96756bc1f6
Same approach as the previous round — apply boundary casts where the code does dict-style mutation on TypedDict-typed values, narrow at return sites, and document the LLMSpecificMessage limitation in realtime adapters that pack history into a single text message. aws_nova_sonic_adapter.py — pure typing + small narrowing fixes: - Filter LLMSpecific items in `_from_universal_context_messages` (documented). - `_from_universal_context_message` now declared `-> AWSNovaSonicConversationHistoryMessage | None` (it already had paths returning None implicitly). - `get_messages_for_logging` returns `dict[str, Any]` per element via `dataclasses.asdict`, matching the declared return type. - Use a local `role` variable so pyright keeps the narrowing across the truthy-content guard. grok_realtime_adapter.py / inworld_realtime_adapter.py — same shape of fix as `open_ai_realtime_adapter.py` from the previous batch. The two files are essentially copies of the OpenAI Realtime adapter, so the same template applies: cast at the boundary, filter LLMSpecificMessage with a documented note, replace the implicit-None fallthrough with `raise ValueError`, and switch the `text_content +=` pattern (which fails when one of the parts is None) to a `text_parts.append(...)` + `" ".join(...)` pattern. open_ai_adapter.py — pure typing. Cast at the `OpenAILLMInvocationParams` return, narrow the system-instruction warning's `initial_content` to `str | None`, and cast the custom-tools list to `list[ChatCompletionToolParam]`. open_ai_responses_adapter.py — pure typing. Same shape: narrow `first_content` to `str | None` for the warning resolver, cast the constructed dict literals at append sites where the target is `ResponseInputItemParam`, and cast `get_messages_for_logging`'s return to the declared `list[dict[str, Any]]`. processors/aggregators/llm_context.py — pure typing. Cast the deepcopied message in the redaction loop in `get_messages` to `dict[str, Any]` and the create_image/audio_message return-dict literals to `LLMContextMessage`. Removes 6 newly-clean files from the pyright ignore list. Net: -77 pyright errors (full-config: 680 -> 603).
🎙️ 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.