Merge pull request #3636 from pipecat-ai/aleix/initial-claude-md

initial CLAUDE.md
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Aleix Conchillo Flaqué
2026-02-03 19:31:16 -08:00
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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
Pipecat is an open-source Python framework for building real-time voice and multimodal conversational AI agents. It orchestrates audio/video, AI services, transports, and conversation pipelines using a frame-based architecture.
## Common Commands
```bash
# Setup development environment
uv sync --group dev --all-extras --no-extra gstreamer --no-extra krisp
# Install pre-commit hooks
uv run pre-commit install
# Run all tests
uv run pytest
# Run a single test file
uv run pytest tests/test_name.py
# Run a specific test
uv run pytest tests/test_name.py::test_function_name
# Preview changelog
towncrier build --draft --version Unreleased
# Update dependencies (after editing pyproject.toml)
uv lock && uv sync
```
## Architecture
### Frame-Based Pipeline Processing
All data flows as **Frame** objects through a pipeline of **FrameProcessors**:
```
Transport Input → Pipeline Source → [Processor1] → [Processor2] → ... → Pipeline Sink → Transport Output
```
**Key components:**
- **Frames** (`src/pipecat/frames/frames.py`): Data units (audio, text, video) and control signals. Flow DOWNSTREAM (input→output) or UPSTREAM (acknowledgments/errors).
- **FrameProcessor** (`src/pipecat/processors/frame_processor.py`): Base processing unit. Each processor receives frames, processes them, and pushes results downstream.
- **Pipeline** (`src/pipecat/pipeline/pipeline.py`): Chains processors together.
- **ParallelPipeline** (`src/pipecat/pipeline/parallel_pipeline.py`): Runs multiple pipelines in parallel.
- **Transports** (`src/pipecat/transports/`): External I/O layer (Daily WebRTC, LiveKit WebRTC, WebSocket, Local). Abstract interface via `BaseTransport`.
- **Services** (`src/pipecat/services/`): 60+ AI provider integrations (STT, TTS, LLM, etc.). Extend base classes: `AIService`, `LLMService`, `STTService`, `TTSService`, `VisionService`.
### Important Patterns
- **Context Aggregation**: `LLMContext` accumulates messages for LLM calls; `UserResponse` aggregates user input
- **Turn Management**: Turn management is done through `LLMUserAggregator` and
`LLMAssistantAggregator`, created with `LLMContextAggregatorPair`
- **User turn strategies**: Detection of when the user starts and stops speaking is done via user turn start/stop strategies. They push `UserStartedSpeakingFrame` and `UserStoppedSpeakingFrame` respectively.
- **Interruptions**: Interruptions are usually triggered by a user turn start strategy (e.g. `VADUserTurnStartStrategy`) but they can be triggered by other processors as well, in which case the user turn start strategies don't need to.
- **Uninterruptible Frames**: These are frames that will not be removed from internal queues even if there's an interruption. For example, `EndFrame` and `StopFrame`.
- **Events**: Most classes in Pipecat have `BaseObject` as the very base class. `BaseObject` has support for events. Events can run in the background in an async task (default) or synchronously (`sync=True`) if we want immediate action. Synchronous event handlers need to exectue fast..
### Key Directories
| Directory | Purpose |
|---------------------------|----------------------------------------------------|
| `src/pipecat/frames/` | Frame definitions (100+ types) |
| `src/pipecat/processors/` | FrameProcessor base + aggregators, filters, audio |
| `src/pipecat/pipeline/` | Pipeline orchestration |
| `src/pipecat/services/` | AI service integrations (60+ providers) |
| `src/pipecat/transports/` | Transport layer (Daily, LiveKit, WebSocket, Local) |
| `src/pipecat/audio/` | VAD, filters, mixers, turn detection, DTMF |
| `src/pipecat/turns/` | User turn management |
## Code Style
- **Docstrings**: Google-style. Classes describe purpose; `__init__` has `Args:` section; dataclasses use `Parameters:` section.
- **Linting**: Ruff (line length 100). Pre-commit hooks enforce formatting.
- **Type hints**: Required for complex async code.
### Docstring Example
```python
class MyService(LLMService):
"""Description of what the service does.
More detailed description.
Event handlers available:
- on_connected: Called when we are connected
Example::
@service.event_handler("on_connected")
async def on_connected(service, frame):
...
"""
def __init__(self, param1: str, **kwargs):
"""Initialize the service.
Args:
param1: Description of param1.
**kwargs: Additional arguments passed to parent.
"""
super().__init__(**kwargs)
```
## Changelog
Every user-facing PR needs a changelog fragment in `changelog/`:
```
changelog/<PR_number>.<type>.md
```
Types: `added`, `changed`, `deprecated`, `removed`, `fixed`, `security`, `other`
Content format (include the `-`):
```markdown
- Added support for new feature X.
```
Skip changelog for: documentation-only, internal refactoring, test-only, CI changes.
## Service Implementation
When adding a new service:
1. Extend the appropriate base class (`STTService`, `TTSService`, `LLMService`, etc.)
2. Implement required abstract methods
3. Handle necessary frames
4. By default, all frames should be pushed in the direction they came
5. Push `ErrorFrame` on failures
6. Add metrics tracking via `MetricsData` if relevant
7. Follow the pattern of existing services in `src/pipecat/services/`