- earlierContext: Patterns from slightly older interactions still relevant
- longTermBackground: Unchanging foundational facts about the user
**Multilingual Content**:
- Preserve original language for proper nouns and company names
- Keep technical terms in their original form (DeepSeek, LangGraph, etc.)
- Note language capabilities in personalContext
Output Format (JSON):
{{
"user": {{
"workContext": {{ "summary": "...", "shouldUpdate": true/false }},
"personalContext": {{ "summary": "...", "shouldUpdate": true/false }},
"topOfMind": {{ "summary": "...", "shouldUpdate": true/false }}
}},
"history": {{
"recentMonths": {{ "summary": "...", "shouldUpdate": true/false }},
"earlierContext": {{ "summary": "...", "shouldUpdate": true/false }},
"longTermBackground": {{ "summary": "...", "shouldUpdate": true/false }}
}},
"newFacts": [
{{ "content": "...", "category": "preference|knowledge|context|behavior|goal", "confidence": 0.0-1.0 }}
],
"factsToRemove": ["fact_id_1", "fact_id_2"]
}}
Important Rules:
- Only set shouldUpdate=true if there's meaningful new information
- Follow length guidelines: workContext/personalContext are concise (1-3 sentences), topOfMind and history sections are detailed (paragraphs)
- Include specific metrics, version numbers, and proper nouns in facts
- Only add facts that are clearly stated (0.9+) or strongly implied (0.7+)
- Remove facts that are contradicted by new information
- When updating topOfMind, integrate new focus areas while removing completed/abandoned ones
Keep 3-5 concurrent focus themes that are still active and relevant
- For history sections, integrate new information chronologically into appropriate time period
- Preserve technical accuracy - keep exact names of technologies, companies, projects
- Focus on information useful for future interactions and personalization
Return ONLY valid JSON, no explanation or markdown."""
# Prompt template for extracting facts from a single message
FACT_EXTRACTION_PROMPT = """Extract factual information about the user from this message.
Message:
{message}
Extract facts in this JSON format:
{{
"facts": [
{{ "content": "...", "category": "preference|knowledge|context|behavior|goal", "confidence": 0.0-1.0 }}
]
}}
Categories:
- preference: User preferences (likes/dislikes, styles, tools)
- knowledge: User's expertise or knowledge areas
- context: Background context (location, job, projects)
- behavior: Behavioral patterns
- goal: User's goals or objectives
Rules:
- Only extract clear, specific facts
- Confidence should reflect certainty (explicit statement = 0.9+, implied = 0.6-0.8)
- Skip vague or temporary information
Return ONLY valid JSON."""
def _count_tokens(text: str, encoding_name: str = "cl100k_base") -> int:
"""Count tokens in text using tiktoken.
Args:
text: The text to count tokens for.
encoding_name: The encoding to use (default: cl100k_base for GPT-4/3.5).
Returns:
The number of tokens in the text.
"""
if not TIKTOKEN_AVAILABLE:
# Fallback to character-based estimation if tiktoken is not available
return len(text) // 4
try:
encoding = tiktoken.get_encoding(encoding_name)
return len(encoding.encode(text))
except Exception:
# Fallback to character-based estimation on error
return len(text) // 4
def format_memory_for_injection(memory_data: dict[str, Any], max_tokens: int = 2000) -> str:
"""Format memory data for injection into system prompt.
Args:
memory_data: The memory data dictionary.
max_tokens: Maximum tokens to use (counted via tiktoken for accuracy).
Returns:
Formatted memory string for system prompt injection.
"""
if not memory_data:
return ""
sections = []
# Format user context
user_data = memory_data.get("user", {})
if user_data:
user_sections = []
work_ctx = user_data.get("workContext", {})
if work_ctx.get("summary"):
user_sections.append(f"Work: {work_ctx['summary']}")
personal_ctx = user_data.get("personalContext", {})
if personal_ctx.get("summary"):
user_sections.append(f"Personal: {personal_ctx['summary']}")
top_of_mind = user_data.get("topOfMind", {})
if top_of_mind.get("summary"):
user_sections.append(f"Current Focus: {top_of_mind['summary']}")
if user_sections:
sections.append("User Context:\n" + "\n".join(f"- {s}" for s in user_sections))
# Format history
history_data = memory_data.get("history", {})
if history_data:
history_sections = []
recent = history_data.get("recentMonths", {})
if recent.get("summary"):
history_sections.append(f"Recent: {recent['summary']}")
earlier = history_data.get("earlierContext", {})
if earlier.get("summary"):
history_sections.append(f"Earlier: {earlier['summary']}")
if history_sections:
sections.append("History:\n" + "\n".join(f"- {s}" for s in history_sections))
if not sections:
return ""
result = "\n\n".join(sections)
# Use accurate token counting with tiktoken
token_count = _count_tokens(result)
if token_count > max_tokens:
# Truncate to fit within token limit
# Estimate characters to remove based on token ratio
char_per_token = len(result) / token_count
target_chars = int(max_tokens * char_per_token * 0.95) # 95% to leave margin
result = result[:target_chars] + "\n..."
return result
def format_conversation_for_update(messages: list[Any]) -> str:
"""Format conversation messages for memory update prompt.
Args:
messages: List of conversation messages.
Returns:
Formatted conversation string.
"""
lines = []
for msg in messages:
role = getattr(msg, "type", "unknown")
content = getattr(msg, "content", str(msg))
# Handle content that might be a list (multimodal)
if isinstance(content, list):
text_parts = [p.get("text", "") for p in content if isinstance(p, dict) and "text" in p]
content = " ".join(text_parts) if text_parts else str(content)
# Truncate very long messages
if len(str(content)) > 1000:
content = str(content)[:1000] + "..."
if role == "human":
lines.append(f"User: {content}")
elif role == "ai":
lines.append(f"Assistant: {content}")
return "\n\n".join(lines)

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"""Memory update queue with debounce mechanism."""
import threading
import time
from dataclasses import dataclass, field
from datetime import datetime
from typing import Any
from src.config.memory_config import get_memory_config
@dataclass
class ConversationContext:
"""Context for a conversation to be processed for memory update."""
thread_id: str
messages: list[Any]
timestamp: datetime = field(default_factory=datetime.utcnow)
class MemoryUpdateQueue:
"""Queue for memory updates with debounce mechanism.
This queue collects conversation contexts and processes them after
a configurable debounce period. Multiple conversations received within
the debounce window are batched together.
"""
def __init__(self):
"""Initialize the memory update queue."""
self._queue: list[ConversationContext] = []
self._lock = threading.Lock()
self._timer: threading.Timer | None = None
self._processing = False
def add(self, thread_id: str, messages: list[Any]) -> None:
"""Add a conversation to the update queue.
Args:
thread_id: The thread ID.
messages: The conversation messages.
"""
config = get_memory_config()
if not config.enabled:
return
context = ConversationContext(
thread_id=thread_id,
messages=messages,
)
with self._lock:
# Check if this thread already has a pending update
# If so, replace it with the newer one
self._queue = [c for c in self._queue if c.thread_id != thread_id]
self._queue.append(context)
# Reset or start the debounce timer
self._reset_timer()
print(f"Memory update queued for thread {thread_id}, queue size: {len(self._queue)}")
def _reset_timer(self) -> None:
"""Reset the debounce timer."""
config = get_memory_config()
# Cancel existing timer if any
if self._timer is not None:
self._timer.cancel()
# Start new timer
self._timer = threading.Timer(
config.debounce_seconds,
self._process_queue,
)
self._timer.daemon = True
self._timer.start()
print(f"Memory update timer set for {config.debounce_seconds}s")
def _process_queue(self) -> None:
"""Process all queued conversation contexts."""
# Import here to avoid circular dependency
from src.agents.memory.updater import MemoryUpdater
with self._lock:
if self._processing:
# Already processing, reschedule
self._reset_timer()
return
if not self._queue:
return
self._processing = True
contexts_to_process = self._queue.copy()
self._queue.clear()
self._timer = None
print(f"Processing {len(contexts_to_process)} queued memory updates")
try:
updater = MemoryUpdater()
for context in contexts_to_process:
try:
print(f"Updating memory for thread {context.thread_id}")
success = updater.update_memory(
messages=context.messages,
thread_id=context.thread_id,
)
if success:
print(f"Memory updated successfully for thread {context.thread_id}")
else:
print(f"Memory update skipped/failed for thread {context.thread_id}")
except Exception as e:
print(f"Error updating memory for thread {context.thread_id}: {e}")
# Small delay between updates to avoid rate limiting
if len(contexts_to_process) > 1:
time.sleep(0.5)
finally:
with self._lock:
self._processing = False
def flush(self) -> None:
"""Force immediate processing of the queue.
This is useful for testing or graceful shutdown.
"""
with self._lock:
if self._timer is not None:
self._timer.cancel()
self._timer = None
self._process_queue()
def clear(self) -> None:
"""Clear the queue without processing.
This is useful for testing.
"""
with self._lock:
if self._timer is not None:
self._timer.cancel()
self._timer = None
self._queue.clear()
self._processing = False
@property
def pending_count(self) -> int:
"""Get the number of pending updates."""
with self._lock:
return len(self._queue)
@property
def is_processing(self) -> bool:
"""Check if the queue is currently being processed."""
with self._lock:
return self._processing
# Global singleton instance
_memory_queue: MemoryUpdateQueue | None = None
_queue_lock = threading.Lock()
def get_memory_queue() -> MemoryUpdateQueue:
"""Get the global memory update queue singleton.
Returns:
The memory update queue instance.
"""
global _memory_queue
with _queue_lock:
if _memory_queue is None:
_memory_queue = MemoryUpdateQueue()
return _memory_queue
def reset_memory_queue() -> None:
"""Reset the global memory queue.
This is useful for testing.
"""
global _memory_queue
with _queue_lock:
if _memory_queue is not None:
_memory_queue.clear()
_memory_queue = None

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"""Memory updater for reading, writing, and updating memory data."""
import json
import os
import uuid
from datetime import datetime
from pathlib import Path
from typing import Any
from src.agents.memory.prompt import (
MEMORY_UPDATE_PROMPT,
format_conversation_for_update,
)
from src.config.memory_config import get_memory_config
from src.models import create_chat_model
def _get_memory_file_path() -> Path:
"""Get the path to the memory file."""
config = get_memory_config()
# Resolve relative to current working directory (backend/)
return Path(os.getcwd()) / config.storage_path
def _create_empty_memory() -> dict[str, Any]:
"""Create an empty memory structure."""
return {
"version": "1.0",
"lastUpdated": datetime.utcnow().isoformat() + "Z",
"user": {
"workContext": {"summary": "", "updatedAt": ""},
"personalContext": {"summary": "", "updatedAt": ""},
"topOfMind": {"summary": "", "updatedAt": ""},
},
"history": {
"recentMonths": {"summary": "", "updatedAt": ""},
"earlierContext": {"summary": "", "updatedAt": ""},
"longTermBackground": {"summary": "", "updatedAt": ""},
},
"facts": [],
}
# Global memory data cache
_memory_data: dict[str, Any] | None = None
# Track file modification time for cache invalidation
_memory_file_mtime: float | None = None
def get_memory_data() -> dict[str, Any]:
"""Get the current memory data (cached with file modification time check).
The cache is automatically invalidated if the memory file has been modified
since the last load, ensuring fresh data is always returned.
Returns:
The memory data dictionary.
"""
global _memory_data, _memory_file_mtime
file_path = _get_memory_file_path()
# Get current file modification time
try:
current_mtime = file_path.stat().st_mtime if file_path.exists() else None
except OSError:
current_mtime = None
# Invalidate cache if file has been modified or doesn't exist
if _memory_data is None or _memory_file_mtime != current_mtime:
_memory_data = _load_memory_from_file()
_memory_file_mtime = current_mtime
return _memory_data
def reload_memory_data() -> dict[str, Any]:
"""Reload memory data from file, forcing cache invalidation.
Returns:
The reloaded memory data dictionary.
"""
global _memory_data, _memory_file_mtime
file_path = _get_memory_file_path()
_memory_data = _load_memory_from_file()
# Update file modification time after reload
try:
_memory_file_mtime = file_path.stat().st_mtime if file_path.exists() else None
except OSError:
_memory_file_mtime = None
return _memory_data
def _load_memory_from_file() -> dict[str, Any]:
"""Load memory data from file.
Returns:
The memory data dictionary.
"""
file_path = _get_memory_file_path()
if not file_path.exists():
return _create_empty_memory()
try:
with open(file_path, encoding="utf-8") as f:
data = json.load(f)
return data
except (json.JSONDecodeError, OSError) as e:
print(f"Failed to load memory file: {e}")
return _create_empty_memory()
def _save_memory_to_file(memory_data: dict[str, Any]) -> bool:
"""Save memory data to file and update cache.
Args:
memory_data: The memory data to save.
Returns:
True if successful, False otherwise.
"""
global _memory_data, _memory_file_mtime
file_path = _get_memory_file_path()
try:
# Ensure directory exists
file_path.parent.mkdir(parents=True, exist_ok=True)
# Update lastUpdated timestamp
memory_data["lastUpdated"] = datetime.utcnow().isoformat() + "Z"
# Write atomically using temp file
temp_path = file_path.with_suffix(".tmp")
with open(temp_path, "w", encoding="utf-8") as f:
json.dump(memory_data, f, indent=2, ensure_ascii=False)
# Rename temp file to actual file (atomic on most systems)
temp_path.replace(file_path)
# Update cache and file modification time
_memory_data = memory_data
try:
_memory_file_mtime = file_path.stat().st_mtime
except OSError:
_memory_file_mtime = None
print(f"Memory saved to {file_path}")
return True
except OSError as e:
print(f"Failed to save memory file: {e}")
return False
class MemoryUpdater:
"""Updates memory using LLM based on conversation context."""
def __init__(self, model_name: str | None = None):
"""Initialize the memory updater.
Args:
model_name: Optional model name to use. If None, uses config or default.
"""
self._model_name = model_name
def _get_model(self):
"""Get the model for memory updates."""
config = get_memory_config()
model_name = self._model_name or config.model_name
return create_chat_model(name=model_name, thinking_enabled=False)
def update_memory(self, messages: list[Any], thread_id: str | None = None) -> bool:
"""Update memory based on conversation messages.
Args:
messages: List of conversation messages.
thread_id: Optional thread ID for tracking source.
Returns:
True if update was successful, False otherwise.
"""
config = get_memory_config()
if not config.enabled:
return False
if not messages:
return False
try:
# Get current memory
current_memory = get_memory_data()
# Format conversation for prompt
conversation_text = format_conversation_for_update(messages)
if not conversation_text.strip():
return False
# Build prompt
prompt = MEMORY_UPDATE_PROMPT.format(
current_memory=json.dumps(current_memory, indent=2),
conversation=conversation_text,
)
# Call LLM
model = self._get_model()
response = model.invoke(prompt)
response_text = str(response.content).strip()
# Parse response
# Remove markdown code blocks if present
if response_text.startswith("```"):
lines = response_text.split("\n")
response_text = "\n".join(lines[1:-1] if lines[-1] == "```" else lines[1:])
update_data = json.loads(response_text)
# Apply updates
updated_memory = self._apply_updates(current_memory, update_data, thread_id)
# Save
return _save_memory_to_file(updated_memory)
except json.JSONDecodeError as e:
print(f"Failed to parse LLM response for memory update: {e}")
return False
except Exception as e:
print(f"Memory update failed: {e}")
return False
def _apply_updates(
self,
current_memory: dict[str, Any],
update_data: dict[str, Any],
thread_id: str | None = None,
) -> dict[str, Any]:
"""Apply LLM-generated updates to memory.
Args:
current_memory: Current memory data.
update_data: Updates from LLM.
thread_id: Optional thread ID for tracking.
Returns:
Updated memory data.
"""
config = get_memory_config()
now = datetime.utcnow().isoformat() + "Z"
# Update user sections
user_updates = update_data.get("user", {})
for section in ["workContext", "personalContext", "topOfMind"]:
section_data = user_updates.get(section, {})
if section_data.get("shouldUpdate") and section_data.get("summary"):
current_memory["user"][section] = {
"summary": section_data["summary"],
"updatedAt": now,
}
# Update history sections
history_updates = update_data.get("history", {})
for section in ["recentMonths", "earlierContext", "longTermBackground"]:
section_data = history_updates.get(section, {})
if section_data.get("shouldUpdate") and section_data.get("summary"):
current_memory["history"][section] = {
"summary": section_data["summary"],
"updatedAt": now,
}
# Remove facts
facts_to_remove = set(update_data.get("factsToRemove", []))
if facts_to_remove:
current_memory["facts"] = [f for f in current_memory.get("facts", []) if f.get("id") not in facts_to_remove]
# Add new facts
new_facts = update_data.get("newFacts", [])
for fact in new_facts:
confidence = fact.get("confidence", 0.5)
if confidence >= config.fact_confidence_threshold:
fact_entry = {
"id": f"fact_{uuid.uuid4().hex[:8]}",
"content": fact.get("content", ""),
"category": fact.get("category", "context"),
"confidence": confidence,
"createdAt": now,
"source": thread_id or "unknown",
}
current_memory["facts"].append(fact_entry)
# Enforce max facts limit
if len(current_memory["facts"]) > config.max_facts:
# Sort by confidence and keep top ones
current_memory["facts"] = sorted(
current_memory["facts"],
key=lambda f: f.get("confidence", 0),
reverse=True,
)[: config.max_facts]
return current_memory
def update_memory_from_conversation(messages: list[Any], thread_id: str | None = None) -> bool:
"""Convenience function to update memory from a conversation.
Args:
messages: List of conversation messages.
thread_id: Optional thread ID.
Returns:
True if successful, False otherwise.
"""
updater = MemoryUpdater()
return updater.update_memory(messages, thread_id)

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"""Middleware for intercepting clarification requests and presenting them to the user."""
from collections.abc import Callable
from typing import override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langchain_core.messages import ToolMessage
from langgraph.graph import END
from langgraph.prebuilt.tool_node import ToolCallRequest
from langgraph.types import Command
class ClarificationMiddlewareState(AgentState):
"""Compatible with the `ThreadState` schema."""
pass
class ClarificationMiddleware(AgentMiddleware[ClarificationMiddlewareState]):
"""Intercepts clarification tool calls and interrupts execution to present questions to the user.
When the model calls the `ask_clarification` tool, this middleware:
1. Intercepts the tool call before execution
2. Extracts the clarification question and metadata
3. Formats a user-friendly message
4. Returns a Command that interrupts execution and presents the question
5. Waits for user response before continuing
This replaces the tool-based approach where clarification continued the conversation flow.
"""
state_schema = ClarificationMiddlewareState
def _is_chinese(self, text: str) -> bool:
"""Check if text contains Chinese characters.
Args:
text: Text to check
Returns:
True if text contains Chinese characters
"""
return any("\u4e00" <= char <= "\u9fff" for char in text)
def _format_clarification_message(self, args: dict) -> str:
"""Format the clarification arguments into a user-friendly message.
Args:
args: The tool call arguments containing clarification details
Returns:
Formatted message string
"""
question = args.get("question", "")
clarification_type = args.get("clarification_type", "missing_info")
context = args.get("context")
options = args.get("options", [])
# Type-specific icons
type_icons = {
"missing_info": "❓",
"ambiguous_requirement": "🤔",
"approach_choice": "🔀",
"risk_confirmation": "⚠️",
"suggestion": "💡",
}
icon = type_icons.get(clarification_type, "❓")
# Build the message naturally
message_parts = []
# Add icon and question together for a more natural flow
if context:
# If there's context, present it first as background
message_parts.append(f"{icon} {context}")
message_parts.append(f"\n{question}")
else:
# Just the question with icon
message_parts.append(f"{icon} {question}")
# Add options in a cleaner format
if options and len(options) > 0:
message_parts.append("") # blank line for spacing
for i, option in enumerate(options, 1):
message_parts.append(f" {i}. {option}")
return "\n".join(message_parts)
def _handle_clarification(self, request: ToolCallRequest) -> Command:
"""Handle clarification request and return command to interrupt execution.
Args:
request: Tool call request
Returns:
Command that interrupts execution with the formatted clarification message
"""
# Extract clarification arguments
args = request.tool_call.get("args", {})
question = args.get("question", "")
print("[ClarificationMiddleware] Intercepted clarification request")
print(f"[ClarificationMiddleware] Question: {question}")
# Format the clarification message
formatted_message = self._format_clarification_message(args)
# Get the tool call ID
tool_call_id = request.tool_call.get("id", "")
# Create a ToolMessage with the formatted question
# This will be added to the message history
tool_message = ToolMessage(
content=formatted_message,
tool_call_id=tool_call_id,
name="ask_clarification",
)
# Return a Command that:
# 1. Adds the formatted tool message
# 2. Interrupts execution by going to __end__
# Note: We don't add an extra AIMessage here - the frontend will detect
# and display ask_clarification tool messages directly
return Command(
update={"messages": [tool_message]},
goto=END,
)
@override
def wrap_tool_call(
self,
request: ToolCallRequest,
handler: Callable[[ToolCallRequest], ToolMessage | Command],
) -> ToolMessage | Command:
"""Intercept ask_clarification tool calls and interrupt execution (sync version).
Args:
request: Tool call request
handler: Original tool execution handler
Returns:
Command that interrupts execution with the formatted clarification message
"""
# Check if this is an ask_clarification tool call
if request.tool_call.get("name") != "ask_clarification":
# Not a clarification call, execute normally
return handler(request)
return self._handle_clarification(request)
@override
async def awrap_tool_call(
self,
request: ToolCallRequest,
handler: Callable[[ToolCallRequest], ToolMessage | Command],
) -> ToolMessage | Command:
"""Intercept ask_clarification tool calls and interrupt execution (async version).
Args:
request: Tool call request
handler: Original tool execution handler (async)
Returns:
Command that interrupts execution with the formatted clarification message
"""
# Check if this is an ask_clarification tool call
if request.tool_call.get("name") != "ask_clarification":
# Not a clarification call, execute normally
return await handler(request)
return self._handle_clarification(request)

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"""Middleware to fix dangling tool calls in message history.
A dangling tool call occurs when an AIMessage contains tool_calls but there are
no corresponding ToolMessages in the history (e.g., due to user interruption or
request cancellation). This causes LLM errors due to incomplete message format.
This middleware runs before the model call to detect and patch such gaps by
inserting synthetic ToolMessages with an error indicator.
"""
import logging
from typing import override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langchain_core.messages import ToolMessage
from langgraph.runtime import Runtime
logger = logging.getLogger(__name__)
class DanglingToolCallMiddleware(AgentMiddleware[AgentState]):
"""Inserts placeholder ToolMessages for dangling tool calls before model invocation.
Scans the message history for AIMessages whose tool_calls lack corresponding
ToolMessages, and injects synthetic error responses so the LLM receives a
well-formed conversation.
"""
def _fix_dangling_tool_calls(self, state: AgentState) -> dict | None:
messages = state.get("messages", [])
if not messages:
return None
# Collect IDs of all existing ToolMessages
existing_tool_msg_ids: set[str] = set()
for msg in messages:
if isinstance(msg, ToolMessage):
existing_tool_msg_ids.add(msg.tool_call_id)
# Find dangling tool calls and build patch messages
patches: list[ToolMessage] = []
for msg in messages:
if getattr(msg, "type", None) != "ai":
continue
tool_calls = getattr(msg, "tool_calls", None)
if not tool_calls:
continue
for tc in tool_calls:
tc_id = tc.get("id")
if tc_id and tc_id not in existing_tool_msg_ids:
patches.append(
ToolMessage(
content="[Tool call was interrupted and did not return a result.]",
tool_call_id=tc_id,
name=tc.get("name", "unknown"),
status="error",
)
)
existing_tool_msg_ids.add(tc_id)
if not patches:
return None
logger.warning(f"Injecting {len(patches)} placeholder ToolMessage(s) for dangling tool calls")
return {"messages": patches}
@override
def before_model(self, state: AgentState, runtime: Runtime) -> dict | None:
return self._fix_dangling_tool_calls(state)
@override
async def abefore_model(self, state: AgentState, runtime: Runtime) -> dict | None:
return self._fix_dangling_tool_calls(state)

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"""Middleware for memory mechanism."""
from typing import Any, override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langgraph.runtime import Runtime
from src.agents.memory.queue import get_memory_queue
from src.config.memory_config import get_memory_config
class MemoryMiddlewareState(AgentState):
"""Compatible with the `ThreadState` schema."""
pass
def _filter_messages_for_memory(messages: list[Any]) -> list[Any]:
"""Filter messages to keep only user inputs and final assistant responses.
This filters out:
- Tool messages (intermediate tool call results)
- AI messages with tool_calls (intermediate steps, not final responses)
Only keeps:
- Human messages (user input)
- AI messages without tool_calls (final assistant responses)
Args:
messages: List of all conversation messages.
Returns:
Filtered list containing only user inputs and final assistant responses.
"""
filtered = []
for msg in messages:
msg_type = getattr(msg, "type", None)
if msg_type == "human":
# Always keep user messages
filtered.append(msg)
elif msg_type == "ai":
# Only keep AI messages that are final responses (no tool_calls)
tool_calls = getattr(msg, "tool_calls", None)
if not tool_calls:
filtered.append(msg)
# Skip tool messages and AI messages with tool_calls
return filtered
class MemoryMiddleware(AgentMiddleware[MemoryMiddlewareState]):
"""Middleware that queues conversation for memory update after agent execution.
This middleware:
1. After each agent execution, queues the conversation for memory update
2. Only includes user inputs and final assistant responses (ignores tool calls)
3. The queue uses debouncing to batch multiple updates together
4. Memory is updated asynchronously via LLM summarization
"""
state_schema = MemoryMiddlewareState
@override
def after_agent(self, state: MemoryMiddlewareState, runtime: Runtime) -> dict | None:
"""Queue conversation for memory update after agent completes.
Args:
state: The current agent state.
runtime: The runtime context.
Returns:
None (no state changes needed from this middleware).
"""
config = get_memory_config()
if not config.enabled:
return None
# Get thread ID from runtime context
thread_id = runtime.context.get("thread_id")
if not thread_id:
print("MemoryMiddleware: No thread_id in context, skipping memory update")
return None
# Get messages from state
messages = state.get("messages", [])
if not messages:
print("MemoryMiddleware: No messages in state, skipping memory update")
return None
# Filter to only keep user inputs and final assistant responses
filtered_messages = _filter_messages_for_memory(messages)
# Only queue if there's meaningful conversation
# At minimum need one user message and one assistant response
user_messages = [m for m in filtered_messages if getattr(m, "type", None) == "human"]
assistant_messages = [m for m in filtered_messages if getattr(m, "type", None) == "ai"]
if not user_messages or not assistant_messages:
return None
# Queue the filtered conversation for memory update
queue = get_memory_queue()
queue.add(thread_id=thread_id, messages=filtered_messages)
return None

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"""Middleware to enforce maximum concurrent subagent tool calls per model response."""
import logging
from typing import override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langgraph.runtime import Runtime
from src.subagents.executor import MAX_CONCURRENT_SUBAGENTS
logger = logging.getLogger(__name__)
# Valid range for max_concurrent_subagents
MIN_SUBAGENT_LIMIT = 2
MAX_SUBAGENT_LIMIT = 4
def _clamp_subagent_limit(value: int) -> int:
"""Clamp subagent limit to valid range [2, 4]."""
return max(MIN_SUBAGENT_LIMIT, min(MAX_SUBAGENT_LIMIT, value))
class SubagentLimitMiddleware(AgentMiddleware[AgentState]):
"""Truncates excess 'task' tool calls from a single model response.
When an LLM generates more than max_concurrent parallel task tool calls
in one response, this middleware keeps only the first max_concurrent and
discards the rest. This is more reliable than prompt-based limits.
Args:
max_concurrent: Maximum number of concurrent subagent calls allowed.
Defaults to MAX_CONCURRENT_SUBAGENTS (3). Clamped to [2, 4].
"""
def __init__(self, max_concurrent: int = MAX_CONCURRENT_SUBAGENTS):
super().__init__()
self.max_concurrent = _clamp_subagent_limit(max_concurrent)
def _truncate_task_calls(self, state: AgentState) -> dict | None:
messages = state.get("messages", [])
if not messages:
return None
last_msg = messages[-1]
if getattr(last_msg, "type", None) != "ai":
return None
tool_calls = getattr(last_msg, "tool_calls", None)
if not tool_calls:
return None
# Count task tool calls
task_indices = [i for i, tc in enumerate(tool_calls) if tc.get("name") == "task"]
if len(task_indices) <= self.max_concurrent:
return None
# Build set of indices to drop (excess task calls beyond the limit)
indices_to_drop = set(task_indices[self.max_concurrent :])
truncated_tool_calls = [tc for i, tc in enumerate(tool_calls) if i not in indices_to_drop]
dropped_count = len(indices_to_drop)
logger.warning(f"Truncated {dropped_count} excess task tool call(s) from model response (limit: {self.max_concurrent})")
# Replace the AIMessage with truncated tool_calls (same id triggers replacement)
updated_msg = last_msg.model_copy(update={"tool_calls": truncated_tool_calls})
return {"messages": [updated_msg]}
@override
def after_model(self, state: AgentState, runtime: Runtime) -> dict | None:
return self._truncate_task_calls(state)
@override
async def aafter_model(self, state: AgentState, runtime: Runtime) -> dict | None:
return self._truncate_task_calls(state)

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import os
from pathlib import Path
from typing import NotRequired, override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langgraph.runtime import Runtime
from src.agents.thread_state import ThreadDataState
from src.sandbox.consts import THREAD_DATA_BASE_DIR
class ThreadDataMiddlewareState(AgentState):
"""Compatible with the `ThreadState` schema."""
thread_data: NotRequired[ThreadDataState | None]
class ThreadDataMiddleware(AgentMiddleware[ThreadDataMiddlewareState]):
"""Create thread data directories for each thread execution.
Creates the following directory structure:
- backend/.deer-flow/threads/{thread_id}/user-data/workspace
- backend/.deer-flow/threads/{thread_id}/user-data/uploads
- backend/.deer-flow/threads/{thread_id}/user-data/outputs
Lifecycle Management:
- With lazy_init=True (default): Only compute paths, directories created on-demand
- With lazy_init=False: Eagerly create directories in before_agent()
"""
state_schema = ThreadDataMiddlewareState
def __init__(self, base_dir: str | None = None, lazy_init: bool = True):
"""Initialize the middleware.
Args:
base_dir: Base directory for thread data. Defaults to the current working directory.
lazy_init: If True, defer directory creation until needed.
If False, create directories eagerly in before_agent().
Default is True for optimal performance.
"""
super().__init__()
self._base_dir = base_dir or os.getcwd()
self._lazy_init = lazy_init
def _get_thread_paths(self, thread_id: str) -> dict[str, str]:
"""Get the paths for a thread's data directories.
Args:
thread_id: The thread ID.
Returns:
Dictionary with workspace_path, uploads_path, and outputs_path.
"""
thread_dir = Path(self._base_dir) / THREAD_DATA_BASE_DIR / thread_id / "user-data"
return {
"workspace_path": str(thread_dir / "workspace"),
"uploads_path": str(thread_dir / "uploads"),
"outputs_path": str(thread_dir / "outputs"),
}
def _create_thread_directories(self, thread_id: str) -> dict[str, str]:
"""Create the thread data directories.
Args:
thread_id: The thread ID.
Returns:
Dictionary with the created directory paths.
"""
paths = self._get_thread_paths(thread_id)
for path in paths.values():
os.makedirs(path, exist_ok=True)
return paths
@override
def before_agent(self, state: ThreadDataMiddlewareState, runtime: Runtime) -> dict | None:
thread_id = runtime.context.get("thread_id")
if thread_id is None:
raise ValueError("Thread ID is required in the context")
if self._lazy_init:
# Lazy initialization: only compute paths, don't create directories
paths = self._get_thread_paths(thread_id)
else:
# Eager initialization: create directories immediately
paths = self._create_thread_directories(thread_id)
print(f"Created thread data directories for thread {thread_id}")
return {
"thread_data": {
**paths,
}
}

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"""Middleware for automatic thread title generation."""
from typing import NotRequired, override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langgraph.runtime import Runtime
from src.config.title_config import get_title_config
from src.models import create_chat_model
class TitleMiddlewareState(AgentState):
"""Compatible with the `ThreadState` schema."""
title: NotRequired[str | None]
class TitleMiddleware(AgentMiddleware[TitleMiddlewareState]):
"""Automatically generate a title for the thread after the first user message."""
state_schema = TitleMiddlewareState
def _should_generate_title(self, state: TitleMiddlewareState) -> bool:
"""Check if we should generate a title for this thread."""
config = get_title_config()
if not config.enabled:
return False
# Check if thread already has a title in state
if state.get("title"):
return False
# Check if this is the first turn (has at least one user message and one assistant response)
messages = state.get("messages", [])
if len(messages) < 2:
return False
# Count user and assistant messages
user_messages = [m for m in messages if m.type == "human"]
assistant_messages = [m for m in messages if m.type == "ai"]
# Generate title after first complete exchange
return len(user_messages) == 1 and len(assistant_messages) >= 1
def _generate_title(self, state: TitleMiddlewareState) -> str:
"""Generate a concise title based on the conversation."""
config = get_title_config()
messages = state.get("messages", [])
# Get first user message and first assistant response
user_msg_content = next((m.content for m in messages if m.type == "human"), "")
assistant_msg_content = next((m.content for m in messages if m.type == "ai"), "")
# Ensure content is string (LangChain messages can have list content)
user_msg = str(user_msg_content) if user_msg_content else ""
assistant_msg = str(assistant_msg_content) if assistant_msg_content else ""
# Use a lightweight model to generate title
model = create_chat_model(thinking_enabled=False)
prompt = config.prompt_template.format(
max_words=config.max_words,
user_msg=user_msg[:500],
assistant_msg=assistant_msg[:500],
)
try:
response = model.invoke(prompt)
# Ensure response content is string
title_content = str(response.content) if response.content else ""
title = title_content.strip().strip('"').strip("'")
# Limit to max characters
return title[: config.max_chars] if len(title) > config.max_chars else title
except Exception as e:
print(f"Failed to generate title: {e}")
# Fallback: use first part of user message (by character count)
fallback_chars = min(config.max_chars, 50) # Use max_chars or 50, whichever is smaller
if len(user_msg) > fallback_chars:
return user_msg[:fallback_chars].rstrip() + "..."
return user_msg if user_msg else "New Conversation"
@override
def after_agent(self, state: TitleMiddlewareState, runtime: Runtime) -> dict | None:
"""Generate and set thread title after the first agent response."""
if self._should_generate_title(state):
title = self._generate_title(state)
print(f"Generated thread title: {title}")
# Store title in state (will be persisted by checkpointer if configured)
return {"title": title}
return None

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"""Middleware to inject uploaded files information into agent context."""
import os
import re
from pathlib import Path
from typing import NotRequired, override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langchain_core.messages import HumanMessage
from langgraph.runtime import Runtime
from src.agents.middlewares.thread_data_middleware import THREAD_DATA_BASE_DIR
class UploadsMiddlewareState(AgentState):
"""State schema for uploads middleware."""
uploaded_files: NotRequired[list[dict] | None]
class UploadsMiddleware(AgentMiddleware[UploadsMiddlewareState]):
"""Middleware to inject uploaded files information into the agent context.
This middleware lists all files in the thread's uploads directory and
adds a system message with the file list before the agent processes the request.
"""
state_schema = UploadsMiddlewareState
def __init__(self, base_dir: str | None = None):
"""Initialize the middleware.
Args:
base_dir: Base directory for thread data. Defaults to the current working directory.
"""
super().__init__()
self._base_dir = base_dir or os.getcwd()
def _get_uploads_dir(self, thread_id: str) -> Path:
"""Get the uploads directory for a thread.
Args:
thread_id: The thread ID.
Returns:
Path to the uploads directory.
"""
return Path(self._base_dir) / THREAD_DATA_BASE_DIR / thread_id / "user-data" / "uploads"
def _list_newly_uploaded_files(self, thread_id: str, last_message_files: set[str]) -> list[dict]:
"""List only newly uploaded files that weren't in the last message.
Args:
thread_id: The thread ID.
last_message_files: Set of filenames that were already shown in previous messages.
Returns:
List of new file information dictionaries.
"""
uploads_dir = self._get_uploads_dir(thread_id)
if not uploads_dir.exists():
return []
files = []
for file_path in sorted(uploads_dir.iterdir()):
if file_path.is_file() and file_path.name not in last_message_files:
stat = file_path.stat()
files.append(
{
"filename": file_path.name,
"size": stat.st_size,
"path": f"/mnt/user-data/uploads/{file_path.name}",
"extension": file_path.suffix,
}
)
return files
def _create_files_message(self, files: list[dict]) -> str:
"""Create a formatted message listing uploaded files.
Args:
files: List of file information dictionaries.
Returns:
Formatted string listing the files.
"""
if not files:
return "<uploaded_files>\nNo files have been uploaded yet.\n</uploaded_files>"
lines = ["<uploaded_files>", "The following files have been uploaded and are available for use:", ""]
for file in files:
size_kb = file["size"] / 1024
if size_kb < 1024:
size_str = f"{size_kb:.1f} KB"
else:
size_str = f"{size_kb / 1024:.1f} MB"
lines.append(f"- {file['filename']} ({size_str})")
lines.append(f" Path: {file['path']}")
lines.append("")
lines.append("You can read these files using the `read_file` tool with the paths shown above.")
lines.append("</uploaded_files>")
return "\n".join(lines)
def _extract_files_from_message(self, content: str) -> set[str]:
"""Extract filenames from uploaded_files tag in message content.
Args:
content: Message content that may contain <uploaded_files> tag.
Returns:
Set of filenames mentioned in the tag.
"""
# Match <uploaded_files>...</uploaded_files> tag
match = re.search(r"<uploaded_files>([\s\S]*?)</uploaded_files>", content)
if not match:
return set()
files_content = match.group(1)
# Extract filenames from lines like "- filename.ext (size)"
# Need to capture everything before the opening parenthesis, including spaces
filenames = set()
for line in files_content.split("\n"):
# Match pattern: - filename with spaces.ext (size)
# Changed from [^\s(]+ to [^(]+ to allow spaces in filename
file_match = re.match(r"^-\s+(.+?)\s*\(", line.strip())
if file_match:
filenames.add(file_match.group(1).strip())
return filenames
@override
def before_agent(self, state: UploadsMiddlewareState, runtime: Runtime) -> dict | None:
"""Inject uploaded files information before agent execution.
Only injects files that weren't already shown in previous messages.
Prepends file info to the last human message content.
Args:
state: Current agent state.
runtime: Runtime context containing thread_id.
Returns:
State updates including uploaded files list.
"""
import logging
logger = logging.getLogger(__name__)
thread_id = runtime.context.get("thread_id")
if thread_id is None:
return None
messages = list(state.get("messages", []))
if not messages:
return None
# Track all filenames that have been shown in previous messages (EXCEPT the last one)
shown_files: set[str] = set()
for msg in messages[:-1]: # Scan all messages except the last one
if isinstance(msg, HumanMessage):
content = msg.content if isinstance(msg.content, str) else ""
extracted = self._extract_files_from_message(content)
shown_files.update(extracted)
if extracted:
logger.info(f"Found previously shown files: {extracted}")
logger.info(f"Total shown files from history: {shown_files}")
# List only newly uploaded files
files = self._list_newly_uploaded_files(thread_id, shown_files)
logger.info(f"Newly uploaded files to inject: {[f['filename'] for f in files]}")
if not files:
return None
# Find the last human message and prepend file info to it
last_message_index = len(messages) - 1
last_message = messages[last_message_index]
if not isinstance(last_message, HumanMessage):
return None
# Create files message and prepend to the last human message content
files_message = self._create_files_message(files)
# Extract original content - handle both string and list formats
original_content = ""
if isinstance(last_message.content, str):
original_content = last_message.content
elif isinstance(last_message.content, list):
# Content is a list of content blocks (e.g., [{"type": "text", "text": "..."}])
text_parts = []
for block in last_message.content:
if isinstance(block, dict) and block.get("type") == "text":
text_parts.append(block.get("text", ""))
original_content = "\n".join(text_parts)
logger.info(f"Original message content: {original_content[:100] if original_content else '(empty)'}")
# Create new message with combined content
updated_message = HumanMessage(
content=f"{files_message}\n\n{original_content}",
id=last_message.id,
additional_kwargs=last_message.additional_kwargs,
)
# Replace the last message
messages[last_message_index] = updated_message
return {
"uploaded_files": files,
"messages": messages,
}

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"""Middleware for injecting image details into conversation before LLM call."""
from typing import NotRequired, override
from langchain.agents import AgentState
from langchain.agents.middleware import AgentMiddleware
from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
from langgraph.runtime import Runtime
from src.agents.thread_state import ViewedImageData
class ViewImageMiddlewareState(AgentState):
"""Compatible with the `ThreadState` schema."""
viewed_images: NotRequired[dict[str, ViewedImageData] | None]
class ViewImageMiddleware(AgentMiddleware[ViewImageMiddlewareState]):
"""Injects image details as a human message before LLM calls when view_image tools have completed.
This middleware:
1. Runs before each LLM call
2. Checks if the last assistant message contains view_image tool calls
3. Verifies all tool calls in that message have been completed (have corresponding ToolMessages)
4. If conditions are met, creates a human message with all viewed image details (including base64 data)
5. Adds the message to state so the LLM can see and analyze the images
This enables the LLM to automatically receive and analyze images that were loaded via view_image tool,
without requiring explicit user prompts to describe the images.
"""
state_schema = ViewImageMiddlewareState
def _get_last_assistant_message(self, messages: list) -> AIMessage | None:
"""Get the last assistant message from the message list.
Args:
messages: List of messages
Returns:
Last AIMessage or None if not found
"""
for msg in reversed(messages):
if isinstance(msg, AIMessage):
return msg
return None
def _has_view_image_tool(self, message: AIMessage) -> bool:
"""Check if the assistant message contains view_image tool calls.
Args:
message: Assistant message to check
Returns:
True if message contains view_image tool calls
"""
if not hasattr(message, "tool_calls") or not message.tool_calls:
return False
return any(tool_call.get("name") == "view_image" for tool_call in message.tool_calls)
def _all_tools_completed(self, messages: list, assistant_msg: AIMessage) -> bool:
"""Check if all tool calls in the assistant message have been completed.
Args:
messages: List of all messages
assistant_msg: The assistant message containing tool calls
Returns:
True if all tool calls have corresponding ToolMessages
"""
if not hasattr(assistant_msg, "tool_calls") or not assistant_msg.tool_calls:
return False
# Get all tool call IDs from the assistant message
tool_call_ids = {tool_call.get("id") for tool_call in assistant_msg.tool_calls if tool_call.get("id")}
# Find the index of the assistant message
try:
assistant_idx = messages.index(assistant_msg)
except ValueError:
return False
# Get all ToolMessages after the assistant message
completed_tool_ids = set()
for msg in messages[assistant_idx + 1 :]:
if isinstance(msg, ToolMessage) and msg.tool_call_id:
completed_tool_ids.add(msg.tool_call_id)
# Check if all tool calls have been completed
return tool_call_ids.issubset(completed_tool_ids)
def _create_image_details_message(self, state: ViewImageMiddlewareState) -> list[str | dict]:
"""Create a formatted message with all viewed image details.
Args:
state: Current state containing viewed_images
Returns:
List of content blocks (text and images) for the HumanMessage
"""
viewed_images = state.get("viewed_images", {})
if not viewed_images:
return ["No images have been viewed."]
# Build the message with image information
content_blocks: list[str | dict] = [{"type": "text", "text": "Here are the images you've viewed:"}]
for image_path, image_data in viewed_images.items():
mime_type = image_data.get("mime_type", "unknown")
base64_data = image_data.get("base64", "")
# Add text description
content_blocks.append({"type": "text", "text": f"\n- **{image_path}** ({mime_type})"})
# Add the actual image data so LLM can "see" it
if base64_data:
content_blocks.append(
{
"type": "image_url",
"image_url": {"url": f"data:{mime_type};base64,{base64_data}"},
}
)
return content_blocks
def _should_inject_image_message(self, state: ViewImageMiddlewareState) -> bool:
"""Determine if we should inject an image details message.
Args:
state: Current state
Returns:
True if we should inject the message
"""
messages = state.get("messages", [])
if not messages:
return False
# Get the last assistant message
last_assistant_msg = self._get_last_assistant_message(messages)
if not last_assistant_msg:
return False
# Check if it has view_image tool calls
if not self._has_view_image_tool(last_assistant_msg):
return False
# Check if all tools have been completed
if not self._all_tools_completed(messages, last_assistant_msg):
return False
# Check if we've already added an image details message
# Look for a human message after the last assistant message that contains image details
assistant_idx = messages.index(last_assistant_msg)
for msg in messages[assistant_idx + 1 :]:
if isinstance(msg, HumanMessage):
content_str = str(msg.content)
if "Here are the images you've viewed" in content_str or "Here are the details of the images you've viewed" in content_str:
# Already added, don't add again
return False
return True
def _inject_image_message(self, state: ViewImageMiddlewareState) -> dict | None:
"""Internal helper to inject image details message.
Args:
state: Current state
Returns:
State update with additional human message, or None if no update needed
"""
if not self._should_inject_image_message(state):
return None
# Create the image details message with text and image content
image_content = self._create_image_details_message(state)
# Create a new human message with mixed content (text + images)
human_msg = HumanMessage(content=image_content)
print("[ViewImageMiddleware] Injecting image details message with images before LLM call")
# Return state update with the new message
return {"messages": [human_msg]}
@override
def before_model(self, state: ViewImageMiddlewareState, runtime: Runtime) -> dict | None:
"""Inject image details message before LLM call if view_image tools have completed (sync version).
This runs before each LLM call, checking if the previous turn included view_image
tool calls that have all completed. If so, it injects a human message with the image
details so the LLM can see and analyze the images.
Args:
state: Current state
runtime: Runtime context (unused but required by interface)
Returns:
State update with additional human message, or None if no update needed
"""
return self._inject_image_message(state)
@override
async def abefore_model(self, state: ViewImageMiddlewareState, runtime: Runtime) -> dict | None:
"""Inject image details message before LLM call if view_image tools have completed (async version).
This runs before each LLM call, checking if the previous turn included view_image
tool calls that have all completed. If so, it injects a human message with the image
details so the LLM can see and analyze the images.
Args:
state: Current state
runtime: Runtime context (unused but required by interface)
Returns:
State update with additional human message, or None if no update needed
"""
return self._inject_image_message(state)

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from typing import Annotated, NotRequired, TypedDict
from langchain.agents import AgentState
class SandboxState(TypedDict):
sandbox_id: NotRequired[str | None]
class ThreadDataState(TypedDict):
workspace_path: NotRequired[str | None]
uploads_path: NotRequired[str | None]
outputs_path: NotRequired[str | None]
class ViewedImageData(TypedDict):
base64: str
mime_type: str
def merge_artifacts(existing: list[str] | None, new: list[str] | None) -> list[str]:
"""Reducer for artifacts list - merges and deduplicates artifacts."""
if existing is None:
return new or []
if new is None:
return existing
# Use dict.fromkeys to deduplicate while preserving order
return list(dict.fromkeys(existing + new))
def merge_viewed_images(existing: dict[str, ViewedImageData] | None, new: dict[str, ViewedImageData] | None) -> dict[str, ViewedImageData]:
"""Reducer for viewed_images dict - merges image dictionaries.
Special case: If new is an empty dict {}, it clears the existing images.
This allows middlewares to clear the viewed_images state after processing.
"""
if existing is None:
return new or {}
if new is None:
return existing
# Special case: empty dict means clear all viewed images
if len(new) == 0:
return {}
# Merge dictionaries, new values override existing ones for same keys
return {**existing, **new}
class ThreadState(AgentState):
sandbox: NotRequired[SandboxState | None]
thread_data: NotRequired[ThreadDataState | None]
title: NotRequired[str | None]
artifacts: Annotated[list[str], merge_artifacts]
todos: NotRequired[list | None]
uploaded_files: NotRequired[list[dict] | None]
viewed_images: Annotated[dict[str, ViewedImageData], merge_viewed_images] # image_path -> {base64, mime_type}

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backend/src/community/aio_sandbox/__init__.py Normal file
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from .aio_sandbox import AioSandbox
from .aio_sandbox_provider import AioSandboxProvider
from .backend import SandboxBackend
from .file_state_store import FileSandboxStateStore
from .local_backend import LocalContainerBackend
from .remote_backend import RemoteSandboxBackend
from .sandbox_info import SandboxInfo
from .state_store import SandboxStateStore
__all__ = [
"AioSandbox",
"AioSandboxProvider",
"FileSandboxStateStore",
"LocalContainerBackend",
"RemoteSandboxBackend",
"SandboxBackend",
"SandboxInfo",
"SandboxStateStore",
]

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import base64
import logging
from agent_sandbox import Sandbox as AioSandboxClient
from src.sandbox.sandbox import Sandbox
logger = logging.getLogger(__name__)
class AioSandbox(Sandbox):
"""Sandbox implementation using the agent-infra/sandbox Docker container.
This sandbox connects to a running AIO sandbox container via HTTP API.
"""
def __init__(self, id: str, base_url: str, home_dir: str | None = None):
"""Initialize the AIO sandbox.
Args:
id: Unique identifier for this sandbox instance.
base_url: URL of the sandbox API (e.g., http://localhost:8080).
home_dir: Home directory inside the sandbox. If None, will be fetched from the sandbox.
"""
super().__init__(id)
self._base_url = base_url
self._client = AioSandboxClient(base_url=base_url, timeout=600)
self._home_dir = home_dir
@property
def base_url(self) -> str:
return self._base_url
@property
def home_dir(self) -> str:
"""Get the home directory inside the sandbox."""
if self._home_dir is None:
context = self._client.sandbox.get_context()
self._home_dir = context.home_dir
return self._home_dir
def execute_command(self, command: str) -> str:
"""Execute a shell command in the sandbox.
Args:
command: The command to execute.
Returns:
The output of the command.
"""
try:
result = self._client.shell.exec_command(command=command)
output = result.data.output if result.data else ""
return output if output else "(no output)"
except Exception as e:
logger.error(f"Failed to execute command in sandbox: {e}")
return f"Error: {e}"
def read_file(self, path: str) -> str:
"""Read the content of a file in the sandbox.
Args:
path: The absolute path of the file to read.
Returns:
The content of the file.
"""
try:
result = self._client.file.read_file(file=path)
return result.data.content if result.data else ""
except Exception as e:
logger.error(f"Failed to read file in sandbox: {e}")
return f"Error: {e}"
def list_dir(self, path: str, max_depth: int = 2) -> list[str]:
"""List the contents of a directory in the sandbox.
Args:
path: The absolute path of the directory to list.
max_depth: The maximum depth to traverse. Default is 2.
Returns:
The contents of the directory.
"""
try:
# Use shell command to list directory with depth limit
# The -L flag limits the depth for the tree command
result = self._client.shell.exec_command(command=f"find {path} -maxdepth {max_depth} -type f -o -type d 2>/dev/null | head -500")
output = result.data.output if result.data else ""
if output:
return [line.strip() for line in output.strip().split("\n") if line.strip()]
return []
except Exception as e:
logger.error(f"Failed to list directory in sandbox: {e}")
return []
def write_file(self, path: str, content: str, append: bool = False) -> None:
"""Write content to a file in the sandbox.
Args:
path: The absolute path of the file to write to.
content: The text content to write to the file.
append: Whether to append the content to the file.
"""
try:
if append:
# Read existing content first and append
existing = self.read_file(path)
if not existing.startswith("Error:"):
content = existing + content
self._client.file.write_file(file=path, content=content)
except Exception as e:
logger.error(f"Failed to write file in sandbox: {e}")
raise
def update_file(self, path: str, content: bytes) -> None:
"""Update a file with binary content in the sandbox.
Args:
path: The absolute path of the file to update.
content: The binary content to write to the file.
"""
try:
base64_content = base64.b64encode(content).decode("utf-8")
self._client.file.write_file(file=path, content=base64_content, encoding="base64")
except Exception as e:
logger.error(f"Failed to update file in sandbox: {e}")
raise

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"""AIO Sandbox Provider — orchestrates sandbox lifecycle with pluggable backends.
This provider composes two abstractions:
- SandboxBackend: how sandboxes are provisioned (local container vs remote/K8s)
- SandboxStateStore: how thread→sandbox mappings are persisted (file vs Redis)
The provider itself handles:
- In-process caching for fast repeated access
- Thread-safe locking (in-process + cross-process via state store)
- Idle timeout management
- Graceful shutdown with signal handling
- Mount computation (thread-specific, skills)
"""
import atexit
import hashlib
import logging
import os
import signal
import threading
import time
import uuid
from pathlib import Path
from src.config import get_app_config
from src.sandbox.consts import THREAD_DATA_BASE_DIR, VIRTUAL_PATH_PREFIX
from src.sandbox.sandbox import Sandbox
from src.sandbox.sandbox_provider import SandboxProvider
from .aio_sandbox import AioSandbox
from .backend import SandboxBackend, wait_for_sandbox_ready
from .file_state_store import FileSandboxStateStore
from .local_backend import LocalContainerBackend
from .remote_backend import RemoteSandboxBackend
from .sandbox_info import SandboxInfo
from .state_store import SandboxStateStore
logger = logging.getLogger(__name__)
# Default configuration
DEFAULT_IMAGE = "enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest"
DEFAULT_PORT = 8080
DEFAULT_CONTAINER_PREFIX = "deer-flow-sandbox"
DEFAULT_IDLE_TIMEOUT = 600 # 10 minutes in seconds
IDLE_CHECK_INTERVAL = 60 # Check every 60 seconds
class AioSandboxProvider(SandboxProvider):
"""Sandbox provider that manages containers running the AIO sandbox.
Architecture:
This provider composes a SandboxBackend (how to provision) and a
SandboxStateStore (how to persist state), enabling:
- Local Docker/Apple Container mode (auto-start containers)
- Remote/K8s mode (connect to pre-existing sandbox URL)
- Cross-process consistency via file-based or Redis state stores
Configuration options in config.yaml under sandbox:
use: src.community.aio_sandbox:AioSandboxProvider
image: <container image>
port: 8080 # Base port for local containers
base_url: http://... # If set, uses remote backend (K8s/external)
auto_start: true # Whether to auto-start local containers
container_prefix: deer-flow-sandbox
idle_timeout: 600 # Idle timeout in seconds (0 to disable)
mounts: # Volume mounts for local containers
- host_path: /path/on/host
container_path: /path/in/container
read_only: false
environment: # Environment variables for containers
NODE_ENV: production
API_KEY: $MY_API_KEY
"""
def __init__(self):
self._lock = threading.Lock()
self._sandboxes: dict[str, AioSandbox] = {} # sandbox_id -> AioSandbox instance
self._sandbox_infos: dict[str, SandboxInfo] = {} # sandbox_id -> SandboxInfo (for destroy)
self._thread_sandboxes: dict[str, str] = {} # thread_id -> sandbox_id
self._thread_locks: dict[str, threading.Lock] = {} # thread_id -> in-process lock
self._last_activity: dict[str, float] = {} # sandbox_id -> last activity timestamp
self._shutdown_called = False
self._idle_checker_stop = threading.Event()
self._idle_checker_thread: threading.Thread | None = None
self._config = self._load_config()
self._backend: SandboxBackend = self._create_backend()
self._state_store: SandboxStateStore = self._create_state_store()
# Register shutdown handler
atexit.register(self.shutdown)
self._register_signal_handlers()
# Start idle checker if enabled
if self._config.get("idle_timeout", DEFAULT_IDLE_TIMEOUT) > 0:
self._start_idle_checker()
# ── Factory methods ──────────────────────────────────────────────────
def _create_backend(self) -> SandboxBackend:
"""Create the appropriate backend based on configuration.
Selection logic (checked in order):
1. ``provisioner_url`` set → RemoteSandboxBackend (provisioner mode)
Provisioner dynamically creates Pods + Services in k3s.
2. ``auto_start`` → LocalContainerBackend (Docker / Apple Container)
"""
provisioner_url = self._config.get("provisioner_url")
if provisioner_url:
logger.info(f"Using remote sandbox backend with provisioner at {provisioner_url}")
return RemoteSandboxBackend(provisioner_url=provisioner_url)
if not self._config.get("auto_start", True):
raise RuntimeError("auto_start is disabled and no base_url is configured")
logger.info("Using local container sandbox backend")
return LocalContainerBackend(
image=self._config["image"],
base_port=self._config["port"],
container_prefix=self._config["container_prefix"],
config_mounts=self._config["mounts"],
environment=self._config["environment"],
)
def _create_state_store(self) -> SandboxStateStore:
"""Create the state store for cross-process sandbox mapping persistence.
Currently uses file-based store. For distributed multi-host deployments,
a Redis-based store can be plugged in here.
"""
# TODO: Support RedisSandboxStateStore for distributed deployments.
# Configuration would be:
# sandbox:
# state_store: redis
# redis_url: redis://localhost:6379/0
# This would enable cross-host sandbox discovery (e.g., multiple K8s pods
# without shared PVC, or multi-node Docker Swarm).
return FileSandboxStateStore(base_dir=os.getcwd())
# ── Configuration ────────────────────────────────────────────────────
def _load_config(self) -> dict:
"""Load sandbox configuration from app config."""
config = get_app_config()
sandbox_config = config.sandbox
return {
"image": sandbox_config.image or DEFAULT_IMAGE,
"port": sandbox_config.port or DEFAULT_PORT,
"base_url": sandbox_config.base_url,
"auto_start": sandbox_config.auto_start if sandbox_config.auto_start is not None else True,
"container_prefix": sandbox_config.container_prefix or DEFAULT_CONTAINER_PREFIX,
"idle_timeout": getattr(sandbox_config, "idle_timeout", None) or DEFAULT_IDLE_TIMEOUT,
"mounts": sandbox_config.mounts or [],
"environment": self._resolve_env_vars(sandbox_config.environment or {}),
# provisioner URL for dynamic pod management (e.g. http://provisioner:8002)
"provisioner_url": getattr(sandbox_config, "provisioner_url", None) or "",
}
@staticmethod
def _resolve_env_vars(env_config: dict[str, str]) -> dict[str, str]:
"""Resolve environment variable references (values starting with $)."""
resolved = {}
for key, value in env_config.items():
if isinstance(value, str) and value.startswith("$"):
env_name = value[1:]
resolved[key] = os.environ.get(env_name, "")
else:
resolved[key] = str(value)
return resolved
# ── Deterministic ID ─────────────────────────────────────────────────
@staticmethod
def _deterministic_sandbox_id(thread_id: str) -> str:
"""Generate a deterministic sandbox ID from a thread ID.
Ensures all processes derive the same sandbox_id for a given thread,
enabling cross-process sandbox discovery without shared memory.
"""
return hashlib.sha256(thread_id.encode()).hexdigest()[:8]
# ── Mount helpers ────────────────────────────────────────────────────
def _get_extra_mounts(self, thread_id: str | None) -> list[tuple[str, str, bool]]:
"""Collect all extra mounts for a sandbox (thread-specific + skills)."""
mounts: list[tuple[str, str, bool]] = []
if thread_id:
mounts.extend(self._get_thread_mounts(thread_id))
logger.info(f"Adding thread mounts for thread {thread_id}: {mounts}")
skills_mount = self._get_skills_mount()
if skills_mount:
mounts.append(skills_mount)
logger.info(f"Adding skills mount: {skills_mount}")
return mounts
@staticmethod
def _get_thread_mounts(thread_id: str) -> list[tuple[str, str, bool]]:
"""Get volume mounts for a thread's data directories.
Creates directories if they don't exist (lazy initialization).
"""
base_dir = os.getcwd()
thread_dir = Path(base_dir) / THREAD_DATA_BASE_DIR / thread_id / "user-data"
mounts = [
(str(thread_dir / "workspace"), f"{VIRTUAL_PATH_PREFIX}/workspace", False),
(str(thread_dir / "uploads"), f"{VIRTUAL_PATH_PREFIX}/uploads", False),
(str(thread_dir / "outputs"), f"{VIRTUAL_PATH_PREFIX}/outputs", False),
]
for host_path, _, _ in mounts:
os.makedirs(host_path, exist_ok=True)
return mounts
@staticmethod
def _get_skills_mount() -> tuple[str, str, bool] | None:
"""Get the skills directory mount configuration."""
try:
config = get_app_config()
skills_path = config.skills.get_skills_path()
container_path = config.skills.container_path
if skills_path.exists():
return (str(skills_path), container_path, True) # Read-only for security
except Exception as e:
logger.warning(f"Could not setup skills mount: {e}")
return None
# ── Idle timeout management ──────────────────────────────────────────
def _start_idle_checker(self) -> None:
"""Start the background thread that checks for idle sandboxes."""
self._idle_checker_thread = threading.Thread(
target=self._idle_checker_loop,
name="sandbox-idle-checker",
daemon=True,
)
self._idle_checker_thread.start()
logger.info(f"Started idle checker thread (timeout: {self._config.get('idle_timeout', DEFAULT_IDLE_TIMEOUT)}s)")
def _idle_checker_loop(self) -> None:
idle_timeout = self._config.get("idle_timeout", DEFAULT_IDLE_TIMEOUT)
while not self._idle_checker_stop.wait(timeout=IDLE_CHECK_INTERVAL):
try:
self._cleanup_idle_sandboxes(idle_timeout)
except Exception as e:
logger.error(f"Error in idle checker loop: {e}")
def _cleanup_idle_sandboxes(self, idle_timeout: float) -> None:
current_time = time.time()
sandboxes_to_release = []
with self._lock:
for sandbox_id, last_activity in self._last_activity.items():
idle_duration = current_time - last_activity
if idle_duration > idle_timeout:
sandboxes_to_release.append(sandbox_id)
logger.info(f"Sandbox {sandbox_id} idle for {idle_duration:.1f}s, marking for release")
for sandbox_id in sandboxes_to_release:
try:
logger.info(f"Releasing idle sandbox {sandbox_id}")
self.release(sandbox_id)
except Exception as e:
logger.error(f"Failed to release idle sandbox {sandbox_id}: {e}")
# ── Signal handling ──────────────────────────────────────────────────
def _register_signal_handlers(self) -> None:
"""Register signal handlers for graceful shutdown."""
self._original_sigterm = signal.getsignal(signal.SIGTERM)
self._original_sigint = signal.getsignal(signal.SIGINT)
def signal_handler(signum, frame):
self.shutdown()
original = self._original_sigterm if signum == signal.SIGTERM else self._original_sigint
if callable(original):
original(signum, frame)
elif original == signal.SIG_DFL:
signal.signal(signum, signal.SIG_DFL)
signal.raise_signal(signum)
try:
signal.signal(signal.SIGTERM, signal_handler)
signal.signal(signal.SIGINT, signal_handler)
except ValueError:
logger.debug("Could not register signal handlers (not main thread)")
# ── Thread locking (in-process) ──────────────────────────────────────
def _get_thread_lock(self, thread_id: str) -> threading.Lock:
"""Get or create an in-process lock for a specific thread_id."""
with self._lock:
if thread_id not in self._thread_locks:
self._thread_locks[thread_id] = threading.Lock()
return self._thread_locks[thread_id]
# ── Core: acquire / get / release / shutdown ─────────────────────────
def acquire(self, thread_id: str | None = None) -> str:
"""Acquire a sandbox environment and return its ID.
For the same thread_id, this method will return the same sandbox_id
across multiple turns, multiple processes, and (with shared storage)
multiple pods.
Thread-safe with both in-process and cross-process locking.
Args:
thread_id: Optional thread ID for thread-specific configurations.
Returns:
The ID of the acquired sandbox environment.
"""
if thread_id:
thread_lock = self._get_thread_lock(thread_id)
with thread_lock:
return self._acquire_internal(thread_id)
else:
return self._acquire_internal(thread_id)
def _acquire_internal(self, thread_id: str | None) -> str:
"""Internal sandbox acquisition with three-layer consistency.
Layer 1: In-process cache (fastest, covers same-process repeated access)
Layer 2: Cross-process state store + file lock (covers multi-process)
Layer 3: Backend discovery (covers containers started by other processes)
"""
# ── Layer 1: In-process cache (fast path) ──
if thread_id:
with self._lock:
if thread_id in self._thread_sandboxes:
existing_id = self._thread_sandboxes[thread_id]
if existing_id in self._sandboxes:
logger.info(f"Reusing in-process sandbox {existing_id} for thread {thread_id}")
self._last_activity[existing_id] = time.time()
return existing_id
else:
del self._thread_sandboxes[thread_id]
# Deterministic ID for thread-specific, random for anonymous
sandbox_id = self._deterministic_sandbox_id(thread_id) if thread_id else str(uuid.uuid4())[:8]
# ── Layer 2 & 3: Cross-process recovery + creation ──
if thread_id:
with self._state_store.lock(thread_id):
# Try to recover from persisted state or discover existing container
recovered_id = self._try_recover(thread_id)
if recovered_id is not None:
return recovered_id
# Nothing to recover — create new sandbox (still under cross-process lock)
return self._create_sandbox(thread_id, sandbox_id)
else:
return self._create_sandbox(thread_id, sandbox_id)
def _try_recover(self, thread_id: str) -> str | None:
"""Try to recover a sandbox from persisted state or backend discovery.
Called under cross-process lock for the given thread_id.
Args:
thread_id: The thread ID.
Returns:
The sandbox_id if recovery succeeded, None otherwise.
"""
info = self._state_store.load(thread_id)
if info is None:
return None
# Re-discover: verifies sandbox is alive and gets current connection info
# (handles cases like port changes after container restart)
discovered = self._backend.discover(info.sandbox_id)
if discovered is None:
logger.info(f"Persisted sandbox {info.sandbox_id} for thread {thread_id} could not be recovered")
self._state_store.remove(thread_id)
return None
# Adopt into this process's memory
sandbox = AioSandbox(id=discovered.sandbox_id, base_url=discovered.sandbox_url)
with self._lock:
self._sandboxes[discovered.sandbox_id] = sandbox
self._sandbox_infos[discovered.sandbox_id] = discovered
self._last_activity[discovered.sandbox_id] = time.time()
self._thread_sandboxes[thread_id] = discovered.sandbox_id
# Update state if connection info changed
if discovered.sandbox_url != info.sandbox_url:
self._state_store.save(thread_id, discovered)
logger.info(f"Recovered sandbox {discovered.sandbox_id} for thread {thread_id} at {discovered.sandbox_url}")
return discovered.sandbox_id
def _create_sandbox(self, thread_id: str | None, sandbox_id: str) -> str:
"""Create a new sandbox via the backend.
Args:
thread_id: Optional thread ID.
sandbox_id: The sandbox ID to use.
Returns:
The sandbox_id.
Raises:
RuntimeError: If sandbox creation or readiness check fails.
"""
extra_mounts = self._get_extra_mounts(thread_id)
info = self._backend.create(thread_id, sandbox_id, extra_mounts=extra_mounts or None)
# Wait for sandbox to be ready
if not wait_for_sandbox_ready(info.sandbox_url, timeout=60):
self._backend.destroy(info)
raise RuntimeError(f"Sandbox {sandbox_id} failed to become ready within timeout at {info.sandbox_url}")
sandbox = AioSandbox(id=sandbox_id, base_url=info.sandbox_url)
with self._lock:
self._sandboxes[sandbox_id] = sandbox
self._sandbox_infos[sandbox_id] = info
self._last_activity[sandbox_id] = time.time()
if thread_id:
self._thread_sandboxes[thread_id] = sandbox_id
# Persist for cross-process discovery
if thread_id:
self._state_store.save(thread_id, info)
logger.info(f"Created sandbox {sandbox_id} for thread {thread_id} at {info.sandbox_url}")
return sandbox_id
def get(self, sandbox_id: str) -> Sandbox | None:
"""Get a sandbox by ID. Updates last activity timestamp.
Args:
sandbox_id: The ID of the sandbox.
Returns:
The sandbox instance if found, None otherwise.
"""
with self._lock:
sandbox = self._sandboxes.get(sandbox_id)
if sandbox is not None:
self._last_activity[sandbox_id] = time.time()
return sandbox
def release(self, sandbox_id: str) -> None:
"""Release a sandbox: clean up in-memory state, persisted state, and backend resources.
Args:
sandbox_id: The ID of the sandbox to release.
"""
info = None
thread_ids_to_remove: list[str] = []
with self._lock:
self._sandboxes.pop(sandbox_id, None)
info = self._sandbox_infos.pop(sandbox_id, None)
thread_ids_to_remove = [tid for tid, sid in self._thread_sandboxes.items() if sid == sandbox_id]
for tid in thread_ids_to_remove:
del self._thread_sandboxes[tid]
self._last_activity.pop(sandbox_id, None)
# Clean up persisted state (outside lock, involves file I/O)
for tid in thread_ids_to_remove:
self._state_store.remove(tid)
# Destroy backend resources (stop container, release port, etc.)
if info:
self._backend.destroy(info)
logger.info(f"Released sandbox {sandbox_id}")
def shutdown(self) -> None:
"""Shutdown all sandboxes. Thread-safe and idempotent."""
with self._lock:
if self._shutdown_called:
return
self._shutdown_called = True
sandbox_ids = list(self._sandboxes.keys())
# Stop idle checker
self._idle_checker_stop.set()
if self._idle_checker_thread is not None and self._idle_checker_thread.is_alive():
self._idle_checker_thread.join(timeout=5)
logger.info("Stopped idle checker thread")
logger.info(f"Shutting down {len(sandbox_ids)} sandbox(es)")
for sandbox_id in sandbox_ids:
try:
self.release(sandbox_id)
except Exception as e:
logger.error(f"Failed to release sandbox {sandbox_id} during shutdown: {e}")

98
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"""Abstract base class for sandbox provisioning backends."""
from __future__ import annotations
import logging
import time
from abc import ABC, abstractmethod
import requests
from .sandbox_info import SandboxInfo
logger = logging.getLogger(__name__)
def wait_for_sandbox_ready(sandbox_url: str, timeout: int = 30) -> bool:
"""Poll sandbox health endpoint until ready or timeout.
Args:
sandbox_url: URL of the sandbox (e.g. http://k3s:30001).
timeout: Maximum time to wait in seconds.
Returns:
True if sandbox is ready, False otherwise.
"""
start_time = time.time()
while time.time() - start_time < timeout:
try:
response = requests.get(f"{sandbox_url}/v1/sandbox", timeout=5)
if response.status_code == 200:
return True
except requests.exceptions.RequestException:
pass
time.sleep(1)
return False
class SandboxBackend(ABC):
"""Abstract base for sandbox provisioning backends.
Two implementations:
- LocalContainerBackend: starts Docker/Apple Container locally, manages ports
- RemoteSandboxBackend: connects to a pre-existing URL (K8s service, external)
"""
@abstractmethod
def create(self, thread_id: str, sandbox_id: str, extra_mounts: list[tuple[str, str, bool]] | None = None) -> SandboxInfo:
"""Create/provision a new sandbox.
Args:
thread_id: Thread ID for which the sandbox is being created. Useful for backends that want to organize sandboxes by thread.
sandbox_id: Deterministic sandbox identifier.
extra_mounts: Additional volume mounts as (host_path, container_path, read_only) tuples.
Ignored by backends that don't manage containers (e.g., remote).
Returns:
SandboxInfo with connection details.
"""
...
@abstractmethod
def destroy(self, info: SandboxInfo) -> None:
"""Destroy/cleanup a sandbox and release its resources.
Args:
info: The sandbox metadata to destroy.
"""
...
@abstractmethod
def is_alive(self, info: SandboxInfo) -> bool:
"""Quick check whether a sandbox is still alive.
This should be a lightweight check (e.g., container inspect)
rather than a full health check.
Args:
info: The sandbox metadata to check.
Returns:
True if the sandbox appears to be alive.
"""
...
@abstractmethod
def discover(self, sandbox_id: str) -> SandboxInfo | None:
"""Try to discover an existing sandbox by its deterministic ID.
Used for cross-process recovery: when another process started a sandbox,
this process can discover it by the deterministic container name or URL.
Args:
sandbox_id: The deterministic sandbox ID to look for.
Returns:
SandboxInfo if found and healthy, None otherwise.
"""
...

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"""File-based sandbox state store.
Uses JSON files for persistence and fcntl file locking for cross-process
mutual exclusion. Works across processes on the same machine or across
K8s pods with a shared PVC mount.
"""
from __future__ import annotations
import fcntl
import json
import logging
import os
from collections.abc import Generator
from contextlib import contextmanager
from pathlib import Path
from .sandbox_info import SandboxInfo
from .state_store import SandboxStateStore
logger = logging.getLogger(__name__)
SANDBOX_STATE_FILE = "sandbox.json"
SANDBOX_LOCK_FILE = "sandbox.lock"
class FileSandboxStateStore(SandboxStateStore):
"""File-based state store using JSON files and fcntl file locking.
State is stored at: {base_dir}/{threads_subdir}/{thread_id}/sandbox.json
Lock files at: {base_dir}/{threads_subdir}/{thread_id}/sandbox.lock
This works across processes on the same machine sharing a filesystem.
For K8s multi-pod scenarios, requires a shared PVC mount at base_dir.
"""
def __init__(self, base_dir: str, threads_subdir: str = ".deer-flow/threads"):
"""Initialize the file-based state store.
Args:
base_dir: Root directory for state files (typically the project root / cwd).
threads_subdir: Subdirectory path for thread state (default: ".deer-flow/threads").
"""
self._base_dir = Path(base_dir)
self._threads_subdir = threads_subdir
def _thread_dir(self, thread_id: str) -> Path:
"""Get the directory for a thread's state files."""
return self._base_dir / self._threads_subdir / thread_id
def save(self, thread_id: str, info: SandboxInfo) -> None:
thread_dir = self._thread_dir(thread_id)
os.makedirs(thread_dir, exist_ok=True)
state_file = thread_dir / SANDBOX_STATE_FILE
try:
state_file.write_text(json.dumps(info.to_dict()))
logger.info(f"Saved sandbox state for thread {thread_id}: {info.sandbox_id}")
except OSError as e:
logger.warning(f"Failed to save sandbox state for thread {thread_id}: {e}")
def load(self, thread_id: str) -> SandboxInfo | None:
state_file = self._thread_dir(thread_id) / SANDBOX_STATE_FILE
if not state_file.exists():
return None
try:
data = json.loads(state_file.read_text())
return SandboxInfo.from_dict(data)
except (OSError, json.JSONDecodeError, KeyError) as e:
logger.warning(f"Failed to load sandbox state for thread {thread_id}: {e}")
return None
def remove(self, thread_id: str) -> None:
state_file = self._thread_dir(thread_id) / SANDBOX_STATE_FILE
try:
if state_file.exists():
state_file.unlink()
logger.info(f"Removed sandbox state for thread {thread_id}")
except OSError as e:
logger.warning(f"Failed to remove sandbox state for thread {thread_id}: {e}")
@contextmanager
def lock(self, thread_id: str) -> Generator[None, None, None]:
"""Acquire a cross-process file lock using fcntl.flock.
The lock is held for the duration of the context manager.
Only one process can hold the lock at a time for a given thread_id.
Note: fcntl.flock is available on macOS and Linux.
"""
thread_dir = self._thread_dir(thread_id)
os.makedirs(thread_dir, exist_ok=True)
lock_path = thread_dir / SANDBOX_LOCK_FILE
lock_file = open(lock_path, "w")
try:
fcntl.flock(lock_file.fileno(), fcntl.LOCK_EX)
yield
finally:
try:
fcntl.flock(lock_file.fileno(), fcntl.LOCK_UN)
lock_file.close()
except OSError:
pass

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"""Local container backend for sandbox provisioning.
Manages sandbox containers using Docker or Apple Container on the local machine.
Handles container lifecycle, port allocation, and cross-process container discovery.
"""
from __future__ import annotations
import logging
import subprocess
from src.utils.network import get_free_port, release_port
from .backend import SandboxBackend, wait_for_sandbox_ready
from .sandbox_info import SandboxInfo
logger = logging.getLogger(__name__)
class LocalContainerBackend(SandboxBackend):
"""Backend that manages sandbox containers locally using Docker or Apple Container.
On macOS, automatically prefers Apple Container if available, otherwise falls back to Docker.
On other platforms, uses Docker.
Features:
- Deterministic container naming for cross-process discovery
- Port allocation with thread-safe utilities
- Container lifecycle management (start/stop with --rm)
- Support for volume mounts and environment variables
"""
def __init__(
self,
*,
image: str,
base_port: int,
container_prefix: str,
config_mounts: list,
environment: dict[str, str],
):
"""Initialize the local container backend.
Args:
image: Container image to use.
base_port: Base port number to start searching for free ports.
container_prefix: Prefix for container names (e.g., "deer-flow-sandbox").
config_mounts: Volume mount configurations from config (list of VolumeMountConfig).
environment: Environment variables to inject into containers.
"""
self._image = image
self._base_port = base_port
self._container_prefix = container_prefix
self._config_mounts = config_mounts
self._environment = environment
self._runtime = self._detect_runtime()
@property
def runtime(self) -> str:
"""The detected container runtime ("docker" or "container")."""
return self._runtime
def _detect_runtime(self) -> str:
"""Detect which container runtime to use.
On macOS, prefer Apple Container if available, otherwise fall back to Docker.
On other platforms, use Docker.
Returns:
"container" for Apple Container, "docker" for Docker.
"""
import platform
if platform.system() == "Darwin":
try:
result = subprocess.run(
["container", "--version"],
capture_output=True,
text=True,
check=True,
timeout=5,
)
logger.info(f"Detected Apple Container: {result.stdout.strip()}")
return "container"
except (FileNotFoundError, subprocess.CalledProcessError, subprocess.TimeoutExpired):
logger.info("Apple Container not available, falling back to Docker")
return "docker"
# ── SandboxBackend interface ──────────────────────────────────────────
def create(self, thread_id: str, sandbox_id: str, extra_mounts: list[tuple[str, str, bool]] | None = None) -> SandboxInfo:
"""Start a new container and return its connection info.
Args:
thread_id: Thread ID for which the sandbox is being created. Useful for backends that want to organize sandboxes by thread.
sandbox_id: Deterministic sandbox identifier (used in container name).
extra_mounts: Additional volume mounts as (host_path, container_path, read_only) tuples.
Returns:
SandboxInfo with container details.
Raises:
RuntimeError: If the container fails to start.
"""
container_name = f"{self._container_prefix}-{sandbox_id}"
port = get_free_port(start_port=self._base_port)
try:
container_id = self._start_container(container_name, port, extra_mounts)
except Exception:
release_port(port)
raise
return SandboxInfo(
sandbox_id=sandbox_id,
sandbox_url=f"http://localhost:{port}",
container_name=container_name,
container_id=container_id,
)
def destroy(self, info: SandboxInfo) -> None:
"""Stop the container and release its port."""
if info.container_id:
self._stop_container(info.container_id)
# Extract port from sandbox_url for release
try:
from urllib.parse import urlparse
port = urlparse(info.sandbox_url).port
if port:
release_port(port)
except Exception:
pass
def is_alive(self, info: SandboxInfo) -> bool:
"""Check if the container is still running (lightweight, no HTTP)."""
if info.container_name:
return self._is_container_running(info.container_name)
return False
def discover(self, sandbox_id: str) -> SandboxInfo | None:
"""Discover an existing container by its deterministic name.
Checks if a container with the expected name is running, retrieves its
port, and verifies it responds to health checks.
Args:
sandbox_id: The deterministic sandbox ID (determines container name).
Returns:
SandboxInfo if container found and healthy, None otherwise.
"""
container_name = f"{self._container_prefix}-{sandbox_id}"
if not self._is_container_running(container_name):
return None
port = self._get_container_port(container_name)
if port is None:
return None
sandbox_url = f"http://localhost:{port}"
if not wait_for_sandbox_ready(sandbox_url, timeout=5):
return None
return SandboxInfo(
sandbox_id=sandbox_id,
sandbox_url=sandbox_url,
container_name=container_name,
)
# ── Container operations ─────────────────────────────────────────────
def _start_container(
self,
container_name: str,
port: int,
extra_mounts: list[tuple[str, str, bool]] | None = None,
) -> str:
"""Start a new container.
Args:
container_name: Name for the container.
port: Host port to map to container port 8080.
extra_mounts: Additional volume mounts.
Returns:
The container ID.
Raises:
RuntimeError: If container fails to start.
"""
cmd = [self._runtime, "run"]
# Docker-specific security options
if self._runtime == "docker":
cmd.extend(["--security-opt", "seccomp=unconfined"])
cmd.extend(
[
"--rm",
"-d",
"-p",
f"{port}:8080",
"--name",
container_name,
]
)
# Environment variables
for key, value in self._environment.items():
cmd.extend(["-e", f"{key}={value}"])
# Config-level volume mounts
for mount in self._config_mounts:
mount_spec = f"{mount.host_path}:{mount.container_path}"
if mount.read_only:
mount_spec += ":ro"
cmd.extend(["-v", mount_spec])
# Extra mounts (thread-specific, skills, etc.)
if extra_mounts:
for host_path, container_path, read_only in extra_mounts:
mount_spec = f"{host_path}:{container_path}"
if read_only:
mount_spec += ":ro"
cmd.extend(["-v", mount_spec])
cmd.append(self._image)
logger.info(f"Starting container using {self._runtime}: {' '.join(cmd)}")
try:
result = subprocess.run(cmd, capture_output=True, text=True, check=True)
container_id = result.stdout.strip()
logger.info(f"Started container {container_name} (ID: {container_id}) using {self._runtime}")
return container_id
except subprocess.CalledProcessError as e:
logger.error(f"Failed to start container using {self._runtime}: {e.stderr}")
raise RuntimeError(f"Failed to start sandbox container: {e.stderr}")
def _stop_container(self, container_id: str) -> None:
"""Stop a container (--rm ensures automatic removal)."""
try:
subprocess.run(
[self._runtime, "stop", container_id],
capture_output=True,
text=True,
check=True,
)
logger.info(f"Stopped container {container_id} using {self._runtime}")
except subprocess.CalledProcessError as e:
logger.warning(f"Failed to stop container {container_id}: {e.stderr}")
def _is_container_running(self, container_name: str) -> bool:
"""Check if a named container is currently running.
This enables cross-process container discovery — any process can detect
containers started by another process via the deterministic container name.
"""
try:
result = subprocess.run(
[self._runtime, "inspect", "-f", "{{.State.Running}}", container_name],
capture_output=True,
text=True,
timeout=5,
)
return result.returncode == 0 and result.stdout.strip().lower() == "true"
except (subprocess.CalledProcessError, subprocess.TimeoutExpired):
return False
def _get_container_port(self, container_name: str) -> int | None:
"""Get the host port of a running container.
Args:
container_name: The container name to inspect.
Returns:
The host port mapped to container port 8080, or None if not found.
"""
try:
result = subprocess.run(
[self._runtime, "port", container_name, "8080"],
capture_output=True,
text=True,
timeout=5,
)
if result.returncode == 0 and result.stdout.strip():
# Output format: "0.0.0.0:PORT" or ":::PORT"
port_str = result.stdout.strip().split(":")[-1]
return int(port_str)
except (subprocess.CalledProcessError, subprocess.TimeoutExpired, ValueError):
pass
return None

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"""Remote sandbox backend — delegates Pod lifecycle to the provisioner service.
The provisioner dynamically creates per-sandbox-id Pods + NodePort Services
in k3s. The backend accesses sandbox pods directly via ``k3s:{NodePort}``.
Architecture:
┌────────────┐ HTTP ┌─────────────┐ K8s API ┌──────────┐
│ this file │ ──────▸ │ provisioner │ ────────▸ │ k3s │
│ (backend) │ │ :8002 │ │ :6443 │
└────────────┘ └─────────────┘ └─────┬────┘
│ creates
┌─────────────┐ ┌─────▼──────┐
│ backend │ ────────▸ │ sandbox │
│ │ direct │ Pod(s) │
└─────────────┘ k3s:NPort └────────────┘
"""
from __future__ import annotations
import logging
import os
import requests
from .backend import SandboxBackend
from .sandbox_info import SandboxInfo
logger = logging.getLogger(__name__)
class RemoteSandboxBackend(SandboxBackend):
"""Backend that delegates sandbox lifecycle to the provisioner service.
All Pod creation, destruction, and discovery are handled by the
provisioner. This backend is a thin HTTP client.
Typical config.yaml::
sandbox:
use: src.community.aio_sandbox:AioSandboxProvider
provisioner_url: http://provisioner:8002
"""
def __init__(self, provisioner_url: str):
"""Initialize with the provisioner service URL.
Args:
provisioner_url: URL of the provisioner service
(e.g., ``http://provisioner:8002``).
"""
self._provisioner_url = provisioner_url.rstrip("/")
@property
def provisioner_url(self) -> str:
return self._provisioner_url
# ── SandboxBackend interface ──────────────────────────────────────────
def create(
self,
thread_id: str,
sandbox_id: str,
extra_mounts: list[tuple[str, str, bool]] | None = None,
) -> SandboxInfo:
"""Create a sandbox Pod + Service via the provisioner.
Calls ``POST /api/sandboxes`` which creates a dedicated Pod +
NodePort Service in k3s.
"""
return self._provisioner_create(thread_id, sandbox_id, extra_mounts)
def destroy(self, info: SandboxInfo) -> None:
"""Destroy a sandbox Pod + Service via the provisioner."""
self._provisioner_destroy(info.sandbox_id)
def is_alive(self, info: SandboxInfo) -> bool:
"""Check whether the sandbox Pod is running."""
return self._provisioner_is_alive(info.sandbox_id)
def discover(self, sandbox_id: str) -> SandboxInfo | None:
"""Discover an existing sandbox via the provisioner.
Calls ``GET /api/sandboxes/{sandbox_id}`` and returns info if
the Pod exists.
"""
return self._provisioner_discover(sandbox_id)
# ── Provisioner API calls ─────────────────────────────────────────────
def _provisioner_create(self, thread_id: str, sandbox_id: str, extra_mounts: list[tuple[str, str, bool]] | None = None) -> SandboxInfo:
"""POST /api/sandboxes → create Pod + Service."""
try:
resp = requests.post(
f"{self._provisioner_url}/api/sandboxes",
json={
"sandbox_id": sandbox_id,
"thread_id": thread_id,
},
timeout=30,
)
resp.raise_for_status()
data = resp.json()
logger.info(f"Provisioner created sandbox {sandbox_id}: sandbox_url={data['sandbox_url']}")
return SandboxInfo(
sandbox_id=sandbox_id,
sandbox_url=data["sandbox_url"],
)
except requests.RequestException as exc:
logger.error(f"Provisioner create failed for {sandbox_id}: {exc}")
raise RuntimeError(f"Provisioner create failed: {exc}") from exc
def _provisioner_destroy(self, sandbox_id: str) -> None:
"""DELETE /api/sandboxes/{sandbox_id} → destroy Pod + Service."""
try:
resp = requests.delete(
f"{self._provisioner_url}/api/sandboxes/{sandbox_id}",
timeout=15,
)
if resp.ok:
logger.info(f"Provisioner destroyed sandbox {sandbox_id}")
else:
logger.warning(f"Provisioner destroy returned {resp.status_code}: {resp.text}")
except requests.RequestException as exc:
logger.warning(f"Provisioner destroy failed for {sandbox_id}: {exc}")
def _provisioner_is_alive(self, sandbox_id: str) -> bool:
"""GET /api/sandboxes/{sandbox_id} → check Pod phase."""
try:
resp = requests.get(
f"{self._provisioner_url}/api/sandboxes/{sandbox_id}",
timeout=10,
)
if resp.ok:
data = resp.json()
return data.get("status") == "Running"
return False
except requests.RequestException:
return False
def _provisioner_discover(self, sandbox_id: str) -> SandboxInfo | None:
"""GET /api/sandboxes/{sandbox_id} → discover existing sandbox."""
try:
resp = requests.get(
f"{self._provisioner_url}/api/sandboxes/{sandbox_id}",
timeout=10,
)
if resp.status_code == 404:
return None
resp.raise_for_status()
data = resp.json()
return SandboxInfo(
sandbox_id=sandbox_id,
sandbox_url=data["sandbox_url"],
)
except requests.RequestException as exc:
logger.debug(f"Provisioner discover failed for {sandbox_id}: {exc}")
return None

41
backend/src/community/aio_sandbox/sandbox_info.py Normal file
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"""Sandbox metadata for cross-process discovery and state persistence."""
from __future__ import annotations
import time
from dataclasses import dataclass, field
@dataclass
class SandboxInfo:
"""Persisted sandbox metadata that enables cross-process discovery.
This dataclass holds all the information needed to reconnect to an
existing sandbox from a different process (e.g., gateway vs langgraph,
multiple workers, or across K8s pods with shared storage).
"""
sandbox_id: str
sandbox_url: str # e.g. http://localhost:8080 or http://k3s:30001
container_name: str | None = None # Only for local container backend
container_id: str | None = None # Only for local container backend
created_at: float = field(default_factory=time.time)
def to_dict(self) -> dict:
return {
"sandbox_id": self.sandbox_id,
"sandbox_url": self.sandbox_url,
"container_name": self.container_name,
"container_id": self.container_id,
"created_at": self.created_at,
}
@classmethod
def from_dict(cls, data: dict) -> SandboxInfo:
return cls(
sandbox_id=data["sandbox_id"],
sandbox_url=data.get("sandbox_url", data.get("base_url", "")),
container_name=data.get("container_name"),
container_id=data.get("container_id"),
created_at=data.get("created_at", time.time()),
)

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"""Abstract base class for sandbox state persistence.
The state store handles cross-process persistence of thread_id → sandbox mappings,
enabling different processes (gateway, langgraph, multiple workers) to find the same
sandbox for a given thread.
"""
from __future__ import annotations
from abc import ABC, abstractmethod
from collections.abc import Generator
from contextlib import contextmanager
from .sandbox_info import SandboxInfo
class SandboxStateStore(ABC):
"""Abstract base for persisting thread_id → sandbox mappings across processes.
Implementations:
- FileSandboxStateStore: JSON files + fcntl file locking (single-host)
- TODO: RedisSandboxStateStore: Redis-based for distributed multi-host deployments
"""
@abstractmethod
def save(self, thread_id: str, info: SandboxInfo) -> None:
"""Save sandbox state for a thread.
Args:
thread_id: The thread ID.
info: Sandbox metadata to persist.
"""
...
@abstractmethod
def load(self, thread_id: str) -> SandboxInfo | None:
"""Load sandbox state for a thread.
Args:
thread_id: The thread ID.
Returns:
SandboxInfo if found, None otherwise.
"""
...
@abstractmethod
def remove(self, thread_id: str) -> None:
"""Remove sandbox state for a thread.
Args:
thread_id: The thread ID.
"""
...
@abstractmethod
@contextmanager
def lock(self, thread_id: str) -> Generator[None, None, None]:
"""Acquire a cross-process lock for a thread's sandbox operations.
Ensures only one process can create/modify a sandbox for a given
thread_id at a time, preventing duplicate sandbox creation.
Args:
thread_id: The thread ID to lock.
Yields:
None — use as a context manager.
"""
...

73
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import json
from firecrawl import FirecrawlApp
from langchain.tools import tool
from src.config import get_app_config
def _get_firecrawl_client() -> FirecrawlApp:
config = get_app_config().get_tool_config("web_search")
api_key = None
if config is not None:
api_key = config.model_extra.get("api_key")
return FirecrawlApp(api_key=api_key) # type: ignore[arg-type]
@tool("web_search", parse_docstring=True)
def web_search_tool(query: str) -> str:
"""Search the web.
Args:
query: The query to search for.
"""
try:
config = get_app_config().get_tool_config("web_search")
max_results = 5
if config is not None:
max_results = config.model_extra.get("max_results", max_results)
client = _get_firecrawl_client()
result = client.search(query, limit=max_results)
# result.web contains list of SearchResultWeb objects
web_results = result.web or []
normalized_results = [
{
"title": getattr(item, "title", "") or "",
"url": getattr(item, "url", "") or "",
"snippet": getattr(item, "description", "") or "",
}
for item in web_results
]
json_results = json.dumps(normalized_results, indent=2, ensure_ascii=False)
return json_results
except Exception as e:
return f"Error: {str(e)}"
@tool("web_fetch", parse_docstring=True)
def web_fetch_tool(url: str) -> str:
"""Fetch the contents of a web page at a given URL.
Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
Do NOT add www. to URLs that do NOT have them.
URLs must include the schema: https://example.com is a valid URL while example.com is an invalid URL.
Args:
url: The URL to fetch the contents of.
"""
try:
client = _get_firecrawl_client()
result = client.scrape(url, formats=["markdown"])
markdown_content = result.markdown or ""
metadata = result.metadata
title = metadata.title if metadata and metadata.title else "Untitled"
if not markdown_content:
return "Error: No content found"
except Exception as e:
return f"Error: {str(e)}"
return f"# {title}\n\n{markdown_content[:4096]}"

3
backend/src/community/image_search/__init__.py Normal file
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from .tools import image_search_tool
__all__ = ["image_search_tool"]

135
backend/src/community/image_search/tools.py Normal file
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"""
Image Search Tool - Search images using DuckDuckGo for reference in image generation.
"""
import json
import logging
from langchain.tools import tool
from src.config import get_app_config
logger = logging.getLogger(__name__)
def _search_images(
query: str,
max_results: int = 5,
region: str = "wt-wt",
safesearch: str = "moderate",
size: str | None = None,
color: str | None = None,
type_image: str | None = None,
layout: str | None = None,
license_image: str | None = None,
) -> list[dict]:
"""
Execute image search using DuckDuckGo.
Args:
query: Search keywords
max_results: Maximum number of results
region: Search region
safesearch: Safe search level
size: Image size (Small/Medium/Large/Wallpaper)
color: Color filter
type_image: Image type (photo/clipart/gif/transparent/line)
layout: Layout (Square/Tall/Wide)
license_image: License filter
Returns:
List of search results
"""
try:
from ddgs import DDGS
except ImportError:
logger.error("ddgs library not installed. Run: pip install ddgs")
return []
ddgs = DDGS(timeout=30)
try:
kwargs = {
"region": region,
"safesearch": safesearch,
"max_results": max_results,
}
if size:
kwargs["size"] = size
if color:
kwargs["color"] = color
if type_image:
kwargs["type_image"] = type_image
if layout:
kwargs["layout"] = layout
if license_image:
kwargs["license_image"] = license_image
results = ddgs.images(query, **kwargs)
return list(results) if results else []
except Exception as e:
logger.error(f"Failed to search images: {e}")
return []
@tool("image_search", parse_docstring=True)
def image_search_tool(
query: str,
max_results: int = 5,
size: str | None = None,
type_image: str | None = None,
layout: str | None = None,
) -> str:
"""Search for images online. Use this tool BEFORE image generation to find reference images for characters, portraits, objects, scenes, or any content requiring visual accuracy.
**When to use:**
- Before generating character/portrait images: search for similar poses, expressions, styles
- Before generating specific objects/products: search for accurate visual references
- Before generating scenes/locations: search for architectural or environmental references
- Before generating fashion/clothing: search for style and detail references
The returned image URLs can be used as reference images in image generation to significantly improve quality.
Args:
query: Search keywords describing the images you want to find. Be specific for better results (e.g., "Japanese woman street photography 1990s" instead of just "woman").
max_results: Maximum number of images to return. Default is 5.
size: Image size filter. Options: "Small", "Medium", "Large", "Wallpaper". Use "Large" for reference images.
type_image: Image type filter. Options: "photo", "clipart", "gif", "transparent", "line". Use "photo" for realistic references.
layout: Layout filter. Options: "Square", "Tall", "Wide". Choose based on your generation needs.
"""
config = get_app_config().get_tool_config("image_search")
# Override max_results from config if set
if config is not None and "max_results" in config.model_extra:
max_results = config.model_extra.get("max_results", max_results)
results = _search_images(
query=query,
max_results=max_results,
size=size,
type_image=type_image,
layout=layout,
)
if not results:
return json.dumps({"error": "No images found", "query": query}, ensure_ascii=False)
normalized_results = [
{
"title": r.get("title", ""),
"image_url": r.get("thumbnail", ""),
"thumbnail_url": r.get("thumbnail", ""),
}
for r in results
]
output = {
"query": query,
"total_results": len(normalized_results),
"results": normalized_results,
"usage_hint": "Use the 'image_url' values as reference images in image generation. Download them first if needed.",
}
return json.dumps(output, indent=2, ensure_ascii=False)

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backend/src/community/jina_ai/jina_client.py Normal file
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import logging
import os
import requests
logger = logging.getLogger(__name__)
class JinaClient:
def crawl(self, url: str, return_format: str = "html", timeout: int = 10) -> str:
headers = {
"Content-Type": "application/json",
"X-Return-Format": return_format,
"X-Timeout": str(timeout),
}
if os.getenv("JINA_API_KEY"):
headers["Authorization"] = f"Bearer {os.getenv('JINA_API_KEY')}"
else:
logger.warning("Jina API key is not set. Provide your own key to access a higher rate limit. See https://jina.ai/reader for more information.")
data = {"url": url}
try:
response = requests.post("https://r.jina.ai/", headers=headers, json=data)
if response.status_code != 200:
error_message = f"Jina API returned status {response.status_code}: {response.text}"
logger.error(error_message)
return f"Error: {error_message}"
if not response.text or not response.text.strip():
error_message = "Jina API returned empty response"
logger.error(error_message)
return f"Error: {error_message}"
return response.text
except Exception as e:
error_message = f"Request to Jina API failed: {str(e)}"
logger.error(error_message)
return f"Error: {error_message}"

28
backend/src/community/jina_ai/tools.py Normal file
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from langchain.tools import tool
from src.community.jina_ai.jina_client import JinaClient
from src.config import get_app_config
from src.utils.readability import ReadabilityExtractor
readability_extractor = ReadabilityExtractor()
@tool("web_fetch", parse_docstring=True)
def web_fetch_tool(url: str) -> str:
"""Fetch the contents of a web page at a given URL.
Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
Do NOT add www. to URLs that do NOT have them.
URLs must include the schema: https://example.com is a valid URL while example.com is an invalid URL.
Args:
url: The URL to fetch the contents of.
"""
jina_client = JinaClient()
timeout = 10
config = get_app_config().get_tool_config("web_fetch")
if config is not None and "timeout" in config.model_extra:
timeout = config.model_extra.get("timeout")
html_content = jina_client.crawl(url, return_format="html", timeout=timeout)
article = readability_extractor.extract_article(html_content)
return article.to_markdown()[:4096]

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backend/src/community/tavily/tools.py Normal file
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import json
from langchain.tools import tool
from tavily import TavilyClient
from src.config import get_app_config
def _get_tavily_client() -> TavilyClient:
config = get_app_config().get_tool_config("web_search")
api_key = None
if config is not None and "api_key" in config.model_extra:
api_key = config.model_extra.get("api_key")
return TavilyClient(api_key=api_key)
@tool("web_search", parse_docstring=True)
def web_search_tool(query: str) -> str:
"""Search the web.
Args:
query: The query to search for.
"""
config = get_app_config().get_tool_config("web_search")
max_results = 5
if config is not None and "max_results" in config.model_extra:
max_results = config.model_extra.get("max_results")
client = _get_tavily_client()
res = client.search(query, max_results=max_results)
normalized_results = [
{
"title": result["title"],
"url": result["url"],
"snippet": result["content"],
}
for result in res["results"]
]
json_results = json.dumps(normalized_results, indent=2, ensure_ascii=False)
return json_results
@tool("web_fetch", parse_docstring=True)
def web_fetch_tool(url: str) -> str:
"""Fetch the contents of a web page at a given URL.
Only fetch EXACT URLs that have been provided directly by the user or have been returned in results from the web_search and web_fetch tools.
This tool can NOT access content that requires authentication, such as private Google Docs or pages behind login walls.
Do NOT add www. to URLs that do NOT have them.
URLs must include the schema: https://example.com is a valid URL while example.com is an invalid URL.
Args:
url: The URL to fetch the contents of.
"""
client = _get_tavily_client()
res = client.extract([url])
if "failed_results" in res and len(res["failed_results"]) > 0:
return f"Error: {res['failed_results'][0]['error']}"
elif "results" in res and len(res["results"]) > 0:
result = res["results"][0]
return f"# {result['title']}\n\n{result['raw_content'][:4096]}"
else:
return "Error: No results found"

13
backend/src/config/__init__.py Normal file
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from .app_config import get_app_config
from .extensions_config import ExtensionsConfig, get_extensions_config
from .memory_config import MemoryConfig, get_memory_config
from .skills_config import SkillsConfig
__all__ = [
"get_app_config",
"SkillsConfig",
"ExtensionsConfig",
"get_extensions_config",
"MemoryConfig",
"get_memory_config",
]

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backend/src/config/app_config.py Normal file
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import os
from pathlib import Path
from typing import Any, Self
import yaml
from dotenv import load_dotenv
from pydantic import BaseModel, ConfigDict, Field
from src.config.extensions_config import ExtensionsConfig
from src.config.memory_config import load_memory_config_from_dict
from src.config.model_config import ModelConfig
from src.config.sandbox_config import SandboxConfig
from src.config.skills_config import SkillsConfig
from src.config.summarization_config import load_summarization_config_from_dict
from src.config.title_config import load_title_config_from_dict
from src.config.tool_config import ToolConfig, ToolGroupConfig
load_dotenv()
class AppConfig(BaseModel):
"""Config for the DeerFlow application"""
models: list[ModelConfig] = Field(default_factory=list, description="Available models")
sandbox: SandboxConfig = Field(description="Sandbox configuration")
tools: list[ToolConfig] = Field(default_factory=list, description="Available tools")
tool_groups: list[ToolGroupConfig] = Field(default_factory=list, description="Available tool groups")
skills: SkillsConfig = Field(default_factory=SkillsConfig, description="Skills configuration")
extensions: ExtensionsConfig = Field(default_factory=ExtensionsConfig, description="Extensions configuration (MCP servers and skills state)")
model_config = ConfigDict(extra="allow", frozen=False)
@classmethod
def resolve_config_path(cls, config_path: str | None = None) -> Path:
"""Resolve the config file path.
Priority:
1. If provided `config_path` argument, use it.
2. If provided `DEER_FLOW_CONFIG_PATH` environment variable, use it.
3. Otherwise, first check the `config.yaml` in the current directory, then fallback to `config.yaml` in the parent directory.
"""
if config_path:
path = Path(config_path)
if not Path.exists(path):
raise FileNotFoundError(f"Config file specified by param `config_path` not found at {path}")
return path
elif os.getenv("DEER_FLOW_CONFIG_PATH"):
path = Path(os.getenv("DEER_FLOW_CONFIG_PATH"))
if not Path.exists(path):
raise FileNotFoundError(f"Config file specified by environment variable `DEER_FLOW_CONFIG_PATH` not found at {path}")
return path
else:
# Check if the config.yaml is in the current directory
path = Path(os.getcwd()) / "config.yaml"
if not path.exists():
# Check if the config.yaml is in the parent directory of CWD
path = Path(os.getcwd()).parent / "config.yaml"
if not path.exists():
raise FileNotFoundError("`config.yaml` file not found at the current directory nor its parent directory")
return path
@classmethod
def from_file(cls, config_path: str | None = None) -> Self:
"""Load config from YAML file.
See `resolve_config_path` for more details.
Args:
config_path: Path to the config file.
Returns:
AppConfig: The loaded config.
"""
resolved_path = cls.resolve_config_path(config_path)
with open(resolved_path) as f:
config_data = yaml.safe_load(f)
config_data = cls.resolve_env_variables(config_data)
# Load title config if present
if "title" in config_data:
load_title_config_from_dict(config_data["title"])
# Load summarization config if present
if "summarization" in config_data:
load_summarization_config_from_dict(config_data["summarization"])
# Load memory config if present
if "memory" in config_data:
load_memory_config_from_dict(config_data["memory"])
# Load extensions config separately (it's in a different file)
extensions_config = ExtensionsConfig.from_file()
config_data["extensions"] = extensions_config.model_dump()
result = cls.model_validate(config_data)
return result
@classmethod
def resolve_env_variables(cls, config: Any) -> Any:
"""Recursively resolve environment variables in the config.
Environment variables are resolved using the `os.getenv` function. Example: $OPENAI_API_KEY
Args:
config: The config to resolve environment variables in.
Returns:
The config with environment variables resolved.
"""
if isinstance(config, str):
if config.startswith("$"):
return os.getenv(config[1:], config)
return config
elif isinstance(config, dict):
return {k: cls.resolve_env_variables(v) for k, v in config.items()}
elif isinstance(config, list):
return [cls.resolve_env_variables(item) for item in config]
return config
def get_model_config(self, name: str) -> ModelConfig | None:
"""Get the model config by name.
Args:
name: The name of the model to get the config for.
Returns:
The model config if found, otherwise None.
"""
return next((model for model in self.models if model.name == name), None)
def get_tool_config(self, name: str) -> ToolConfig | None:
"""Get the tool config by name.
Args:
name: The name of the tool to get the config for.
Returns:
The tool config if found, otherwise None.
"""
return next((tool for tool in self.tools if tool.name == name), None)
def get_tool_group_config(self, name: str) -> ToolGroupConfig | None:
"""Get the tool group config by name.
Args:
name: The name of the tool group to get the config for.
Returns:
The tool group config if found, otherwise None.
"""
return next((group for group in self.tool_groups if group.name == name), None)
_app_config: AppConfig | None = None
def get_app_config() -> AppConfig:
"""Get the DeerFlow config instance.
Returns a cached singleton instance. Use `reload_app_config()` to reload
from file, or `reset_app_config()` to clear the cache.
"""
global _app_config
if _app_config is None:
_app_config = AppConfig.from_file()
return _app_config
def reload_app_config(config_path: str | None = None) -> AppConfig:
"""Reload the config from file and update the cached instance.
This is useful when the config file has been modified and you want
to pick up the changes without restarting the application.
Args:
config_path: Optional path to config file. If not provided,
uses the default resolution strategy.
Returns:
The newly loaded AppConfig instance.
"""
global _app_config
_app_config = AppConfig.from_file(config_path)
return _app_config
def reset_app_config() -> None:
"""Reset the cached config instance.
This clears the singleton cache, causing the next call to
`get_app_config()` to reload from file. Useful for testing
or when switching between different configurations.
"""
global _app_config
_app_config = None
def set_app_config(config: AppConfig) -> None:
"""Set a custom config instance.
This allows injecting a custom or mock config for testing purposes.
Args:
config: The AppConfig instance to use.
"""
global _app_config
_app_config = config

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"""Unified extensions configuration for MCP servers and skills."""
import json
import os
from pathlib import Path
from typing import Any
from pydantic import BaseModel, ConfigDict, Field
class McpServerConfig(BaseModel):
"""Configuration for a single MCP server."""
enabled: bool = Field(default=True, description="Whether this MCP server is enabled")
type: str = Field(default="stdio", description="Transport type: 'stdio', 'sse', or 'http'")
command: str | None = Field(default=None, description="Command to execute to start the MCP server (for stdio type)")
args: list[str] = Field(default_factory=list, description="Arguments to pass to the command (for stdio type)")
env: dict[str, str] = Field(default_factory=dict, description="Environment variables for the MCP server")
url: str | None = Field(default=None, description="URL of the MCP server (for sse or http type)")
headers: dict[str, str] = Field(default_factory=dict, description="HTTP headers to send (for sse or http type)")
description: str = Field(default="", description="Human-readable description of what this MCP server provides")
model_config = ConfigDict(extra="allow")
class SkillStateConfig(BaseModel):
"""Configuration for a single skill's state."""
enabled: bool = Field(default=True, description="Whether this skill is enabled")
class ExtensionsConfig(BaseModel):
"""Unified configuration for MCP servers and skills."""
mcp_servers: dict[str, McpServerConfig] = Field(
default_factory=dict,
description="Map of MCP server name to configuration",
alias="mcpServers",
)
skills: dict[str, SkillStateConfig] = Field(
default_factory=dict,
description="Map of skill name to state configuration",
)
model_config = ConfigDict(extra="allow", populate_by_name=True)
@classmethod
def resolve_config_path(cls, config_path: str | None = None) -> Path | None:
"""Resolve the extensions config file path.
Priority:
1. If provided `config_path` argument, use it.
2. If provided `DEER_FLOW_EXTENSIONS_CONFIG_PATH` environment variable, use it.
3. Otherwise, check for `extensions_config.json` in the current directory, then in the parent directory.
4. For backward compatibility, also check for `mcp_config.json` if `extensions_config.json` is not found.
5. If not found, return None (extensions are optional).
Args:
config_path: Optional path to extensions config file.
Returns:
Path to the extensions config file if found, otherwise None.
"""
if config_path:
path = Path(config_path)
if not path.exists():
raise FileNotFoundError(f"Extensions config file specified by param `config_path` not found at {path}")
return path
elif os.getenv("DEER_FLOW_EXTENSIONS_CONFIG_PATH"):
path = Path(os.getenv("DEER_FLOW_EXTENSIONS_CONFIG_PATH"))
if not path.exists():
raise FileNotFoundError(f"Extensions config file specified by environment variable `DEER_FLOW_EXTENSIONS_CONFIG_PATH` not found at {path}")
return path
else:
# Check if the extensions_config.json is in the current directory
path = Path(os.getcwd()) / "extensions_config.json"
if path.exists():
return path
# Check if the extensions_config.json is in the parent directory of CWD
path = Path(os.getcwd()).parent / "extensions_config.json"
if path.exists():
return path
# Backward compatibility: check for mcp_config.json
path = Path(os.getcwd()) / "mcp_config.json"
if path.exists():
return path
path = Path(os.getcwd()).parent / "mcp_config.json"
if path.exists():
return path
# Extensions are optional, so return None if not found
return None
@classmethod
def from_file(cls, config_path: str | None = None) -> "ExtensionsConfig":
"""Load extensions config from JSON file.
See `resolve_config_path` for more details.
Args:
config_path: Path to the extensions config file.
Returns:
ExtensionsConfig: The loaded config, or empty config if file not found.
"""
resolved_path = cls.resolve_config_path(config_path)
if resolved_path is None:
# Return empty config if extensions config file is not found
return cls(mcp_servers={}, skills={})
with open(resolved_path) as f:
config_data = json.load(f)
cls.resolve_env_variables(config_data)
return cls.model_validate(config_data)
@classmethod
def resolve_env_variables(cls, config: dict[str, Any]) -> dict[str, Any]:
"""Recursively resolve environment variables in the config.
Environment variables are resolved using the `os.getenv` function. Example: $OPENAI_API_KEY
Args:
config: The config to resolve environment variables in.
Returns:
The config with environment variables resolved.
"""
for key, value in config.items():
if isinstance(value, str):
if value.startswith("$"):
env_value = os.getenv(value[1:], None)
if env_value is not None:
config[key] = env_value
else:
config[key] = value
elif isinstance(value, dict):
config[key] = cls.resolve_env_variables(value)
elif isinstance(value, list):
config[key] = [cls.resolve_env_variables(item) if isinstance(item, dict) else item for item in value]
return config
def get_enabled_mcp_servers(self) -> dict[str, McpServerConfig]:
"""Get only the enabled MCP servers.
Returns:
Dictionary of enabled MCP servers.
"""
return {name: config for name, config in self.mcp_servers.items() if config.enabled}
def is_skill_enabled(self, skill_name: str, skill_category: str) -> bool:
"""Check if a skill is enabled.
Args:
skill_name: Name of the skill
skill_category: Category of the skill
Returns:
True if enabled, False otherwise
"""
skill_config = self.skills.get(skill_name)
if skill_config is None:
# Default to enable for public & custom skill
return skill_category in ("public", "custom")
return skill_config.enabled
_extensions_config: ExtensionsConfig | None = None
def get_extensions_config() -> ExtensionsConfig:
"""Get the extensions config instance.
Returns a cached singleton instance. Use `reload_extensions_config()` to reload
from file, or `reset_extensions_config()` to clear the cache.
Returns:
The cached ExtensionsConfig instance.
"""
global _extensions_config
if _extensions_config is None:
_extensions_config = ExtensionsConfig.from_file()
return _extensions_config
def reload_extensions_config(config_path: str | None = None) -> ExtensionsConfig:
"""Reload the extensions config from file and update the cached instance.
This is useful when the config file has been modified and you want
to pick up the changes without restarting the application.
Args:
config_path: Optional path to extensions config file. If not provided,
uses the default resolution strategy.
Returns:
The newly loaded ExtensionsConfig instance.
"""
global _extensions_config
_extensions_config = ExtensionsConfig.from_file(config_path)
return _extensions_config
def reset_extensions_config() -> None:
"""Reset the cached extensions config instance.
This clears the singleton cache, causing the next call to
`get_extensions_config()` to reload from file. Useful for testing
or when switching between different configurations.
"""
global _extensions_config
_extensions_config = None
def set_extensions_config(config: ExtensionsConfig) -> None:
"""Set a custom extensions config instance.
This allows injecting a custom or mock config for testing purposes.
Args:
config: The ExtensionsConfig instance to use.
"""
global _extensions_config
_extensions_config = config

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"""Configuration for memory mechanism."""
from pydantic import BaseModel, Field
class MemoryConfig(BaseModel):
"""Configuration for global memory mechanism."""
enabled: bool = Field(
default=True,
description="Whether to enable memory mechanism",
)
storage_path: str = Field(
default=".deer-flow/memory.json",
description="Path to store memory data (relative to backend directory)",
)
debounce_seconds: int = Field(
default=30,
ge=1,
le=300,
description="Seconds to wait before processing queued updates (debounce)",
)
model_name: str | None = Field(
default=None,
description="Model name to use for memory updates (None = use default model)",
)
max_facts: int = Field(
default=100,
ge=10,
le=500,
description="Maximum number of facts to store",
)
fact_confidence_threshold: float = Field(
default=0.7,
ge=0.0,
le=1.0,
description="Minimum confidence threshold for storing facts",
)
injection_enabled: bool = Field(
default=True,
description="Whether to inject memory into system prompt",
)
max_injection_tokens: int = Field(
default=2000,
ge=100,
le=8000,
description="Maximum tokens to use for memory injection",
)
# Global configuration instance
_memory_config: MemoryConfig = MemoryConfig()
def get_memory_config() -> MemoryConfig:
"""Get the current memory configuration."""
return _memory_config
def set_memory_config(config: MemoryConfig) -> None:
"""Set the memory configuration."""
global _memory_config
_memory_config = config
def load_memory_config_from_dict(config_dict: dict) -> None:
"""Load memory configuration from a dictionary."""
global _memory_config
_memory_config = MemoryConfig(**config_dict)

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from pydantic import BaseModel, ConfigDict, Field
class ModelConfig(BaseModel):
"""Config section for a model"""
name: str = Field(..., description="Unique name for the model")
display_name: str | None = Field(..., default_factory=lambda: None, description="Display name for the model")
description: str | None = Field(..., default_factory=lambda: None, description="Description for the model")
use: str = Field(
...,
description="Class path of the model provider(e.g. langchain_openai.ChatOpenAI)",
)
model: str = Field(..., description="Model name")
model_config = ConfigDict(extra="allow")
supports_thinking: bool = Field(default_factory=lambda: False, description="Whether the model supports thinking")
when_thinking_enabled: dict | None = Field(
default_factory=lambda: None,
description="Extra settings to be passed to the model when thinking is enabled",
)
supports_vision: bool = Field(default_factory=lambda: False, description="Whether the model supports vision/image inputs")

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from pydantic import BaseModel, ConfigDict, Field
class VolumeMountConfig(BaseModel):
"""Configuration for a volume mount."""
host_path: str = Field(..., description="Path on the host machine")
container_path: str = Field(..., description="Path inside the container")
read_only: bool = Field(default=False, description="Whether the mount is read-only")
class SandboxConfig(BaseModel):
"""Config section for a sandbox.
Common options:
use: Class path of the sandbox provider (required)
AioSandboxProvider specific options:
image: Docker image to use (default: enterprise-public-cn-beijing.cr.volces.com/vefaas-public/all-in-one-sandbox:latest)
port: Base port for sandbox containers (default: 8080)
base_url: If set, uses existing sandbox instead of starting new container
auto_start: Whether to automatically start Docker container (default: true)
container_prefix: Prefix for container names (default: deer-flow-sandbox)
idle_timeout: Idle timeout in seconds before sandbox is released (default: 600 = 10 minutes). Set to 0 to disable.
mounts: List of volume mounts to share directories with the container
environment: Environment variables to inject into the container (values starting with $ are resolved from host env)
"""
use: str = Field(
...,
description="Class path of the sandbox provider (e.g. src.sandbox.local:LocalSandboxProvider)",
)
image: str | None = Field(
default=None,
description="Docker image to use for the sandbox container",
)
port: int | None = Field(
default=None,
description="Base port for sandbox containers",
)
base_url: str | None = Field(
default=None,
description="If set, uses existing sandbox at this URL instead of starting new container",
)
auto_start: bool | None = Field(
default=None,
description="Whether to automatically start Docker container",
)
container_prefix: str | None = Field(
default=None,
description="Prefix for container names",
)
idle_timeout: int | None = Field(
default=None,
description="Idle timeout in seconds before sandbox is released (default: 600 = 10 minutes). Set to 0 to disable.",
)
mounts: list[VolumeMountConfig] = Field(
default_factory=list,
description="List of volume mounts to share directories between host and container",
)
environment: dict[str, str] = Field(
default_factory=dict,
description="Environment variables to inject into the sandbox container. Values starting with $ will be resolved from host environment variables.",
)
model_config = ConfigDict(extra="allow")

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from pathlib import Path
from pydantic import BaseModel, Field
class SkillsConfig(BaseModel):
"""Configuration for skills system"""
path: str | None = Field(
default=None,
description="Path to skills directory. If not specified, defaults to ../skills relative to backend directory",
)
container_path: str = Field(
default="/mnt/skills",
description="Path where skills are mounted in the sandbox container",
)
def get_skills_path(self) -> Path:
"""
Get the resolved skills directory path.
Returns:
Path to the skills directory
"""
if self.path:
# Use configured path (can be absolute or relative)
path = Path(self.path)
if not path.is_absolute():
# If relative, resolve from current working directory
path = Path.cwd() / path
return path.resolve()
else:
# Default: ../skills relative to backend directory
from src.skills.loader import get_skills_root_path
return get_skills_root_path()
def get_skill_container_path(self, skill_name: str, category: str = "public") -> str:
"""
Get the full container path for a specific skill.
Args:
skill_name: Name of the skill (directory name)
category: Category of the skill (public or custom)
Returns:
Full path to the skill in the container
"""
return f"{self.container_path}/{category}/{skill_name}"

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"""Configuration for conversation summarization."""
from typing import Literal
from pydantic import BaseModel, Field
ContextSizeType = Literal["fraction", "tokens", "messages"]
class ContextSize(BaseModel):
"""Context size specification for trigger or keep parameters."""
type: ContextSizeType = Field(description="Type of context size specification")
value: int | float = Field(description="Value for the context size specification")
def to_tuple(self) -> tuple[ContextSizeType, int | float]:
"""Convert to tuple format expected by SummarizationMiddleware."""
return (self.type, self.value)
class SummarizationConfig(BaseModel):
"""Configuration for automatic conversation summarization."""
enabled: bool = Field(
default=False,
description="Whether to enable automatic conversation summarization",
)
model_name: str | None = Field(
default=None,
description="Model name to use for summarization (None = use a lightweight model)",
)
trigger: ContextSize | list[ContextSize] | None = Field(
default=None,
description="One or more thresholds that trigger summarization. When any threshold is met, summarization runs. "
"Examples: {'type': 'messages', 'value': 50} triggers at 50 messages, "
"{'type': 'tokens', 'value': 4000} triggers at 4000 tokens, "
"{'type': 'fraction', 'value': 0.8} triggers at 80% of model's max input tokens",
)
keep: ContextSize = Field(
default_factory=lambda: ContextSize(type="messages", value=20),
description="Context retention policy after summarization. Specifies how much history to preserve. "
"Examples: {'type': 'messages', 'value': 20} keeps 20 messages, "
"{'type': 'tokens', 'value': 3000} keeps 3000 tokens, "
"{'type': 'fraction', 'value': 0.3} keeps 30% of model's max input tokens",
)
trim_tokens_to_summarize: int | None = Field(
default=4000,
description="Maximum tokens to keep when preparing messages for summarization. Pass null to skip trimming.",
)
summary_prompt: str | None = Field(
default=None,
description="Custom prompt template for generating summaries. If not provided, uses the default LangChain prompt.",
)
# Global configuration instance
_summarization_config: SummarizationConfig = SummarizationConfig()
def get_summarization_config() -> SummarizationConfig:
"""Get the current summarization configuration."""
return _summarization_config
def set_summarization_config(config: SummarizationConfig) -> None:
"""Set the summarization configuration."""
global _summarization_config
_summarization_config = config
def load_summarization_config_from_dict(config_dict: dict) -> None:
"""Load summarization configuration from a dictionary."""
global _summarization_config
_summarization_config = SummarizationConfig(**config_dict)

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"""Configuration for automatic thread title generation."""
from pydantic import BaseModel, Field
class TitleConfig(BaseModel):
"""Configuration for automatic thread title generation."""
enabled: bool = Field(
default=True,
description="Whether to enable automatic title generation",
)
max_words: int = Field(
default=6,
ge=1,
le=20,
description="Maximum number of words in the generated title",
)
max_chars: int = Field(
default=60,
ge=10,
le=200,
description="Maximum number of characters in the generated title",
)
model_name: str | None = Field(
default=None,
description="Model name to use for title generation (None = use default model)",
)
prompt_template: str = Field(
default=("Generate a concise title (max {max_words} words) for this conversation.\nUser: {user_msg}\nAssistant: {assistant_msg}\n\nReturn ONLY the title, no quotes, no explanation."),
description="Prompt template for title generation",
)
# Global configuration instance
_title_config: TitleConfig = TitleConfig()
def get_title_config() -> TitleConfig:
"""Get the current title configuration."""
return _title_config
def set_title_config(config: TitleConfig) -> None:
"""Set the title configuration."""
global _title_config
_title_config = config
def load_title_config_from_dict(config_dict: dict) -> None:
"""Load title configuration from a dictionary."""
global _title_config
_title_config = TitleConfig(**config_dict)

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from pydantic import BaseModel, ConfigDict, Field
class ToolGroupConfig(BaseModel):
"""Config section for a tool group"""
name: str = Field(..., description="Unique name for the tool group")
model_config = ConfigDict(extra="allow")
class ToolConfig(BaseModel):
"""Config section for a tool"""
name: str = Field(..., description="Unique name for the tool")
group: str = Field(..., description="Group name for the tool")
use: str = Field(
...,
description="Variable name of the tool provider(e.g. src.sandbox.tools:bash_tool)",
)
model_config = ConfigDict(extra="allow")

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from .app import app, create_app
from .config import GatewayConfig, get_gateway_config
__all__ = ["app", "create_app", "GatewayConfig", "get_gateway_config"]

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import logging
from collections.abc import AsyncGenerator
from contextlib import asynccontextmanager
from fastapi import FastAPI
from src.gateway.config import get_gateway_config
from src.gateway.routers import artifacts, mcp, memory, models, skills, uploads
# Configure logging
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
datefmt="%Y-%m-%d %H:%M:%S",
)
logger = logging.getLogger(__name__)
@asynccontextmanager
async def lifespan(app: FastAPI) -> AsyncGenerator[None, None]:
"""Application lifespan handler."""
config = get_gateway_config()
logger.info(f"Starting API Gateway on {config.host}:{config.port}")
# NOTE: MCP tools initialization is NOT done here because:
# 1. Gateway doesn't use MCP tools - they are used by Agents in the LangGraph Server
# 2. Gateway and LangGraph Server are separate processes with independent caches
# MCP tools are lazily initialized in LangGraph Server when first needed
yield
logger.info("Shutting down API Gateway")
def create_app() -> FastAPI:
"""Create and configure the FastAPI application.
Returns:
Configured FastAPI application instance.
"""
app = FastAPI(
title="DeerFlow API Gateway",
description="""
## DeerFlow API Gateway
API Gateway for DeerFlow - A LangGraph-based AI agent backend with sandbox execution capabilities.
### Features
- **Models Management**: Query and retrieve available AI models
- **MCP Configuration**: Manage Model Context Protocol (MCP) server configurations
- **Memory Management**: Access and manage global memory data for personalized conversations
- **Skills Management**: Query and manage skills and their enabled status
- **Artifacts**: Access thread artifacts and generated files
- **Health Monitoring**: System health check endpoints
### Architecture
LangGraph requests are handled by nginx reverse proxy.
This gateway provides custom endpoints for models, MCP configuration, skills, and artifacts.
""",
version="0.1.0",
lifespan=lifespan,
docs_url="/docs",
redoc_url="/redoc",
openapi_url="/openapi.json",
openapi_tags=[
{
"name": "models",
"description": "Operations for querying available AI models and their configurations",
},
{
"name": "mcp",
"description": "Manage Model Context Protocol (MCP) server configurations",
},
{
"name": "memory",
"description": "Access and manage global memory data for personalized conversations",
},
{
"name": "skills",
"description": "Manage skills and their configurations",
},
{
"name": "artifacts",
"description": "Access and download thread artifacts and generated files",
},
{
"name": "uploads",
"description": "Upload and manage user files for threads",
},
{
"name": "health",
"description": "Health check and system status endpoints",
},
],
)
# CORS is handled by nginx - no need for FastAPI middleware
# Include routers
# Models API is mounted at /api/models
app.include_router(models.router)
# MCP API is mounted at /api/mcp
app.include_router(mcp.router)
# Memory API is mounted at /api/memory
app.include_router(memory.router)
# Skills API is mounted at /api/skills
app.include_router(skills.router)
# Artifacts API is mounted at /api/threads/{thread_id}/artifacts
app.include_router(artifacts.router)
# Uploads API is mounted at /api/threads/{thread_id}/uploads
app.include_router(uploads.router)
@app.get("/health", tags=["health"])
async def health_check() -> dict:
"""Health check endpoint.
Returns:
Service health status information.
"""
return {"status": "healthy", "service": "deer-flow-gateway"}
return app
# Create app instance for uvicorn
app = create_app()

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import os
from pydantic import BaseModel, Field
class GatewayConfig(BaseModel):
"""Configuration for the API Gateway."""
host: str = Field(default="0.0.0.0", description="Host to bind the gateway server")
port: int = Field(default=8001, description="Port to bind the gateway server")
cors_origins: list[str] = Field(default_factory=lambda: ["http://localhost:3000"], description="Allowed CORS origins")
_gateway_config: GatewayConfig | None = None
def get_gateway_config() -> GatewayConfig:
"""Get gateway config, loading from environment if available."""
global _gateway_config
if _gateway_config is None:
cors_origins_str = os.getenv("CORS_ORIGINS", "http://localhost:3000")
_gateway_config = GatewayConfig(
host=os.getenv("GATEWAY_HOST", "0.0.0.0"),
port=int(os.getenv("GATEWAY_PORT", "8001")),
cors_origins=cors_origins_str.split(","),
)
return _gateway_config

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"""Shared path resolution for thread virtual paths (e.g. mnt/user-data/outputs/...)."""
import os
from pathlib import Path
from fastapi import HTTPException
from src.agents.middlewares.thread_data_middleware import THREAD_DATA_BASE_DIR
# Virtual path prefix used in sandbox environments (without leading slash for URL path matching)
VIRTUAL_PATH_PREFIX = "mnt/user-data"
def resolve_thread_virtual_path(thread_id: str, virtual_path: str) -> Path:
"""Resolve a virtual path to the actual filesystem path under thread user-data.
Args:
thread_id: The thread ID.
virtual_path: The virtual path (e.g., mnt/user-data/outputs/file.txt).
Leading slashes are stripped.
Returns:
The resolved filesystem path.
Raises:
HTTPException: If the path is invalid or outside allowed directories.
"""
virtual_path = virtual_path.lstrip("/")
if not virtual_path.startswith(VIRTUAL_PATH_PREFIX):
raise HTTPException(status_code=400, detail=f"Path must start with /{VIRTUAL_PATH_PREFIX}")
relative_path = virtual_path[len(VIRTUAL_PATH_PREFIX) :].lstrip("/")
base_dir = Path(os.getcwd()) / THREAD_DATA_BASE_DIR / thread_id / "user-data"
actual_path = base_dir / relative_path
try:
actual_path = actual_path.resolve()
base_resolved = base_dir.resolve()
if not str(actual_path).startswith(str(base_resolved)):
raise HTTPException(status_code=403, detail="Access denied: path traversal detected")
except (ValueError, RuntimeError):
raise HTTPException(status_code=400, detail="Invalid path")
return actual_path

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from . import artifacts, mcp, models, skills, uploads
__all__ = ["artifacts", "mcp", "models", "skills", "uploads"]

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import logging
import mimetypes
import zipfile
from pathlib import Path
from urllib.parse import quote
from fastapi import APIRouter, HTTPException, Request
from fastapi.responses import FileResponse, HTMLResponse, PlainTextResponse, Response
from src.gateway.path_utils import resolve_thread_virtual_path
logger = logging.getLogger(__name__)
router = APIRouter(prefix="/api", tags=["artifacts"])
def is_text_file_by_content(path: Path, sample_size: int = 8192) -> bool:
"""Check if file is text by examining content for null bytes."""
try:
with open(path, "rb") as f:
chunk = f.read(sample_size)
# Text files shouldn't contain null bytes
return b"\x00" not in chunk
except Exception:
return False
def _extract_file_from_skill_archive(zip_path: Path, internal_path: str) -> bytes | None:
"""Extract a file from a .skill ZIP archive.
Args:
zip_path: Path to the .skill file (ZIP archive).
internal_path: Path to the file inside the archive (e.g., "SKILL.md").
Returns:
The file content as bytes, or None if not found.
"""
if not zipfile.is_zipfile(zip_path):
return None
try:
with zipfile.ZipFile(zip_path, "r") as zip_ref:
# List all files in the archive
namelist = zip_ref.namelist()
# Try direct path first
if internal_path in namelist:
return zip_ref.read(internal_path)
# Try with any top-level directory prefix (e.g., "skill-name/SKILL.md")
for name in namelist:
if name.endswith("/" + internal_path) or name == internal_path:
return zip_ref.read(name)
# Not found
return None
except (zipfile.BadZipFile, KeyError):
return None
@router.get(
"/threads/{thread_id}/artifacts/{path:path}",
summary="Get Artifact File",
description="Retrieve an artifact file generated by the AI agent. Supports text, HTML, and binary files.",
)
async def get_artifact(thread_id: str, path: str, request: Request) -> FileResponse:
"""Get an artifact file by its path.
The endpoint automatically detects file types and returns appropriate content types.
Use the `?download=true` query parameter to force file download.
Args:
thread_id: The thread ID.
path: The artifact path with virtual prefix (e.g., mnt/user-data/outputs/file.txt).
request: FastAPI request object (automatically injected).
Returns:
The file content as a FileResponse with appropriate content type:
- HTML files: Rendered as HTML
- Text files: Plain text with proper MIME type
- Binary files: Inline display with download option
Raises:
HTTPException:
- 400 if path is invalid or not a file
- 403 if access denied (path traversal detected)
- 404 if file not found
Query Parameters:
download (bool): If true, returns file as attachment for download
Example:
- Get HTML file: `/api/threads/abc123/artifacts/mnt/user-data/outputs/index.html`
- Download file: `/api/threads/abc123/artifacts/mnt/user-data/outputs/data.csv?download=true`
"""
# Check if this is a request for a file inside a .skill archive (e.g., xxx.skill/SKILL.md)
if ".skill/" in path:
# Split the path at ".skill/" to get the ZIP file path and internal path
skill_marker = ".skill/"
marker_pos = path.find(skill_marker)
skill_file_path = path[: marker_pos + len(".skill")] # e.g., "mnt/user-data/outputs/my-skill.skill"
internal_path = path[marker_pos + len(skill_marker) :] # e.g., "SKILL.md"
actual_skill_path = resolve_thread_virtual_path(thread_id, skill_file_path)
if not actual_skill_path.exists():
raise HTTPException(status_code=404, detail=f"Skill file not found: {skill_file_path}")
if not actual_skill_path.is_file():
raise HTTPException(status_code=400, detail=f"Path is not a file: {skill_file_path}")
# Extract the file from the .skill archive
content = _extract_file_from_skill_archive(actual_skill_path, internal_path)
if content is None:
raise HTTPException(status_code=404, detail=f"File '{internal_path}' not found in skill archive")
# Determine MIME type based on the internal file
mime_type, _ = mimetypes.guess_type(internal_path)
# Add cache headers to avoid repeated ZIP extraction (cache for 5 minutes)
cache_headers = {"Cache-Control": "private, max-age=300"}
if mime_type and mime_type.startswith("text/"):
return PlainTextResponse(content=content.decode("utf-8"), media_type=mime_type, headers=cache_headers)
# Default to plain text for unknown types that look like text
try:
return PlainTextResponse(content=content.decode("utf-8"), media_type="text/plain", headers=cache_headers)
except UnicodeDecodeError:
return Response(content=content, media_type=mime_type or "application/octet-stream", headers=cache_headers)
actual_path = resolve_thread_virtual_path(thread_id, path)
logger.info(f"Resolving artifact path: thread_id={thread_id}, requested_path={path}, actual_path={actual_path}")
if not actual_path.exists():
raise HTTPException(status_code=404, detail=f"Artifact not found: {path}")
if not actual_path.is_file():
raise HTTPException(status_code=400, detail=f"Path is not a file: {path}")
mime_type, _ = mimetypes.guess_type(actual_path)
# Encode filename for Content-Disposition header (RFC 5987)
encoded_filename = quote(actual_path.name)
# if `download` query parameter is true, return the file as a download
if request.query_params.get("download"):
return FileResponse(path=actual_path, filename=actual_path.name, media_type=mime_type, headers={"Content-Disposition": f"attachment; filename*=UTF-8''{encoded_filename}"})
if mime_type and mime_type == "text/html":
return HTMLResponse(content=actual_path.read_text())
if mime_type and mime_type.startswith("text/"):
return PlainTextResponse(content=actual_path.read_text(), media_type=mime_type)
if is_text_file_by_content(actual_path):
return PlainTextResponse(content=actual_path.read_text(), media_type=mime_type)
return Response(content=actual_path.read_bytes(), media_type=mime_type, headers={"Content-Disposition": f"inline; filename*=UTF-8''{encoded_filename}"})

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import json
import logging
from pathlib import Path
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel, Field
from src.config.extensions_config import ExtensionsConfig, get_extensions_config, reload_extensions_config
logger = logging.getLogger(__name__)
router = APIRouter(prefix="/api", tags=["mcp"])
class McpServerConfigResponse(BaseModel):
"""Response model for MCP server configuration."""
enabled: bool = Field(default=True, description="Whether this MCP server is enabled")
type: str = Field(default="stdio", description="Transport type: 'stdio', 'sse', or 'http'")
command: str | None = Field(default=None, description="Command to execute to start the MCP server (for stdio type)")
args: list[str] = Field(default_factory=list, description="Arguments to pass to the command (for stdio type)")
env: dict[str, str] = Field(default_factory=dict, description="Environment variables for the MCP server")
url: str | None = Field(default=None, description="URL of the MCP server (for sse or http type)")
headers: dict[str, str] = Field(default_factory=dict, description="HTTP headers to send (for sse or http type)")
description: str = Field(default="", description="Human-readable description of what this MCP server provides")
class McpConfigResponse(BaseModel):
"""Response model for MCP configuration."""
mcp_servers: dict[str, McpServerConfigResponse] = Field(
default_factory=dict,
description="Map of MCP server name to configuration",
)
class McpConfigUpdateRequest(BaseModel):
"""Request model for updating MCP configuration."""
mcp_servers: dict[str, McpServerConfigResponse] = Field(
...,
description="Map of MCP server name to configuration",
)
@router.get(
"/mcp/config",
response_model=McpConfigResponse,
summary="Get MCP Configuration",
description="Retrieve the current Model Context Protocol (MCP) server configurations.",
)
async def get_mcp_configuration() -> McpConfigResponse:
"""Get the current MCP configuration.
Returns:
The current MCP configuration with all servers.
Example:
```json
{
"mcp_servers": {
"github": {
"enabled": true,
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-github"],
"env": {"GITHUB_TOKEN": "ghp_xxx"},
"description": "GitHub MCP server for repository operations"
}
}
}
```
"""
config = get_extensions_config()
return McpConfigResponse(mcp_servers={name: McpServerConfigResponse(**server.model_dump()) for name, server in config.mcp_servers.items()})
@router.put(
"/mcp/config",
response_model=McpConfigResponse,
summary="Update MCP Configuration",
description="Update Model Context Protocol (MCP) server configurations and save to file.",
)
async def update_mcp_configuration(request: McpConfigUpdateRequest) -> McpConfigResponse:
"""Update the MCP configuration.
This will:
1. Save the new configuration to the mcp_config.json file
2. Reload the configuration cache
3. Reset MCP tools cache to trigger reinitialization
Args:
request: The new MCP configuration to save.
Returns:
The updated MCP configuration.
Raises:
HTTPException: 500 if the configuration file cannot be written.
Example Request:
```json
{
"mcp_servers": {
"github": {
"enabled": true,
"command": "npx",
"args": ["-y", "@modelcontextprotocol/server-github"],
"env": {"GITHUB_TOKEN": "$GITHUB_TOKEN"},
"description": "GitHub MCP server for repository operations"
}
}
}
```
"""
try:
# Get the current config path (or determine where to save it)
config_path = ExtensionsConfig.resolve_config_path()
# If no config file exists, create one in the parent directory (project root)
if config_path is None:
config_path = Path.cwd().parent / "extensions_config.json"
logger.info(f"No existing extensions config found. Creating new config at: {config_path}")
# Load current config to preserve skills configuration
current_config = get_extensions_config()
# Convert request to dict format for JSON serialization
config_data = {
"mcpServers": {name: server.model_dump() for name, server in request.mcp_servers.items()},
"skills": {name: {"enabled": skill.enabled} for name, skill in current_config.skills.items()},
}
# Write the configuration to file
with open(config_path, "w") as f:
json.dump(config_data, f, indent=2)
logger.info(f"MCP configuration updated and saved to: {config_path}")
# NOTE: No need to reload/reset cache here - LangGraph Server (separate process)
# will detect config file changes via mtime and reinitialize MCP tools automatically
# Reload the configuration and update the global cache
reloaded_config = reload_extensions_config()
return McpConfigResponse(mcp_servers={name: McpServerConfigResponse(**server.model_dump()) for name, server in reloaded_config.mcp_servers.items()})
except Exception as e:
logger.error(f"Failed to update MCP configuration: {e}", exc_info=True)
raise HTTPException(status_code=500, detail=f"Failed to update MCP configuration: {str(e)}")

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"""Memory API router for retrieving and managing global memory data."""
from fastapi import APIRouter
from pydantic import BaseModel, Field
from src.agents.memory.updater import get_memory_data, reload_memory_data
from src.config.memory_config import get_memory_config
router = APIRouter(prefix="/api", tags=["memory"])
class ContextSection(BaseModel):
"""Model for context sections (user and history)."""
summary: str = Field(default="", description="Summary content")
updatedAt: str = Field(default="", description="Last update timestamp")
class UserContext(BaseModel):
"""Model for user context."""
workContext: ContextSection = Field(default_factory=ContextSection)
personalContext: ContextSection = Field(default_factory=ContextSection)
topOfMind: ContextSection = Field(default_factory=ContextSection)
class HistoryContext(BaseModel):
"""Model for history context."""
recentMonths: ContextSection = Field(default_factory=ContextSection)
earlierContext: ContextSection = Field(default_factory=ContextSection)
longTermBackground: ContextSection = Field(default_factory=ContextSection)
class Fact(BaseModel):
"""Model for a memory fact."""
id: str = Field(..., description="Unique identifier for the fact")
content: str = Field(..., description="Fact content")
category: str = Field(default="context", description="Fact category")
confidence: float = Field(default=0.5, description="Confidence score (0-1)")
createdAt: str = Field(default="", description="Creation timestamp")
source: str = Field(default="unknown", description="Source thread ID")
class MemoryResponse(BaseModel):
"""Response model for memory data."""
version: str = Field(default="1.0", description="Memory schema version")
lastUpdated: str = Field(default="", description="Last update timestamp")
user: UserContext = Field(default_factory=UserContext)
history: HistoryContext = Field(default_factory=HistoryContext)
facts: list[Fact] = Field(default_factory=list)
class MemoryConfigResponse(BaseModel):
"""Response model for memory configuration."""
enabled: bool = Field(..., description="Whether memory is enabled")
storage_path: str = Field(..., description="Path to memory storage file")
debounce_seconds: int = Field(..., description="Debounce time for memory updates")
max_facts: int = Field(..., description="Maximum number of facts to store")
fact_confidence_threshold: float = Field(..., description="Minimum confidence threshold for facts")
injection_enabled: bool = Field(..., description="Whether memory injection is enabled")
max_injection_tokens: int = Field(..., description="Maximum tokens for memory injection")
class MemoryStatusResponse(BaseModel):
"""Response model for memory status."""
config: MemoryConfigResponse
data: MemoryResponse
@router.get(
"/memory",
response_model=MemoryResponse,
summary="Get Memory Data",
description="Retrieve the current global memory data including user context, history, and facts.",
)
async def get_memory() -> MemoryResponse:
"""Get the current global memory data.
Returns:
The current memory data with user context, history, and facts.
Example Response:
```json
{
"version": "1.0",
"lastUpdated": "2024-01-15T10:30:00Z",
"user": {
"workContext": {"summary": "Working on DeerFlow project", "updatedAt": "..."},
"personalContext": {"summary": "Prefers concise responses", "updatedAt": "..."},
"topOfMind": {"summary": "Building memory API", "updatedAt": "..."}
},
"history": {
"recentMonths": {"summary": "Recent development activities", "updatedAt": "..."},
"earlierContext": {"summary": "", "updatedAt": ""},
"longTermBackground": {"summary": "", "updatedAt": ""}
},
"facts": [
{
"id": "fact_abc123",
"content": "User prefers TypeScript over JavaScript",
"category": "preference",
"confidence": 0.9,
"createdAt": "2024-01-15T10:30:00Z",
"source": "thread_xyz"
}
]
}
```
"""
memory_data = get_memory_data()
return MemoryResponse(**memory_data)
@router.post(
"/memory/reload",
response_model=MemoryResponse,
summary="Reload Memory Data",
description="Reload memory data from the storage file, refreshing the in-memory cache.",
)
async def reload_memory() -> MemoryResponse:
"""Reload memory data from file.
This forces a reload of the memory data from the storage file,
useful when the file has been modified externally.
Returns:
The reloaded memory data.
"""
memory_data = reload_memory_data()
return MemoryResponse(**memory_data)
@router.get(
"/memory/config",
response_model=MemoryConfigResponse,
summary="Get Memory Configuration",
description="Retrieve the current memory system configuration.",
)
async def get_memory_config_endpoint() -> MemoryConfigResponse:
"""Get the memory system configuration.
Returns:
The current memory configuration settings.
Example Response:
```json
{
"enabled": true,
"storage_path": ".deer-flow/memory.json",
"debounce_seconds": 30,
"max_facts": 100,
"fact_confidence_threshold": 0.7,
"injection_enabled": true,
"max_injection_tokens": 2000
}
```
"""
config = get_memory_config()
return MemoryConfigResponse(
enabled=config.enabled,
storage_path=config.storage_path,
debounce_seconds=config.debounce_seconds,
max_facts=config.max_facts,
fact_confidence_threshold=config.fact_confidence_threshold,
injection_enabled=config.injection_enabled,
max_injection_tokens=config.max_injection_tokens,
)
@router.get(
"/memory/status",
response_model=MemoryStatusResponse,
summary="Get Memory Status",
description="Retrieve both memory configuration and current data in a single request.",
)
async def get_memory_status() -> MemoryStatusResponse:
"""Get the memory system status including configuration and data.
Returns:
Combined memory configuration and current data.
"""
config = get_memory_config()
memory_data = get_memory_data()
return MemoryStatusResponse(
config=MemoryConfigResponse(
enabled=config.enabled,
storage_path=config.storage_path,
debounce_seconds=config.debounce_seconds,
max_facts=config.max_facts,
fact_confidence_threshold=config.fact_confidence_threshold,
injection_enabled=config.injection_enabled,
max_injection_tokens=config.max_injection_tokens,
),
data=MemoryResponse(**memory_data),
)

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from fastapi import APIRouter, HTTPException
from pydantic import BaseModel, Field
from src.config import get_app_config
router = APIRouter(prefix="/api", tags=["models"])
class ModelResponse(BaseModel):
"""Response model for model information."""
name: str = Field(..., description="Unique identifier for the model")
display_name: str | None = Field(None, description="Human-readable name")
description: str | None = Field(None, description="Model description")
supports_thinking: bool = Field(default=False, description="Whether model supports thinking mode")
class ModelsListResponse(BaseModel):
"""Response model for listing all models."""
models: list[ModelResponse]
@router.get(
"/models",
response_model=ModelsListResponse,
summary="List All Models",
description="Retrieve a list of all available AI models configured in the system.",
)
async def list_models() -> ModelsListResponse:
"""List all available models from configuration.
Returns model information suitable for frontend display,
excluding sensitive fields like API keys and internal configuration.
Returns:
A list of all configured models with their metadata.
Example Response:
```json
{
"models": [
{
"name": "gpt-4",
"display_name": "GPT-4",
"description": "OpenAI GPT-4 model",
"supports_thinking": false
},
{
"name": "claude-3-opus",
"display_name": "Claude 3 Opus",
"description": "Anthropic Claude 3 Opus model",
"supports_thinking": true
}
]
}
```
"""
config = get_app_config()
models = [
ModelResponse(
name=model.name,
display_name=model.display_name,
description=model.description,
supports_thinking=model.supports_thinking,
)
for model in config.models
]
return ModelsListResponse(models=models)
@router.get(
"/models/{model_name}",
response_model=ModelResponse,
summary="Get Model Details",
description="Retrieve detailed information about a specific AI model by its name.",
)
async def get_model(model_name: str) -> ModelResponse:
"""Get a specific model by name.
Args:
model_name: The unique name of the model to retrieve.
Returns:
Model information if found.
Raises:
HTTPException: 404 if model not found.
Example Response:
```json
{
"name": "gpt-4",
"display_name": "GPT-4",
"description": "OpenAI GPT-4 model",
"supports_thinking": false
}
```
"""
config = get_app_config()
model = config.get_model_config(model_name)
if model is None:
raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found")
return ModelResponse(
name=model.name,
display_name=model.display_name,
description=model.description,
supports_thinking=model.supports_thinking,
)

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import json
import logging
import re
import shutil
import tempfile
import zipfile
from pathlib import Path
import yaml
from fastapi import APIRouter, HTTPException
from pydantic import BaseModel, Field
from src.config.extensions_config import ExtensionsConfig, SkillStateConfig, get_extensions_config, reload_extensions_config
from src.gateway.path_utils import resolve_thread_virtual_path
from src.skills import Skill, load_skills
from src.skills.loader import get_skills_root_path
logger = logging.getLogger(__name__)
router = APIRouter(prefix="/api", tags=["skills"])
class SkillResponse(BaseModel):
"""Response model for skill information."""
name: str = Field(..., description="Name of the skill")
description: str = Field(..., description="Description of what the skill does")
license: str | None = Field(None, description="License information")
category: str = Field(..., description="Category of the skill (public or custom)")
enabled: bool = Field(default=True, description="Whether this skill is enabled")
class SkillsListResponse(BaseModel):
"""Response model for listing all skills."""
skills: list[SkillResponse]
class SkillUpdateRequest(BaseModel):
"""Request model for updating a skill."""
enabled: bool = Field(..., description="Whether to enable or disable the skill")
class SkillInstallRequest(BaseModel):
"""Request model for installing a skill from a .skill file."""
thread_id: str = Field(..., description="The thread ID where the .skill file is located")
path: str = Field(..., description="Virtual path to the .skill file (e.g., mnt/user-data/outputs/my-skill.skill)")
class SkillInstallResponse(BaseModel):
"""Response model for skill installation."""
success: bool = Field(..., description="Whether the installation was successful")
skill_name: str = Field(..., description="Name of the installed skill")
message: str = Field(..., description="Installation result message")
# Allowed properties in SKILL.md frontmatter
ALLOWED_FRONTMATTER_PROPERTIES = {"name", "description", "license", "allowed-tools", "metadata"}
def _validate_skill_frontmatter(skill_dir: Path) -> tuple[bool, str, str | None]:
"""Validate a skill directory's SKILL.md frontmatter.
Args:
skill_dir: Path to the skill directory containing SKILL.md.
Returns:
Tuple of (is_valid, message, skill_name).
"""
skill_md = skill_dir / "SKILL.md"
if not skill_md.exists():
return False, "SKILL.md not found", None
content = skill_md.read_text()
if not content.startswith("---"):
return False, "No YAML frontmatter found", None
# Extract frontmatter
match = re.match(r"^---\n(.*?)\n---", content, re.DOTALL)
if not match:
return False, "Invalid frontmatter format", None
frontmatter_text = match.group(1)
# Parse YAML frontmatter
try:
frontmatter = yaml.safe_load(frontmatter_text)
if not isinstance(frontmatter, dict):
return False, "Frontmatter must be a YAML dictionary", None
except yaml.YAMLError as e:
return False, f"Invalid YAML in frontmatter: {e}", None
# Check for unexpected properties
unexpected_keys = set(frontmatter.keys()) - ALLOWED_FRONTMATTER_PROPERTIES
if unexpected_keys:
return False, f"Unexpected key(s) in SKILL.md frontmatter: {', '.join(sorted(unexpected_keys))}", None
# Check required fields
if "name" not in frontmatter:
return False, "Missing 'name' in frontmatter", None
if "description" not in frontmatter:
return False, "Missing 'description' in frontmatter", None
# Validate name
name = frontmatter.get("name", "")
if not isinstance(name, str):
return False, f"Name must be a string, got {type(name).__name__}", None
name = name.strip()
if not name:
return False, "Name cannot be empty", None
# Check naming convention (hyphen-case: lowercase with hyphens)
if not re.match(r"^[a-z0-9-]+$", name):
return False, f"Name '{name}' should be hyphen-case (lowercase letters, digits, and hyphens only)", None
if name.startswith("-") or name.endswith("-") or "--" in name:
return False, f"Name '{name}' cannot start/end with hyphen or contain consecutive hyphens", None
if len(name) > 64:
return False, f"Name is too long ({len(name)} characters). Maximum is 64 characters.", None
# Validate description
description = frontmatter.get("description", "")
if not isinstance(description, str):
return False, f"Description must be a string, got {type(description).__name__}", None
description = description.strip()
if description:
if "<" in description or ">" in description:
return False, "Description cannot contain angle brackets (< or >)", None
if len(description) > 1024:
return False, f"Description is too long ({len(description)} characters). Maximum is 1024 characters.", None
return True, "Skill is valid!", name
def _skill_to_response(skill: Skill) -> SkillResponse:
"""Convert a Skill object to a SkillResponse."""
return SkillResponse(
name=skill.name,
description=skill.description,
license=skill.license,
category=skill.category,
enabled=skill.enabled,
)
@router.get(
"/skills",
response_model=SkillsListResponse,
summary="List All Skills",
description="Retrieve a list of all available skills from both public and custom directories.",
)
async def list_skills() -> SkillsListResponse:
"""List all available skills.
Returns all skills regardless of their enabled status.
Returns:
A list of all skills with their metadata.
Example Response:
```json
{
"skills": [
{
"name": "PDF Processing",
"description": "Extract and analyze PDF content",
"license": "MIT",
"category": "public",
"enabled": true
},
{
"name": "Frontend Design",
"description": "Generate frontend designs and components",
"license": null,
"category": "custom",
"enabled": false
}
]
}
```
"""
try:
# Load all skills (including disabled ones)
skills = load_skills(enabled_only=False)
return SkillsListResponse(skills=[_skill_to_response(skill) for skill in skills])
except Exception as e:
logger.error(f"Failed to load skills: {e}", exc_info=True)
raise HTTPException(status_code=500, detail=f"Failed to load skills: {str(e)}")
@router.get(
"/skills/{skill_name}",
response_model=SkillResponse,
summary="Get Skill Details",
description="Retrieve detailed information about a specific skill by its name.",
)
async def get_skill(skill_name: str) -> SkillResponse:
"""Get a specific skill by name.
Args:
skill_name: The name of the skill to retrieve.
Returns:
Skill information if found.
Raises:
HTTPException: 404 if skill not found.
Example Response:
```json
{
"name": "PDF Processing",
"description": "Extract and analyze PDF content",
"license": "MIT",
"category": "public",
"enabled": true
}
```
"""
try:
skills = load_skills(enabled_only=False)
skill = next((s for s in skills if s.name == skill_name), None)
if skill is None:
raise HTTPException(status_code=404, detail=f"Skill '{skill_name}' not found")
return _skill_to_response(skill)
except HTTPException:
raise
except Exception as e:
logger.error(f"Failed to get skill {skill_name}: {e}", exc_info=True)
raise HTTPException(status_code=500, detail=f"Failed to get skill: {str(e)}")
@router.put(
"/skills/{skill_name}",
response_model=SkillResponse,
summary="Update Skill",
description="Update a skill's enabled status by modifying the skills_state_config.json file.",
)
async def update_skill(skill_name: str, request: SkillUpdateRequest) -> SkillResponse:
"""Update a skill's enabled status.
This will modify the skills_state_config.json file to update the enabled state.
The SKILL.md file itself is not modified.
Args:
skill_name: The name of the skill to update.
request: The update request containing the new enabled status.
Returns:
The updated skill information.
Raises:
HTTPException: 404 if skill not found, 500 if update fails.
Example Request:
```json
{
"enabled": false
}
```
Example Response:
```json
{
"name": "PDF Processing",
"description": "Extract and analyze PDF content",
"license": "MIT",
"category": "public",
"enabled": false
}
```
"""
try:
# Find the skill to verify it exists
skills = load_skills(enabled_only=False)
skill = next((s for s in skills if s.name == skill_name), None)
if skill is None:
raise HTTPException(status_code=404, detail=f"Skill '{skill_name}' not found")
# Get or create config path
config_path = ExtensionsConfig.resolve_config_path()
if config_path is None:
# Create new config file in parent directory (project root)
config_path = Path.cwd().parent / "extensions_config.json"
logger.info(f"No existing extensions config found. Creating new config at: {config_path}")
# Load current configuration
extensions_config = get_extensions_config()
# Update the skill's enabled status
extensions_config.skills[skill_name] = SkillStateConfig(enabled=request.enabled)
# Convert to JSON format (preserve MCP servers config)
config_data = {
"mcpServers": {name: server.model_dump() for name, server in extensions_config.mcp_servers.items()},
"skills": {name: {"enabled": skill_config.enabled} for name, skill_config in extensions_config.skills.items()},
}
# Write the configuration to file
with open(config_path, "w") as f:
json.dump(config_data, f, indent=2)
logger.info(f"Skills configuration updated and saved to: {config_path}")
# Reload the extensions config to update the global cache
reload_extensions_config()
# Reload the skills to get the updated status (for API response)
skills = load_skills(enabled_only=False)
updated_skill = next((s for s in skills if s.name == skill_name), None)
if updated_skill is None:
raise HTTPException(status_code=500, detail=f"Failed to reload skill '{skill_name}' after update")
logger.info(f"Skill '{skill_name}' enabled status updated to {request.enabled}")
return _skill_to_response(updated_skill)
except HTTPException:
raise
except Exception as e:
logger.error(f"Failed to update skill {skill_name}: {e}", exc_info=True)
raise HTTPException(status_code=500, detail=f"Failed to update skill: {str(e)}")
@router.post(
"/skills/install",
response_model=SkillInstallResponse,
summary="Install Skill",
description="Install a skill from a .skill file (ZIP archive) located in the thread's user-data directory.",
)
async def install_skill(request: SkillInstallRequest) -> SkillInstallResponse:
"""Install a skill from a .skill file.
The .skill file is a ZIP archive containing a skill directory with SKILL.md
and optional resources (scripts, references, assets).
Args:
request: The install request containing thread_id and virtual path to .skill file.
Returns:
Installation result with skill name and status message.
Raises:
HTTPException:
- 400 if path is invalid or file is not a valid .skill file
- 403 if access denied (path traversal detected)
- 404 if file not found
- 409 if skill already exists
- 500 if installation fails
Example Request:
```json
{
"thread_id": "abc123-def456",
"path": "/mnt/user-data/outputs/my-skill.skill"
}
```
Example Response:
```json
{
"success": true,
"skill_name": "my-skill",
"message": "Skill 'my-skill' installed successfully"
}
```
"""
try:
# Resolve the virtual path to actual file path
skill_file_path = resolve_thread_virtual_path(request.thread_id, request.path)
# Check if file exists
if not skill_file_path.exists():
raise HTTPException(status_code=404, detail=f"Skill file not found: {request.path}")
# Check if it's a file
if not skill_file_path.is_file():
raise HTTPException(status_code=400, detail=f"Path is not a file: {request.path}")
# Check file extension
if not skill_file_path.suffix == ".skill":
raise HTTPException(status_code=400, detail="File must have .skill extension")
# Verify it's a valid ZIP file
if not zipfile.is_zipfile(skill_file_path):
raise HTTPException(status_code=400, detail="File is not a valid ZIP archive")
# Get the custom skills directory
skills_root = get_skills_root_path()
custom_skills_dir = skills_root / "custom"
# Create custom directory if it doesn't exist
custom_skills_dir.mkdir(parents=True, exist_ok=True)
# Extract to a temporary directory first for validation
with tempfile.TemporaryDirectory() as temp_dir:
temp_path = Path(temp_dir)
# Extract the .skill file
with zipfile.ZipFile(skill_file_path, "r") as zip_ref:
zip_ref.extractall(temp_path)
# Find the skill directory (should be the only top-level directory)
extracted_items = list(temp_path.iterdir())
if len(extracted_items) == 0:
raise HTTPException(status_code=400, detail="Skill archive is empty")
# Handle both cases: single directory or files directly in root
if len(extracted_items) == 1 and extracted_items[0].is_dir():
skill_dir = extracted_items[0]
else:
# Files are directly in the archive root
skill_dir = temp_path
# Validate the skill
is_valid, message, skill_name = _validate_skill_frontmatter(skill_dir)
if not is_valid:
raise HTTPException(status_code=400, detail=f"Invalid skill: {message}")
if not skill_name:
raise HTTPException(status_code=400, detail="Could not determine skill name")
# Check if skill already exists
target_dir = custom_skills_dir / skill_name
if target_dir.exists():
raise HTTPException(status_code=409, detail=f"Skill '{skill_name}' already exists. Please remove it first or use a different name.")
# Move the skill directory to the custom skills directory
shutil.copytree(skill_dir, target_dir)
logger.info(f"Skill '{skill_name}' installed successfully to {target_dir}")
return SkillInstallResponse(success=True, skill_name=skill_name, message=f"Skill '{skill_name}' installed successfully")
except HTTPException:
raise
except Exception as e:
logger.error(f"Failed to install skill: {e}", exc_info=True)
raise HTTPException(status_code=500, detail=f"Failed to install skill: {str(e)}")

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"""Upload router for handling file uploads."""
import logging
import os
from pathlib import Path
from fastapi import APIRouter, File, HTTPException, UploadFile
from pydantic import BaseModel
from src.agents.middlewares.thread_data_middleware import THREAD_DATA_BASE_DIR
from src.sandbox.sandbox_provider import get_sandbox_provider
logger = logging.getLogger(__name__)
router = APIRouter(prefix="/api/threads/{thread_id}/uploads", tags=["uploads"])
# File extensions that should be converted to markdown
CONVERTIBLE_EXTENSIONS = {
".pdf",
".ppt",
".pptx",
".xls",
".xlsx",
".doc",
".docx",
}
class UploadResponse(BaseModel):
"""Response model for file upload."""
success: bool
files: list[dict[str, str]]
message: str
def get_uploads_dir(thread_id: str) -> Path:
"""Get the uploads directory for a thread.
Args:
thread_id: The thread ID.
Returns:
Path to the uploads directory.
"""
base_dir = Path(os.getcwd()) / THREAD_DATA_BASE_DIR / thread_id / "user-data" / "uploads"
base_dir.mkdir(parents=True, exist_ok=True)
return base_dir
async def convert_file_to_markdown(file_path: Path) -> Path | None:
"""Convert a file to markdown using markitdown.
Args:
file_path: Path to the file to convert.
Returns:
Path to the markdown file if conversion was successful, None otherwise.
"""
try:
from markitdown import MarkItDown
md = MarkItDown()
result = md.convert(str(file_path))
# Save as .md file with same name
md_path = file_path.with_suffix(".md")
md_path.write_text(result.text_content, encoding="utf-8")
logger.info(f"Converted {file_path.name} to markdown: {md_path.name}")
return md_path
except Exception as e:
logger.error(f"Failed to convert {file_path.name} to markdown: {e}")
return None
@router.post("", response_model=UploadResponse)
async def upload_files(
thread_id: str,
files: list[UploadFile] = File(...),
) -> UploadResponse:
"""Upload multiple files to a thread's uploads directory.
For PDF, PPT, Excel, and Word files, they will be converted to markdown using markitdown.
All files (original and converted) are saved to /mnt/user-data/uploads.
Args:
thread_id: The thread ID to upload files to.
files: List of files to upload.
Returns:
Upload response with success status and file information.
"""
if not files:
raise HTTPException(status_code=400, detail="No files provided")
uploads_dir = get_uploads_dir(thread_id)
uploaded_files = []
sandbox_provider = get_sandbox_provider()
sandbox_id = sandbox_provider.acquire(thread_id)
sandbox = sandbox_provider.get(sandbox_id)
for file in files:
if not file.filename:
continue
try:
# Save the original file
file_path = uploads_dir / file.filename
content = await file.read()
# Build relative path from backend root
relative_path = f".deer-flow/threads/{thread_id}/user-data/uploads/{file.filename}"
virtual_path = f"/mnt/user-data/uploads/{file.filename}"
sandbox.update_file(virtual_path, content)
file_info = {
"filename": file.filename,
"size": str(len(content)),
"path": relative_path, # Actual filesystem path (relative to backend/)
"virtual_path": virtual_path, # Path for Agent in sandbox
"artifact_url": f"/api/threads/{thread_id}/artifacts/mnt/user-data/uploads/{file.filename}", # HTTP URL
}
logger.info(f"Saved file: {file.filename} ({len(content)} bytes) to {relative_path}")
# Check if file should be converted to markdown
file_ext = file_path.suffix.lower()
if file_ext in CONVERTIBLE_EXTENSIONS:
md_path = await convert_file_to_markdown(file_path)
if md_path:
md_relative_path = f".deer-flow/threads/{thread_id}/user-data/uploads/{md_path.name}"
file_info["markdown_file"] = md_path.name
file_info["markdown_path"] = md_relative_path
file_info["markdown_virtual_path"] = f"/mnt/user-data/uploads/{md_path.name}"
file_info["markdown_artifact_url"] = f"/api/threads/{thread_id}/artifacts/mnt/user-data/uploads/{md_path.name}"
uploaded_files.append(file_info)
except Exception as e:
logger.error(f"Failed to upload {file.filename}: {e}")
raise HTTPException(status_code=500, detail=f"Failed to upload {file.filename}: {str(e)}")
return UploadResponse(
success=True,
files=uploaded_files,
message=f"Successfully uploaded {len(uploaded_files)} file(s)",
)
@router.get("/list", response_model=dict)
async def list_uploaded_files(thread_id: str) -> dict:
"""List all files in a thread's uploads directory.
Args:
thread_id: The thread ID to list files for.
Returns:
Dictionary containing list of files with their metadata.
"""
uploads_dir = get_uploads_dir(thread_id)
if not uploads_dir.exists():
return {"files": [], "count": 0}
files = []
for file_path in sorted(uploads_dir.iterdir()):
if file_path.is_file():
stat = file_path.stat()
relative_path = f".deer-flow/threads/{thread_id}/user-data/uploads/{file_path.name}"
files.append(
{
"filename": file_path.name,
"size": stat.st_size,
"path": relative_path, # Actual filesystem path (relative to backend/)
"virtual_path": f"/mnt/user-data/uploads/{file_path.name}", # Path for Agent in sandbox
"artifact_url": f"/api/threads/{thread_id}/artifacts/mnt/user-data/uploads/{file_path.name}", # HTTP URL
"extension": file_path.suffix,
"modified": stat.st_mtime,
}
)
return {"files": files, "count": len(files)}
@router.delete("/{filename}")
async def delete_uploaded_file(thread_id: str, filename: str) -> dict:
"""Delete a file from a thread's uploads directory.
Args:
thread_id: The thread ID.
filename: The filename to delete.
Returns:
Success message.
"""
uploads_dir = get_uploads_dir(thread_id)
file_path = uploads_dir / filename
if not file_path.exists():
raise HTTPException(status_code=404, detail=f"File not found: {filename}")
# Security check: ensure the path is within the uploads directory
try:
file_path.resolve().relative_to(uploads_dir.resolve())
except ValueError:
raise HTTPException(status_code=403, detail="Access denied")
try:
file_path.unlink()
logger.info(f"Deleted file: {filename}")
return {"success": True, "message": f"Deleted {filename}"}
except Exception as e:
logger.error(f"Failed to delete {filename}: {e}")
raise HTTPException(status_code=500, detail=f"Failed to delete {filename}: {str(e)}")

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backend/src/mcp/__init__.py Normal file
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"""MCP (Model Context Protocol) integration using langchain-mcp-adapters."""
from .cache import get_cached_mcp_tools, initialize_mcp_tools, reset_mcp_tools_cache
from .client import build_server_params, build_servers_config
from .tools import get_mcp_tools
__all__ = [
"build_server_params",
"build_servers_config",
"get_mcp_tools",
"initialize_mcp_tools",
"get_cached_mcp_tools",
"reset_mcp_tools_cache",
]

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backend/src/mcp/cache.py Normal file
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"""Cache for MCP tools to avoid repeated loading."""
import asyncio
import logging
import os
from langchain_core.tools import BaseTool
logger = logging.getLogger(__name__)
_mcp_tools_cache: list[BaseTool] | None = None
_cache_initialized = False
_initialization_lock = asyncio.Lock()
_config_mtime: float | None = None # Track config file modification time
def _get_config_mtime() -> float | None:
"""Get the modification time of the extensions config file.
Returns:
The modification time as a float, or None if the file doesn't exist.
"""
from src.config.extensions_config import ExtensionsConfig
config_path = ExtensionsConfig.resolve_config_path()
if config_path and config_path.exists():
return os.path.getmtime(config_path)
return None
def _is_cache_stale() -> bool:
"""Check if the cache is stale due to config file changes.
Returns:
True if the cache should be invalidated, False otherwise.
"""
global _config_mtime
if not _cache_initialized:
return False # Not initialized yet, not stale
current_mtime = _get_config_mtime()
# If we couldn't get mtime before or now, assume not stale
if _config_mtime is None or current_mtime is None:
return False
# If the config file has been modified since we cached, it's stale
if current_mtime > _config_mtime:
logger.info(f"MCP config file has been modified (mtime: {_config_mtime} -> {current_mtime}), cache is stale")
return True
return False
async def initialize_mcp_tools() -> list[BaseTool]:
"""Initialize and cache MCP tools.
This should be called once at application startup.
Returns:
List of LangChain tools from all enabled MCP servers.
"""
global _mcp_tools_cache, _cache_initialized, _config_mtime
async with _initialization_lock:
if _cache_initialized:
logger.info("MCP tools already initialized")
return _mcp_tools_cache or []
from src.mcp.tools import get_mcp_tools
logger.info("Initializing MCP tools...")
_mcp_tools_cache = await get_mcp_tools()
_cache_initialized = True
_config_mtime = _get_config_mtime() # Record config file mtime
logger.info(f"MCP tools initialized: {len(_mcp_tools_cache)} tool(s) loaded (config mtime: {_config_mtime})")
return _mcp_tools_cache
def get_cached_mcp_tools() -> list[BaseTool]:
"""Get cached MCP tools with lazy initialization.
If tools are not initialized, automatically initializes them.
This ensures MCP tools work in both FastAPI and LangGraph Studio contexts.
Also checks if the config file has been modified since last initialization,
and re-initializes if needed. This ensures that changes made through the
Gateway API (which runs in a separate process) are reflected in the
LangGraph Server.
Returns:
List of cached MCP tools.
"""
global _cache_initialized
# Check if cache is stale due to config file changes
if _is_cache_stale():
logger.info("MCP cache is stale, resetting for re-initialization...")
reset_mcp_tools_cache()
if not _cache_initialized:
logger.info("MCP tools not initialized, performing lazy initialization...")
try:
# Try to initialize in the current event loop
loop = asyncio.get_event_loop()
if loop.is_running():
# If loop is already running (e.g., in LangGraph Studio),
# we need to create a new loop in a thread
import concurrent.futures
with concurrent.futures.ThreadPoolExecutor() as executor:
future = executor.submit(asyncio.run, initialize_mcp_tools())
future.result()
else:
# If no loop is running, we can use the current loop
loop.run_until_complete(initialize_mcp_tools())
except RuntimeError:
# No event loop exists, create one
asyncio.run(initialize_mcp_tools())
except Exception as e:
logger.error(f"Failed to lazy-initialize MCP tools: {e}")
return []
return _mcp_tools_cache or []
def reset_mcp_tools_cache() -> None:
"""Reset the MCP tools cache.
This is useful for testing or when you want to reload MCP tools.
"""
global _mcp_tools_cache, _cache_initialized, _config_mtime
_mcp_tools_cache = None
_cache_initialized = False
_config_mtime = None
logger.info("MCP tools cache reset")

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backend/src/mcp/client.py Normal file
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"""MCP client using langchain-mcp-adapters."""
import logging
from typing import Any
from src.config.extensions_config import ExtensionsConfig, McpServerConfig
logger = logging.getLogger(__name__)
def build_server_params(server_name: str, config: McpServerConfig) -> dict[str, Any]:
"""Build server parameters for MultiServerMCPClient.
Args:
server_name: Name of the MCP server.
config: Configuration for the MCP server.
Returns:
Dictionary of server parameters for langchain-mcp-adapters.
"""
transport_type = config.type or "stdio"
params: dict[str, Any] = {"transport": transport_type}
if transport_type == "stdio":
if not config.command:
raise ValueError(f"MCP server '{server_name}' with stdio transport requires 'command' field")
params["command"] = config.command
params["args"] = config.args
# Add environment variables if present
if config.env:
params["env"] = config.env
elif transport_type in ("sse", "http"):
if not config.url:
raise ValueError(f"MCP server '{server_name}' with {transport_type} transport requires 'url' field")
params["url"] = config.url
# Add headers if present
if config.headers:
params["headers"] = config.headers
else:
raise ValueError(f"MCP server '{server_name}' has unsupported transport type: {transport_type}")
return params
def build_servers_config(extensions_config: ExtensionsConfig) -> dict[str, dict[str, Any]]:
"""Build servers configuration for MultiServerMCPClient.
Args:
extensions_config: Extensions configuration containing all MCP servers.
Returns:
Dictionary mapping server names to their parameters.
"""
enabled_servers = extensions_config.get_enabled_mcp_servers()
if not enabled_servers:
logger.info("No enabled MCP servers found")
return {}
servers_config = {}
for server_name, server_config in enabled_servers.items():
try:
servers_config[server_name] = build_server_params(server_name, server_config)
logger.info(f"Configured MCP server: {server_name}")
except Exception as e:
logger.error(f"Failed to configure MCP server '{server_name}': {e}")
return servers_config

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backend/src/mcp/tools.py Normal file
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"""Load MCP tools using langchain-mcp-adapters."""
import logging
from langchain_core.tools import BaseTool
from src.config.extensions_config import ExtensionsConfig
from src.mcp.client import build_servers_config
logger = logging.getLogger(__name__)
async def get_mcp_tools() -> list[BaseTool]:
"""Get all tools from enabled MCP servers.
Returns:
List of LangChain tools from all enabled MCP servers.
"""
try:
from langchain_mcp_adapters.client import MultiServerMCPClient
except ImportError:
logger.warning("langchain-mcp-adapters not installed. Install it to enable MCP tools: pip install langchain-mcp-adapters")
return []
# NOTE: We use ExtensionsConfig.from_file() instead of get_extensions_config()
# to always read the latest configuration from disk. This ensures that changes
# made through the Gateway API (which runs in a separate process) are immediately
# reflected when initializing MCP tools.
extensions_config = ExtensionsConfig.from_file()
servers_config = build_servers_config(extensions_config)
if not servers_config:
logger.info("No enabled MCP servers configured")
return []
try:
# Create the multi-server MCP client
logger.info(f"Initializing MCP client with {len(servers_config)} server(s)")
client = MultiServerMCPClient(servers_config)
# Get all tools from all servers
tools = await client.get_tools()
logger.info(f"Successfully loaded {len(tools)} tool(s) from MCP servers")
return tools
except Exception as e:
logger.error(f"Failed to load MCP tools: {e}", exc_info=True)
return []

3
backend/src/models/__init__.py Normal file
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from .factory import create_chat_model
__all__ = ["create_chat_model"]

40
backend/src/models/factory.py Normal file
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from langchain.chat_models import BaseChatModel
from src.config import get_app_config
from src.reflection import resolve_class
def create_chat_model(name: str | None = None, thinking_enabled: bool = False, **kwargs) -> BaseChatModel:
"""Create a chat model instance from the config.
Args:
name: The name of the model to create. If None, the first model in the config will be used.
Returns:
A chat model instance.
"""
config = get_app_config()
if name is None:
name = config.models[0].name
model_config = config.get_model_config(name)
if model_config is None:
raise ValueError(f"Model {name} not found in config") from None
model_class = resolve_class(model_config.use, BaseChatModel)
model_settings_from_config = model_config.model_dump(
exclude_none=True,
exclude={
"use",
"name",
"display_name",
"description",
"supports_thinking",
"when_thinking_enabled",
"supports_vision",
},
)
if thinking_enabled and model_config.when_thinking_enabled is not None:
if not model_config.supports_thinking:
raise ValueError(f"Model {name} does not support thinking. Set `supports_thinking` to true in the `config.yaml` to enable thinking.") from None
model_settings_from_config.update(model_config.when_thinking_enabled)
model_instance = model_class(**kwargs, **model_settings_from_config)
return model_instance

65
backend/src/models/patched_deepseek.py Normal file
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"""Patched ChatDeepSeek that preserves reasoning_content in multi-turn conversations.
This module provides a patched version of ChatDeepSeek that properly handles
reasoning_content when sending messages back to the API. The original implementation
stores reasoning_content in additional_kwargs but doesn't include it when making
subsequent API calls, which causes errors with APIs that require reasoning_content
on all assistant messages when thinking mode is enabled.
"""
from typing import Any
from langchain_core.language_models import LanguageModelInput
from langchain_core.messages import AIMessage
from langchain_deepseek import ChatDeepSeek
class PatchedChatDeepSeek(ChatDeepSeek):
"""ChatDeepSeek with proper reasoning_content preservation.
When using thinking/reasoning enabled models, the API expects reasoning_content
to be present on ALL assistant messages in multi-turn conversations. This patched
version ensures reasoning_content from additional_kwargs is included in the
request payload.
"""
def _get_request_payload(
self,
input_: LanguageModelInput,
*,
stop: list[str] | None = None,
**kwargs: Any,
) -> dict:
"""Get request payload with reasoning_content preserved.
Overrides the parent method to inject reasoning_content from
additional_kwargs into assistant messages in the payload.
"""
# Get the original messages before conversion
original_messages = self._convert_input(input_).to_messages()
# Call parent to get the base payload
payload = super()._get_request_payload(input_, stop=stop, **kwargs)
# Match payload messages with original messages to restore reasoning_content
payload_messages = payload.get("messages", [])
# The payload messages and original messages should be in the same order
# Iterate through both and match by position
if len(payload_messages) == len(original_messages):
for payload_msg, orig_msg in zip(payload_messages, original_messages):
if payload_msg.get("role") == "assistant" and isinstance(orig_msg, AIMessage):
reasoning_content = orig_msg.additional_kwargs.get("reasoning_content")
if reasoning_content is not None:
payload_msg["reasoning_content"] = reasoning_content
else:
# Fallback: match by counting assistant messages
ai_messages = [m for m in original_messages if isinstance(m, AIMessage)]
assistant_payloads = [(i, m) for i, m in enumerate(payload_messages) if m.get("role") == "assistant"]
for (idx, payload_msg), ai_msg in zip(assistant_payloads, ai_messages):
reasoning_content = ai_msg.additional_kwargs.get("reasoning_content")
if reasoning_content is not None:
payload_messages[idx]["reasoning_content"] = reasoning_content
return payload

3
backend/src/reflection/__init__.py Normal file
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from .resolvers import resolve_class, resolve_variable
__all__ = ["resolve_class", "resolve_variable"]

71
backend/src/reflection/resolvers.py Normal file
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from importlib import import_module
from typing import TypeVar
T = TypeVar("T")
def resolve_variable[T](
variable_path: str,
expected_type: type[T] | tuple[type, ...] | None = None,
) -> T:
"""Resolve a variable from a path.
Args:
variable_path: The path to the variable (e.g. "parent_package_name.sub_package_name.module_name:variable_name").
expected_type: Optional type or tuple of types to validate the resolved variable against.
If provided, uses isinstance() to check if the variable is an instance of the expected type(s).
Returns:
The resolved variable.
Raises:
ImportError: If the module path is invalid or the attribute doesn't exist.
ValueError: If the resolved variable doesn't pass the validation checks.
"""
try:
module_path, variable_name = variable_path.rsplit(":", 1)
except ValueError as err:
raise ImportError(f"{variable_path} doesn't look like a variable path. Example: parent_package_name.sub_package_name.module_name:variable_name") from err
try:
module = import_module(module_path)
except ImportError as err:
raise ImportError(f"Could not import module {module_path}") from err
try:
variable = getattr(module, variable_name)
except AttributeError as err:
raise ImportError(f"Module {module_path} does not define a {variable_name} attribute/class") from err
# Type validation
if expected_type is not None:
if not isinstance(variable, expected_type):
type_name = expected_type.__name__ if isinstance(expected_type, type) else " or ".join(t.__name__ for t in expected_type)
raise ValueError(f"{variable_path} is not an instance of {type_name}, got {type(variable).__name__}")
return variable
def resolve_class[T](class_path: str, base_class: type[T] | None = None) -> type[T]:
"""Resolve a class from a module path and class name.
Args:
class_path: The path to the class (e.g. "langchain_openai:ChatOpenAI").
base_class: The base class to check if the resolved class is a subclass of.
Returns:
The resolved class.
Raises:
ImportError: If the module path is invalid or the attribute doesn't exist.
ValueError: If the resolved object is not a class or not a subclass of base_class.
"""
model_class = resolve_variable(class_path, expected_type=type)
if not isinstance(model_class, type):
raise ValueError(f"{class_path} is not a valid class")
if base_class is not None and not issubclass(model_class, base_class):
raise ValueError(f"{class_path} is not a subclass of {base_class.__name__}")
return model_class

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