e06e0c0282
When tools change mid-conversation, LLMs can produce a few different flavors of tool-call-related hallucination: calling tools that have been removed, avoiding tools that have been re-added, or hallucinating output (made-up answers or tool-call-shaped non-tool-calls) when tools are unavailable. This change introduces an opt-in ``add_tool_change_messages`` flag on the LLM aggregators (preferred entry point: ``LLMContextAggregatorPair( ..., add_tool_change_messages=True)``) that appends a developer-role message to the context whenever ``LLMSetToolsFrame`` changes the set of advertised standard tools. Helps the LLM stay coherent across tool changes by spelling out exactly what just became available or unavailable. Both aggregators participate; whichever handles the frame first wins, and the other (if any) sees an empty diff against the shared context and stays silent — order-independent regardless of whether the frame flows downstream or upstream. Also tightens the existing missing-handler path (introduced in #4301): - Reworded the terminal tool result to a neutral "The function ``X`` is not currently available." (overridable via ``LLMService.MISSING_FUNCTION_CALL_MESSAGE_TEMPLATE``). Previously read "Error: function 'X' is not registered." - Logs at the call site now distinguish developer error (tool advertised but no handler registered → ``logger.error``) from hallucination (tool not advertised → ``logger.warning``). Includes a manual validation harness (``examples/features/features-add-tool-change-messages.py``) that exercises the new ``add_tool_change_messages`` mitigation by flipping tool availability on a turn counter so its effect can be observed end-to-end with the flag on vs. off.
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.