Turn Tracking Observer

Track conversation turns and events in your Pipecat pipeline

The TurnTrackingObserver monitors and tracks conversational turns in your Pipecat pipeline, providing events when turns start and end. It intelligently identifies when a user-bot interaction cycle begins and completes.

Turn Lifecycle

A turn represents a complete user-bot interaction cycle:

1. Start: When the user starts speaking (or pipeline starts for first turn)
2. Processing: User speaks, bot processes and responds
3. End: After the bot finishes speaking and either:
   • The user starts speaking again
   • A timeout period elapses with no further activity

Events

The observer emits two main events:

• on_turn_started: When a new turn begins
  • Parameters: turn_number (int)
• on_turn_ended: When a turn completes
  • Parameters: turn_number (int), duration (float, in seconds), was_interrupted (bool)

Usage

The observer is automatically created when you initialize a PipelineTask with enable_turn_tracking=True (which is the default):

task = PipelineTask(
    pipeline,
    # Turn tracking is enabled by default
)

# Access the observer
turn_observer = task.turn_tracking_observer

# Register event handlers
@turn_observer.event_handler("on_turn_started")
async def on_turn_started(observer, turn_number):
    logger.info(f"Turn {turn_number} started")

@turn_observer.event_handler("on_turn_ended")
async def on_turn_ended(observer, turn_number, duration, was_interrupted):
    status = "interrupted" if was_interrupted else "completed"
    logger.info(f"Turn {turn_number} {status} in {duration:.2f}s")

Configuration

You can configure the observer’s behavior when creating a PipelineTask:

from pipecat.observers.turn_tracking_observer import TurnTrackingObserver

# Create a custom observer instance
custom_turn_tracker = TurnTrackingObserver(
    turn_end_timeout_secs=3.5,     # Turn end timeout (default: 2.5)
)

# Add it as a regular observer
task = PipelineTask(
    pipeline,
    observers=[custom_turn_tracker],
    # Disable the default one if adding your own
    enable_turn_tracking=False,
)

Interruptions

The observer automatically detects interruptions when the user starts speaking while the bot is still speaking. In this case:

• The current turn is marked as interrupted (was_interrupted=True)
• A new turn begins immediately

How It Works

The observer monitors specific frame types to track conversation flow:

• StartFrame: Initiates the first turn
• UserStartedSpeakingFrame: Starts user speech or triggers a new turn
• BotStartedSpeakingFrame: Marks bot speech beginning
• BotStoppedSpeakingFrame: Starts the turn end timeout

After a bot stops speaking, the observer waits for the configured timeout period. If no further bot speech occurs, the turn ends; otherwise, it continues as part of the same turn.

Use Cases

• Analytics: Measure turn durations, interruption rates, and conversation flow
• Logging: Record turn-based logs for diagnostics and analysis
• Visualization: Show turn-based conversation timelines in UIs
• Tracing: Group spans and metrics by conversation turns