bff741a647
Pass realtime_service_mode=RealtimeServiceModeConfig() through every realtime LLM service example (base, async-tool, video, text-output, persistent-context, update-settings, MCP) so context aggregation uses the new realtime-mode semantics instead of relying on local VAD as a workaround. Where examples previously wired SileroVADAnalyzer into LLMUserAggregatorParams to coax turn frames out of services that don't emit them server-side (AWS Nova Sonic, Ultravox, Gemini Live), the local VAD is now removed. realtime_service_mode keeps context writes correct without it, and the Phase 1.5 server-side InterruptionFrame fixes for Nova Sonic and Ultravox keep the bot from talking past the user when they barge in. Transcript-logging event handlers move from on_user_turn_stopped / on_assistant_turn_stopped to on_user_message_added / on_assistant_message_added, which carry the finalized text in realtime mode (the turn-stopped events fire before the message is finalized, so their `content` is None in that mode). For services that don't emit user-turn frames (Gemini Live, AWS Nova Sonic, Ultravox) the example now carries a Tier 1 comment block that spells out which downstream processors won't activate, how to add local VAD if needed, and the caveat that locally-generated turn boundaries are a heuristic that may diverge from server-side ground truth. Adds examples/realtime/realtime-openai-local-vad.py, a new variant of the OpenAI Realtime example that disables OpenAI's server-side turn detection and drives turn boundaries locally — useful when you want a turn analyzer like LocalSmartTurnV3 to decide when the user is done speaking. Server-emitted turn frames are still preferred when available. The Gemini Live local-VAD variant already existed; it's been updated in place rather than rewritten.
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.