4703df8686
A fourth pass over low-error-count files. Drops 8 files (57 → 49) and full-pyright errors from 525 → 496. Default pyright stays clean. Optional access on transport/client receivers (4 files). Same fix shape as #4359 — a receiver typed `X | None` accessed without a guard. For "should never happen" cases (caller's lifecycle ensures the field is non-None when the method runs), used `assert` rather than silent early-return so an invariant violation surfaces loudly: - `transports/whatsapp/client.py` (5 errors): `_validate_whatsapp_webhook_request` was typed `bytes` / `str` but called with `bytes | None` / `str | None`. Widened the helper signature and pushed the explicit None-check inside (matching its existing empty-string check). Also handled `pipecat_connection.get_answer()` returning `None` — would have crashed at `.get("sdp")` before. - `transports/websocket/client.py` (5 errors): four are the deprecated `websockets.WebSocketClientProtocol` alias (same `# pyright: ignore[reportAttributeAccessIssue]` as the `services/websocket_service.py` fix from earlier in this PR). The fifth was `async for message in self._websocket` — traced the call chain and confirmed `_client_task` is created only after `self._websocket` is assigned and cancelled before it's cleared, so the field is never None when `_client_task_handler` runs. Used `assert`. - `services/openai/stt.py` (4 errors): same pattern. `_receive_messages` is started by `_connect()` only when `self._websocket` is set, and the reconnect loop in `WebsocketService._receive_task_handler` re-establishes it before each retry. `assert` at entry. Plus L478/L483: the `try`/`except ModuleNotFoundError` import-guard makes `websocket_connect` and `State` `<type> | None`; `__init__` already raises `ImportError` if either is None, so an `assert` at the `_connect_websocket` use site is honest. Plus an L538 `Language | str` cast (same shape as last batch). - `services/deepgram/flux/base.py` (2 errors): `event = data.get("event")` flowed into `_handle_turn_resumed(event: str)` as `Any | None`. Tightened with an `isinstance(event, str)` guard before the `FluxEventType(event)` lookup. The other error (`average_confidence > min_confidence` where `min_confidence: float | None`) was a latent crash on missing confidence data — restored the original `not min_confidence` (which treats both `None` and `0.0` as "no filter") and added an explicit drop-on-missing-confidence-data branch. `gemini_live` Settings/InputParams (vertex). The deprecated `InputParams` declares `modalities: GeminiModalities | None` and `media_resolution: GeminiMediaResolution | None`, but their downstream usage at `services/google/gemini_live/llm.py:952,959` calls `.value` on each — `None` would crash. Rather than touching the deprecated input model, translate `None` to the canonical defaults (`GeminiModalities.AUDIO`, `GeminiMediaResolution.UNSPECIFIED`) at the assignment site in `vertex/llm.py`. Also fixed an unrelated annotation bug: `_get_credentials` was annotated `-> str` but actually returns `service_account.Credentials` (used correctly by the caller — only the annotation was wrong). `moondream/vision.py` (3 errors). `frame.format` is `str | None` but `Image.frombytes(mode, ...)` requires `str`; raise instead of crashing on missing format. The other two errors are pyright thinking the moondream2-custom `encode_image` and `query` methods are `Tensor` (rather than callables) — those are provided by the model code via `trust_remote_code=True` and aren't visible to pyright on the base `AutoModelForCausalLM` type. Scoped `# pyright: ignore[reportCallIssue]` on the two call sites. `transports/base_output.py` (3 errors). Two are `self._mixer.mix(...)` calls in `with_mixer`, a closure invoked only when `self._mixer` is truthy at the call site — captured the mixer to a local variable inside the closure with an `assert`, then used that. Third is the PIL `frombytes(mode, ...)` shape — `frame.format is None` early- return guard at the top of `resize_frame` so the main resize logic reads cleanly. `elevenlabs/tts.py` (4 errors). The payload-building dict at L1271 was typed `dict[str, str | dict[str, float | bool]]` — an aspirational shape that matched only the first two assignments. Subsequent code assigned `list[dict[...]]` (pronunciation locators) and bools, all violating the annotation. Same pattern at L926 (the WebSocket-init `msg`). Both widened to `dict[str, Any]`, which is the honest shape for a JSON request payload and what similar code uses elsewhere. Files dropped from the ignore list (57 → 49): services/deepgram/flux/base.py, services/elevenlabs/tts.py, services/google/gemini_live/vertex/llm.py, services/moondream/vision.py, services/openai/stt.py, transports/base_output.py, transports/websocket/client.py, transports/whatsapp/client.py.
🎙️ Pipecat: Real-Time Voice & Multimodal AI Agents
Pipecat is an open-source Python framework for building real-time voice and multimodal conversational agents. Orchestrate audio and video, AI services, different transports, and conversation pipelines effortlessly—so you can focus on what makes your agent unique.
Want to dive right in? Run
pipecat init quickstartor follow the quickstart guide.
🚀 What You Can Build
- Voice Assistants – natural, streaming conversations with AI
- AI Companions – coaches, meeting assistants, characters
- Multimodal Interfaces – voice, video, images, and more
- Interactive Storytelling – creative tools with generative media
- Business Agents – customer intake, support bots, guided flows
- Complex Dialog Systems – design logic with structured conversations
🧠 Why Pipecat?
- Voice-first: Integrates speech recognition, text-to-speech, and conversation handling
- Pluggable: Supports many AI services and tools
- Composable Pipelines: Build complex behavior from modular components
- Real-Time: Ultra-low latency interaction with different transports (e.g. WebSockets or WebRTC)
🌐 Pipecat Ecosystem
🧩 Multi-agent systems
Need multiple AI agents working together? Pipecat Subagents lets you build distributed multi-agent systems where each agent runs its own pipeline and communicates through a shared message bus. Hand off conversations between specialists, dispatch background tasks, and scale agents across processes or machines.
📱 Client SDKs
Building client applications? You can connect to Pipecat from any platform using our official SDKs:
JavaScript | React | React Native | Swift | Kotlin | C++ | ESP32
🧭 Structured conversations
Looking to build structured conversations? Check out Pipecat Flows for managing complex conversational states and transitions.
🪄 Beautiful UIs
Want to build beautiful and engaging experiences? Checkout the Voice UI Kit, a collection of components, hooks and templates for building voice AI applications quickly.
🛠️ Create and deploy projects
Create a new project in under a minute with the Pipecat CLI. Then use the CLI to monitor and deploy your agent to production.
🔍 Debugging
Looking for help debugging your pipeline and processors? Check out Whisker, a real-time Pipecat debugger.
🖥️ Terminal
Love terminal applications? Check out Tail, a terminal dashboard for Pipecat.
🤖 Claude Code Skills
Use Pipecat Skills with Claude Code to scaffold projects, deploy to Pipecat Cloud, and more. Install the marketplace with:
claude plugin marketplace add pipecat-ai/skills
and install any of the available plugins.
🧩 Community Integrations
Build and share your own Pipecat service integrations! Browse existing community integrations or check out our guide to create your own.
📺️ Pipecat TV Channel
Catch new features, interviews, and how-tos on our Pipecat TV channel.
🎬 See it in action
🧩 Available services
📚 View full services documentation →
⚡ Getting started
You can get started with Pipecat running on your local machine, then move your agent processes to the cloud when you're ready.
-
Install uv
curl -LsSf https://astral.sh/uv/install.sh | shNeed help? Refer to the uv install documentation.
-
Install the module
# For new projects uv init my-pipecat-app cd my-pipecat-app uv add pipecat-ai # Or for existing projects uv add pipecat-ai -
Set up your environment
cp env.example .env -
To keep things lightweight, only the core framework is included by default. If you need support for third-party AI services, you can add the necessary dependencies with:
uv add "pipecat-ai[option,...]"
Using pip? You can still use
pip install pipecat-aiandpip install "pipecat-ai[option,...]"to get set up.
🧪 Code examples
- Foundational — small snippets that build on each other, introducing one or two concepts at a time
- Example apps — complete applications that you can use as starting points for development
🛠️ Contributing to the framework
Prerequisites
Minimum Python Version: 3.11 Recommended Python Version: >= 3.12
Setup Steps
-
Clone the repository and navigate to it:
git clone https://github.com/pipecat-ai/pipecat.git cd pipecat -
Install development and testing dependencies:
uv sync --group dev --all-extras \ --no-extra gstreamer \ --no-extra local \ -
Install the git pre-commit hooks:
uv run pre-commit install
Note
: Some extras (local, gstreamer) require system dependencies. See documentation if you encounter build errors.
Claude Code Skills
Install development workflow skills for contributing to Pipecat with Claude Code:
claude plugin marketplace add pipecat-ai/pipecat
claude plugin install pipecat-dev@pipecat-dev-skills
Running tests
To run all tests, from the root directory:
uv run pytest
Run a specific test suite:
uv run pytest tests/test_name.py
🤝 Contributing
We welcome contributions from the community! Whether you're fixing bugs, improving documentation, or adding new features, here's how you can help:
- Found a bug? Open an issue
- Have a feature idea? Start a discussion
- Want to contribute code? Check our CONTRIBUTING.md guide
- Documentation improvements? Docs PRs are always welcome
Before submitting a pull request, please check existing issues and PRs to avoid duplicates.
We aim to review all contributions promptly and provide constructive feedback to help get your changes merged.