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hush/AGENT
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AGENTS.md
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AGENTS.md
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# AGENTS.md
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## Project Overview
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Pipecat is an open-source Python framework for building real-time voice and multimodal conversational AI agents. The codebase is organized around a pipeline architecture where data flows through connected services (STT → LLM → TTS).
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## Development Environment Setup
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### Prerequisites
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- **Minimum Python Version:** 3.10
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- **Recommended Python Version:** 3.12
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- **Package Manager:** uv (recommended) or pip
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### Setup Commands
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```bash
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# Clone the repository
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git clone https://github.com/pipecat-ai/pipecat.git
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cd pipecat
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# Install dependencies with uv (recommended)
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uv sync --group dev --all-extras \
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--no-extra gstreamer \
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--no-extra krisp \
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--no-extra local \
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--no-extra ultravox
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# Or with pip
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pip install -e ".[dev]"
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# Install pre-commit hooks
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uv run pre-commit install
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# Set up environment variables
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cp env.example .env
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```
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## Build and Test Commands
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### Running Tests
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```bash
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# Run all tests
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uv run pytest
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# Run specific test file
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uv run pytest tests/test_name.py
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# Run tests with coverage
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uv run pytest --cov=pipecat --cov-report=html
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```
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### Code Quality
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```bash
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# Format code (required before commits)
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uv run ruff format
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# Lint code
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uv run ruff check
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# Type checking
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uv run mypy src/pipecat
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# Run pre-commit checks manually
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uv run pre-commit run --all-files
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```
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### Documentation
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```bash
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# Build API documentation
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cd docs/api
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./build-docs.sh
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# Build docs manually
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sphinx-build -b html . _build/html -W --keep-going
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```
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## Code Style Guidelines
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### Python Standards
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- **Formatting:** Strict PEP 8 via Ruff
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- **Docstrings:** Google-style format
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- **Type Hints:** Required for all public APIs
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- **Import Organization:** Automated via Ruff
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### Docstring Conventions
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- **Classes:** Describe purpose + `__init__` with complete `Args:` section
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- **Dataclasses:** Use `Parameters:` section, no `__init__` docstring
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- **Methods:** Include `Args:` and `Returns:` sections
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- **Properties:** Must have `Returns:` section
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- **Examples:** Use `Examples:` section with `::` syntax
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### File Organization
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```
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src/pipecat/ # Main package
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├── processors/ # Frame processors
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├── services/ # AI service integrations
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├── transports/ # Communication layers
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├── frames/ # Data frame definitions
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└── pipeline/ # Pipeline orchestration
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examples/foundational/ # Step-by-step tutorials
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tests/ # Test suite
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```
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## Testing Instructions
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### Test Structure
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- **Unit Tests:** Test individual components in isolation
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- **Integration Tests:** Test service interactions
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- **Example Tests:** Validate foundational examples work
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### Adding Tests
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```bash
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# Test naming convention
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test_<component>_<functionality>.py
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# Run specific test pattern
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uv run pytest -k "test_pipeline"
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# Run with debugging
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uv run pytest -s -vv tests/test_name.py::test_function
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```
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### Pre-commit Requirements
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All commits must pass:
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- Ruff formatting
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- Ruff linting
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- Type checking
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- Basic test suite
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## Dependency Management
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### Using uv (Recommended)
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```bash
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# Add runtime dependency
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uv add package-name
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# Add optional dependency
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uv add --optional service package-name
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# Add development dependency
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uv add --group dev package-name
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# Update lockfile
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uv lock
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# Sync dependencies
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uv sync
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```
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### Important Notes
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- **Always commit both `pyproject.toml` and `uv.lock` together**
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- **Never manually edit `uv.lock`** - it's auto-generated
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- **Use extras for optional service dependencies** (e.g., `[openai]`, `[cartesia]`)
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## Project Structure Guidelines
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### Service Integration
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When adding new AI services:
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1. Create service class in `src/pipecat/services/<provider>/`
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2. Follow existing patterns (e.g., STTService, LLMService)
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3. Add to appropriate extras in `pyproject.toml`
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4. Include tests in `tests/`
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5. Add documentation examples
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### Frame Processing
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For custom processors:
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1. Inherit from `FrameProcessor`
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2. Implement `process_frame()` method. ALWAYS explicitly call `await super().process_frame(frame, direction)` at the top of this method.
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3. Handle frame direction (FrameDirection.UPSTREAM/DOWNSTREAM)
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4. Add proper type hints and docstrings
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### Transport Implementation
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For new transport layers:
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1. Inherit from `BaseTransport`
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2. Implement required abstract methods
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3. Handle connection lifecycle
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4. Support both input and output streams
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## Security Considerations
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### API Keys
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- **Never commit API keys** to the repository
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- **Use environment variables** for all secrets
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- **Reference `env.example`** for required variables
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- **Use `.env` files** for local development
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### Input Validation
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- **Validate all external inputs** (audio, text, API responses)
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- **Sanitize user data** before processing
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- **Handle rate limiting** for external services
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- **Implement proper timeout handling**
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## Performance Guidelines
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### Memory Management
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- **Clean up resources** in transport disconnection handlers
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- **Use async context managers** for service connections
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- **Implement proper frame lifecycle** management
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### Latency Optimization
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- **Choose appropriate STT services** for latency requirements
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- **Use streaming TTS** when possible
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- **Implement connection pooling** for HTTP services
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- **Consider WebRTC** for real-time applications
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## Common Patterns
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### Error Handling
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```python
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@transport.event_handler("on_error")
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async def on_error(transport, error):
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logger.error(f"Transport error: {error}")
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# Shutdown the pipeline
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await task.queue_frame(EndFrame())
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```
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### Service Configuration
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```python
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# Use environment variables for configuration
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service = OpenAILLMService(
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api_key=os.getenv("OPENAI_API_KEY", ""),
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model="gpt-4o",
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params={"temperature": 0.7}
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)
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```
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### Pipeline Assembly
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```python
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pipeline = Pipeline([
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transport.input(),
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stt_service,
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context_aggregator.user(),
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llm_service,
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tts_service,
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transport.output(),
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context_aggregator.assistant(),
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])
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```
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## Commit and PR Guidelines
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### Commit Message Format
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```
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<type>(<scope>): <description>
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[optional body]
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[optional footer]
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```
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Types: `feat`, `fix`, `docs`, `style`, `refactor`, `test`, `chore`
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### PR Requirements
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- **All tests must pass**
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- **Code must be properly formatted** (Ruff)
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- **Include appropriate tests** for new functionality
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- **Update documentation** if needed
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- **Reference related issues** in description
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### Review Process
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1. Automated checks must pass
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2. Manual code review by maintainers
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3. Documentation review for user-facing changes
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4. Integration testing for service additions
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## Troubleshooting
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### Common Issues
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- **Import errors:** Run `uv sync` to ensure dependencies are installed
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- **Test failures:** Check environment variables in `.env`
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- **Format errors:** Run `uv run ruff format` before committing
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- **Type errors:** Ensure all public methods have type hints
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### Development Tips
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- **Use foundational examples** as starting points for testing
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- **Check existing services** for integration patterns
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- **Run tests frequently** during development
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- **Use IDE integration** for Ruff formatting
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### Getting Help
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||||
- **Documentation:** [docs.pipecat.ai](https://docs.pipecat.ai)
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- **Issues:** [GitHub Issues](https://github.com/pipecat-ai/pipecat/issues)
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