- Add Nebius LLM service wrapping OpenAI-compatible Token Factory API
- Set supports_developer_role = False (Nebius rejects developer role)
- Default to openai/gpt-oss-120b model (supports function calling)
- Add Nebius function-calling example and env.example entry
- Fix Sarvam developer role support
- Update examples to use developer role for intro messages
Gemini 3.1 Flash Live won't reliably report ending its turn until
after it says something following a tool call. Restructure the system
instruction so the model says goodbye *after* calling
end_conversation, and add a comment explaining the deferred EndFrame
behavior that makes this work.
All recent Gemini Live models (including the default
gemini-2.5-flash-native-audio-preview-12-2025, and going at least as
far back as gemini-2.5-flash-native-audio-preview-09-2025) only
support AUDIO as a response modality. We considered using
`modalities=TEXT` as a Pipecat-level signal to suppress audio output
frames (so developers could pair Gemini Live with an external TTS),
but the output transcription from the API arrives too late relative
to the audio to be useful for driving an external TTS service.
For now, just log a warning when a TEXT modality is configured
(at init or via set_model_modalities) and proceed as normal. The 26d
text-modality example is removed since it no longer represents a
viable configuration.
Expose a public method for retrieving all stored memories outside the
pipeline, avoiding the need for callers to reimplement client branching,
OR filter construction, and asyncio.to_thread wrapping. Simplify the
example get_initial_greeting() to use it.
When Gemini Live was configured with local VAD (server-side VAD disabled),
the service was listening for the wrong frame types and not sending
ActivityStart/ActivityEnd events to the server. Now it listens for
VADUserStartedSpeakingFrame/VADUserStoppedSpeakingFrame and sends the
appropriate activity signals when local VAD is in use.
Also removes the unnecessary local SileroVADAnalyzer from server-side VAD
examples and adds a new 26a example demonstrating local VAD configuration.
Add DeepgramFluxSageMakerSTTService that combines SageMaker's HTTP/2
transport with Flux's JSON turn detection protocol (StartOfTurn,
EndOfTurn, EagerEndOfTurn, TurnResumed). Includes mid-stream Configure
support, silence watchdog, and an example bot.
Both GrokLLMService and XAIHttpTTSService use the same xAI API (api.x.ai),
so move Grok source files into the xai module. Leave deprecation shims in
the old grok/ paths for backward compatibility.
- Rename XAITTSService → XAIHttpTTSService and XAITTSSettings → XAIHttpTTSSettings
- Add language_to_xai_language() with explicit LANGUAGE_MAP using resolve_language()
- Remove deprecated InputParams, params, voice, language init params
- Remove XAI_DEFAULT_SAMPLE_RATE and XAI_PCM_CODEC constants; add encoding param
- Set sample_rate=None default (picked up from PipelineParams or user)
- Use Language.EN enum instead of string "en" for default language
- Add changelog/4031.added.md
- Add 07e-interruptible-xai.py foundational example
- Update 14g-function-calling-grok.py to use XAIHttpTTSService
- Register 07e in run-release-evals.py
Add system_instruction parameter to the Grok Realtime adapter's
get_llm_invocation_params() and call _resolve_system_instruction() to
prefer init-provided over context-provided system instructions and
warn on conflicts. Previously context-provided took precedence.
Update the Grok Realtime example to use settings.system_instruction
instead of session_properties.instructions.
These messages are developer instructions to the assistant (e.g. "Please
introduce yourself to the user"), not simulated user input. The
"developer" role is semantically correct for this purpose.
Adds SmallestTTSService, a WebSocket-based TTS service using Smallest AI's
Lightning v3.1 model. Follows current Pipecat service conventions:
- SmallestTTSSettings dataclass with runtime-updatable settings (voice,
language, speed, etc.)
- Reconnects on model change; keepalive every 30s to prevent idle timeout
- TTS settings default to None so the API applies its own defaults
- Model enum: SmallestTTSModel.LIGHTNING_V3_1
Includes a foundational example (07zl-interruptible-smallest.py) using
Deepgram STT + Smallest TTS + OpenAI LLM.
STT integration will follow in a separate PR once the hallucination/finalize
behaviour is resolved.
Made-with: Cursor
Adds a FrameOrder enum with ARRIVAL (default, existing behavior) and
PIPELINE (pushes frames in pipeline definition order). This lets callers
guarantee output ordering between parallel pipelines — e.g. ensuring
image frames precede audio frames — without needing a separate reordering
processor downstream.
Updates the 05-sync-speech-and-image example to use FrameOrder.PIPELINE,
removing the ImageBeforeAudioReorderer class entirely.
Add a processor after SyncParallelPipeline that ensures each image frame
precedes its corresponding TTS audio frames. SyncParallelPipeline batches
them together but doesn't guarantee branch ordering. The reorderer detects
when TTS frames arrive before their image (via context_id tracking) and
holds them until the image arrives.
Also rename ImageAudioSync to MarkImageForPlaybackSync for clarity.
