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.
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.
Add a `sync_with_audio` field to `OutputImageRawFrame` that routes image
frames through the audio queue in the output transport, ensuring images
are only displayed after all preceding audio has been sent. This enables
proper audio/image synchronization in pipelines like the calendar month
narration example.
Update the 05-sync-speech-and-image example to use an `ImageAudioSync`
processor that sets this flag on image frames.
- Add WakePhraseUserTurnStartStrategy for gating interaction behind wake
phrase detection, with timeout and single_activation modes
- Add default_user_turn_start_strategies() and
default_user_turn_stop_strategies() helper functions
- Deprecate WakeCheckFilter in favor of the new strategy
- Extend ProcessFrameResult to stop strategies for short-circuit evaluation
- Fix MinWordsUserTurnStartStrategy including filtered text in output
Add OpenAIResponsesLLMService using the Responses API, with a dedicated
adapter that converts LLMContext messages to Responses API input items
(system→developer, tool_calls→function_call, tool→function_call_output,
multimodal content conversion, and tools schema flattening).
- New adapter: open_ai_responses_adapter.py
- New service: openai/responses/llm.py
- Examples: 07-interruptible and 14-function-calling variants
- 19 unit tests for adapter conversion logic
- Eval entries for both examples
