b03247f360
Replaces every "task" identifier that referred to the BaseTask abstraction with "worker". Asyncio task plumbing (asyncio.Task, BaseTaskManager, TaskManager, create_task, cancel_task, etc.) stays untouched. Highlights: - Classes: BaseTask → BaseWorker, PipelineTask → PipelineWorker, LLMTask → LLMWorker, LLMContextTask → LLMContextWorker, TaskBus → WorkerBus, TaskRegistry → WorkerRegistry, TaskActivationArgs → WorkerActivationArgs, TaskReadyData → WorkerReadyData, TaskRegistryEntry → WorkerRegistryEntry, TaskObserver → WorkerObserver, all Bus*TaskMessage → Bus*WorkerMessage, BusAddTaskMessage.task field → worker, BusWorkerRegistryMessage.tasks field → workers. - Methods/decorators: activate_task → activate_worker, deactivate_task → deactivate_worker, add_task → add_worker, watch_task → watch_worker, @task_ready → @worker_ready, setup_pipeline_task hook → setup_pipeline_worker. - Params/fields: FrameProcessorSetup.pipeline_task and FunctionCallParams.pipeline_task → pipeline_worker. Parameter names like task_name → worker_name; spawn/run accept worker:. - Files: pipeline/base_task.py → base_worker.py, pipeline/task.py → worker.py (plus a re-export shim at pipeline/task.py), task_observer.py → worker_observer.py, task_ready_decorator.py → worker_ready_decorator.py, pipecat.tasks → pipecat.workers, llm_task.py → llm_worker.py, llm_context_task.py → llm_context_worker.py, examples/multi-task → examples/multi-worker. Back-compat: - PipelineTask kept as a deprecated subclass of PipelineWorker that warns on construction. - pipecat.pipeline.task re-exports PipelineWorker/PipelineTask/etc. so existing user imports keep working. - FrameProcessor.pipeline_task kept as a deprecated property that forwards to pipeline_worker. Local variables in examples that hold a worker (task = PipelineTask(...)) are renamed to worker = PipelineWorker(...). Asyncio-task locals (runner_task, etc.) are preserved.
Pipecat Examples
This directory contains examples showing how to build voice and multimodal agents with Pipecat.
Setup
-
Follow the README steps to get your local environment configured.
Run from root directory: Make sure you are running the steps from the root directory.
Using local audio?: The
LocalAudioTransportrequires a system dependency forportaudio. Install the dependency to use the transport. -
Copy the
env.examplefile and add API keys for services you plan to use:cp env.example .env # Edit .env with your API keys -
Run any example:
uv run python getting-started/01-say-one-thing.py -
Open the web interface at http://localhost:7860/client/ and click "Connect"
Running examples with other transports
Most examples support running with other transports, like Twilio or Daily.
Daily
You need to create a Daily account at https://dashboard.daily.co/u/signup. Once signed up, you can create your own room from the dashboard and set the environment variables DAILY_ROOM_URL and DAILY_API_KEY. Alternatively, you can let the example create a room for you (still needs DAILY_API_KEY environment variable). Then, start any example with -t daily:
uv run getting-started/06-voice-agent.py -t daily
Twilio
It is also possible to run the example through a Twilio phone number. You will need to setup a few things:
- Install and run ngrok.
ngrok http 7860
- Configure your Twilio phone number. One way is to setup a TwiML app and set the request URL to the ngrok URL from step (1). Then, set your phone number to use the new TwiML app.
Then, run the example with:
uv run getting-started/06-voice-agent.py -t twilio -x NGROK_HOST_NAME
Directory Structure
getting-started/
Progressive introduction to Pipecat, from minimal TTS to a full voice agent with function calling.
voice/
Full STT + LLM + TTS voice agent pipelines showcasing different speech service providers (Deepgram, ElevenLabs, Cartesia, etc.)
function-calling/
Function calling with different LLM providers (OpenAI, Anthropic, Google, etc.)
transcription/
Speech-to-text examples with various STT providers.
vision/
Image description and vision capabilities with different multimodal LLMs.
realtime/
Realtime and multimodal live APIs (OpenAI Realtime, Gemini Live, AWS Nova Sonic, Ultravox, Grok).
persistent-context/
Maintaining conversation context across sessions with different providers.
context-summarization/
Summarizing conversation context to manage token limits.
update-settings/
Changing service settings at runtime, organized by service type:
turn-management/
Turn detection, interruption handling, and user input management.
thinking-and-mcp/
LLM thinking/reasoning modes and MCP (Model Context Protocol) tool server integration.
transports/
Transport layer examples (WebRTC, Daily, LiveKit).
video-avatar/
Video avatar integrations (Tavus, HeyGen, Simli, LemonSlice).
video-processing/
Video processing, mirroring, GStreamer, and custom video tracks.
audio/
Audio recording, background sounds, and sound effects.
observability/
Pipeline monitoring: observers, heartbeats, and Sentry metrics.
rag/
Retrieval-augmented generation, grounding, and long-term memory (Mem0, Gemini).
features/
Miscellaneous features: wake phrases, live translation, service switching, voice switching, and more.
Advanced Usage
Customizing Network Settings
uv run python <example-name> --host 0.0.0.0 --port 8080
Troubleshooting
- No audio/video: Check browser permissions for microphone and camera
- Connection errors: Verify API keys in
.envfile - Port conflicts: Use
--portto change the port
For more examples, visit the pipecat-examples repository.