4864eddbc7
Replaces the prior "log a warning and skip" approach with actual handling of async-tool messages on Ultravox. The catch with Ultravox is that its API freezes the conversation between client_tool_invocation and the matching client_tool_result — there's no "keep talking while the tool runs" channel like NON_BLOCKING on Gemini or function_call_output-without-blocking on OpenAI Realtime. So: - When the model invokes an async-registered function (cancel_on_inter ruption=False), the service immediately ships a placeholder client_tool_result that tells the model "the actual result isn't ready yet; a follow-up will arrive shortly; keep the conversation going". This unfreezes the conversation. The placeholder is sent from _handle_tool_invocation, since the started async-tool message doesn't reach the context-frame path until later. - When the real tool finishes, the final async-tool message lands in the context. _handle_context now forward-iterates and routes async-tool messages: started is a no-op (placeholder already sent), intermediate is logged-as-error and dropped (matching the other realtime services), and final is injected as user-side text via user_text_message with bracketed framing — the only mechanism Ultravox offers for adding non-tool input mid-conversation. Hoists the registry-lookup helper to LLMService as _function_is_async(name) so future services can use the same pattern without re-implementing it. Adds an async-tool example file for Ultravox modeled on the existing ones for the other realtime services.
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.