pipecat-ai 1.12.0 on Python PyPI

Added

TTS sentence aggregation follows Settings.language, defaulting to English when unspecified. Language updates apply with TTS settings.
(PR #5737)
Added TTSService.pronunciation_transform_ipa(), which builds a text transform from a word-to-IPA mapping so a voice says names and terms it would otherwise guess from spelling. Each matched word is replaced with the service's format_pronunciation() output, so one mapping works with any service that supports pronunciation hints; a word the service cannot use is reported once when the transform is built and spoken as written. Cartesia writes IPA as inline phoneme blocks, ElevenLabs as SSML <phoneme> tags (read by eleven_flash_v2 and eleven_turbo_v2, and on the WebSocket service only with enable_ssml_parsing=True; on any other setup the transform is skipped with a warning and words are spoken as written), Eleven v3 and Inworld as IPA between slashes, and Deepgram Aura-2 as an inline pronunciation object. Deepgram Flux has no pronunciation markup, so its words are spoken as written. pipecat.utils.text.phonemes holds the IPA parsing and normalization the formatters share. Register the transform last in text_transforms, so no later transform rewrites the markup it inserts.
```
pronounce = CartesiaTTSService.pronunciation_transform_ipa({"Metformin":
"mɛtˈfɔɹmɪn"})

tts = CartesiaTTSService(
    text_transforms=[
        ("*", strip_markdown),
        ("*", pronounce),  # last, so nothing rewrites its markup
    ],
)
```
(PR #5798)
Added client-mode reconnect to MOQTransport: when the relay session drops, the transport redials with backoff for as long as MOQParams.connection_timeout allows the client to be missing, keeps its broadcast across dials, and reports the client gone only when that time is up. A relay that vanishes without closing the session is detected by watching the connection's traffic counters, rather than waiting out the QUIC idle timeout. Each side now sends a session-ending marker on its transcript before leaving; a peer whose tracks end without it is treated as an outage rather than a hangup. on_disconnected and on_connected now fire for each relay session, so a redial shows up as a disconnect followed by a connect; end the call from on_client_disconnected, which fires only once the client is gone.
(PR #5800)
Added MOQRunnerArguments.relay_url for dialing a relay by its full URL, query string included. host and port are optional when it is set, and create_transport passes it through to MOQParams.relay_url. When host or port is given as well, relay_url wins and a warning is logged.
(PR #5800)
Added interruptible to every frame: True by default and False by default for UninterruptibleFrame subclasses, and what the frame queue, the processor's interruption handling and the speculation gate decide by. Set it on a frame before pushing it to keep that one frame through an interruption, or to let one frame of a protected type be dropped, without a frame class of your own.
(PR #5835)
JudgeVerdict now has a confidence, from 0 to 1, saying how sure the judge is. With Jev the number is calibrated. With an LLM it is the LLM's own guess.
(PR #5857)
The eval judge can now use a classifier. Put a factory: in the scenario's judge.eval: block that returns a BaseClassifier, or an LLM service as before. Our release evals judge with Jev this way, through evals/judges.py. Jev answers each check in a few hundred milliseconds and says how sure it is. It needs the jev extra and TYPESAFE_API_KEY.
(PR #5857)
Added allow_continue to a scenario's judge.eval: block and to EvalJudge. Set it to false when every reply you judge is a final answer. The judge then answers yes or no and never continue, so it never waits for more text that isn't coming.
(PR #5857)
Added LLMClassifier, which answers classifier questions with any Pipecat LLM service that supports run_inference(). All the questions about one state go to the LLM in one call, and the LLM replies with one JSON object holding an answer per question. It waits timeout seconds for the LLM, 10 by default, and raises ClassifierError if the LLM does not answer in time or the call fails.
```
classifier = LLMClassifier(llm=OpenAILLMService(model="gpt-4o-mini"))
results = await classifier.yes_no(
    "Hi, you've reached Dana. Leave a message.",
    {"voicemail": YesNoQuestion(instructions="is this a voicemail greeting?")},
)
results["voicemail"].is_yes  # True
results["voicemail"].probability  # 0.97
```
(PR #5863)
Classifiers report metrics through an on_metrics event: after every call, the time it took as ProcessingMetricsData and, for JevClassifier, the tokens it used as LLMUsageMetricsData. A classifier cannot push frames, so its owner puts the data in a MetricsFrame.
(PR #5863)
Added Pipecat Classifiers. A classifier is a small object that answers typed questions about some state. A question is a YesNoQuestion, a ChoiceQuestion among options, or a ScoreQuestion on a scale. Questions are asked by name, several about one state at once. ask() takes any mix of kinds, and yes_no(), choice() and score() take questions of one kind and return typed results: a YesNoResult with the probability of yes and is_yes, a ChoiceResult with the choice and a probability per option, and a ScoreResult with the score on the scale and a probability per level. JevClassifier answers them through Jev, TypeSafe's classification model, in one request. A choice question can have at most 255 options, Jev's limit (JEV_MAX_CHOICE_OPTIONS). Several classifiers can share one JevClient, which handles the HTTP/2 connection, auth, retries and token accounting. Install with uv add "pipecat-ai[jev]". examples/features/features-classifiers.py asks one question of each kind.
```
classifier = JevClassifier(api_key=os.getenv("TYPESAFE_API_KEY"))
results = await classifier.choice(
    "I'd like to book a table for, um",
    {
        "turn": ChoiceQuestion(
            instructions="is the user's turn over?",
            options={
                "complete": "the user finished",
                "short": "a brief pause",
                "long": "asked for time",
            },
        )
    },
)
results["turn"].choice  # "short"
```
(PR #5863)
Added a read-only settings property to AIService, so code outside a service can read its current settings, such as the model. Settings are still changed through a ServiceUpdateSettingsFrame.
(PR #5863)
Added an optional response_schema argument to LLMService.run_inference(), a JSON schema the reply must follow. OpenAI, Anthropic and Google have the provider enforce it, so the reply is JSON text in that shape. A service or model that cannot enforce one, such as Bedrock, DeepSeek or an OpenAI model before gpt-4o-mini, ignores it with a warning, and supports_response_schema says whether a service can at all.
```
reply = await llm.run_inference(context, response_schema=schema)
```
(PR #5876)
Added LLMUserAggregatorParams.empty_user_turn, an EmptyUserTurnConfig for user turns that end with no transcript, such as a cough, background noise, or speech the STT could not recognize. It is on by default, even when empty_user_turn isn't passed, and treats two cases differently:
- Interrupted: the turn cut the bot off. The bot would otherwise stay silent mid-response, so the aggregator appends a developer message (interrupted_prompt) and runs the LLM once, and the bot asks the user to repeat or picks up where it left off. On by default.
- Idle: the bot had finished and was waiting for the user. The conversation isn't stuck, and answering what may be noise would be intrusive, so these turns get no answer unless idle_prompt is set.
```
LLMUserAggregatorParams(
    empty_user_turn=EmptyUserTurnConfig(
        idle_prompt=(
            "The user may have said something, but it was not "
            "recognized. Briefly ask them to repeat it."
        ),
    ),
)
```
Setting interrupted_prompt=None turns off the interrupted case, and empty_user_turn=None turns off both. max_consecutive_recoveries (default 1) limits how many empty turns in a row get an answer.
(PR #5907)
UIWorker can now use a classifier for small decisions about the screen, with no LLM turn: which element the user means (which_element), whether something is true on the screen (check_screen), which elements match a description (select_elements), whether a UI event deserves a reply (should_respond), and act on an element named in words. Pass a classifier, for example a JevClassifier. If you don't, the worker's own LLM answers through an LLMClassifier, which works but is slower.
```
worker = MyUIWorker("ui", llm=OpenAILLMService(api_key=...),
classifier=JevClassifier(api_key=...))
ref = await worker.which_element("the checkout button")
```
(PR #5909)
A voice LLM can now ask a UIWorker about the screen without ever seeing it. The worker has a built-in screen job with an action: find (which element a description means), check (whether something is true on the screen), select (which elements match a description), list (what is on the screen), selection (the text the user has selected) and click, scroll_to, highlight, select_text or fill (do that to the element a description means). Every answer is short data, never the page. screen_tools("ui") gives the voice LLM the tool that sends it.
```
context = LLMContext(tools=screen_tools("ui"))
```
(PR #5909)
UIWorker now has a snapshot property with the latest page snapshot and a selection property with the text the user has selected, so a subclass can read the screen with plain code.
(PR #5909)
Added RTVIFunctionCallReportLevel.ARGUMENTS, between NAME and FULL. It reports the function name and the arguments, and no result.
(PR #5918)

Changed

InworldRealtimeLLMService now sends providerData.auto_tool_response=false, leaving Pipecat responsible for requesting continuation after submitting a tool result.
- InworldRealtimeLLMService now generates a unique session key for every connection, including connections opened within the same millisecond.
  (PR #5116)
Sentence aggregation uses self-contained sentencex rules without NLTK data downloads or tokenizer warm-up. Language-specific sentence boundaries may differ from Punkt.
(PR #5737)
MOQTransport numbers every transcript record with seq and epoch and drops replayed records on subscribe, so a reconnect on either side no longer redelivers the whole RTVI log and re-fires client-ready. Both fields are stripped before the message reaches the pipeline; records without them pass through unchanged, so older peers keep working.
(PR #5800)
MOQTransport failures now reach the pipeline as an ErrorFrame with an error category, in addition to the on_error event. A relay that refuses the token, at the dial or by closing the session as unauthorized, is not retried and marks the transport unusable, as does a relay that cannot be reached within connection_timeout.
(PR #5800)
The moq extra requires moq-rs 0.4.6 or later. moq-rs is the client library MOQTransport dials a relay with; moq-relay is the separate server. The transport is tested against moq-relay 0.14. A relay from an incompatible release line can accept the connection and still drop the transcript track without an error.
(PR #5800)
MOQParams.connection_timeout is now the one limit on how long the client may be missing, and defaults to 60 s (was 30 s). It bounds the wait for the client to join and, in client mode, an outage: the time from the relay session dropping until the client's data flows again, across every redial. Set it longer than the time your load balancer takes to fail a dead relay out.
(PR #5800)
XAISTTService now sends its model setting to xAI, and defaults to grok-voice-transcribe-2.0. Previously no model was sent, so xAI used its server default, grok-voice-transcribe-1.0. To keep the previous model, pass settings=XAISTTService.Settings(model="grok-voice-transcribe-1.0").
(PR #5847)
The local-smart-turn and moondream extras now require transformers>=5.10.0. The moondream extra's accelerate, einops, pyvips and timm pins are relaxed to a lower bound with a major-version cap.
(PR #5850)
EvalJudge now decides every verdict with a classifier. By default that is an LLMClassifier over the LLM the judge.eval: block names, so existing scenarios work as before. A classifier gives no reasons, so the judge asks an LLM, the explainer, for the reason behind every no and every verdict below explain_below. The explainer is the judging LLM unless an explainer: block names another one, and explainer: false turns reasons off. The classifier's verdict always stands.
(PR #5857)
pipecat eval suite now keeps concurrency runs going at all times, taking the next run in manifest order. Before, each entry ran its scenarios one at a time on one slot, so a manifest with fewer entries than slots left slots idle, and one entry with ten scenarios ran them one by one. An entry whose provider rate-limits sets its own concurrency: to cap its runs in flight, as the turn-completion manifest does.
(PR #5857)
The mcp extra now requires mcp 2 (mcp[cli]>=2.1.1,<3). MCPClient works the same; it no longer runs on the 1.x SDK.
(PR #5857)
An LLM judge now classifies instead of answering a prose prompt: it gets the conversation as structured state and picks one of the outcomes. A borderline reply can get a different verdict than before. A simulation is judged one bot turn per call instead of the whole run in one call, so it costs more calls.
(PR #5857)
VoicemailDetector now takes a classifier and is a single processor instead of a parallel pipeline with its own LLM. It asks the classifier after each transcription and acts once the caller has been quiet for decision_timeout (default 1 s), when the latest answer decides. No verdict acts on a fragment: "hi, this is Sam" is what a person says and how a greeting starts, and only the silence that follows tells them apart. Then the held-back speech is released or dropped as before. The on_voicemail_detected and on_conversation_detected handlers receive the detector itself.
```
# Before
detector = VoicemailDetector(llm=OpenAILLMService(api_key=...))

# After
detector = VoicemailDetector(classifier=JevClassifier(api_key=...))
```
(PR #5869)
Observers created with observe_every_push=False are told about a frame once, on its first push, instead of on every push by every processor that passes it along. The built-in observers that handle a frame once do so, and FramePushed.first_push tells the first push from the ones that follow for the ones that observe every push.
(PR #5908)
A UIWorker now answers a respond job with the reply its LLM writes, so the smallest UI worker is a UIWorker with an LLM and a system prompt. A @tool that calls respond_to_job still answers instead when it needs to, for example to speak the answer through TTS.
(PR #5909)
UI_SNAPSHOT_EVENT_NAME and UI_CANCEL_JOB_GROUP_EVENT_NAME are now public in pipecat.bus.ui. They are the bus event names of the client's screen snapshot and of the client asking to cancel a job group.
(PR #5909)

Deprecated

Deprecated UninterruptibleFrame. A frame class that should be uninterruptible by default declares interruptible: bool = field(default=False, init=False) instead. The marker still sets the flag until it is removed in 2.0.0.
(PR #5835)
Passing an LLM service to EvalJudge, as the first argument or as service=, is deprecated and will be removed in 2.0.0. Pass EvalJudge(LLMClassifier(llm=service), explainer=service) instead, which is what the old form did.
(PR #5857)
Deprecated the startup warming timings reported by StartupTimingObserver, all removed in 2.0.0: the StartupTimingReport.warmup field, StartupWarmupTiming, StartupWarmup, and the on_startup_warmup() hook on BaseObserver, WorkerObserver and StartupTimingObserver. Pipecat warms no deferred imports while the pipeline sets up, so warmup is always None and the hook is never called. The rest of the report — the phase totals and the per-processor timings — is unchanged.
(PR #5859)
VoicemailDetector's llm and custom_system_prompt parameters are deprecated. Pass a classifier instead. Until removal, the llm is wrapped in an LLMClassifier, with the custom prompt in front of the classifier's instructions.
(PR #5869)
The max_frames parameters of TurnTrackingObserver and UserBotLatencyObserver are now deprecated, removed in 2.0.0. Observers no longer keep a window of the frames they have seen.
(PR #5908)
tts_speak on UIWorker.respond_to_job is deprecated and will be removed in 2.0.0. A UI worker should not speak; respond with the answer and let the voice LLM say it.
(PR #5909)
BaseUIWorker is deprecated and will be removed in 2.0.0. Use UIWorker instead. UIWorker now reports its job groups to the client on its own, so instead of a separate BaseUIWorker dispatcher, dispatch job groups from a @job handler in your UIWorker.
(PR #5909)
ReplyToolMixin is deprecated and will be removed in 2.0.0. Use screen_tools instead: the voice LLM asks the UI worker through the screen job and says the answer itself.
(PR #5909)

Removed

Removed NotifierGate, ClassifierGate, ConversationGate and ClassificationProcessor from pipecat.extensions.voicemail.voicemail_detector, and the CLASSIFIER_RESPONSE_INSTRUCTION and DEFAULT_SYSTEM_PROMPT attributes of VoicemailDetector. They were parts of the old parallel-pipeline detector and its prompt.
(PR #5869)

Fixed

Fixed an LLMService regression where re-advertising a tool with the same name but a different handler (for example, a per-node handler in Pipecat Flows) silently kept the previous handler bound. Auto-registered handlers are now rebound when the advertised handler changes, while explicit register_function registrations are still left untouched.
(PR #4823)
Fixed InworldRealtimeLLMService resending a server-VAD user transcript after a fast tool result context update, which duplicated user turns and responses.
(PR #5116)
InworldRealtimeLLMService now serializes Inworld extensions under providerData instead of provider_data, allowing the server to apply them.
(PR #5116)
Applied NVIDIA STT settings changes to the running stream. NvidiaSTTService rebuilt its recognition config on a settings update but never reconnected, so the open gRPC stream kept transcribing with the previous settings while self._settings reported the new ones.
(PR #5632)
Fixed Flows node transitions where an interruption could leave the LLM running with the previous node's context and tools. The LLMMessagesAppendFrame or LLMMessagesUpdateFrame and the LLMSetToolsFrame a transition queues are now uninterruptible, so they are still delivered.
(PR #5837)
TelnyxFrameSerializer now sends OutputTransportMessageFrame and OutputTransportMessageUrgentFrame to the client as JSON, matching the other telephony serializers. Previously these messages (including RTVI messages) were silently dropped.
(PR #5841)
Fixed the eval harness treating the bot's earlier speech as its reply when a scenario interrupts the bot. Speech from before the interruption is now dropped.
(PR #5846)
Fixed MoondreamService with transformers 5: loading the model on a GPU or Apple Silicon failed with AttributeError: 'HfMoondream' object has no attribute 'all_tied_weights_keys', and a model that did load produced garbage descriptions.
(PR #5856)
Fixed a bridged PipelineWorker reporting every frame from its LLM twice over RTVI, once in its own pipeline and once when the frame crossed the bridge, which doubled every word of a bridged worker's reply in the client's LLM text and in the evals. enable_rtvi now defaults to off for a bridged pipeline worker, which has no client of its own; LLMWorker already did this. Pass enable_rtvi=True to keep it.
(PR #5857)
Fixed TTS word tracking falling out of step on markdown-heavy replies. Most word-timestamp events were dropped with "Dropping word ... not recognised by any slot" warnings, and the rest of the reply arrived in large chunks, so word highlighting stalled and then jumped ahead. Two token shapes triggered it: the period Cartesia adds to the last token of a line (images:., ---.), and a symbol a provider reports differently from the text (ElevenLabs reports → as -) followed by more symbols such as ### or **.
(PR #5866)
Fixed WorkerRunner skipping a worker that another worker added while the runner was still starting, for example a child worker added by a processor during its setup.
(PR #5872)
Fixed pcm_to_wav() dropping complete samples when given a typed memoryview.
(PR #5879)
Fixed is_silence() misclassifying full-scale negative int16 PCM samples as silence.
(PR #5881)
Fixed MOQTransport ending the call when a relay refused a subscription to the peer's broadcast with dropped, which a surviving relay in a mesh does while it still holds a route through a relay that went away. The refusal is now treated as the peer's tracks ending, so the transport retries and redials as it does for any other relay loss.
(PR #5892)
Fixed RTVIObserver remembering the IDs of frames it does not handle, such as every audio frame. Its memory still grows with the frames it handles, but much more slowly.
(PR #5906)
Fixed on_user_turn_idle never firing after a user turn that ended with no transcript, since that turn cancelled the idle timer and nothing restarted it.
(PR #5907)
Fixed observers keeping the IDs of every frame they had seen for the life of the session, the pipeline worker's idle detection included.
(PR #5908)
The runner extra now requires pipecat-ai-prebuilt>=1.2.2. The conversation panel in the prebuilt client UI served by the development runner now keeps autoscrolling while long bot replies stream in.
(PR #5917)

Performance

RTVIObserver now skips audio frames before checking anything else when audio levels are not reported, instead of testing every condition on every push.
(PR #5906)

pipecat-ai 1.12.0 v1.12.0 on Python PyPI

Added

Changed

Deprecated

Removed

Fixed

Performance

pipecat-ai 1.12.0
v1.12.0

on Python PyPI