pipecat-ai 1.9.0 on Python PyPI

Added

Added LatencyBreakdown.contributions, a timeline of the user-to-bot interval whose durations sum to the measured latency. It names the time no service reports — VAD silence, turn detection, turn-completion markers and holds, sentence aggregation, and function handlers — and attributes time a setting governs to that setting rather than to the service it runs inside.
The first thing the bot says is measured too, from where StartupTimingObserver stops — the pipeline ready with a client on it — so the two reports meet rather than overlap. Waiting to be asked is usually most of that wait, and no service reports it.
Each part carries a stable key and an owner_kind — service, setting, bot or pipeline — so data keyed on them survives a label being reworded, and the breakdown says what it was measured_from alongside the total_secs it measured. Print it with LatencyBreakdown.turn_contribution_lines(); UserBotLatencyObserver(min_contribution_secs=...) sets how brief a part must be before it rolls into a single pipeline entry.
```
```
0.200s  endpointing wait     [config: VAD stop_secs]
0.125s  transcription        [DeepgramSTTService#0]
0.336s  LLM inference        [OpenAILLMService#0]
0.024s  turn completion      [config: filter_incomplete_user_turns]
0.359s  speech synthesis     [CartesiaTTSService#0]
1.044s  TOTAL
```
```
(PR #5445)
Eval scenario turns can play an audio file as the user instead of synthesizing text. A turn's audio: names a recording, resolved relative to the scenario file, in any format soundfile reads (WAV, MP3, FLAC, OGG, ...); multi-channel audio is downmixed to mono and the file keeps its own sample rate. user: is required alongside it and is what the recording says, so the judge and text_contains still have the turn's input. A scenario whose audio turns all name a file needs no user.speech: block.
(PR #5473)
Added a cancellable_by_llm argument to LLMSwitcher.register_function(), which forwards it to every LLM the switcher fronts. Tools registered through a switcher can now opt into LLM-side cancellation, matching what LLMService.register_function() accepts.
(PR #5474)
Added AssemblyAISyncSTTService, a segmented speech-to-text service backed by AssemblyAI's Sync API: pipeline VAD segments the audio and each segment (up to 120 seconds) is transcribed in one HTTP request, with no streaming session to manage. It takes an aiohttp_session, base_url selects a data-residency endpoint, and Settings supports model, language, prompt, keyterms_prompt, and conversation_context.
Recent user and agent turns are sent automatically as conversation_context on each request, bounded by max_context_turns (0 disables) and max_context_chars; setting conversation_context yourself replaces that buffer. The connection is pre-warmed when the user starts speaking so the transcription request skips the handshake (enable_prewarming).
(PR #5488)
AzureSTTService.Settings gained segmentation_silence_timeout_ms, which sets how much silence (100–5000 ms) Azure allows inside a phrase before it emits a final transcript. Azure's default of 500 ms applies when it's unset.
(PR #5511)
Added on_progress and on_update event handlers to the eval framework. EvalSession and EvalSuite are now BaseObjects, so eval progress is observed the same way as every other Pipecat event. EvalSuite's on_update handlers run synchronously and should return promptly, since an EvalRun is mutated in place as it executes; EvalSession's on_progress handlers run as tasks, and EvalSession.run() waits for them before it returns.
```
  @session.event_handler("on_progress")
  async def on_progress(session, progress):
      print(progress.event_name, progress.status)
```
(PR #5520)
Added a voice_parameters setting to AzureTTSService and AzureHttpTTSService, which passes SSML's parameters attribute on <voice> through to Azure so HD voices can be tuned (temperature, top_p, top_k, cfg_scale, enhancePronunciation).
(PR #5522)
Added profanity_filter and redact to DeepgramFluxSTTService.Settings and DeepgramFluxSageMakerSTTService.Settings, exposing Flux's profanity masking and number redaction.
(PR #5523)
Added a version field to DeepgramSTTService.Settings (and DeepgramSageMakerSTTService.Settings), pinning transcription to a specific Deepgram model version instead of whichever one latest currently resolves to. Previously reachable only as an untyped extra key, which continues to work.
(PR #5527)
Added thinking to DeepSeekLLMService.Settings, which controls DeepSeek's thinking mode: settings=DeepSeekLLMService.Settings(thinking={"type": "disabled"}) turns off the reasoning pass that V4 models run by default.
(PR #5528)
CartesiaTurnsSTTSettings gained turn_start_threshold, turn_eager_end_threshold, turn_end_threshold and turn_end_timeout_ms, Cartesia's turn detection tuning parameters. Unset by default, so the server's own defaults apply.
(PR #5533)
Added a denoiser_config setting to GoogleSTTService, which passes Google's Chirp 3 background-noise removal (denoise_audio, snr_threshold) through to the recognition request.
(PR #5536)
Added a temperature field to HumeTTSService.Settings, passing Hume's sampling temperature through to the synthesis request. Higher values increase variation, lower values increase consistency; when unset, Hume applies its own per-model default.
(PR #5540)
Added a speed setting to DeepgramTTSService and DeepgramHttpTTSService, passing Deepgram's Aura speech-rate multiplier (0.7 to 1.5) through to /v1/speak.
(PR #5552)
Added a no_verbatim setting to ElevenLabsSTTService and ElevenLabsRealtimeSTTService, which asks Scribe to drop filler words, false starts and non-speech sounds from the transcript.
(PR #5555)
Added a hotwords field to WhisperSTTService.Settings, which biases Faster Whisper's transcription towards the words or phrases it names, such as product names or domain jargon.
(PR #5558)
Added a reduce_silence setting to SonioxTTSService, which shortens the pauses between words on models that support silence reduction.
(PR #5567)
Added an include_results setting to SpeechmaticsSTTService, which asks Speechmatics to include word-level results in transcript messages.
(PR #5568)
WebsocketService now accepts reconnect_backoff_min_wait and reconnect_backoff_max_wait to configure the wait between reconnection attempts. Defaults (4s / 10s) preserve the previous hardcoded behavior.
(PR #5586)
Added MetaSTTService, a streaming speech-to-text service using Meta's muse-voice-transcribe-1.0 model over the Muse Voice realtime API. Install it with pipecat-ai[meta], or pick Meta when scaffolding with pipecat init. MetaSTTService.Settings takes language or language_bias to bias recognition across the 25 supported languages, keywords for domain-specific terms, and mode to select the model's own endpointing, client-delimited turns, or diarization.
(PR #5587)
Added universal-3-6-pro as a supported AssemblyAISTTService model. It is universal-3-5-pro upgraded — the same model with the same feature set — and is recognized as part of the Universal-3 Pro family, so every u3-rt-pro feature (built-in turn detection, prompting, continuous partials, interruption_delay, context carryover, and voice focus) applies to it as well. universal-3-5-pro remains supported and stays the default.
(PR #5598)
StartupTimingReport.warmup reports what warming Pipecat's deferred imports cost startup, which no processor accounts for and which is often the largest single contributor to a cascaded bot's start. warmup.duration_secs is how long warming took and warmup.blocking_duration_secs is what it added beyond the slowest processor — zero for a pipeline whose services take longer to connect than warming takes to load. Observers can also handle the new BaseObserver.on_startup_warmup() event directly.
(PR #5600)
StartupTimingReport now accounts for the whole of a pipeline's startup, so a slow start can be read off the report rather than inferred. setup_phase_secs and start_phase_secs split the span into the two phases that compose differently: processors are set up concurrently, so the longest single piece of work decides that phase, while the StartFrame reaches them one at a time, so what each spends on it adds up. ProcessorStartupTiming.start_duration_secs reports the latter per processor alongside the existing setup_duration_secs.
(PR #5600)
Added language_codes and timestamps settings to AssemblyAISyncSTTService, aligning it with the Sync API's config surface. language_codes (e.g. [Language.EN, Language.ES]) declares the audio's languages for multilingual or code-switching audio, and is bound in preference to the single language when both are set; regional variants resolve to their base code and duplicates are dropped, preserving declaration order. timestamps requests per-word start/end timings on the transcription result, and is unset by default so the API default of False applies.
(PR #5603)
Added ServiceMetricsObserver, which reports each metric a service publishes as a record rather than a log line. on_service_latency carries time to first byte, first audio and first answer token, with the parts each decomposes into; on_service_usage carries speech-to-text audio seconds, text-to-speech characters, and the token counts an LLM reports. One record per metric, never summed, so a consumer groups them by turn, session or model.
(PR #5607)
Added SpeakingObserver, which reports a conversation's speaking lifecycle through on_speech_event: user_speech_started / user_speech_stopped as the detector heard them, user_turn_started / user_turn_stopped as the turn strategy ruled on them, bot_speech_started / bot_speech_stopped, and interruption. Speech is timed to where it began and ended rather than to where the detector confirmed it, and a moment that closes a stretch of speech names its started_at, so an interval reads whole from one record.
(PR #5612)
Added ErrorObserver, which reports every error a pipeline raises through on_error as an ErrorEvent: the message, the category it was attributed to, the exception_type behind it, the processor that raised it, and whether that processor can still do its job (processor_usable). Errors are read where they are raised, so ones a processor answers for itself — a ServiceSwitcher failing over, or a service it holds in reserve — are reported too; PipelineWorker's on_pipeline_error sees only the errors that reach the top of the pipeline.
(PR #5618)
Added FunctionCallObserver, which reports each function call through on_function_call_event as a FunctionCallEvent: function_call_started when the LLM asks for it, function_call_in_progress when it begins running, and one of function_call_completed, function_call_failed, function_call_timed_out or function_call_cancelled when it settles. Each moment names the one before it, so the wait to run and the time spent running each read from one record. Arguments travel by default and results do not; include_arguments and include_results change either.
(PR #5622)
DeepgramFluxSTTService, DeepgramFluxSageMakerSTTService and CartesiaTurnsSTTService push an EagerTranscriptionFrame when they predict an end of turn and an EagerEndOfTurnCancelFrame when they withdraw it, which EagerUserTurnStrategies acts on. Both frames are emitted only while enable_eager_end_of_turn is on.
(PR #5625)
Added support for answering an eager end of turn: a response is generated from a service's predicted end of turn and held until the turn is confirmed, so the gap before the committed end of turn is spent generating rather than waiting. Turn it on with enable_eager_end_of_turn=True on DeepgramFluxSTTService, DeepgramFluxSageMakerSTTService or CartesiaTurnsSTTService, which then recommend EagerUserTurnStrategies in place of ExternalUserTurnStrategies. The LLM service holds the response, so no extra processor is needed. The response is discarded if the user resumes speaking or if the committed transcript differs from the predicted one, per the strategy's match_policy (NormalizedMatch by default, which ignores the capitalization and punctuation services commonly add when committing a transcript, or ExactMatch to require the two to be identical). An unconfirmed turn never reaches the user or the LLM context, and tool calls are never executed for one.
(PR #5625)
Added FlowConfig, which describes a Pipecat Flows conversation as data so a bot can load its flow at runtime. A config names the initial node and, for each node, its messages, the tools it offers, and where each tool leads. Load it with FlowConfig.from_file(), from_yaml(), or from_json(), construct a Flow from it and the module holding your direct functions and action handlers, or a list of modules, and hand that to FlowManager.
Tools stay in Python and return (result, TRANSITION_IN_YAML); a function
that only moves the conversation is written in the config alone, as a
transition_only entry with a description. The config picks the next node,
by name or with a branch table keyed on a field of the result, where case
keys may be strings, booleans, or numbers. Constructing the Flow reports
every tool, handler, or variable the config names but cannot resolve,
together, as a FlowReferenceError. Actions use the built-in tts_say,
end_conversation, and function types, or a custom type registered in code
or given a handler name in the config. Prompts may use {{ key }}
placeholders, which FlowManager fills from its state on entering the
node, and a YAML config can keep long prompts in their own files with
!include path, resolved relative to the config. The format's JSON Schema is
kept in the repository at src/pipecat/flows/flow_config.schema.json for
editors and other tools to vendor.
```
initial_node: greet
nodes:
  greet:
    role_message: You take food orders for {{ restaurant_name }}.
    task_messages:
      - role: developer
        content: Greet the caller and ask: pizza or sushi?
    functions:
      - name: choose_pizza
        transition_only: true
        description: The caller wants pizza.
        transition_to: pizza
  pizza:
    task_messages:
      - role: developer
        content: Take the pizza order.
    functions:
      - name: select_pizza_order
        transition_to:
          field: status
          cases:
            ok: confirm
            unavailable: pizza
```
```
import handlers  # direct functions: select_pizza_order, ...; action
handlers

config = FlowConfig.from_file("flow.yaml")
flow = Flow(config, handlers=handlers)
flow_manager = FlowManager(
    worker=worker,
    llm=llm,
    context_aggregator=context_aggregator,
    global_functions=flow.global_functions,
)
flow_manager.state["restaurant_name"] = "Luigi's"  # fills {{
restaurant_name }}
await flow_manager.initialize(flow.initial_node)
```
The flows examples are grouped by form: examples/flows/yaml/ holds a
folder per YAML flow with bot.py, flow.yaml, and handlers.py
(hello_world, food_ordering, restaurant_reservation, patient_intake,
insurance_quote, and podcast_interview), and examples/flows/python/ holds
the flows that show what needs code. The README says when to write a flow in
each form.
(PR #5628)
Added pipecat.utils.yaml.include_loader(), which returns a yaml.SafeLoader subclass that resolves !include <relative-path> tags against a base directory. Flow configs and eval scenarios use it; any YAML-based configuration can.
(PR #5628)
A suite manifest lists scripted and simulated scenarios side by side under scenarios:, and pipecat eval suite -k simulation (or -k script) runs only one kind.
(PR #5646)
Simulations for pipecat eval: give a scenario a persona: and a goal:
instead of scripted turns, and an LLM plays the caller, holding the whole
conversation with your bot, in text or over synthesized speech, and hanging
up once it has what it came for. A judge then reads the transcript, the bot's
tool calls in place, and decides whether the bot did its job (success:) and
how every reply scored on your metrics:, judged per turn (criterion, with
min_score as the share of turns that must pass) or measured from the run
(measure: turns, duration, words, or latency against a range, or
function_calls against the list of calls the bot should make, [] for
none). pipecat eval run plays one; a suite runs it runs: times and every
run must pass.
```
persona: "Jamie, booking dinner for two tonight."
goal: "Book a table for two at 6 PM, then end the call."
success: "the bot confirmed a reservation for two at 6 PM"
metrics:
  - name: politeness
    criterion: "the reply is courteous, never curt or dismissive"
    min_score: 1
  - measure: latency
    max_value: 5
runs: 3
```
(PR #5646)
RTVIClientTransport and RTVIClientSerializer let a Pipecat pipeline join a bot as an RTVI client. The eval harness now runs on them: its STT, TTS, persona, and recording are stages of a pipeline of its own, and each suite run gets its own process.
(PR #5646)
Added require_given() to pipecat.utils.types. It narrows a settings value past NOT_GIVEN and None like assert_given(), and also rejects the empty string, raising ValueError("<what> must be specified") for settings a service cannot run without.
(PR #5670)
A simulation's results tell judge trouble from bot trouble: a turn the judge left unanswered carries verdict: none, a failed metric carries a failure_kind (judge_no, judge_no_verdict, out_of_range, function_calls), and a judge that gives no verdict on the goal makes the run an error with kind judge_no_verdict, kept out of the suite's pass rate. A failed latency measure's reason says what was timed, the first token in text mode or the first spoken sentence in audio mode.
(PR #5678)
A simulation ends as soon as it is going nowhere: silence when neither side does anything for max_silence_s (30 s by default), and an error the moment the harness's own pipeline fails, the persona LLM first among them, instead of waiting out max_duration_s.
(PR #5678)
A simulation's simulator: block is optional: without one the persona runs on the same local Ollama model as the default judge, gemma4:12b, so a simulation needs no API key. The release-eval simulations use it.
(PR #5678)
Added BackendLLMWorker and BackendOutput (pipecat.workers.llm): a worker that runs any LLM service, with its own context and multi-step tool calling, as the backend OpenAILiveLLMService client delegation hands work to. Everything the backend produces comes back as a BackendOutput saying what it is and whether the user may hear it, and transform_output decides that per output, or rewrites the text on its way out — see examples/realtime/realtime-openai-live-client-delegation-spoken-updates.py.
(PR #5688)
Added OpenAILiveLLMService, a speech-to-speech service for the OpenAI Live API (gpt-live-1). The live model is full-duplex — it listens and speaks at the same time and handles being interrupted itself — and delegates search, reasoning and tool use to a backend model while the conversation continues. Both delegation modes are supported: OpenAILiveLLMService.ResponsesDelegation lets OpenAI host the backend (Responses API) model, with its function calls executed by the pipeline's registered tool handlers; OpenAILiveLLMService.ClientDelegation runs any Pipecat LLM service as the backend through a BackendLLMWorker. See examples/realtime/realtime-openai-live-responses-delegation.py and examples/realtime/realtime-openai-live-client-delegation.py, plus examples/realtime/realtime-openai-live-client-delegation-spoken-updates.py for a backend that chooses what the user hears.
(PR #5688)

Changed

⚠️ Behavior change: DeepgramSTTService and DeepgramSageMakerSTTService no longer send profanity_filter, so Deepgram's own default of off applies. Deepgram's filter rewrites the words it matches rather than tagging them, so a false positive silently corrupts a transcript that may be stored, analyzed, or sent downstream. To keep the previous behavior, pass settings=DeepgramSTTService.Settings(profanity_filter=True).
(PR #5462)
CartesiaTTSService and CartesiaHttpTTSService now default to sonic-3.6, Cartesia's current Sonic model. Pass model="sonic-3.5" to stay on the previous default.
(PR #5472)
soundfile is now a required dependency instead of an optional extra, so SoundfileMixer and the eval harness can load audio files with no extra install step. The pipecat-ai[soundfile] extra still resolves and is now a no-op, so existing installs keep working unchanged.
(PR #5473)
text_contains on a user_transcription eval expectation now accumulates the STT's final transcription segments within the turn before checking, so an STT that finalizes an utterance in pieces can still satisfy a phrase. Substring checks also ignore whitespace differences between the expected text and the event's text.
(PR #5473)
Raised the pipecat-ai[cli] Context Hub floor to pipecat-ai-context-hub 0.6.0, which registers its MCP server with Claude Code for every directory rather than only the one install ran in. A project scaffolded after the hub was set up now has the server without re-running install.
(PR #5480)
Bots scaffolded by pipecat init build their WorkerRunner before wiring up event handlers, so handlers can end the session with await runner.cancel(). The runner takes handle_sigint=runner_args.handle_sigint, letting a bot run directly install its own signal handler while a hosted one leaves signals to its host.
(PR #5482)
The AssemblyAI STT examples and docs now name universal-3-5-pro, AssemblyAI's current Universal-3 Pro streaming model. The u3-rt-pro names are legacy aliases AssemblyAI redirects to it and are still accepted by AssemblyAISTTService.
(PR #5507)
AWSTranscribeSTTService now maps 46 more AWS Transcribe streaming languages, including Turkish, Hungarian, Tamil, Telugu, Swahili, Mexican Spanish and Welsh. Passing one of these as a Language enum previously sent a bare base code such as tr, which AWS Transcribe rejects.
(PR #5509)
AWSPollyTTSService now maps Language.EN_IE and Language.EN_SG to Polly's en-IE and en-SG locales, so Irish and Singaporean English no longer log an unverified-language warning.
(PR #5510)
Raised the pipecat-ai[cli] Context Hub floor to pipecat-ai-context-hub 0.7.0, whose refresh indexes the framework at its newest release tag rather than tracking its default branch. Hub answers stop mixing in unreleased APIs stamped with the previous release's number, which version_compatibility reported as compatible to anyone running that release.
Pass --framework-version head to index the default branch — the right choice when developing Pipecat itself or building against unreleased code. A --framework-version tag that does not exist now fails the refresh instead of logging a warning and exiting 0.
(PR #5513)
SarvamTTSService and SarvamHttpTTSService now default to bulbul:v3, Sarvam's current TTS model. Sarvam's API no longer serves bulbul:v2 and rejects requests for it, so the previous default could not synthesize. The v3 defaults come with it: shubh as the speaker and a 24000 Hz sample rate.
(PR #5517)
SmallestTTSService now maps all 31 languages the Waves Lightning API accepts, and no longer maps Language.HE, which the API rejects.
(PR #5519)
FireworksLLMService now defaults to accounts/fireworks/models/nemotron-3-ultra-nvfp4. Fireworks no longer serves the previous default, accounts/fireworks/models/firefunction-v2, on its serverless endpoint, so requests using it returned a 404.
(PR #5524)
CartesiaTTSService and CartesiaHttpTTSService now map Language.OR (Odia) and Language.UR (Urdu), the two languages Sonic 3.6 added.
(PR #5532)
Added a turn_coverage setting to GeminiLiveLLMService.Settings, which selects how much of the realtime input stream a user turn covers (for example TURN_INCLUDES_ONLY_ACTIVITY to keep video frames outside detected audio activity out of the turn).
(PR #5535)
GroqTTSService now supports a variety of sample rates, defaulting to 24khz.
(PR #5538)
LiveAvatarNewSessionRequest now defaults to H264 video encoding, matching the LiveAvatar API's own default. LiveAvatar has deprecated VP8; pass video_settings=VideoSettings(encoding=VideoEncoding.VP8) to keep the previous encoding while it lasts.
(PR #5539)
KokoroTTSService.Settings has a speed field, kokoro-onnx's speech rate multiplier (0.5 to 2.0, default 1.0), settable at construction and at runtime.
(PR #5543)
OpenAIResponsesLLMService's service_tier documentation now lists OpenAI's full set of tiers, including fast — the low-latency tier that replaced the priority name.
(PR #5548)
The OpenAI server-side turn detection example now uses gpt-transcribe, replacing gpt-4o-transcribe, which shuts down on 2027-02-26.
(PR #5549)
OpenAISTTService now defaults to gpt-transcribe, OpenAI's replacement for gpt-4o-transcribe, which shuts down on 2027-02-26. With include_prob_metrics=True, GPT transcription models now request logprobs alongside a json response; only Whisper models use verbose_json.
(PR #5549)
PocketTTSService now loads pocket-tts's distilled models for German, Spanish, Italian and Portuguese instead of the 24-layer variants, which are too slow to synthesize in real time on a typical CPU.
(PR #5553)
FalImageGenService.Settings now accepts negative_prompt and guidance_scale, passed through to Fal's image generation request when set.
(PR #5556)
MoonshineSTTService now maps Language.DE (German) and Language.TL / Language.FIL (Tagalog/Filipino), the languages Moonshine added to its model catalogue.
(PR #5557)
TogetherLLMService now defaults to zai-org/GLM-5.2. The previous default, zai-org/GLM-5.1, has been retired from Together's serverless inference and returns model_not_available. Pass settings=TogetherLLMService.Settings(model=...) to choose a different model.
(PR #5570)
NeuphonicTTSService and NeuphonicHttpTTSService now accept temperature in their settings, passing Neuphonic's synthesis randomness control (0.0–1.0) through to the API. Left unset, Neuphonic's own default applies.
(PR #5578)
NvidiaLLMService now defaults to nvidia/nemotron-3-super-120b-a12b. The previous default, nvidia/nemotron-3-nano-30b-a3b, reached end of life on NVIDIA's NIM cloud endpoint and now returns HTTP 410.a
(PR #5579)
⚠️ Behavior change: A word-timestamp event that matches nothing left to speak is now dropped instead of being emitted as a TTSTextFrame. Passing it through put text the LLM never wrote into the conversation context. Nothing is lost: the text that word should have covered is carried by the word that puts the sentence back in step, or emitted when the audio context ends.
(PR #5585)
⚠️ Mem0MemoryService now adds retrieved memories with the "developer" role instead of "system" when add_as_system_message is set. Memories are extra guidance for the model, not a system prompt. Services without a "developer" role receive them as "user" — the same treatment non-OpenAI services already gave the "system" message.
(PR #5596)
SegmentedSTTService now appends 0.5 s of silence to each speech segment before transcribing it, so the model hears the end of speech and finishes the last word instead of dropping or garbling it. This applies to every service built on it, such as WhisperSTTService, OpenAISTTService, GroqSTTService and MoonshineSTTService. The padding is submitted to the provider, so it counts toward STT usage metrics. Pass trailing_silence_secs=0 to the service to disable it, or another value to tune it.
(PR #5621)
FunctionCallResultFrame now carries an error field, set when a call ends because its handler raised, so a failed call can be told apart from a successful one. The result is unchanged: it remains the stand-in message the LLM reads.
(PR #5622)
⚠️ Behavior change: the Deepgram Flux services no longer push their eager end-of-turn transcript as an InterimTranscriptionFrame. That transcript is a prediction the service may withdraw, and it now travels as an EagerTranscriptionFrame, emitted only under enable_eager_end_of_turn, so it is no longer surfaced to clients as partial user speech. Flux emits no interim transcriptions as a result; subscribe to on_update for incremental transcript text.
(PR #5625)
The on_user_turn_inference_triggered event emitted by UserTurnController and by BaseUserTurnStopStrategy now carries a UserTurnSpeculation | None as its last argument, set when the inference answers a turn that hasn't ended yet and carrying the turn text it was run against. A custom stop strategy requests one by passing it to trigger_user_turn_inference_triggered(). The events of the same name on LLMUserAggregator and UserTurnProcessor are unchanged, so handlers registered on those keep their signature.
```
```python
@controller.event_handler("on_user_turn_inference_triggered")
async def on_user_turn_inference_triggered(controller, strategy,
```
speculation): ...
```
(PR #5625)
LLMContextFrame carries a speculation flag marking an inference as speculative, so the response it produces is held until the turn it answers is confirmed.
(PR #5625)
The AGENTS.md that pipecat init writes now describes Pipecat Flows as part of Pipecat (pipecat.flows) and directs coding agents to build a new flow as a FlowConfig YAML file plus a Python tools module, reserving Python-built flows for routing or prompts that need code.
(PR #5628)
Eval suite results.jsonl records carry scenario and kind (script or simulation), and a repeated sweep (--repeat) reports pass rates and exits 0.
(PR #5646)
pipecat eval loads the nearest .env (walking up from the working directory, shell variables winning) before running, so a simulation's persona LLM and a hosted judge find their credentials the way the bots do. python-dotenv is now a core dependency.
(PR #5646)
CrusoeLLMService now defaults to openai/gpt-oss-120b. The previous default was zai/GLM-5.2. Pass settings=CrusoeLLMService.Settings(model=...) to choose a different model.
(PR #5656)
The Speechify service now sends the standard Speechify-Caller-Version attribution header (valued from pipecat_version()), replacing the non-standard X-Pipecat-Version header.
(PR #5668)
The runner extra now requires pipecat-ai-prebuilt>=1.1.0. The prebuilt client UI served by the development runner gains a LiveKit option in its transport selector, backed by @pipecat-ai/livekit-transport 1.0.0, and moves to @pipecat-ai/voice-ui-kit 0.14.0.
(PR #5675)
EvalSession.from_scenario() builds the session for a loaded scenario of either kind, an EvalScriptSession for a scripted scenario or an EvalSimulationSession for a simulation, and EvalSession is the base class of both. Constructing EvalSession(...) directly is no longer supported; use the kind's own class.
(PR #5676)
EvalClientParams holds only what the scenario asks of the bot, and EvalClient takes it together with the run's EvalSessionParams and the services. EvalClient.for_scenario() and EvalClient.for_simulation() are gone; the sessions construct the client.
(PR #5676)
The eval sessions and EvalSuite.run() take how a run behaves as one EvalSessionParams (connect_timeout_s, default_timeout_ms, record_path, cache_dir, use_cache, stop_bot, trigger_disconnect), and the services a run uses (judge, user_tts, bot_stt, and a simulation's persona_llm) as keyword arguments on both from_scenario() and the constructors.
(PR #5676)
Cartesia word timestamps for Chinese and Japanese now keep one token per reported entry, each with its own start time, instead of joining a whole word_timestamps message into a single token.
(PR #5679)
PiperTTSService downloads voice models to ~/.cache/pipecat/piper by default, the same cache location KokoroTTSService uses, instead of the current working directory. Pass download_dir to keep models elsewhere; a model already downloaded into a working directory is fetched again into the cache on first use.
(PR #5687)

Deprecated

Deprecated LatencyBreakdown.chronological_events(). Use LatencyBreakdown.turn_contribution_lines() instead, which names every part of the interval rather than the services that happened to report a metric, and whose lines sum to the measured latency. Will be removed in 2.0.0.
(PR #5445)
Deprecated SarvamTTSModel.BULBUL_V2. Sarvam's API rejects bulbul:v2 on both the REST and the WebSocket endpoint, so it cannot synthesize; use SarvamTTSModel.BULBUL_V3. Constructing SarvamTTSService or SarvamHttpTTSService with it still resolves a model config and now emits a DeprecationWarning. It will be removed in 2.0.0.
(PR #5517)
Deprecated the on_progress parameter of EvalSession() and EvalSession.from_scenario(), and the on_update parameter of EvalSuite.run(). Use the event handlers of the same name instead. Passing a callback still works, keeps its existing signature, and emits a DeprecationWarning. They will be removed in 2.0.0.
(PR #5520)
Setting the system prompt as a "system" message at the start of LLMContext is deprecated and will stop working in 2.0.0. Set it on the LLM service instead:
```
```python
# Before
context = LLMContext([{"role": "system", "content": "Be helpful."}])

# After
llm = OpenAILLMService(api_key=..., system_instruction="Be helpful.")
context = LLMContext()
```
```
To change the prompt at runtime, push an LLMUpdateSettingsFrame carrying LLMSettings(system_instruction=...) instead of rewriting the first context message.
(PR #5596)
EvalScenario, EvalResult, EvalTurn, EvalTurnResult, and EvalTurnProgress are now EvalScriptScenario, EvalScriptResult, EvalScriptTurn, EvalScriptTurnResult, and EvalScriptTurnProgress, the scripted kind of scenario. The old names remain as aliases until 2.0.0.
(PR #5646)
pipecat.evals.harness moved to pipecat.evals.script_session. The old module path remains until 2.0.0.
(PR #5646)
RTVIEvalSerializer is now EvalSerializer, the serializer of the bot's EvalTransport; its client-side counterpart is EvalClientSerializer. The old name remains as an alias until 2.0.0.
(PR #5646)
Deprecated the connect_timeout_s, default_timeout_ms, record_path, cache_dir, use_cache, stop_bot, and trigger_disconnect keyword arguments of EvalSession.from_scenario() and EvalScriptSession.from_scenario(), and the use_cache and default_timeout_ms keyword arguments of EvalSuite.run(). Pass params=EvalSessionParams(...) instead. The old arguments still work, override the params field of the same name, and emit a DeprecationWarning. They will be removed in 2.0.0. EvalSimulationSession.from_scenario() is new and takes params only.
(PR #5676)
Deprecated service: openai in a scenario's judge.eval: and simulator: blocks and service: cartesia in its user.speech: block, along with the openai_service() and cartesia_service() builders behind them. The built-in names are the local, keyless services (Ollama, Kokoro, Moonshine, Whisper); any other provider is a factory:, a dotted path to a callable that takes the block and returns the service, documented with examples in pipecat.evals.services. The old names still work and emit a DeprecationWarning; they will be removed in 2.0.0.
(PR #5678)

Removed

Removed SEND_CHUNK_MS from pipecat.evals.harness. The harness now sends the user's audio through its own output transport, which paces it like any Pipecat transport does.\n
(PR #5646)

Fixed

Fixed AsyncAITTSService ignoring runtime model, voice and language updates. Those three are sent only in the websocket init message and never repeated per utterance, so a change to any of them now opens a new session instead of being stored and warned about.
(PR #5464)
Deepgram Flux STT now applies a model or numerals update by reconnecting, since Flux reads those only from the connection URL. The reconnect waits until the user stops speaking, and settings Flux accepts on a live connection (keyterm, eot_threshold, eager_eot_threshold, eot_timeout_ms, language_hints) are still sent without dropping it.
(PR #5477)
A Deepgram Flux FatalError now reports the code and description Flux sends, instead of a generic Unknown error.
(PR #5477)
Deepgram Flux STT now fails instead of hanging when a connection cannot be established. A connection setting the endpoint rejects leaves the SageMaker session opening forever and produces no confirmation on either transport, so both the session handshake and the wait for Flux to confirm it are now bounded and reported through on_connection_error.
(PR #5498)
Deepgram Flux STT now marks itself unusable once it can no longer transcribe — after a fatal error from Flux, or a connection that never establishes — so the failure reaches the pipeline instead of the service staying connected and silent.
(PR #5498)
Fixed GladiaSTTService dropping Gladia's stop_recording message on a graceful end. The websocket closed before the message was sent, so sessions ended by dropping the connection rather than closing cleanly, and the service's on_disconnected event fired twice.
(PR #5499)
Fixed the pipeline idle timeout cancelling a live session while the user is still talking, on pipelines that get their turns from a provider instead of local VAD. idle_timeout_frames now counts UserStartedSpeakingFrame, TranscriptionFrame and InterimTranscriptionFrame as user activity, alongside BotSpeakingFrame and the VAD-driven UserSpeakingFrame.
(PR #5502)
Corrected the Nova Sonic region lists in AWSNovaSonicLLMService's docstring and example: eu-north-1 is a supported region for both Nova 2 Sonic and Nova Sonic.
(PR #5508)
Fixed the Vonage-Google package conflict so it no longer prevents GeminiSTTService usage in environments where the Vonage connector can't be installed anyway, such as development environments on macOS. The repo lockfile resolved google-genai to 2.8.0 — below the 2.9.0 the service needs — for every Python 3.13 environment, to satisfy vonage-video-connector's pydantic<2.12 pin, though the connector is installable only on Python 3.13 Linux. That environment is now resolved apart from every other. Installs from PyPI were never affected.
(PR #5512)
Fixed SarvamHttpTTSService ignoring its sample_rate. It named the field sample_rate in the request body where Sarvam's REST API reads speech_sample_rate, so synthesis always came back at the model's default rate while the audio frames were tagged with the requested one — audio played back at the wrong speed for any other rate.
(PR #5517)
Fixed the Fireworks function-calling example, which named accounts/fireworks/models/gpt-oss-20b. Fireworks retired that model from its serverless endpoint, so the example now uses accounts/fireworks/models/nemotron-3-ultra-nvfp4.
(PR #5524)
Fixed the examples/flows/llm_switching.py Bedrock service, which pinned a Claude model Bedrock has retired and a latency mode Bedrock does not offer for its replacement. It now uses us.anthropic.claude-haiku-4-5-20251001-v1:0.
(PR #5525)
Corrected GeminiTTSService's documented model names: they differ between the Cloud Text-to-Speech and Gemini API backends, and the docstrings named only Cloud Text-to-Speech's gemini-2.5-flash-tts / gemini-2.5-pro-tts, which the Gemini API rejects with a 404.
(PR #5526)
Fixed Deepgram STT settings passed through extra being discarded when the key matched a declared settings field (for example extra={"diarize": True}). Such keys are now promoted to their field and sent to Deepgram.
(PR #5527)
Fixed GeminiTTSService ignoring its language setting on the Gemini API (GenAI) backend. The configured language is now sent as the speech config's language_code, as it already is on the Cloud Text-to-Speech backend.
(PR #5537)
LmntTTSService now maps all 31 languages LMNT's Blizzard model speaks, adding Assamese, Bengali, Czech, Danish, Finnish, Malayalam, Marathi, Slovak, Tamil and Telugu. These languages already reached the API through the base-code fallback, which logged a "not verified" warning for each of them.
(PR #5544)
Fixed an OpenAISTTService error where include_prob_metrics=True sent a request that diarization models reject. Those models report no per-token or per-segment probabilities, so the service now transcribes without requesting them and warns once.
(PR #5549)
Fixed the examples/voice/voice-pockettts.py import of PocketTTSService, which named a module that does not exist.
(PR #5553)
Fixed an issue where ElevenLabsRealtimeSTTService ignored the realtime API's quota_exceeded, unaccepted_terms and invalid_request messages instead of reporting them as errors.
(PR #5555)
Fixed the PCM sample rates GrokRealtimeLLMService accepts: the Grok Voice Agent API's 22050 Hz option is now allowed, and 21050 Hz — which the API rejects — is not.
(PR #5560)
Fixed the Rime TTS services' language mapping: Arabic, Italian, Japanese and Portuguese — all served by Rime's coda model — now resolve to Rime's language codes, and a region-qualified language such as Language.EN_US falls back to a base code Rime accepts instead of a BCP-47 tag that made Rime close the connection without synthesizing.
(PR #5564)
SambaNovaLLMService now sends the frequency_penalty, presence_penalty and seed settings, which SambaNova's chat completions API accepts. They were previously dropped from the request.
(PR #5565)
Fixed a SpeechmaticsSTTService error where setting split_sentences raised ValueError: "VoiceAgentConfig" object has no field "split_sentences" and the service failed to construct.
(PR #5568)
Fixed SpeechmaticsTTSService reporting the speechmatics-rt SDK version, rather than Pipecat's, in the sm-app tag it sends to Speechmatics. The tag now carries the Pipecat version, matching SpeechmaticsSTTService.
(PR #5569)
Fixed the NVIDIA examples update-settings/llm/llm-nvidia.py and voice/voice-nvidia-sagemaker.py, which requested Llama models that NVIDIA's NIM cloud endpoint no longer serves.
(PR #5579)
Corrected the model docstring in OneShotInputParams for UltravoxRealtimeLLMService: the field defaults to None, which lets Ultravox pick its current default model, rather than to the legacy fixie-ai/ultravox alias.
(PR #5582)
Fixed word-level tracking stalling for the rest of a sentence when a TTS service reports a word-timestamp event that doesn't match what is being spoken. The next correctly reported word is now matched past the gap and carries the text that was never reported, so TTSTextFrames and RTVI progress keep pace with the audio. Previously the sentence stayed stuck at the first unmatched word, every word after it went unmatched too, and the remainder arrived as a single frame once the audio context ended.
(PR #5585)
Fixed NvidiaLLMService emitting reasoning frames before TTFB metrics, so observers receive TTFB before reasoning output for structured reasoning fields and inline <think> blocks.
(PR #5589)
The LLM usage debug log now includes cache creation tokens alongside cache reads.
(PR #5602)
OpenAI-compatible LLM services now report prompt-cache write tokens as LLMTokenUsage.cache_creation_input_tokens, so usage-based cost tracking accounts for them. Covers BaseOpenAILLMService and the services inheriting it, OpenAIResponsesLLMService, and SambaNovaLLMService. Providers that do not report the count leave the field unset.
(PR #5602)
OutputAudioRawFrames pushed from upstream of an STT service (for example a sound effect or a recording played into the pipeline before the STT) now reach the output transport untouched. STT services only transcribe InputAudioRawFrames. Previously any audio frame was treated as user speech, so the audio was sent to the transcription provider and, with audio_passthrough=False, never reached the transport.
(PR #5609)
VoicemailDetector no longer swallows EndWorkerFrame travelling upstream through a closed gate, so a handler that ends the call reaches PipelineWorker. Closed classifier and conversation gates already admitted CancelWorkerFrame (a system frame) but dropped other worker lifecycle frames.
(PR #5614)
SegmentedSTTService (Whisper, Moonshine, and the other segmented STT services) now transcribes each speech segment in a background task. Previously the transcription blocked the service's input audio frames, so a VAD downstream could not analyze them until it finished. Transcripts are still pushed in segment order.
(PR #5630)
A punctuation mark reported as its own word-timestamp entry no longer shifts the text around it or records the mark twice. Trailing punctuation is attributed to the word before it, so the mark's own event arrived with nothing left to account for and spent a character of the following word instead — no text was lost and the cursors resynced a word later, but the words in between were recorded with the break misplaced ("Yeah", ",", "I" gave "Yeah, I c an help"), and a turn ending inside that window kept the misplaced split. Where a service forwards raw tokens (pre_merge_tokens=False) the mark was also written to the context a second time. Most visible for languages reported one character at a time, where every mark is affected, though not specific to them.
(PR #5633)
LLMService now logs the composed system instruction only when it changes. Previously every tool sync recomposed it, so the full prompt was logged once per turn.
(PR #5635)
Fixed LLMContextSummarizer's message-count trigger (max_unsummarized_messages), which never reset after a summary was applied and counted the injected summary message as a new conversation turn.
(PR #5642)
Bots scaffolded by pipecat init with Sarvam, Whisper, or Moonshine STT, Sarvam TTS, or Google STT no longer crash at startup. Sarvam and Google STT now use each service's default model or location, and Whisper and Moonshine read an optional WHISPER_MODEL or MOONSHINE_MODEL variable that falls back to the default model. Previously the generated code read a variable the generated .env.example never listed, and the unset variable arrived as an explicit None that overrode the working default and failed validation.
(PR #5661)
The generated .env.example now lists every variable the scaffolded bot reads. This adds entries for ElevenLabs realtime STT, Gemini STT and TTS, Mistral STT, xAI STT, Soniox TTS, Deepgram Flux TTS, Deepgram Flux SageMaker TTS, and the NVIDIA TTS voice ID, names the Fal key FAL_API_KEY to match the generated code, and reads the AWS Transcribe session token from AWS_SESSION_TOKEN instead of the misspelled AWS_AWS_SESSION_TOKEN.
(PR #5661)
Fixed WhisperSTTService raising ValueError at construction when Settings(language=None) is paired with a multilingual model. None asks Whisper to detect the language itself, which every model supports; the language check previously looked None up in the model's supported-language list.
(PR #5669)
Fixed the Model.DISTIL_LARGE_V2 and MLXModel.DISTIL_LARGE_V3 Whisper models being documented as multilingual. Every Distil-Whisper release is English-only.
(PR #5669)
Local STT and TTS services now reject a missing or blank model or voice at startup with a "must be specified" error. Piper, Pocket TTS, Kokoro, and XTTS check the voice in the constructor (Pocket TTS before loading its model, and Kokoro also against the loaded voices file), and Whisper, Whisper MLX, and Moonshine check the model. Previously a blank value, which is what a copied .env.example produces, reached the underlying library and failed with an unrelated message, sometimes only on the first utterance. XTTS also reports an unknown studio speaker as an error frame instead of raising a KeyError.
(PR #5670)
Fixed XAISTTService emitting each utterance's text twice. xAI sends a chunk final for every locked segment and then an utterance final that restates the whole utterance, and both were pushed as TranscriptionFrame, so LLM context aggregators and other consumers that accumulate finals saw the text duplicated. Chunk finals are now pushed as InterimTranscriptionFrame and only the utterance final becomes a TranscriptionFrame, always with finalized=True.
(PR #5673)
Fixed SonioxSTTService dropping Soniox's end-of-audio frame on a graceful end. The websocket closed before it was sent, so sessions ended by dropping the connection rather than closing cleanly, and the service's on_disconnected event fired twice.
(PR #5682)
Fixed SarvamSTTService mislabeling every transcript as Hindi when no language was detected, the default behavior in transcribe mode. Unresolved languages now stay None, and ur-IN, mai-IN, sd-IN, and kok-IN are now correctly mapped.
(PR #5692)
The pipecat eval suite dashboard fits the terminal: rows are windowed to the active runs, including in a repeated sweep, and never wrap, so the tally at the bottom is always on screen. A running row's spinner animates and each row carries a clock, from its first attempt's start to its last attempt's end; the failures list is set apart from its header by a blank line.
(PR #5694)

Other

CartesiaSTTService, DeepgramFluxSTTService, and DeepgramFluxSageMakerSTTService now log the provider's request ID when the connection is established, so a session can be traced in provider-side diagnostics.
(PR #5475)
Documented SonioxSTTService's context setting as accepting either a SonioxContextObject or a plain string of background text on any current Soniox model, along with the 8k-token cap Soniox applies to the context object.
(PR #5566)

pipecat-ai 1.9.0 v1.9.0 on Python PyPI

Added

Changed

Deprecated

Removed

Fixed

Other

pipecat-ai 1.9.0
v1.9.0

on Python PyPI