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.
This adds user-to-bot response latency tracking to OpenTelemetry spans:
- Created UserBotLatencyObserver as a reusable component for tracking
user-to-bot response latency
- Records the value as an attribute on turn spans (turn.user_bot_latency_seconds)
- Updated TurnTraceObserver to use UserBotLatencyObserver, following the same pattern as TurnTrackingObserver
- Updated PipelineTask to automatically create and wire UserBotLatencyObserver
when tracing is enabled (same as TurnTrackingObserver)
With all these examples updated, we no longer need dedicated examples illustrating `LLMContext`, so they're removed.
Here’s where we *don’t* yet use `LLMContext` and associated machinery:
- Realtime services: OpenAI Realtime, Gemini Live, and AWS Nova Sonic (support coming soon)
- `GoogleLLMOpenAIBetaService` (it’s deprecated, so we didn’t bother adding support)
- `LLMLogObserver` (support coming soon)
- `GatedOpenAILLMContextAggregator` (support coming soon)
- `LangchainProcessor` (support coming soon)
- `Mem0MemoryService` (support coming soon)
- Examples that use LLM-specific tools definitions as opposed to `ToolsSchema` (these will be updated soon)
- Examples that rely `GoogleLLMContext.upgrade_to_google` (TBD what to do with these)
Examples that use `LLMLogObserver`:
- 30-
Examples that use `GatedOpenAILLMContextAggregator`:
- 22-
Examples that use `LangchainProcessor`:
- 07b-
Examples that use `Mem0MemoryService`:
- 37-
Examples that need updating to use `ToolsSchema`:
- 15-
- 15a-
- 20a-
- 20c-
- 20d-
- 22b-
- 22c-
- 33-
- 36-
Examples that use `GoogleLLMContext.upgrade_to_google`:
- 22d-
- 25-
