* VIVA SDK TT v3 support
* Format fix.
* Renamed the API naming, removed '3' from the name.
* Implementation of User turn start strategy using Krisp VIVA Interruption Prediction in scope of TT v3 support.
* Typo fix in voice-krisp-viva example to use KrispVivaFilter class
* style fix.
* test run error fixes.
* some test related changes.
* Fixed tests
* Stule fixes.
When the LLM returned zero text tokens (e.g. it was interrupted before producing
tokens or about to push tokens), push_aggregation() returned an empty string and
on_assistant_turn_stopped was never emitted. This left consumers waiting for an
event that would never arrive.
Now on_assistant_turn_stopped always fires, with an empty content string when
the LLM produced no text tokens.
Fixes#4292
Only treat messages[0] as the initial system prompt when determining the
summarization range. Previously, the code scanned the entire context for
the first system-role message, which caused failures when the only system
message was a mid-conversation injection (e.g. "The user has been quiet").
In that case summary_start exceeded summary_end, producing an empty range
and "No messages to summarize" errors.
Fixes#4286
When the STT p99 timeout fires without a transcript, the turn stop
strategy previously did nothing — falling through to the 5-second
user_turn_stop_timeout. Now, a _timeout_expired flag tracks when the
timeout has elapsed so that a late transcript triggers the turn stop
immediately instead of waiting for the fallback.
The strategy schedules background tasks during setup. Fast-running
tests could observe state before those tasks had a chance to run;
yielding once via asyncio.sleep(0) ensures they do.
Enable callers to get a compact version of context messages suitable
for serialization, logging, and debugging tools. For standard
messages, known binary data (base64 images, audio) is fully elided.
For LLM-specific messages, long string values are recursively
truncated. Adapter get_messages_for_logging() methods now use this.
Replaces the per-frame asyncio.Event signaling with a monotonic
timestamp updated on each audio frame. The handler sleeps until the
next deadline (last_audio_time + timeout), recomputing on each wake-up
to account for audio arriving during sleep.
This avoids waking the handler on every audio frame (~50/s at 20ms
chunks), and guarantees detection latency is bounded by timeout rather
than 2 * timeout.
Also renames audio_starvation_timeout to audio_idle_timeout and
associated identifiers for consistency with existing pipecat naming
(user_idle_timeout, etc.).
These are TypedDicts (plain dicts at runtime), so no behavioral change
— just more descriptive type hints for readers. Use ToolParam instead
of FunctionToolParam for the Responses adapter to reflect that custom
non-function tools are supported. Use ChatCompletionToolParam instead
of Any for the completions adapter return type. Update tests to use
typed params in expected values.
Move event.clear() from finally block to success path in
IdleFrameProcessor and UserIdleProcessor._idle_task_handler().
The finally block unconditionally cleared signals set during
async timeout callbacks, causing false-positive idle detection.
Closes#3402
* Add Inworld Realtime LLM service
Adds a WebSocket-based realtime service for Inworld's cascade
STT/LLM/TTS API with semantic VAD, function calling, and streaming
transcription support.
New files:
- src/pipecat/services/inworld/realtime/ (service, events)
- src/pipecat/adapters/services/inworld_realtime_adapter.py
- examples/foundational/19zb-inworld-realtime.py
Also includes:
- websockets dependency for inworld extra in pyproject.toml
- Adapter and settings tests matching OpenAI/Grok realtime patterns
- Fix for double-response when server-side VAD is enabled
* Prefer init-provided system instruction in Inworld Realtime
Adopt _resolve_system_instruction() from BaseLLMAdapter, matching the
pattern applied to OpenAI Realtime, Grok Realtime, Gemini Live, and
Nova Sonic in the pk/realtime-services-init-v-context-system-instructions-cleanup
branch.
* Update changelog entry with PR number
* Fix changelog format to use bullet point
* Polish PR: default model, example cleanup, changelog update
- Change default model from gpt-4.1-nano to gpt-4.1-mini
- Add function calling demo to example
- Remove demo-testing artifact from system instruction
- Mention Router support in changelog
* Address PR review feedback for Inworld Realtime
- Move example to examples/realtime/realtime-inworld.py
- Change initial context role from "user" to "developer"
- Remove explicit sample rates from example; sync them in
_ensure_audio_config so Inworld gets the transport's actual rates
- Add audio race condition guard in _handle_evt_audio_delta (matches
OpenAI realtime pattern)
- Convert remaining "system"/"developer" messages to "user" in adapter
- Add clarifying comment for local-VAD vs server-VAD metrics paths
* Simplify example, add provider tracking, remove local VAD path
- Remove function calling from example, switch model to xai/grok-4-1-fast-non-reasoning
- Add pipecat-realtime session key prefix and provider_data metadata
for Inworld traffic attribution
- Remove local VAD code path (Inworld only supports server-side VAD)
- Use typed InputAudioBufferAppendEvent for audio sends
* Default TTS model to inworld-tts-1.5-max
* Remove dead shimmed tools code, set STT/VAD defaults
- Remove non-functional AdapterType.SHIM custom tools code from adapter
- Default STT model to assemblyai/u3-rt-pro
- Default VAD eagerness to low
Add tests for LLMRunFrame, LLMMessagesAppendFrame, LLMMessagesUpdateFrame,
and LLMMessagesTransformFrame sent upstream to LLMAssistantAggregator,
mirroring the existing LLMUserAggregator downstream tests. Add
frames_to_send_direction param to run_test helper to support this.
The previous approach required the caller to directly grab a reference to the context object, grab a "snapshot" of its messages *at that point in time*, transform the messages, and then push an `LLMMessagesUpdateFrame` with the transformed messages. This approach can lead to problems: what if there had already been a change to the context queued in the pipeline? The transformed messages would simply overwrite it without consideration.
Deepgram's built-in VAD events were deprecated in 0.0.99 in favor of
Silero VAD. This removes vad_events from settings and LiveOptions,
the should_interrupt parameter, the vad_enabled property,
_on_speech_started/_on_utterance_end handlers, and simplifies
_on_message and process_frame accordingly.
UserBotLatencyLogObserver (deprecated 0.0.102) is replaced by
UserBotLatencyObserver. UserIdleProcessor (deprecated 0.0.100) is
replaced by LLMUserAggregator with user_idle_timeout.
The interruption_strategies mechanism was deprecated in v0.0.99 in favor
of LLMUserAggregator's user_turn_strategies. All evaluation logic was
already removed — this removes the remaining field definitions, property,
StartFrame propagation, conditional check in base_input.py, strategy
files, and test.
Add WebsocketLLMService as a base class for WebSocket-based LLM services,
parallel to WebsocketTTSService/WebsocketSTTService but codifying a
transactional request-response model rather than a continuous background
receive loop.
WebsocketLLMService provides:
- Connection lifecycle (start/stop/cancel → connect/disconnect)
- _ws_send/_ws_recv with transparent ConnectionClosed handling
(auto-reconnect via exponential backoff → WebsocketReconnectedError)
- _ensure_connected with retry via _try_reconnect
OpenAIResponsesLLMService now inherits from WebsocketLLMService, removing
duplicated connection management code (_connect, _disconnect, _reconnect,
_ensure_connected, _ws_send, start, stop, cancel) and simplifying
_process_context from a loop with attempt tracking to a flat try/except
with a single retry.
When a WebSocket server accepts the handshake but immediately closes the
connection (e.g. invalid API key returning close code 1008), the existing
exponential backoff does not help because the handshake keeps succeeding.
This tracks how long each connection survives and emits a non-fatal
ErrorFrame after 3 consecutive sub-5s failures, allowing ServiceSwitcher
failover instead of killing the pipeline.
Fixes#3711
Instead of trying to filter stale events inline (unreliable — the API
doesn't provide a way to correlate events to a specific response),
drain remaining events from a cancelled response before starting the
next one. On cancellation, send response.cancel and set a drain flag.
At the start of the next _process_context, read and discard events
until a terminal event arrives, ensuring a clean connection. Falls
back to reconnecting if draining times out.
When using previous_response_id, the server already knows its own
output from the previous response. Store the raw response output and,
on the next call, compare it against the items following the matched
input prefix — checking role and text content for messages, and call_id
for function calls. If the items match, skip them and send only truly
new input (user messages, tool results). Falls back to full context if
either the prefix or the output comparison fails.
Introduce a WebSocket variant of the OpenAI Responses API service that
maintains a persistent connection to wss://api.openai.com/v1/responses
for lower-latency inference. The WebSocket variant automatically uses
previous_response_id to send only incremental context when possible,
falling back to full context on reconnection or cache miss.
The WebSocket variant becomes the new default OpenAIResponsesLLMService,
and the HTTP variant is renamed to OpenAIResponsesHttpLLMService. Both
share a private base class with common settings, parameter building,
and run_inference (always HTTP) logic.
