feat: add Langflow
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Sun-ZhenXing
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src/langflow/README.md
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src/langflow/README.md
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# Langflow
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Langflow is a low-code visual framework for building AI applications. It's Python-based and agnostic to any model, API, or database, making it easy to build RAG applications, multi-agent systems, and custom AI workflows.
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## Features
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- **Visual Flow Builder**: Drag-and-drop interface for building AI applications
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- **Multi-Model Support**: Works with OpenAI, Anthropic, Google, HuggingFace, and more
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- **RAG Components**: Built-in support for vector databases and retrieval
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- **Custom Components**: Create your own Python components
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- **Agent Support**: Build multi-agent systems with memory and tools
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- **Real-Time Monitoring**: Track executions and debug flows
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- **API Integration**: REST API for programmatic access
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## Quick Start
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1. Copy `.env.example` to `.env`:
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```bash
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cp .env.example .env
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```
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2. (Optional) Edit `.env` to customize settings:
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- Generate a secure `LANGFLOW_SECRET_KEY` for production
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- Set `LANGFLOW_AUTO_LOGIN=false` to require authentication
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- Configure superuser credentials
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- Add API keys for LLM providers
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3. Start Langflow:
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```bash
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docker compose up -d
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```
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4. Wait for services to be ready
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5. Access Langflow UI at `http://localhost:7860`
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6. Start building your AI application!
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## Default Configuration
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| Service | Port | Description |
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| ---------- | ---- | ------------------- |
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| Langflow | 7860 | Web UI and API |
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| PostgreSQL | 5432 | Database (internal) |
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**Default Credentials** (if authentication enabled):
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- Username: `langflow`
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- Password: `langflow`
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## Environment Variables
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Key environment variables (see `.env.example` for full list):
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| Variable | Description | Default |
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| ----------------------------- | ----------------------------- | ---------- |
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| `LANGFLOW_VERSION` | Langflow image version | `latest` |
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| `LANGFLOW_PORT_OVERRIDE` | Host port for UI | `7860` |
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| `POSTGRES_PASSWORD` | Database password | `langflow` |
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| `LANGFLOW_AUTO_LOGIN` | Auto-login (disable for auth) | `true` |
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| `LANGFLOW_SUPERUSER` | Superuser username | `langflow` |
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| `LANGFLOW_SUPERUSER_PASSWORD` | Superuser password | `langflow` |
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| `LANGFLOW_SECRET_KEY` | Secret key for sessions | (empty) |
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| `LANGFLOW_COMPONENTS_PATH` | Custom components directory | (empty) |
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| `LANGFLOW_LOAD_FLOWS_PATH` | Auto-load flows directory | (empty) |
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| `TZ` | Timezone | `UTC` |
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## Resource Requirements
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**Minimum**:
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- CPU: 1 core
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- RAM: 1GB
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- Disk: 5GB
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**Recommended**:
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- CPU: 2+ cores
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- RAM: 2GB+
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- Disk: 20GB+
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## Volumes
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- `postgres_data`: PostgreSQL database data
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- `langflow_data`: Langflow configuration, flows, and logs
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## Using Langflow
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### Building Your First Flow
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1. Access the UI at `http://localhost:7860`
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2. Click "New Flow" or use a template
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3. Drag components from the sidebar to the canvas
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4. Connect components by dragging between ports
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5. Configure component parameters
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6. Click "Run" to test your flow
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7. Use the API or integrate with your application
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### Adding API Keys
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You can add API keys for LLM providers in two ways:
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#### Option 1: Global Variables (Recommended)
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1. Click your profile icon → Settings
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2. Go to "Global Variables"
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3. Add your API keys (e.g., `OPENAI_API_KEY`)
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4. Reference them in components using `{OPENAI_API_KEY}`
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#### Option 2: Environment Variables
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Add to your `.env` file:
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```text
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OPENAI_API_KEY=sk-...
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ANTHROPIC_API_KEY=sk-ant-...
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GOOGLE_API_KEY=...
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```
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Langflow will automatically create global variables from these.
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### Using the API
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Get your API token from the UI:
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1. Click your profile icon → Settings
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2. Go to "API Keys"
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3. Create a new API key
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Example: Run a flow
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```bash
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curl -X POST http://localhost:7860/api/v1/run/YOUR_FLOW_ID \
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-H "Authorization: Bearer YOUR_API_KEY" \
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-H "Content-Type: application/json" \
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-d '{"input_value": "Hello"}'
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```
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### Custom Components
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1. Create a directory for your components
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2. Set `LANGFLOW_COMPONENTS_PATH` in `.env`
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3. Create Python files with your component classes
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4. Restart Langflow to load them
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Example component structure:
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```python
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from langflow import CustomComponent
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class MyComponent(CustomComponent):
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display_name = "My Component"
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description = "Does something cool"
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def build(self):
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# Your component logic
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return result
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```
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## Security Considerations
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1. **Secret Key**: Generate a strong `LANGFLOW_SECRET_KEY` for production:
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```bash
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python -c "from secrets import token_urlsafe; print(token_urlsafe(32))"
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```
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2. **Authentication**: Set `LANGFLOW_AUTO_LOGIN=false` to require login
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3. **Database Password**: Use a strong PostgreSQL password
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4. **API Keys**: Store sensitive keys as global variables, not in flows
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5. **SSL/TLS**: Use reverse proxy with HTTPS in production
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6. **Network Access**: Restrict access with firewall rules
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## Upgrading
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To upgrade Langflow:
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1. Update `LANGFLOW_VERSION` in `.env` (or use `latest`)
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2. Pull and restart:
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```bash
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docker compose pull
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docker compose up -d
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```
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3. Check logs:
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```bash
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docker compose logs -f langflow
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```
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## Troubleshooting
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**Service won't start:**
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- Check logs: `docker compose logs langflow`
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- Verify database: `docker compose ps postgres`
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- Ensure sufficient resources allocated
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**Cannot access UI:**
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- Check port 7860 is not in use: `netstat -an | findstr 7860`
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- Verify firewall settings
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- Check container health: `docker compose ps`
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**API key not working:**
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- Verify the key is set in Global Variables
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- Check the variable name matches in your components
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- Ensure `LANGFLOW_STORE_ENVIRONMENT_VARIABLES=true`
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**Flow execution errors:**
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- Check component configurations
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- Review logs in the UI under each component
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- Verify API keys have sufficient credits/permissions
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## References
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- Official Website: <https://langflow.org>
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- Documentation: <https://docs.langflow.org>
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- GitHub: <https://github.com/langflow-ai/langflow>
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- Discord Community: <https://discord.gg/EqksyE2EX9>
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- Docker Hub: <https://hub.docker.com/r/langflowai/langflow>
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## License
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Langflow is licensed under MIT. See [LICENSE](https://github.com/langflow-ai/langflow/blob/main/LICENSE) for more information.
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