chore: unify the call logs and real time events

api/services/pipecat/event_handlers.py

@@ -47,7 +47,6 @@ def register_transport_event_handlers(
         num_channels=num_channels,
     )
     in_memory_transcript_buffer = InMemoryTranscriptBuffer(workflow_run_id)
-    in_memory_logs_buffer = InMemoryLogsBuffer(workflow_run_id)
 
     @transport.event_handler("on_client_connected")
     async def on_client_connected(transport, participant):
@@ -71,7 +70,7 @@ def register_transport_event_handlers(
             await task.cancel()
 
     # Return the buffers so they can be passed to other handlers
-    return in_memory_audio_buffer, in_memory_transcript_buffer, in_memory_logs_buffer
+    return in_memory_audio_buffer, in_memory_transcript_buffer
 
 
 def register_task_event_handler(

api/services/pipecat/realtime_feedback_observer.py

@@ -1,11 +1,16 @@
 """Real-time feedback observer for sending pipeline events to the frontend.
 
 This observer watches pipeline frames and sends relevant events (transcriptions,
-bot text) over WebSocket to provide real-time feedback in the UI.
+bot text, function calls, TTFB metrics) over WebSocket to provide real-time
+feedback in the UI.
 
 For frames with presentation timestamps (pts), like TTSTextFrame, we respect
 the timing by queuing them and sending at the appropriate time, similar to
 how base_output.py handles timed frames.
+
+Note: Node transition events are sent directly from PipecatEngine.set_node()
+rather than being observed here, to ensure precise timing at the moment of
+node changes.
 """
 
 import asyncio
@@ -24,20 +29,30 @@ from pipecat.frames.frames import (
     FunctionCallResultFrame,
     InterimTranscriptionFrame,
     InterruptionFrame,
+    MetricsFrame,
     StopFrame,
     TranscriptionFrame,
     TTSTextFrame,
 )
+from pipecat.metrics.metrics import TTFBMetricsData
 from pipecat.observers.base_observer import BaseObserver, FramePushed
 from pipecat.processors.frame_processor import FrameDirection
 from pipecat.utils.time import nanoseconds_to_seconds
 
 
 class RealtimeFeedbackObserver(BaseObserver):
-    """Observer that sends real-time transcription and bot response events via WebSocket.
+    """Observer that sends real-time transcription, bot response, and metrics via WebSocket.
+
+    Observes pipeline frames and sends events for:
+    - User transcriptions (interim and final)
+    - Bot TTS text (with pts-based timing)
+    - Function calls (start/end)
+    - TTFB metrics (LLM generation time only - filters to processors containing "LLM")
 
     For frames with pts (presentation timestamp), we queue them and send at the
     appropriate time to sync with audio playback.
+
+    Note: Node transitions are handled by PipecatEngine.set_node() callback.
     """
 
     def __init__(
@@ -132,6 +147,8 @@ class RealtimeFeedbackObserver(BaseObserver):
         frame = data.frame
         frame_direction = data.direction
 
+        logger.trace(f"{self} Received Frame: {frame} Direction: {frame_direction}")
+
         # Handle pipeline termination - stop clock task
         if isinstance(frame, (EndFrame, CancelFrame, StopFrame)):
             await self._cancel_clock_task()
@@ -226,6 +243,23 @@ class RealtimeFeedbackObserver(BaseObserver):
                     },
                 }
             )
+        # Handle TTFB metrics - capture LLM generation time only
+        elif isinstance(frame, MetricsFrame):
+            # Check if this MetricsFrame contains TTFB data from an LLM processor
+            for metric_data in frame.data:
+                if isinstance(metric_data, TTFBMetricsData):
+                    # Only send TTFB if it's from an LLM processor
+                    if metric_data.processor and "LLM" in metric_data.processor:
+                        await self._send_message(
+                            {
+                                "type": "rtf-ttfb-metric",
+                                "payload": {
+                                    "ttfb_seconds": metric_data.value,
+                                    "processor": metric_data.processor,
+                                    "model": metric_data.model,
+                                },
+                            }
+                        )
 
     async def _send_message(self, message: dict):
         """Send message via WebSocket AND append to logs buffer, handling errors gracefully."""

api/services/pipecat/run_pipeline.py

@@ -14,6 +14,7 @@ from api.services.pipecat.event_handlers import (
     register_transcript_handler,
     register_transport_event_handlers,
 )
+from api.services.pipecat.in_memory_buffers import InMemoryLogsBuffer
 from api.services.pipecat.pipeline_builder import (
     build_pipeline,
     create_pipeline_components,
@@ -467,11 +468,45 @@ async def _run_pipeline(
         ReactFlowDTO.model_validate(workflow.workflow_definition_with_fallback)
     )
 
+    # Create in-memory logs buffer early so it can be used by engine callbacks
+    in_memory_logs_buffer = InMemoryLogsBuffer(workflow_run_id)
+
+    # Create node transition callback if WebSocket sender is available
+    node_transition_callback = None
+    ws_sender = get_ws_sender(workflow_run_id)
+    if ws_sender:
+
+        async def send_node_transition(
+            node_name: str, previous_node: Optional[str]
+        ) -> None:
+            """Send node transition event via WebSocket AND log to buffer."""
+            message = {
+                "type": "rtf-node-transition",
+                "payload": {
+                    "node_name": node_name,
+                    "previous_node": previous_node,
+                },
+            }
+            # Send via WebSocket
+            try:
+                await ws_sender(message)
+            except Exception as e:
+                logger.debug(f"Failed to send node transition via WebSocket: {e}")
+
+            # Log to in-memory buffer
+            try:
+                await in_memory_logs_buffer.append(message)
+            except Exception as e:
+                logger.error(f"Failed to append node transition to logs buffer: {e}")
+
+        node_transition_callback = send_node_transition
+
     engine = PipecatEngine(
         llm=llm,
         workflow=workflow_graph,
         call_context_vars=merged_call_context_vars,
         workflow_run_id=workflow_run_id,
+        node_transition_callback=node_transition_callback,
     )
 
     # Create pipeline components with audio configuration and engine
@@ -573,7 +608,7 @@ async def _run_pipeline(
     await engine.initialize()
 
     # Register event handlers
-    in_memory_audio_buffer, in_memory_transcript_buffer, in_memory_logs_buffer = (
+    in_memory_audio_buffer, in_memory_transcript_buffer = (
         register_transport_event_handlers(
             task,
             transport,
@@ -585,10 +620,11 @@ async def _run_pipeline(
     )
 
     # Add real-time feedback observer if WebSocket sender is available
-    ws_sender = get_ws_sender(workflow_run_id)
+    # Note: ws_sender was already fetched earlier for node_transition_callback
     if ws_sender:
         feedback_observer = RealtimeFeedbackObserver(
-            ws_sender=ws_sender, logs_buffer=in_memory_logs_buffer
+            ws_sender=ws_sender,
+            logs_buffer=in_memory_logs_buffer,
         )
         task.add_observer(feedback_observer)