# 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
   ```

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
   ```
   `make docker-start` reads `config.yaml` and starts `provisioner` only for provisioner/Kubernetes sandbox mode.

   All services will start with hot-reload enabled:
   - Frontend changes are automatically reloaded
   - Backend changes trigger automatic restart
   - Gateway-hosted LangGraph-compatible runtime supports hot-reload

4. **Access the application**:
   - Web Interface: http://localhost:2026
   - API Gateway: http://localhost:2026/api/*
   - LangGraph-compatible API: http://localhost:2026/api/langgraph/*

#### Docker Commands

```bash
# Build the custom k3s image (with pre-cached sandbox image)
make docker-init
# Start Docker services (mode-aware, localhost:2026)
make docker-start
# Stop Docker development services
make docker-stop
# View Docker development logs
make docker-logs
# View Docker frontend logs
make docker-logs-frontend
# View Docker gateway logs
make docker-logs-gateway
```

If Docker builds are slow in your network, you can override the default package registries before running `make docker-init` or `make docker-start`:

```bash
export UV_INDEX_URL=https://pypi.org/simple
export NPM_REGISTRY=https://registry.npmjs.org
```

#### Recommended host resources

Use these as practical starting points for development and review environments:

| Scenario | Starting point | Recommended | Notes |
|---------|-----------|------------|-------|
| `make dev` on one machine | 4 vCPU, 8 GB RAM | 8 vCPU, 16 GB RAM | Best when DeerFlow uses hosted model APIs. |
| `make docker-start` review environment | 4 vCPU, 8 GB RAM | 8 vCPU, 16 GB RAM | Docker image builds and sandbox containers need extra headroom. |
| Shared Linux test server | 8 vCPU, 16 GB RAM | 16 vCPU, 32 GB RAM | Prefer this for heavier multi-agent runs or multiple reviewers. |

`2 vCPU / 4 GB` environments often fail to start reliably or become unresponsive under normal DeerFlow workloads.

#### Linux: Docker daemon permission denied

If `make docker-init`, `make docker-start`, or `make docker-stop` fails on Linux with an error like below, your current user likely does not have permission to access the Docker daemon socket:

```text
unable to get image 'deer-flow-gateway': permission denied while trying to connect to the Docker daemon socket at unix:///var/run/docker.sock
```

Recommended fix: add your current user to the `docker` group so Docker commands work without `sudo`.

1. Confirm the `docker` group exists:
   ```bash
   getent group docker
   ```
2. Add your current user to the `docker` group:
   ```bash
   sudo usermod -aG docker $USER
   ```
3. Apply the new group membership. The most reliable option is to log out completely and then log back in. If you want to refresh the current shell session instead, run:
   ```bash
   newgrp docker
   ```
4. Verify Docker access:
   ```bash
   docker ps
   ```
5. Retry the DeerFlow command:
   ```bash
   make docker-stop
   make docker-start
   ```

If `docker ps` still reports a permission error after `usermod`, fully log out and log back in before retrying.

#### Docker Architecture

```
Host Machine
  ↓
Docker Compose (deer-flow-dev)
  ├→ nginx (port 2026) ← Reverse proxy
  ├→ web (port 3000) ← Frontend with hot-reload
  ├→ gateway (port 8001) ← Gateway API + LangGraph-compatible runtime with hot-reload
  └→ provisioner (optional, port 8002) ← Started only in provisioner/K8s sandbox mode
```

**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** (this also sets up pre-commit hooks):
   ```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 service**:
   ```bash
   # Terminal 1: Start Gateway API + embedded agent runtime (port 8001)
   cd backend
   make dev

   # Terminal 2: Start Frontend (port 3000)
   cd frontend
   pnpm dev
   ```

2. **Start nginx** (run from the repo root):
   ```bash
   make nginx
   ```

   This runs `scripts/nginx.sh`, which launches nginx in the foreground the same way `scripts/serve.sh` (used by `make dev` / `make start`) does: it pre-creates the `logs/` and `temp/` directories and uses the local dev config at `docker/nginx/nginx.local.conf`.

3. **Access the application**:
   - Web Interface: http://localhost:2026

#### Nginx Configuration

The nginx configuration provides:
- Unified entry point on port 2026
- Rewrites `/api/langgraph/*` to Gateway's LangGraph-compatible API (8001)
- Routes other `/api/*` endpoints to Gateway API (8001)
- Routes non-API requests to Frontend (3000)
- Same-origin API routing; split-origin or port-forwarded browser clients should use the Gateway `GATEWAY_CORS_ORIGINS` allowlist
- 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
│   ├── packages/harness/   # deerflow-harness package (import: deerflow.*)
│   │   └── deerflow/       # Agents, tools, sandbox, MCP, skills, config
│   ├── app/                # FastAPI Gateway + IM channels (import: app.*)
│   │   ├── gateway/        # Gateway API and LangGraph-compatible runtime (port 8001)
│   │   └── channels/       # IM channel integrations
│   ├── 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/* and /api/langgraph/* (LangGraph-compatible 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. **Format and lint your code** (CI will reject unformatted code):
   ```bash
   # Backend
   cd backend
   make format   # ruff check --fix + ruff format

   # Frontend
   cd frontend
   pnpm format:write   # Prettier
   ```

4. **Test your changes** thoroughly

5. **Commit your changes**:
   ```bash
   git add .
   git commit -m "feat: description of your changes"
   ```

6. **Push and create a Pull Request**:
   ```bash
   git push origin feature/your-feature-name
   ```

## AI assistance disclosure

DeerFlow is an AI project and we welcome AI-assisted contributions. To help
reviewers calibrate how closely to read a change, **every pull request must
complete the "AI assistance" section of the
[PR template](.github/pull_request_template.md)**:

- which tool(s) you used (or `none`),
- how you used them, and
- a confirmation that a human has read, understands, and takes responsibility
  for the change.

Please don't delete the section. PRs that ignore it may be asked to fill it in
before review.

## Testing

```bash
# Backend tests (offline by default; excludes live external-API tests)
cd backend
make test

# Live DeerFlowClient integration tests (explicit opt-in)
# Requires a valid root config.yaml and API credentials.
make test-live

# Frontend unit tests
cd frontend
make test

# Frontend E2E tests (requires Chromium; builds and auto-starts the Next.js production server)
cd frontend
make test-e2e
```

`make test-live` calls real external APIs and may incur API costs or create
local sandboxes, artifacts, and files. It is never run by the default backend
test command or CI. Direct pytest invocations of `tests/test_client_live.py`
must also set `DEER_FLOW_RUN_LIVE_TESTS=1`.

### PR Regression Checks

Every pull request triggers the following CI workflows:

- **Backend unit tests** — [.github/workflows/backend-unit-tests.yml](.github/workflows/backend-unit-tests.yml)
- **Frontend unit tests** — [.github/workflows/frontend-unit-tests.yml](.github/workflows/frontend-unit-tests.yml)
- **Frontend E2E tests** — [.github/workflows/e2e-tests.yml](.github/workflows/e2e-tests.yml) (triggered only when `frontend/` files change)

## Code Style

- **Backend (Python)**: We use `ruff` for linting and formatting. Run `make format` before committing.
- **Frontend (TypeScript)**: We use ESLint and Prettier. Run `pnpm format:write` before committing.
- CI enforces formatting — PRs with unformatted code will fail the lint check.

## Documentation

- [Configuration Guide](backend/docs/CONFIGURATION.md) - Setup and configuration
- [Architecture Overview](backend/CLAUDE.md) - Technical architecture
- [MCP Setup Guide](backend/docs/MCP_SERVER.md) - Model Context Protocol configuration

## Troubleshooting Bundle

For setup, configuration, sandbox, or runtime issues, generate a redacted support
summary before filing:

```bash
make support-bundle
```

The command prints reporter next steps, writes a `*-issue-summary.md` file that
you can paste into the issue, writes a `*-issue-draft.md` file for AI-assisted
issue filing, and writes an optional evidence zip under
`.deer-flow/support-bundles/`. The zip includes toolchain versions, sanitized
`config.yaml` and `extensions_config.json` summaries, enabled tool/skill/MCP
structure, git metadata, and redacted `make doctor` output.

When filing the issue, paste the generated `*-issue-summary.md` into the issue
body. If an AI assistant files the issue, start from `*-issue-draft.md` and
replace every REQUIRED placeholder before filing; the draft intentionally does
not invent reproduction steps, expected behavior, or a problem summary. Attach
the zip only if a maintainer asks for the evidence bundle, or if the summary
alone is not enough to diagnose the issue. Maintainers and AI-assisted triage
should start with `triage.json`, which contains stable signals such as
`config_missing`, `node_version_too_old`, `doctor_failed`, and suggested next
steps. The other JSON files are evidence for follow-up inspection.

It intentionally does **not** include `.env`, raw conversation messages, or the
contents of files in thread workspaces/uploads/outputs. If you need to include a
thread, run `cd backend && uv run python ../scripts/support_bundle.py --thread-id
<thread-id> --include-doctor`; this adds file manifests only. Please still review
the generated zip before attaching it to a public issue.

## 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).