Add `system_instruction` field to `LLMSettings` so it is runtime-updatable via settings.
For Google (GoogleLLMService, GoogleVertexLLMService), deprecate the init-time arg since it was already shipped. For Anthropic, AWS Bedrock, and OpenAI, remove the init-time arg entirely since it was never shipped.
Still need to handle realtime services (OpenAI Realtime, Grok Realtime, Gemini Live).
Widen version ranges for stable packages (anthropic, azure, deepgram,
groq, livekit, nvidia-riva-client, fastapi, ormsgpack, opentelemetry,
faster-whisper) and add upper bounds to previously uncapped packages
(hume, pyjwt, livekit-api, camb).
Replace CartesiaHttpTTSService's internal use of the Cartesia SDK with
direct aiohttp calls, accepting an optional aiohttp_session parameter.
Replace fal-client SDK calls in FalSTTService and FalImageGenService
with direct HTTP to bypass the SDK's aggressive retry/backoff logic
that caused significant latency regressions.
Enables .model_dump() serialization for Pipecat Cloud collection.
All metrics now include start_time (Unix timestamp) for timeline
plotting alongside duration_secs.
Add per-service latency breakdown metrics alongside existing user-to-bot
latency measurement. When enable_metrics=True, the observer now emits an
on_latency_breakdown event with TTFB, text aggregation, and user turn
duration metrics collected between VADUserStoppedSpeakingFrame and
BotStartedSpeakingFrame.
- Add LatencyBreakdown dataclass with ttfb, text_aggregation,
user_turn_secs fields
- Accumulate MetricsFrame data during user→bot cycles
- Reset accumulators on InterruptionFrame to discard stale metrics
- Measure user_turn_secs from actual user silence (VAD timestamp -
stop_secs) to turn release (UserStoppedSpeakingFrame)
- Filter zero-value TTFB entries from startup metric resets
- Add frame deduplication using bounded deque + set pattern
- Update example 29 with latency breakdown display
Add BotConnectedFrame (SystemFrame) pushed by SFU transports (Daily,
LiveKit, HeyGen, Tavus) when the bot joins the room. Replace the
on_transport_readiness_measured event with on_transport_timing_report
which includes both bot_connected_secs and client_connected_secs.
Introduce ClientConnectedFrame (SystemFrame) pushed by all transports
when a client connects. StartupTimingObserver uses this to measure
transport readiness — the time from StartFrame to first client
connection — via a new on_transport_readiness_measured event.
Tracks how long each processor start method takes during pipeline
startup by measuring StartFrame arrive/leave deltas. Emits a timing
report via the on_startup_timing_report event and auto-logs a summary.
Internal pipeline processors are excluded from reports by default.
- Update 13d-assemblyai-transcription.py to explicitly use u3-rt-pro model
- Update 55d-update-settings-assemblyai-stt.py to demonstrate keyterms updates instead of language updates
- Add helpful logging to show before/after keyterms boosting effect
- Use difficult names (Xiomara, Saoirse, Krzystof) to demonstrate boosting effectiveness
- Add "beta feature" note to custom prompt warning
- Rename min_end_of_turn_silence_when_confident parameter to min_turn_silence across all AssemblyAI code
- Update documentation, examples, and test files to use new parameter name
- 07o-interruptible-assemblyai.py: Basic example using Pipecat VAD mode
- 07o-interruptible-assemblyai-stt.py: Advanced example using STT-controlled
turn detection with comprehensive documentation on u3-rt-pro features
(turn detection tuning, prompt-based enhancement, speaker diarization)
- Add InterruptionFrame handling with stop_all_metrics()
- Add processing metrics (start/stop) at response boundaries
- Fix agent transcript handling for voice and text modalities:
- Voice mode: push LLMTextFrame (append_to_context=False) and
TTSTextFrame for deltas, skip duplicated final text
- Text mode: push LLMTextFrame with proper response lifecycle,
no TTSTextFrame (downstream TTS handles audio)
- Add output_medium parameter to AgentInputParams and OneShotInputParams
- Improve TTFB measurement using VAD speech end time
- Update example with user turn strategies and transcript events
- Add text-only output example (50a-ultravox-realtime-text.py)
Move the sentence vs token aggregation concern into text aggregators
so all text flows through them regardless of mode. This enables
pattern detection and tag handling to work in TOKEN mode.
- Add TextAggregationMode enum (SENTENCE, TOKEN) as the user-facing
TTS setting, separate from the internal AggregationType
- Add TOKEN mode support to Simple, SkipTags, and PatternPair aggregators
- Add text_aggregation_mode parameter to TTSService and all TTS subclasses
- Deprecate aggregate_sentences in favor of text_aggregation_mode
- Merge TTSService._process_text_frame() into a single codepath
