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The Observer pattern in Pipecat allows non-intrusive monitoring of frames as they flow through the pipeline. Observers can watch frame traffic without affecting the pipeline’s core functionality.

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Base Observer

All observers must inherit from BaseObserver and can implement these methods:
  • on_push_frame(data: FramePushed): Called when a frame is pushed from one processor to another
  • on_process_frame(data: FrameProcessed): Called when a frame is being processed by a processor
  • on_pipeline_started(): Called after the StartFrame has been processed by all processors in the pipeline
from pipecat.observers.base_observer import BaseObserver, FramePushed, FrameProcessed

class CustomObserver(BaseObserver):
    async def on_push_frame(self, data: FramePushed):
        # Your frame observation logic here
        pass

    async def on_process_frame(self, data: FrameProcessed):
        # Your frame processing observation logic here
        pass

    async def on_pipeline_started(self):
        # Called when the pipeline has fully started
        pass

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Lifecycle and Cleanup

The pipeline calls setup() on each observer when it starts and cleanup() when it stops. If your observer spawns background work, use self.create_task() so the task is tracked by the pipeline’s task manager, and override cleanup() to cancel it. Always call super().cleanup(), which waits for any in-flight event handlers to finish. 
class CustomObserver(BaseObserver):
    def __init__(self):
        super().__init__()
        self._task = None

    async def on_pipeline_started(self):
        # Use create_task (not asyncio.create_task) so the task is tracked.
        self._task = self.create_task(self._run())

    async def _run(self):
        # Your long-running background logic here.
        pass

    async def cleanup(self):
        if self._task:
            await self.cancel_task(self._task)
        await super().cleanup()
If you give your observer a custom __init__, you must call super().__init__(). Skipping it leaves the observer partially initialized and raises errors such as 'CustomObserver' object has no attribute '_name' at runtime.

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Available Observers

Pipecat provides several built-in observers:
  • LLMLogObserver: Logs LLM activity and responses
  • TranscriptionLogObserver: Logs speech-to-text transcription events
  • RTVIObserver: Converts internal frames to RTVI protocol messages for server to client messaging
  • StartupTimingObserver: Measures processor startup times and transport readiness
  • UserBotLatencyObserver: Measures user-to-bot response latency
  • TurnTrackingObserver: Tracks conversation turns and events

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Using Multiple Observers

You can attach multiple observers to a pipeline task. Each observer will be notified of all frames: 
task = PipelineWorker(
    pipeline,
    params=PipelineParams(
        observers=[LLMLogObserver(), TranscriptionLogObserver(), CustomObserver()],
    ),
)

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Example: Debug Observer

Here’s an example observer that logs interruptions and bot speaking events: 
from pipecat.observers.base_observer import BaseObserver, FramePushed, FrameProcessed
from pipecat.frames.frames import (
    InterruptionFrame,
    BotStartedSpeakingFrame,
    BotStoppedSpeakingFrame,
)
from pipecat.processors.frame_processor import FrameDirection
from loguru import logger

class DebugObserver(BaseObserver):
    """Observer to log interruptions and bot speaking events to the console.

    Logs all frame instances of:
    - InterruptionFrame
    - BotStartedSpeakingFrame
    - BotStoppedSpeakingFrame

    This allows you to see the frame flow from processor to processor through the pipeline for these frames.
    Log format: [EVENT TYPE]: [source processor] → [destination processor] at [timestamp]s
    """

    async def on_push_frame(self, data: FramePushed):
        time_sec = data.timestamp / 1_000_000_000
        arrow = "→" if data.direction == FrameDirection.DOWNSTREAM else "←"

        if isinstance(data.frame, InterruptionFrame):
            logger.info(f"⚡ INTERRUPTION START: {data.source} {arrow} {data.destination} at {time_sec:.2f}s")
        elif isinstance(data.frame, BotStartedSpeakingFrame):
            logger.info(f"🤖 BOT START SPEAKING: {data.source} {arrow} {data.destination} at {time_sec:.2f}s")
        elif isinstance(data.frame, BotStoppedSpeakingFrame):
            logger.info(f"🤖 BOT STOP SPEAKING: {data.source} {arrow} {data.destination} at {time_sec:.2f}s")

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Common Use Cases

Observers are particularly useful for:
  • Debugging frame flow
  • Logging specific events
  • Monitoring pipeline behavior
  • Collecting metrics
  • Converting internal frames to external messages