8330c3487d
Splits ``_maybe_emit_user_turn_stopped`` into three focused methods — ``_flush_user_message_to_context`` (push aggregation, return content + timestamp), ``_finalize_user_turn`` (default-mode flow, emits both events), and ``_finalize_delayed_user_message`` (delayed-mode flow, emits only ``on_user_turn_message_finalized``). Fixes a side-issue where ``on_user_turn_stopped`` could fire from non-end-of-turn paths in delayed-transcript mode; that event now has a single origin (the end-of-turn handler). Standardizes vocabulary across docstrings and comments: - "Default mode" / "Delayed-transcript mode" (with ``_expect_delayed_transcripts == False/True``) - "End of turn" (not "audible stop" or "audible end of turn") - "User message finalization" (the moment user-text is flushed to context + ``on_user_turn_message_finalized`` fires) - "Pending finalization" (the in-between state in delayed mode) - Transcripts (plural — the aggregator combines multiple per turn) The timer that triggers user message finalization is no longer described as a "backstop" — it's the sole trigger for finalization in delayed-transcript mode, not a fallback. Renamed accordingly: ``_pending_finalization_task``, ``_pending_finalization_handler``, ``_run_pending_finalization``, ``_discard_pending_finalization``. Adds a separate message class for the two events: ``UserTurnStoppedMessage.content`` is now ``str | None`` (``None`` at end-of-turn in delayed-transcript mode), and a new ``UserMessageFinalizedMessage`` carries the always-populated ``content`` for the finalization event.
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.