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 implement the on_push_frame method:

from pipecat.observers.base_observer import BaseObserver

class CustomObserver(BaseObserver):
    async def on_push_frame(
        self,
        src: FrameProcessor,
        dst: FrameProcessor,
        frame: Frame,
        direction: FrameDirection,
        timestamp: int,
    ):
        # Your frame observation logic here
        pass

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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

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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 = PipelineTask(
    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:

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

    Logs all frame instances of:
    - StartInterruptionFrame
    - 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,
        src: FrameProcessor,
        dst: FrameProcessor,
        frame: Frame,
        direction: FrameDirection,
        timestamp: int,
    ):
        time_sec = timestamp / 1_000_000_000
        arrow = "→" if direction == FrameDirection.DOWNSTREAM else "←"

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