{"timestamp": "2026-03-28T01:36:27.074Z", "level": "INFO", "logger": "uvicorn.protocols.http.httptools_impl", "function": "send", "line": 476, "message": "35.191.57.78:45508 - \"GET /api/v4/check-limits?device_id=4c7990ca3869fa1d&persona_id=edit_photos HTTP/1.1\" 200", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:37:08.114Z", "level": "INFO", "logger": "uvicorn.protocols.http.httptools_impl", "function": "send", "line": 476, "message": "35.191.57.76:57980 - \"GET /api/v4/check-limits?device_id=4c7990ca3869fa1d&persona_id=edit_photos HTTP/1.1\" 200", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:39:55.549Z", "level": "INFO", "logger": "uvicorn.protocols.http.httptools_impl", "function": "send", "line": 476, "message": "35.191.57.78:40682 - \"GET /api/v4/check-limits?device_id=4c7990ca3869fa1d&persona_id=edit_photos HTTP/1.1\" 200", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:00.358Z", "level": "INFO", "logger": "src.routes.bot", "function": "bot_connect", "line": 186, "message": "CONNECT_INPUT_BODY: {'device_id': '4c7990ca3869fa1d', 'version': 'explore', 'character_id': None, 'persona_id': 'edit_photos', 'app_key': 'app_v2', 'language': 'te', 'source': None}", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:00.359Z", "level": "INFO", "logger": "src.routes.bot", "function": "bot_connect", "line": 197, "message": "SESSION_START: device_id=4c7990ca3869fa1d version=explore persona_id=edit_photos app_key=app_v2 language=te source=app transport_mode=smallwebrtc", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:01.257Z", "level": "INFO", "logger": "src.bot.core.main", "function": "run_bot_webrtc_global_agent", "line": 60, "message": "SESSION_START: persona=edit_photos name=Edit Photos content_type=image_gen tools=['cf_image'] transport=smallwebrtc", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:01.278Z", "level": "INFO", "logger": "src.bot.core.config", "function": "load_system_instruction", "line": 409, "message": "PROMPT_LOADED: persona=edit_photos lang=te global=1047 lang_inst=90 intro=0 personality=193 total=1368", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:01.298Z", "level": "INFO", "logger": "src.bot.core.main", "function": "run_bot_webrtc_global_agent", "line": 89, "message": "USAGE_STATE: session_id=1f181fde-319b-4183-826f-e4ea0870250a device_id=4c7990ca3869fa1d persona=edit_photos content=image_gen free=0/3 plan=95 topup=0 transport=smallwebrtc", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:01.774Z", "level": "INFO", "logger": "src.bot.core.main", "function": "run_bot_webrtc_global_agent", "line": 131, "message": "GEMINI_LIVE_SESSION: session_id=1f181fde-319b-4183-826f-e4ea0870250a language=te persona=edit_photos transport=smallwebrtc", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:01.790Z", "level": "WARNING", "logger": "warnings", "function": "_showwarnmsg", "line": 112, "message": "/app/venv/lib/python3.12/site-packages/pipecat/services/google/gemini_live/llm.py:1297: DeprecationWarning: Setting `LiveConnectConfig.generation_config` is deprecated, please set the fields on `LiveConnectConfig` directly. This will become an error in a future version (not before Q3 2025)\n  async with self._client.aio.live.connect(\n", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:02.310Z", "level": "ERROR", "logger": "asyncio.runners", "function": "run", "line": 118, "message": "Task exception was never retrieved\nfuture: <Task finished name='Task-179' coro=<TurnClientMixin.send_data() done, defined at /app/venv/lib/python3.12/site-packages/aioice/turn.py:262> exception=TransactionFailed(Message(message_method=Method.CHANNEL_BIND, message_class=Class.ERROR, transaction_id=b'\\xcasK\\xf5\\xbdt\\xf4\\x07\\xe5Ru\\x18'))>", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "error", "error.kind": "ERROR", "error.type": "TransactionFailed", "error.message": "STUN transaction failed (403 - Forbidden IP)", "error.stack": "Traceback (most recent call last):\n  File \"/app/venv/lib/python3.12/site-packages/aioice/turn.py\", line 281, in send_data\n    await self.channel_bind(channel, addr)\n  File \"/app/venv/lib/python3.12/site-packages/aioice/turn.py\", line 102, in channel_bind\n    await self.request_with_retry(request)\n  File \"/app/venv/lib/python3.12/site-packages/aioice/turn.py\", line 234, in request_with_retry\n    response, addr = await self.request(request)\n                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/venv/lib/python3.12/site-packages/aioice/turn.py\", line 221, in request\n    return await transaction.run()\n           ^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/venv/lib/python3.12/site-packages/aioice/stun.py\", line 323, in run\n    return await self.__future\n           ^^^^^^^^^^^^^^^^^^^\naioice.stun.TransactionFailed: STUN transaction failed (403 - Forbidden IP)\n"}
{"timestamp": "2026-03-28T01:40:02.331Z", "level": "ERROR", "logger": "asyncio.runners", "function": "run", "line": 118, "message": "Task exception was never retrieved\nfuture: <Task finished name='Task-181' coro=<TurnClientMixin.send_data() done, defined at /app/venv/lib/python3.12/site-packages/aioice/turn.py:262> exception=TransactionFailed(Message(message_method=Method.CHANNEL_BIND, message_class=Class.ERROR, transaction_id=b'\\x81\\xcd\\x08\\xa5\\x1d\\x94\\xf3\\xe3\\xcfp(\\xf3'))>", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "error", "error.kind": "ERROR", "error.type": "TransactionFailed", "error.message": "STUN transaction failed (403 - Forbidden IP)", "error.stack": "Traceback (most recent call last):\n  File \"/app/venv/lib/python3.12/site-packages/aioice/turn.py\", line 281, in send_data\n    await self.channel_bind(channel, addr)\n  File \"/app/venv/lib/python3.12/site-packages/aioice/turn.py\", line 102, in channel_bind\n    await self.request_with_retry(request)\n  File \"/app/venv/lib/python3.12/site-packages/aioice/turn.py\", line 234, in request_with_retry\n    response, addr = await self.request(request)\n                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/venv/lib/python3.12/site-packages/aioice/turn.py\", line 221, in request\n    return await transaction.run()\n           ^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/venv/lib/python3.12/site-packages/aioice/stun.py\", line 323, in run\n    return await self.__future\n           ^^^^^^^^^^^^^^^^^^^\naioice.stun.TransactionFailed: STUN transaction failed (403 - Forbidden IP)\n"}
{"timestamp": "2026-03-28T01:40:30.176Z", "level": "INFO", "logger": "src.bot.core.rtvi_events", "function": "send_generation_started", "line": 39, "message": "GENERATION_START_EVENT_SENT: session_id=1f181fde-319b-4183-826f-e4ea0870250a device_id=4c7990ca3869fa1d persona=edit_photos persona_name=Edit Photos id=image_1774662030", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:33.585Z", "level": "INFO", "logger": "src.bot.services.image_service", "function": "generate_image_async", "line": 231, "message": "CF_IMAGE_GENERATED: image_id=image_1774662030 session_id=1f181fde-319b-4183-826f-e4ea0870250a device_id=4c7990ca3869fa1d persona=edit_photos persona_name=Edit Photos request_id=180df4f0 provider=flux-2-klein-4b total_latency=3.408s api_latency=3.081s s3_url=https://storage.googleapis.com/maya-session-data/4c7990ca3869fa1d/1f181fde-319b-4183-826f-e4ea0870250a/images/image_1774662030.png", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:33.585Z", "level": "INFO", "logger": "src.bot.core.rtvi_events", "function": "send_generation_complete", "line": 91, "message": "GENERATION_COMPLETE_EVENT_SENT: session_id=1f181fde-319b-4183-826f-e4ea0870250a device_id=4c7990ca3869fa1d persona=edit_photos persona_name=Edit Photos id=image_1774662030 s3_url=https://storage.googleapis.com/maya-session-data/4c7990ca3869fa1d/1f181fde-319b-4183-826f-e4ea0870250a/images/image_1774662030.png", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:38.400Z", "level": "WARNING", "logger": "pipecat.transports.smallwebrtc.transport", "function": "read_audio_frame", "line": 381, "message": "Received an unexpected media stream error while reading the audio.", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:39.660Z", "level": "INFO", "logger": "src.bot.core.session_handlers", "function": "handle_participant_left", "line": 525, "message": "SESSION_SAVED: session_id=1f181fde-319b-4183-826f-e4ea0870250a transport=smallwebrtc persona=edit_photos images=1 videos=0 ppts=0 music=0", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:39.683Z", "level": "INFO", "logger": "src.bot.core.session_handlers", "function": "handle_participant_left", "line": 536, "message": "CREDITS_PERSISTED_PLAN: session_id=1f181fde-319b-4183-826f-e4ea0870250a transport=smallwebrtc sub=sub_d8310614c5dd4b1e used=1", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:39.685Z", "level": "INFO", "logger": "pipecat.observers.loggers.user_bot_latency_log_observer", "function": "_log_summary", "line": 97, "message": "⏱ LATENCY FROM USER STOPPED SPEAKING TO BOT STARTED SPEAKING - Avg: 3.540s, Min: 1.769s, Max: 5.311s", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:53.905Z", "level": "WARNING", "logger": "aioice.turn", "function": "_send", "line": 337, "message": "socket.send() raised exception.", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:41:09.905Z", "level": "WARNING", "logger": "aioice.turn", "function": "_send", "line": 337, "message": "socket.send() raised exception.", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:37:12.018Z", "level": "INFO", "logger": "src.routes.bot", "function": "bot_connect", "line": 186, "message": "CONNECT_INPUT_BODY: {'device_id': '4c7990ca3869fa1d', 'version': 'explore', 'character_id': None, 'persona_id': 'edit_photos', 'app_key': 'app_v2', 'language': 'te', 'source': None}", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:37:12.018Z", "level": "INFO", "logger": "src.routes.bot", "function": "bot_connect", "line": 197, "message": "SESSION_START: device_id=4c7990ca3869fa1d version=explore persona_id=edit_photos app_key=app_v2 language=te source=app transport_mode=smallwebrtc", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:37:12.927Z", "level": "INFO", "logger": "src.bot.core.main", "function": "run_bot_webrtc_global_agent", "line": 60, "message": "SESSION_START: persona=edit_photos name=Edit Photos content_type=image_gen tools=['cf_image'] transport=smallwebrtc", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:37:13.020Z", "level": "INFO", "logger": "src.bot.core.config", "function": "load_system_instruction", "line": 409, "message": "PROMPT_LOADED: persona=edit_photos lang=te global=1047 lang_inst=90 intro=0 personality=193 total=1368", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:37:13.038Z", "level": "INFO", "logger": "src.bot.core.main", "function": "run_bot_webrtc_global_agent", "line": 89, "message": "USAGE_STATE: session_id=439abfa6-9231-4934-a8fe-59d2619971fa device_id=4c7990ca3869fa1d persona=edit_photos content=image_gen free=0/3 plan=96 topup=0 transport=smallwebrtc", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:37:13.547Z", "level": "INFO", "logger": "src.bot.core.main", "function": "run_bot_webrtc_global_agent", "line": 131, "message": "GEMINI_LIVE_SESSION: session_id=439abfa6-9231-4934-a8fe-59d2619971fa language=te persona=edit_photos transport=smallwebrtc", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:37:13.564Z", "level": "WARNING", "logger": "warnings", "function": "_showwarnmsg", "line": 112, "message": "/app/venv/lib/python3.12/site-packages/pipecat/services/google/gemini_live/llm.py:1297: DeprecationWarning: Setting `LiveConnectConfig.generation_config` is deprecated, please set the fields on `LiveConnectConfig` directly. This will become an error in a future version (not before Q3 2025)\n  async with self._client.aio.live.connect(\n", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:37:13.941Z", "level": "ERROR", "logger": "asyncio.runners", "function": "run", "line": 118, "message": "Task exception was never retrieved\nfuture: <Task finished name='Task-99' coro=<TurnClientMixin.send_data() done, defined at /app/venv/lib/python3.12/site-packages/aioice/turn.py:262> exception=TransactionFailed(Message(message_method=Method.CHANNEL_BIND, message_class=Class.ERROR, transaction_id=b'\\xd5\\xc5J(C\\x1b\\x187:$o\\xaa'))>", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "error", "error.kind": "ERROR", "error.type": "TransactionFailed", "error.message": "STUN transaction failed (403 - Forbidden IP)", "error.stack": "Traceback (most recent call last):\n  File \"/app/venv/lib/python3.12/site-packages/aioice/turn.py\", line 281, in send_data\n    await self.channel_bind(channel, addr)\n  File \"/app/venv/lib/python3.12/site-packages/aioice/turn.py\", line 102, in channel_bind\n    await self.request_with_retry(request)\n  File \"/app/venv/lib/python3.12/site-packages/aioice/turn.py\", line 234, in request_with_retry\n    response, addr = await self.request(request)\n                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/venv/lib/python3.12/site-packages/aioice/turn.py\", line 221, in request\n    return await transaction.run()\n           ^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/venv/lib/python3.12/site-packages/aioice/stun.py\", line 323, in run\n    return await self.__future\n           ^^^^^^^^^^^^^^^^^^^\naioice.stun.TransactionFailed: STUN transaction failed (403 - Forbidden IP)\n"}
{"timestamp": "2026-03-28T01:37:24.862Z", "level": "INFO", "logger": "src.bot.core.rtvi_events", "function": "send_generation_started", "line": 39, "message": "GENERATION_START_EVENT_SENT: session_id=439abfa6-9231-4934-a8fe-59d2619971fa device_id=4c7990ca3869fa1d persona=edit_photos persona_name=Edit Photos id=image_1774661844", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:37:27.209Z", "level": "ERROR", "logger": "src.service.cf_flux_client", "function": "generate_image", "line": 138, "message": "CF_FLUX_GENERATE_HTTP_ERROR: account=dheemanth status=500 response={\"error\":\"Failed to generate image\",\"details\":\"3043: Internal server error\",\"hint\":\"Internal server error. This might be a temporary issue with Workers AI.\"}", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "error", "error.kind": "ERROR"}
{"timestamp": "2026-03-28T01:37:27.209Z", "level": "WARNING", "logger": "src.bot.services.image_service", "function": "_generate_with_fallback", "line": 70, "message": "CF_image_1774661844_FAILED, retrying with Replicate: Service error: 500", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:37:29.381Z", "level": "INFO", "logger": "src.bot.services.image_service", "function": "generate_image_async", "line": 231, "message": "CF_IMAGE_GENERATED: image_id=image_1774661844 session_id=439abfa6-9231-4934-a8fe-59d2619971fa device_id=4c7990ca3869fa1d persona=edit_photos persona_name=Edit Photos request_id=05ea854a provider=replicate-flux-2-klein-4b total_latency=4.519s api_latency=1.860s s3_url=https://storage.googleapis.com/maya-session-data/4c7990ca3869fa1d/439abfa6-9231-4934-a8fe-59d2619971fa/images/image_1774661844.png", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:37:29.382Z", "level": "INFO", "logger": "src.bot.core.rtvi_events", "function": "send_generation_complete", "line": 91, "message": "GENERATION_COMPLETE_EVENT_SENT: session_id=439abfa6-9231-4934-a8fe-59d2619971fa device_id=4c7990ca3869fa1d persona=edit_photos persona_name=Edit Photos id=image_1774661844 s3_url=https://storage.googleapis.com/maya-session-data/4c7990ca3869fa1d/439abfa6-9231-4934-a8fe-59d2619971fa/images/image_1774661844.png", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:37:48.195Z", "level": "WARNING", "logger": "pipecat.transports.smallwebrtc.transport", "function": "read_audio_frame", "line": 381, "message": "Received an unexpected media stream error while reading the audio.", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:37:49.466Z", "level": "INFO", "logger": "src.bot.core.session_handlers", "function": "handle_participant_left", "line": 525, "message": "SESSION_SAVED: session_id=439abfa6-9231-4934-a8fe-59d2619971fa transport=smallwebrtc persona=edit_photos images=1 videos=0 ppts=0 music=0", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:37:49.489Z", "level": "INFO", "logger": "src.bot.core.session_handlers", "function": "handle_participant_left", "line": 536, "message": "CREDITS_PERSISTED_PLAN: session_id=439abfa6-9231-4934-a8fe-59d2619971fa transport=smallwebrtc sub=sub_d8310614c5dd4b1e used=1", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:37:49.492Z", "level": "INFO", "logger": "pipecat.observers.loggers.user_bot_latency_log_observer", "function": "_log_summary", "line": 97, "message": "⏱ LATENCY FROM USER STOPPED SPEAKING TO BOT STARTED SPEAKING - Avg: 3.695s, Min: 0.983s, Max: 6.407s", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:38:03.701Z", "level": "WARNING", "logger": "aioice.turn", "function": "_send", "line": 337, "message": "socket.send() raised exception.", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:38:19.701Z", "level": "WARNING", "logger": "aioice.turn", "function": "_send", "line": 337, "message": "socket.send() raised exception.", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}


Looking at the logs, I see two sessions:

1. Session `439abfa6` (01:37:12 - 01:37:49): Image generated successfully (CF failed, Replicate fallback), session ended at 37.48s with "unexpected media stream error while reading the audio" - user disconnected after ~35s. Only 1 image generated, no edit attempted.

2. Session `1f181fde` (01:40:00 - 01:40:39): Image generated successfully via CF Flux, session ended at 40.38s with same "unexpected media stream error" - user disconnected after ~38s.

Both sessions follow the same pattern: image generates, user tries to speak for an edit, then the audio stream error occurs and the session terminates. The key question is whether the agent stops responding before the user disconnects, or if the user disconnects because the agent has already stopped speaking.

Looking at the timing in session `1f181fde`, the user only stayed about 5 seconds after the image was generated before leaving—suggesting they may have requested an edit that the agent failed to process. The issue could be that after image generation, the system message about the image being ready isn't properly communicated to Gemini Live, preventing the agent from acknowledging the edit request.

I'm checking the code flow and found a critical bug: the notification about image completion is only sent for the traditional pipeline, but there's a conditional that explicitly skips it for Gemini Live sessions. This means when using Gemini Live, the agent never gets told the image is ready and available for editing.


I see the issue. Looking at both sessions — image generates successfully but user disconnects ~5 seconds later without getting an edit response. Let me check the Gemini Live notification path:


   245→            })
   246→            await rtvi_events.send_generation_ended("image", image_id)
   247→            await _trigger_review(trigger_review_callback)
   248→
   249→            # Decrement credit
   250→            from src.bot.services.limit_checker import use_credit
   251→            use_credit(bot_session)
   252→
   253→            # Notify LLM that image is ready (traditional pipeline runs in background)
   254→            if task and not getattr(bot_session, 'use_gemini_live', False):
   255→                from pipecat.frames.frames import LLMMessagesAppendFrame
   256→                await task.queue_frames([
   257→                    LLMMessagesAppendFrame(
   258→                        messages=[{"role": "user", "content": "[System: Image generation is complete. The image is now on screen and ready for editing. If user asks to edit, use cf_image with operation=edit. Do NOT ask them to upload an image.]"}],
   259→                        run_llm=False
   260→                    )
   261→                ])
   262→
   263→            return {"status": "completed", "image_url": s3_url, "image_id": image_id, "provider": provider, "model": result.model,
   264→                    "assistant_instruction": "Image is ready! Tell the user their image is generated and they can see it on screen. Be enthusiastic! Reply in the user's language."}
   265→
   266→        except Exception as e:
   267→            return await handle_generation_error(
   268→                e, image_id, request_id, session_info, rtvi_events, task,
   269→                generation_type="image", description=prompt
   270→            )
   271→
   272→    # Gemini Live: await result directly so it waits before speaking.
   273→    # Traditional pipeline: queue in background (HTTP LLM blocks on function calls anyway).
   274→    use_sync = getattr(bot_session, 'use_gemini_live', False)

            })
            await rtvi_events.send_generation_ended("image", image_id)
            await _trigger_review(trigger_review_callback)

            # Decrement credit
            from src.bot.services.limit_checker import use_credit
            use_credit(bot_session)

            # Notify LLM that image is ready (traditional pipeline runs in background)
            if task and not getattr(bot_session, 'use_gemini_live', False):
                from pipecat.frames.frames import LLMMessagesAppendFrame
                await task.queue_frames([
                    LLMMessagesAppendFrame(
                        messages=[{"role": "user", "content": "[System: Image generation is complete. The image is now on screen and ready for editing. If user asks to edit, use cf_image with operation=edit. Do NOT ask them to upload an image.]"}],
                        run_llm=False
                    )
                ])

            return {"status": "completed", "image_url": s3_url, "image_id": image_id, "provider": provider, "model": result.model,
                    "assistant_instruction": "Image is ready! Tell the user their image is generated and they can see it on screen. Be enthusiastic! Reply in the user's language."}

        except Exception as e:
            return await handle_generation_error(
                e, image_id, request_id, session_info, rtvi_events, task,
                generation_type="image", description=prompt
            )

    # Gemini Live: await result directly so it waits before speaking.
    # Traditional pipeline: queue in background (HTTP LLM blocks on function calls anyway).
    use_sync = getattr(bot_session, 'use_gemini_live', False)


Found the issue. Lines 254 and 432: **For Gemini Live sessions, the LLM notification about image completion is skipped entirely.** The `assistant_instruction` in the return dict at line 264 tells the LLM what to say, but the explicit `LLMMessagesAppendFrame` that makes the bot actually speak is gated behind `not use_gemini_live`.

For Gemini Live, the function call result goes back through the tool response, so the `assistant_instruction` in the return dict should work. Let me check how Gemini Live handles function call results:


src/bot/services/video_service.py:82:    await params.result_callback(json.dumps({
src/bot/services/video_service.py:95:    await params.result_callback(json.dumps({
src/bot/services/video_service.py:204:            await params.result_callback(json.dumps({
src/bot/services/video_service.py:258:            await params.result_callback(json.dumps({
src/bot/services/music_service.py:152:            await params.result_callback(json.dumps({
src/bot/services/music_service.py:159:            await params.result_callback(json.dumps({
src/bot/services/music_service.py:173:            await params.result_callback(json.dumps({
src/bot/services/music_service.py:231:            await params.result_callback(json.dumps({
src/bot/services/music_service.py:244:            await params.result_callback(json.dumps({
src/bot/services/ppt_service.py:178:            await params.result_callback(json.dumps({
src/bot/services/ppt_service.py:185:            await params.result_callback(json.dumps({
src/bot/services/ppt_service.py:199:            await params.result_callback(json.dumps({
src/bot/services/ppt_service.py:267:            await params.result_callback(json.dumps({
src/bot/services/ppt_service.py:281:            await params.result_callback(json.dumps({
src/bot/services/image_service.py:151:            await params.result_callback(json.dumps({
src/bot/services/image_service.py:164:                await params.result_callback(json.dumps({
src/bot/services/image_service.py:281:            await params.result_callback(json.dumps(
src/bot/services/image_service.py:289:        await params.result_callback(json.dumps({
src/bot/services/image_service.py:318:        await params.result_callback(json.dumps({
src/bot/services/image_service.py:455:            await params.result_callback(json.dumps(
src/bot/services/image_service.py:463:        await params.result_callback(json.dumps({


   272→    # Gemini Live: await result directly so it waits before speaking.
   273→    # Traditional pipeline: queue in background (HTTP LLM blocks on function calls anyway).
   274→    use_sync = getattr(bot_session, 'use_gemini_live', False)
   275→
   276→    if use_sync:
   277→        # Gemini Live: run synchronously, return actual result
   278→        bot_session.is_processing = True
   279→        try:
   280→            result_data = await generate_image_async()
   281→            await params.result_callback(json.dumps(
   282→                result_data if result_data else {"status": "completed", "image_id": image_id}
   283→            ))
   284→        finally:
   285→            bot_session.is_processing = False
   286→    else:
   287→        # Traditional pipeline: queue in background
   288→        await state.add_to_queue("image", generate_image_async, {"image_id": image_id, "description": prompt, "style": style})
   289→        await params.result_callback(json.dumps({
   290→            "status": "in_progress",
   291→            "assistant_instruction": "Image generation has STARTED for this request. Say a short waiting message in the user's language. CRITICAL: Do NOT call cf_image again for this same request. Reply in the user's language.",
   292→            "image_id": image_id, "description": prompt
   293→        }))
   294→
   295→
   296→# ── Editing ─────────────────────────────────────────────────────
   297→
   298→
   299→async def _handle_edit(
   300→    params, prompt, style, request_id, session_info,
   301→    state, rtvi_processor, bot_session, transcript_manager,

    # Gemini Live: await result directly so it waits before speaking.
    # Traditional pipeline: queue in background (HTTP LLM blocks on function calls anyway).
    use_sync = getattr(bot_session, 'use_gemini_live', False)

    if use_sync:
        # Gemini Live: run synchronously, return actual result
        bot_session.is_processing = True
        try:
            result_data = await generate_image_async()
            await params.result_callback(json.dumps(
                result_data if result_data else {"status": "completed", "image_id": image_id}
            ))
        finally:
            bot_session.is_processing = False
    else:
        # Traditional pipeline: queue in background
        await state.add_to_queue("image", generate_image_async, {"image_id": image_id, "description": prompt, "style": style})
        await params.result_callback(json.dumps({
            "status": "in_progress",
            "assistant_instruction": "Image generation has STARTED for this request. Say a short waiting message in the user's language. CRITICAL: Do NOT call cf_image again for this same request. Reply in the user's language.",
            "image_id": image_id, "description": prompt
        }))

# ── Editing ─────────────────────────────────────────────────────

async def _handle_edit(
    params, prompt, style, request_id, session_info,
    state, rtvi_processor, bot_session, transcript_manager,


OK so for Gemini Live, `use_sync=True`, it awaits `generate_image_async()` and passes the result via `result_callback`. The `assistant_instruction` in the result dict tells Gemini Live what to say. This should work — Gemini Live gets the function result and responds.

Let me look at what happens with the edit flow more closely. The user's complaint is "agent stops speaking after asking for edit." Let me check the edit path for Gemini Live:


   299→async def _handle_edit(
   300→    params, prompt, style, request_id, session_info,
   301→    state, rtvi_processor, bot_session, transcript_manager,
   302→    cf_client, replicate_client, task, llm_service, trigger_review_callback,
   303→    gemini_client=None,
   304→    openai_client=None,
   305→):
   306→    """Handle image editing."""
   307→    edit_id = f"edit_{int(time.time())}"
   308→    rtvi_events = RTVIEventSender(rtvi_processor, session_info)
   309→
   310→    logger.info(f"CF_IMAGE_EDIT_FUNCTION_CALLED: {session_info} edit_id={edit_id} request_id={request_id}")
   311→
   312→    # Validate image exists
   313→    image_to_edit = bot_session.get_image_to_edit()
   314→    if not image_to_edit:
   315→        logger.warning(f"CF_IMAGE_EDIT_NO_IMAGE: {session_info} edit_id={edit_id}")
   316→        from pipecat.frames.frames import TTSSpeakFrame
   317→        await llm_service.push_frame(TTSSpeakFrame(constants.ERROR_NO_IMAGE_FOR_EDIT))
   318→        await params.result_callback(json.dumps({
   319→            "status": "error", "message": "No image available. User needs to generate or upload an image first. Reply in the user's language.", "edit_id": edit_id
   320→        }))
   321→        return
   322→
   323→    # Prepare image description
   324→    use_sync = getattr(bot_session, 'use_gemini_live', False)
   325→    image_source = image_to_edit.get("source", "unknown")
   326→
   327→    if use_sync:
   328→        # Gemini Live: skip describe entirely (saves 8-10s latency)
   329→        image_description = None
   330→        batch_size = 0
   331→        logger.info(f"CF_IMAGE_EDIT_SKIP_DESCRIBE: edit_id={edit_id} (gemini_live)")
   332→    else:
   333→        try:
   334→            image_batch = bot_session.get_image_batch()
   335→            batch_size = bot_session.get_batch_size()
   336→            has_batch = batch_size >= 2
   337→            image_description = await _get_edit_description(image_to_edit, image_batch, has_batch, image_source, edit_id)
   338→        except Exception as e:
   339→            logger.error(f"CF_IMAGE_EDIT_DESCRIPTION_FAILED: edit_id={edit_id} error={e}")
   340→            image_description = None
   341→            batch_size = 0
   342→
   343→    logger.info(f"CF_IMAGE_EDIT_STARTING: {session_info} edit_id={edit_id} image_source={image_source} batch_size={batch_size} has_description={bool(image_description)}")
   344→
   345→    # Send RTVI started FIRST - user sees loader immediately
   346→    await rtvi_events.send_generation_started("image_edit", edit_id, constants.IMAGE_LOADER_TIME_SECONDS, edit_instruction=prompt)
   347→
   348→    # Gemini Live: skip prompt writer (adds latency, direct request is fine)
   349→    if use_sync:
   350→        enhanced_prompt = prompt
   351→        logger.info(f"CF_PROMPT_DIRECT: edit_id={edit_id} len={len(prompt)} (gemini_live, skipped prompt_writer)")
   352→    else:
   353→        try:
   354→            enhanced_prompt = await write_image_prompt(prompt, image_description)
   355→            logger.info(f"CF_PROMPT_ENHANCED: edit_id={edit_id} original_len={len(prompt)} enhanced_len={len(enhanced_prompt)}")
   356→        except Exception as e:
   357→            logger.error(f"CF_PROMPT_ENHANCE_FAILED: edit_id={edit_id} error={e}")
   358→            enhanced_prompt = prompt
   359→
   360→    async def edit_image_async():
   361→        start_time = time.time()
   362→        try:
   363→            image_data_b64 = image_to_edit.get("data")
   364→            input_mime_type = image_to_edit.get("mime_type", "image/jpeg")
   365→
   366→            # Prepare batch images
   367→            current_batch = bot_session.get_image_batch()
   368→            second_image_b64 = None
   369→            is_batch_edit = len(current_batch) >= 2
   370→            original_image_urls = []
   371→
   372→            if is_batch_edit:
   373→                image_data_b64 = current_batch[0].get("data")

async def _handle_edit(
    params, prompt, style, request_id, session_info,
    state, rtvi_processor, bot_session, transcript_manager,
    cf_client, replicate_client, task, llm_service, trigger_review_callback,
    gemini_client=None,
    openai_client=None,
):
    """Handle image editing."""
    edit_id = f"edit_{int(time.time())}"
    rtvi_events = RTVIEventSender(rtvi_processor, session_info)

    logger.info(f"CF_IMAGE_EDIT_FUNCTION_CALLED: {session_info} edit_id={edit_id} request_id={request_id}")

    # Validate image exists
    image_to_edit = bot_session.get_image_to_edit()
    if not image_to_edit:
        logger.warning(f"CF_IMAGE_EDIT_NO_IMAGE: {session_info} edit_id={edit_id}")
        from pipecat.frames.frames import TTSSpeakFrame
        await llm_service.push_frame(TTSSpeakFrame(constants.ERROR_NO_IMAGE_FOR_EDIT))
        await params.result_callback(json.dumps({
            "status": "error", "message": "No image available. User needs to generate or upload an image first. Reply in the user's language.", "edit_id": edit_id
        }))
        return

    # Prepare image description
    use_sync = getattr(bot_session, 'use_gemini_live', False)
    image_source = image_to_edit.get("source", "unknown")

    if use_sync:
        # Gemini Live: skip describe entirely (saves 8-10s latency)
        image_description = None
        batch_size = 0
        logger.info(f"CF_IMAGE_EDIT_SKIP_DESCRIBE: edit_id={edit_id} (gemini_live)")
    else:
        try:
            image_batch = bot_session.get_image_batch()
            batch_size = bot_session.get_batch_size()
            has_batch = batch_size >= 2
            image_description = await _get_edit_description(image_to_edit, image_batch, has_batch, image_source, edit_id)
        except Exception as e:
            logger.error(f"CF_IMAGE_EDIT_DESCRIPTION_FAILED: edit_id={edit_id} error={e}")
            image_description = None
            batch_size = 0

    logger.info(f"CF_IMAGE_EDIT_STARTING: {session_info} edit_id={edit_id} image_source={image_source} batch_size={batch_size} has_description={bool(image_description)}")

    # Send RTVI started FIRST - user sees loader immediately
    await rtvi_events.send_generation_started("image_edit", edit_id, constants.IMAGE_LOADER_TIME_SECONDS, edit_instruction=prompt)

    # Gemini Live: skip prompt writer (adds latency, direct request is fine)
    if use_sync:
        enhanced_prompt = prompt
        logger.info(f"CF_PROMPT_DIRECT: edit_id={edit_id} len={len(prompt)} (gemini_live, skipped prompt_writer)")
    else:
        try:
            enhanced_prompt = await write_image_prompt(prompt, image_description)
            logger.info(f"CF_PROMPT_ENHANCED: edit_id={edit_id} original_len={len(prompt)} enhanced_len={len(enhanced_prompt)}")
        except Exception as e:
            logger.error(f"CF_PROMPT_ENHANCE_FAILED: edit_id={edit_id} error={e}")
            enhanced_prompt = prompt

    async def edit_image_async():
        start_time = time.time()
        try:
            image_data_b64 = image_to_edit.get("data")
            input_mime_type = image_to_edit.get("mime_type", "image/jpeg")

            # Prepare batch images
            current_batch = bot_session.get_image_batch()
            second_image_b64 = None
            is_batch_edit = len(current_batch) >= 2
            original_image_urls = []

            if is_batch_edit:
                image_data_b64 = current_batch[0].get("data")


   373→                image_data_b64 = current_batch[0].get("data")
   374→                second_image_b64 = current_batch[1].get("data")
   375→                original_image_urls = [img.get("s3_url") for img in current_batch if img.get("s3_url")]
   376→                logger.info(f"CF_IMAGE_EDIT_BATCH: edit_id={edit_id} batch_size={len(current_batch)}")
   377→
   378→            if gemini_client:
   379→                result = await gemini_client.edit_image(image_base64=image_data_b64, prompt=enhanced_prompt, reference_image_b64=second_image_b64)
   380→            elif openai_client:
   381→                result = await openai_client.edit_image(image_base64=image_data_b64, prompt=enhanced_prompt)
   382→            else:
   383→                result = await _generate_with_fallback(
   384→                    cf_client, replicate_client, edit_id,
   385→                    cf_func=lambda: cf_client.edit_image(image_base64=image_data_b64, prompt=enhanced_prompt, reference_image_b64=second_image_b64, width=1024, height=1024, num_steps=25),
   386→                    replicate_func=lambda: replicate_client.edit_image(image_base64=image_data_b64, prompt=enhanced_prompt, reference_image_b64=second_image_b64)
   387→                )
   388→
   389→            s3_key = _build_s3_key(bot_session, "edits", edit_id)
   390→            s3_url = await upload_to_s3(result.image_bytes, s3_key, result.mime_type)
   391→
   392→            # Upload original image for single edits
   393→            original_s3_url = None
   394→            if not is_batch_edit:
   395→                original_s3_url = await _upload_original_image(image_data_b64, input_mime_type, bot_session, edit_id, session_info)
   396→
   397→            _update_session_image(bot_session, result, s3_url, edit_id, prompt, is_edit=True)
   398→
   399→            provider = _get_provider(result.model)
   400→            total_latency = time.time() - start_time
   401→            logger.info(
   402→                f"CF_IMAGE_EDITED: edit_id={edit_id} {session_info} request_id={request_id} "
   403→                f"provider={provider} total_latency={total_latency:.3f}s api_latency={result.api_latency:.3f}s s3_url={s3_url}"
   404→            )
   405→
   406→            original_url_value = original_image_urls if is_batch_edit and original_image_urls else original_s3_url
   407→
   408→            await rtvi_events.send_generation_complete(
   409→                "image_edit", edit_id, s3_url, provider,
   410→                edit_instruction=prompt, original_image_url=original_url_value
   411→            )
   412→
   413→            transcript_entry = {
   414→                "edit_id": edit_id, "edit_instruction": prompt, "edited_s3_url": s3_url,
   415→                "original_s3_url": original_url_value, "provider": provider,
   416→                "latency_seconds": round(total_latency, 3), "api_latency_seconds": round(result.api_latency, 3),
   417→            }
   418→            if config_repo.is_image_describer_enabled() and image_description:
   419→                transcript_entry["image_description"] = image_description
   420→            if config_repo.is_prompt_writer_enabled() and enhanced_prompt != prompt:
   421→                transcript_entry["gpt_prompt"] = enhanced_prompt
   422→            transcript_manager.add_generation("image_edit", transcript_entry)
   423→
   424→            await rtvi_events.send_generation_ended("image_edit", edit_id)
   425→            await _trigger_review(trigger_review_callback)
   426→
   427→            # Increment in-memory counter (edits count toward image limit)
   428→            from src.bot.services.limit_checker import use_credit
   429→            use_credit(bot_session)
   430→
   431→            # Notify LLM that edit is ready (traditional pipeline runs in background)
   432→            if task and not getattr(bot_session, 'use_gemini_live', False):
   433→                from pipecat.frames.frames import LLMMessagesAppendFrame
   434→                await task.queue_frames([
   435→                    LLMMessagesAppendFrame(
   436→                        messages=[{"role": "user", "content": "[System: Image edit is complete. The edited image is now on screen and ready for further editing. If user asks to edit again, use cf_image with operation=edit. Do NOT ask them to upload an image.]"}],
   437→                        run_llm=False
   438→                    )
   439→                ])
   440→
   441→            return {"status": "completed", "edit_id": edit_id, "edited_image_url": s3_url, "original_image_url": original_url_value, "provider": provider,
   442→                    "assistant_instruction": "Edit is done! Tell the user their edited image is ready and they can see it on screen. Ask what other changes they want. Reply in the user's language."}
   443→
   444→        except Exception as e:
   445→            return await handle_generation_error(
   446→                e, edit_id, request_id, session_info, rtvi_events, task,
   447→                generation_type="edit"
   448→            )
   449→
   450→    if use_sync:
   451→        logger.info(f"CF_IMAGE_EDIT_SYNC: {session_info} edit_id={edit_id}")
   452→        bot_session.is_processing = True
   453→        try:
   454→            result_data = await edit_image_async()
   455→            await params.result_callback(json.dumps(
   456→                result_data if result_data else {"status": "completed", "edit_id": edit_id}
   457→            ))
   458→        finally:
   459→            bot_session.is_processing = False
   460→    else:
   461→        logger.info(f"CF_IMAGE_EDIT_QUEUEING: {session_info} edit_id={edit_id}")
   462→        await state.add_to_queue("image", edit_image_async, {"edit_id": edit_id, "edit_instruction": prompt})
   463→        await params.result_callback(json.dumps({
   464→            "status": "in_progress",
   465→            "assistant_instruction": "Image edit has STARTED for this request. Say a short waiting message in the user's language. CRITICAL: Do NOT call cf_image again for this same request. Reply in the user's language.",
   466→            "edit_id": edit_id, "edit_instruction": prompt
   467→        }))
   468→
   469→
   470→# ── Edit helpers ────────────────────────────────────────────────
   471→
   472→

                image_data_b64 = current_batch[0].get("data")
                second_image_b64 = current_batch[1].get("data")
                original_image_urls = [img.get("s3_url") for img in current_batch if img.get("s3_url")]
                logger.info(f"CF_IMAGE_EDIT_BATCH: edit_id={edit_id} batch_size={len(current_batch)}")

            if gemini_client:
                result = await gemini_client.edit_image(image_base64=image_data_b64, prompt=enhanced_prompt, reference_image_b64=second_image_b64)
            elif openai_client:
                result = await openai_client.edit_image(image_base64=image_data_b64, prompt=enhanced_prompt)
            else:
                result = await _generate_with_fallback(
                    cf_client, replicate_client, edit_id,
                    cf_func=lambda: cf_client.edit_image(image_base64=image_data_b64, prompt=enhanced_prompt, reference_image_b64=second_image_b64, width=1024, height=1024, num_steps=25),
                    replicate_func=lambda: replicate_client.edit_image(image_base64=image_data_b64, prompt=enhanced_prompt, reference_image_b64=second_image_b64)
                )

            s3_key = _build_s3_key(bot_session, "edits", edit_id)
            s3_url = await upload_to_s3(result.image_bytes, s3_key, result.mime_type)

            # Upload original image for single edits
            original_s3_url = None
            if not is_batch_edit:
                original_s3_url = await _upload_original_image(image_data_b64, input_mime_type, bot_session, edit_id, session_info)

            _update_session_image(bot_session, result, s3_url, edit_id, prompt, is_edit=True)

            provider = _get_provider(result.model)
            total_latency = time.time() - start_time
            logger.info(
                f"CF_IMAGE_EDITED: edit_id={edit_id} {session_info} request_id={request_id} "
                f"provider={provider} total_latency={total_latency:.3f}s api_latency={result.api_latency:.3f}s s3_url={s3_url}"
            )

            original_url_value = original_image_urls if is_batch_edit and original_image_urls else original_s3_url

            await rtvi_events.send_generation_complete(
                "image_edit", edit_id, s3_url, provider,
                edit_instruction=prompt, original_image_url=original_url_value
            )

            transcript_entry = {
                "edit_id": edit_id, "edit_instruction": prompt, "edited_s3_url": s3_url,
                "original_s3_url": original_url_value, "provider": provider,
                "latency_seconds": round(total_latency, 3), "api_latency_seconds": round(result.api_latency, 3),
            }
            if config_repo.is_image_describer_enabled() and image_description:
                transcript_entry["image_description"] = image_description
            if config_repo.is_prompt_writer_enabled() and enhanced_prompt != prompt:
                transcript_entry["gpt_prompt"] = enhanced_prompt
            transcript_manager.add_generation("image_edit", transcript_entry)

            await rtvi_events.send_generation_ended("image_edit", edit_id)
            await _trigger_review(trigger_review_callback)

            # Increment in-memory counter (edits count toward image limit)
            from src.bot.services.limit_checker import use_credit
            use_credit(bot_session)

            # Notify LLM that edit is ready (traditional pipeline runs in background)
            if task and not getattr(bot_session, 'use_gemini_live', False):
                from pipecat.frames.frames import LLMMessagesAppendFrame
                await task.queue_frames([
                    LLMMessagesAppendFrame(
                        messages=[{"role": "user", "content": "[System: Image edit is complete. The edited image is now on screen and ready for further editing. If user asks to edit again, use cf_image with operation=edit. Do NOT ask them to upload an image.]"}],
                        run_llm=False
                    )
                ])

            return {"status": "completed", "edit_id": edit_id, "edited_image_url": s3_url, "original_image_url": original_url_value, "provider": provider,
                    "assistant_instruction": "Edit is done! Tell the user their edited image is ready and they can see it on screen. Ask what other changes they want. Reply in the user's language."}

        except Exception as e:
            return await handle_generation_error(
                e, edit_id, request_id, session_info, rtvi_events, task,
                generation_type="edit"
            )

    if use_sync:
        logger.info(f"CF_IMAGE_EDIT_SYNC: {session_info} edit_id={edit_id}")
        bot_session.is_processing = True
        try:
            result_data = await edit_image_async()
            await params.result_callback(json.dumps(
                result_data if result_data else {"status": "completed", "edit_id": edit_id}
            ))
        finally:
            bot_session.is_processing = False
    else:
        logger.info(f"CF_IMAGE_EDIT_QUEUEING: {session_info} edit_id={edit_id}")
        await state.add_to_queue("image", edit_image_async, {"edit_id": edit_id, "edit_instruction": prompt})
        await params.result_callback(json.dumps({
            "status": "in_progress",
            "assistant_instruction": "Image edit has STARTED for this request. Say a short waiting message in the user's language. CRITICAL: Do NOT call cf_image again for this same request. Reply in the user's language.",
            "edit_id": edit_id, "edit_instruction": prompt
        }))

# ── Edit helpers ────────────────────────────────────────────────


Looking at the logs more carefully, I see that in both sessions, only 1 image was generated (images=1) and there's NO edit log at all. The user says "after asking for edit, agent stops speaking" - so the edit function isn't even being called. The agent generates an image, then when user asks for an edit, the agent doesn't respond at all.

This could be a Gemini Live issue - the LLM isn't calling the `cf_image` tool with `operation=edit`. Or, the Gemini Live session might be crashing/disconnecting after the first function call.

Let me check more specific logs for the session to see what happens after image generation.


The logs show NO edit was ever attempted in either session. The agent generates an image, user asks for edit, but the agent never calls `cf_image(operation=edit)`. Let me check if Gemini Live is dropping the connection after the first function call:


Bash

kubectl logs -l app=maya-pipecat-v4 --tail=2000 --since=15m 2>/dev/null | grep "1f181fde" | grep -i "gemini\|llm\|function\|tool\|turn\|speak\|audio\|disconnect\|error\|exception\|closed\|cancel" | tail -30

Check Gemini Live behavior after image gen in session 1f181fde


{"timestamp": "2026-03-28T01:40:04.432Z", "level": "INFO", "logger": "src.bot.core.session_handlers", "function": "on_client_connected", "line": 166, "message": "USER_JOINED: session_id=1f181fde-319b-4183-826f-e4ea0870250a participant_id=SmallWebRTCConnection#0-13f88967d300474b8ac9fba40e7585f4 elapsed=3.2s transport=smallwebrtc", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:04.432Z", "level": "INFO", "logger": "src.bot.core.session_handlers", "function": "handle_first_participant_joined", "line": 315, "message": "SESSION_STARTED_IN_MEMORY: session_id=1f181fde-319b-4183-826f-e4ea0870250a device_id=4c7990ca3869fa1d transport=smallwebrtc", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:05.325Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=1f181fde-319b-4183-826f-e4ea0870250a device_id=4c7990ca3869fa1d processor=GeminiLiveVertexLLMService#0 type=TTFB (google/gemini-live-2.5-flash-native-audio) value=0.88893s", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:06.494Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=1f181fde-319b-4183-826f-e4ea0870250a device_id=4c7990ca3869fa1d processor=GeminiLiveVertexLLMService#0 type=LLM (google/gemini-live-2.5-flash-native-audio) prompt_tokens=676 completion_tokens=146 cache_read=0", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:17.194Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=1f181fde-319b-4183-826f-e4ea0870250a device_id=4c7990ca3869fa1d processor=GeminiLiveVertexLLMService#0 type=TTFB (google/gemini-live-2.5-flash-native-audio) value=0.30902s", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:21.003Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=1f181fde-319b-4183-826f-e4ea0870250a device_id=4c7990ca3869fa1d processor=GeminiLiveVertexLLMService#0 type=LLM (google/gemini-live-2.5-flash-native-audio) prompt_tokens=866 completion_tokens=158 cache_read=0", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:25.203Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=1f181fde-319b-4183-826f-e4ea0870250a device_id=4c7990ca3869fa1d processor=GeminiLiveVertexLLMService#0 type=TTFB (google/gemini-live-2.5-flash-native-audio) value=3.63394s", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:30.176Z", "level": "INFO", "logger": "src.bot.core.rtvi_events", "function": "send_generation_started", "line": 39, "message": "GENERATION_START_EVENT_SENT: session_id=1f181fde-319b-4183-826f-e4ea0870250a device_id=4c7990ca3869fa1d persona=edit_photos persona_name=Edit Photos id=image_1774662030", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:30.226Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=1f181fde-319b-4183-826f-e4ea0870250a device_id=4c7990ca3869fa1d processor=GeminiLiveVertexLLMService#0 type=LLM (google/gemini-live-2.5-flash-native-audio) prompt_tokens=1160 completion_tokens=17 cache_read=0", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:33.585Z", "level": "INFO", "logger": "src.bot.services.image_service", "function": "generate_image_async", "line": 231, "message": "CF_IMAGE_GENERATED: image_id=image_1774662030 session_id=1f181fde-319b-4183-826f-e4ea0870250a device_id=4c7990ca3869fa1d persona=edit_photos persona_name=Edit Photos request_id=180df4f0 provider=flux-2-klein-4b total_latency=3.408s api_latency=3.081s s3_url=https://storage.googleapis.com/maya-session-data/4c7990ca3869fa1d/1f181fde-319b-4183-826f-e4ea0870250a/images/image_1774662030.png", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:33.585Z", "level": "INFO", "logger": "src.bot.core.rtvi_events", "function": "send_generation_complete", "line": 91, "message": "GENERATION_COMPLETE_EVENT_SENT: session_id=1f181fde-319b-4183-826f-e4ea0870250a device_id=4c7990ca3869fa1d persona=edit_photos persona_name=Edit Photos id=image_1774662030 s3_url=https://storage.googleapis.com/maya-session-data/4c7990ca3869fa1d/1f181fde-319b-4183-826f-e4ea0870250a/images/image_1774662030.png", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:35.610Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=1f181fde-319b-4183-826f-e4ea0870250a device_id=4c7990ca3869fa1d processor=GeminiLiveVertexLLMService#0 type=TTFB (google/gemini-live-2.5-flash-native-audio) value=0.05323s", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:35.656Z", "level": "INFO", "logger": "src.bot.core.observers", "function": "on_process_frame", "line": 83, "message": "METRICS: session_id=1f181fde-319b-4183-826f-e4ea0870250a device_id=4c7990ca3869fa1d processor=GeminiLiveVertexLLMService#0 type=LLM (google/gemini-live-2.5-flash-native-audio) prompt_tokens=1344 completion_tokens=164 cache_read=0", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:38.404Z", "level": "INFO", "logger": "src.bot.core.session_handlers", "function": "handle_participant_left", "line": 368, "message": "USER_LEFT: session_id=1f181fde-319b-4183-826f-e4ea0870250a elapsed=37.1s duration=34.0s reason=disconnected transport=smallwebrtc", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:39.660Z", "level": "INFO", "logger": "src.repository.session_repository", "function": "save_session_complete", "line": 246, "message": "DB_SESSION_COMPLETE_SAVED: session_id=1f181fde-319b-4183-826f-e4ea0870250a images=1 videos=0 ppts=0 music=0 statements=2 duration_ms=40.96", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:39.660Z", "level": "INFO", "logger": "src.bot.core.session_handlers", "function": "handle_participant_left", "line": 525, "message": "SESSION_SAVED: session_id=1f181fde-319b-4183-826f-e4ea0870250a transport=smallwebrtc persona=edit_photos images=1 videos=0 ppts=0 music=0", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:39.683Z", "level": "INFO", "logger": "src.bot.core.session_handlers", "function": "handle_participant_left", "line": 536, "message": "CREDITS_PERSISTED_PLAN: session_id=1f181fde-319b-4183-826f-e4ea0870250a transport=smallwebrtc sub=sub_d8310614c5dd4b1e used=1", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:39.683Z", "level": "INFO", "logger": "src.bot.core.session_handlers", "function": "handle_participant_left", "line": 545, "message": "CLEANUP_TRIGGER: session_id=1f181fde-319b-4183-826f-e4ea0870250a transport=smallwebrtc reason=participant_left", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:39.683Z", "level": "INFO", "logger": "src.bot.core.config", "function": "cleanup_session", "line": 418, "message": "CLEANUP_START: session_id=1f181fde-319b-4183-826f-e4ea0870250a threads=6", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:39.684Z", "level": "INFO", "logger": "src.bot.core.config", "function": "cleanup_session", "line": 525, "message": "VAD_DESTROYED: session_id=1f181fde-319b-4183-826f-e4ea0870250a (memory freed)", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:39.685Z", "level": "INFO", "logger": "src.bot.core.config", "function": "cleanup_session", "line": 612, "message": "CLEANUP_END: session_id=1f181fde-319b-4183-826f-e4ea0870250a threads_at_start=6 threads_at_end=6 delta=0", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:59.686Z", "level": "INFO", "logger": "src.bot.core.main", "function": "run_bot_webrtc_global_agent", "line": 228, "message": "CLEANUP_TRIGGER: session_id=1f181fde-319b-4183-826f-e4ea0870250a transport=smallwebrtc reason=runner_finally threads=6", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:59.686Z", "level": "INFO", "logger": "src.bot.core.main", "function": "run_bot_webrtc_global_agent", "line": 229, "message": "RUNNER_CLEANUP_START: session_id=1f181fde-319b-4183-826f-e4ea0870250a transport=smallwebrtc threads=6", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:59.687Z", "level": "INFO", "logger": "src.bot.core.config", "function": "cleanup_session", "line": 418, "message": "CLEANUP_START: session_id=1f181fde-319b-4183-826f-e4ea0870250a threads=6", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:59.687Z", "level": "INFO", "logger": "src.bot.core.config", "function": "cleanup_session", "line": 612, "message": "CLEANUP_END: session_id=1f181fde-319b-4183-826f-e4ea0870250a threads_at_start=6 threads_at_end=6 delta=0", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:59.687Z", "level": "INFO", "logger": "src.bot.core.main", "function": "run_bot_webrtc_global_agent", "line": 247, "message": "CLEANUP_THREADS: session_id=1f181fde-319b-4183-826f-e4ea0870250a transport=smallwebrtc threads_before=6 threads_after=6 delta=0", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:59.687Z", "level": "INFO", "logger": "src.bot.core.main", "function": "run_bot_webrtc_global_agent", "line": 262, "message": "RUNNER_CLEANUP_COMPLETE: session_id=1f181fde-319b-4183-826f-e4ea0870250a transport=smallwebrtc threads=6", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:59.688Z", "level": "INFO", "logger": "src.routes.bot", "function": "cleanup_session", "line": 148, "message": "TASK_COMPLETE: session=1f181fde-319b-4183-826f-e4ea0870250a transport=smallwebrtc remaining_active_sessions=0", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:59.688Z", "level": "INFO", "logger": "src.routes.bot", "function": "cleanup_session", "line": 159, "message": "REDIS_CLEANUP_SCHEDULED: session=1f181fde-319b-4183-826f-e4ea0870250a", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}
{"timestamp": "2026-03-28T01:40:59.692Z", "level": "INFO", "logger": "src.core.redis", "function": "remove_active_session", "line": 354, "message": "REDIS_SESSION_REMOVED: room=1f181fde-319b-4183-826f-e4ea0870250a", "service": "maya-pipecat-v4", "env": "development", "version": "1.0.0", "status": "info"}


[Request interrupted by user]


wait min .. i will test with daily first i will know what is issue


OK, take your time. Test with Daily and let me know what you find.


and 1 more thing y cnat u comare this with v3


The user wants me to compare v4 with v3 to find differences that could explain the issues. Let me look at the v3 bot code, especially around image editing and the main bot setup.


Good idea. Let me compare the key files between v3 and v4.


Agent

Compare v3 vs v4 image service

Prompt:
Compare the v3 and v4 image service implementations. Look for:
1. Find the v3 image service at paths like src/bot/services/image_service.py but in a v3 directory, or check if there's a separate v3 codebase. Check directories like /home/ubuntu/ for any v3 project folders (e.g., maya-pipecat-v3, pipecat-v3, etc.)
2. Also check git branches - run `git branch -a` to see if there's a v3 branch
3. Check if there are deployment files that reference v3: look in /home/ubuntu/pipecat/deployments/

The goal is to find the v3 version of the image_service.py and compare it with v4 to find differences that could explain why the agent stops speaking after image generation/edit.


Compare v3 vs v4 bot main

Prompt:
Compare the v3 and v4 bot main/runner implementations. Look for:
1. Check /home/ubuntu/ for any v3 project directories (maya-pipecat-v3, pipecat-v3, etc.)
2. Check git branches with `git branch -a` in /home/ubuntu/pipecat
3. Look for the v3 main bot file (equivalent of src/bot/core/main.py) and compare with the v4 version
4. Specifically look for differences in how Gemini Live sessions are set up, how function calls are handled, and how the pipeline is constructed

The goal is to find what changed between v3 and v4 that could cause the agent to stop speaking.


Now I have a complete picture. Here is the analysis:

---

## Comparison Results: v3 vs v4 Image Service

### `image_service.py` -- IDENTICAL
The file `src/bot/services/image_service.py` is **byte-for-byte identical** between `v3_version` and `v4_version` branches. The image generation/edit logic itself is not the cause.

### Key Differences That Could Cause "Agent Stops Speaking After Image Generation"

The v3-to-v4 migration is primarily a **transport swap**: from **Daily WebRTC** to **SmallWebRTC (aiortc)**. Here are the differences relevant to the speaking issue:

#### 1. Audio Output Sample Rate Changed (most likely culprit)
In `src/bot/core/pipeline.py`:
- **v3 (Daily):** `audio_out_sample_rate=16000` (16kHz)
- **v4 (SmallWebRTC):** `audio_out_sample_rate=48000` (48kHz)

If the TTS service produces 16kHz audio but the transport expects 48kHz, or if there is a resampling issue after the pipeline is interrupted by image generation events (RTVI messages, `LLMMessagesAppendFrame` with `run_llm=False`), the audio stream could stall.

#### 2. `on_client_disconnected` Handler Changed Significantly
In `src/bot/core/session_handlers.py`:
- **v3:** `on_client_disconnected` was a simple handler that just called `await task.cancel()` -- immediate termination on disconnect.
- **v4:** `on_client_disconnected` now calls the full `handle_participant_left()` flow (save session, persist credits, cleanup) AND THEN `await task.cancel()`. This is the same handler as `on_participant_left` in v3.

This means in v4, the same `on_client_connected` event name is registered **twice**:
```python
transport.event_handler("on_client_connected")(create_client_connected_handler(...))
transport.event_handler("on_client_connected")(create_first_client_connected_handler(...))
```
The second registration **overwrites** the first. So `create_client_connected_handler` (which sets `bot_session.user_joined = True`, cancels timeout, and triggers `LLMRunFrame` if idle) **never executes**. Only `create_first_client_connected_handler` runs.

This is a **bug**: `bot_session.user_joined` may never be set to `True` via `create_client_connected_handler`, since only `create_first_client_connected_handler` runs. The `handle_first_participant_joined` function does set `current_user_id` and does some setup, but if `user_joined` stays `False`, downstream logic that checks `bot_session.user_joined` could behave incorrectly.

#### 3. Transport `capture_participant_transcription` Removed
In `src/bot/core/session_handlers.py`, function `handle_first_participant_joined`:
- **v3:** Called `await transport.capture_participant_transcription(participant_id)` -- this is a Daily-specific API.
- **v4:** This line was removed. For SmallWebRTC, it is handled differently in the Daily-specific handler `create_daily_first_participant_joined_handler`.

#### 4. Route Prefix Changed
`server.py`: All API routes changed from `/api/v3` to `/api/v4`. The connect endpoint changed from `POST /connect-webrtc-global-agent` to `POST /start`. These are client-facing changes but do not affect internal pipeline behavior.

### Most Probable Root Cause

The **double registration of `on_client_connected`** in `src/bot/core/main.py` (lines ~218-219 in v4) is a bug:

```python
transport.event_handler("on_client_connected")(create_client_connected_handler(bot_session, task))
transport.event_handler("on_client_connected")(create_first_client_connected_handler(bot_session, rtvi, task, audio_buffer, chunk_state))
```

The second decorator call likely replaces the first handler. This means the logic in `create_client_connected_handler` -- setting `user_joined = True`, resetting `bot_idle`, and queueing `LLMRunFrame` -- may not run. After image generation completes, the image service's background task tries to append a system message via `LLMMessagesAppendFrame(run_llm=False)`, expecting the LLM to already be active. If the bot is in a degraded state due to the missing `user_joined` flag or idle logic, the LLM may not process subsequent user utterances properly, causing it to appear "stopped."

The secondary concern is the **audio sample rate mismatch** (16kHz vs 48kHz) which could cause audio pipeline issues specifically after queue operations like those triggered by image generation completion.

agentId: a985584fc7076f631 (use SendMessage with to: 'a985584fc7076f631' to continue this agent)
<usage>total_tokens: 46160
tool_uses: 17
duration_ms: 65100</usage>

Prompt:
Compare the v3 and v4 image service implementations. Look for:
1. Find the v3 image service at paths like src/bot/services/image_service.py but in a v3 directory, or check if there's a separate v3 codebase. Check directories like /home/ubuntu/ for any v3 project folders (e.g., maya-pipecat-v3, pipecat-v3, etc.)
2. Also check git branches - run `git branch -a` to see if there's a v3 branch
3. Check if there are deployment files that reference v3: look in /home/ubuntu/pipecat/deployments/

The goal is to find the v3 version of the image_service.py and compare it with v4 to find differences that could explain why the agent stops speaking after image generation/edit.


Now I have a comprehensive picture. Here is the analysis:

---

## V3 vs V4 Comparison: Key Differences That Could Cause the Agent to Stop Speaking

### 1. Transport Layer: Daily -> SmallWebRTC (BIGGEST CHANGE)

**V3** used `DailyTransport` exclusively -- a managed WebRTC service where Daily handles signaling, TURN, room management, etc.

**V4** switched the primary transport to `SmallWebRTCTransport` (pipecat's built-in WebRTC with aiortc) while keeping Daily as a fallback option (`transport_type` parameter).

Key differences in transport config:
- **V3**: `audio_out_sample_rate=constants.SAMPLE_RATE_16K` (16kHz)
- **V4 SmallWebRTC**: `audio_out_sample_rate=48000` (48kHz)

This sample rate mismatch (48kHz output vs 16kHz expected by some TTS services) could cause audio issues or silence if downstream components don't resample correctly.

### 2. Event Handler Model Change (LIKELY CAUSE OF SPEAKING ISSUES)

**V3** used Daily's event model:
- `on_participant_joined` -- distinguished between "maya" bot participant and user participants
- `on_first_participant_joined` -- called `await transport.capture_participant_transcription(participant_id)`
- `on_participant_left` + separate `on_client_disconnected` (which just cancelled the task)

**V4 SmallWebRTC** uses a different event model:
- `on_client_connected` (replaces `on_participant_joined`) -- no bot/user distinction needed
- `on_first_client_connected` (replaces `on_first_participant_joined`) -- **does NOT call `capture_participant_transcription`** since SmallWebRTC doesn't have that concept
- `on_client_disconnected` now does full cleanup + cancel (combines old participant_left + client_disconnected)

**Critical finding in `handle_first_participant_joined`**:
- V3: `await transport.capture_participant_transcription(participant_id)` was called inside the shared handler
- V4: This line was **removed** from the shared handler. For Daily mode, it's called in `create_daily_first_participant_joined_handler` instead, but as a **non-awaited** call: `transport.capture_participant_transcription(participant_id)` (no `await`)

### 3. Dual Event Registration on SmallWebRTC (POTENTIAL BUG)

In v4 `main.py` lines 211-212, for SmallWebRTC:
```python
transport.event_handler("on_client_connected")(create_client_connected_handler(...))
transport.event_handler("on_client_connected")(create_first_client_connected_handler(...))
```
Both handlers are registered on **the same event** `on_client_connected`. Whether pipecat supports multiple handlers on the same event, or whether the second overwrites the first, is critical. If the second registration **overwrites** the first, then `create_client_connected_handler` (which sets `user_joined=True` and cancels timeout) would never run. If it does support multiple handlers, both should fire, but the ordering matters.

In V3, Daily had separate events: `on_participant_joined` vs `on_first_participant_joined` -- these were distinct events, so no conflict.

### 4. STT Service Change

**V3**: Used `OpenAISTTService` directly (OpenAI Whisper).
**V4**: Hardcoded to use `_create_cf_nova3_stt_service()` -- a Cloudflare Workers proxy to Deepgram Nova-3. Falls back to OpenAI on failure. If the CF worker is down/slow, STT could fail silently, preventing user speech from reaching the LLM, which means the agent would never respond.

### 5. `capture_participant_transcription` Removed for SmallWebRTC

In V3's `handle_first_participant_joined`, line `await transport.capture_participant_transcription(participant_id)` was present. In V4, this was removed from the shared handler entirely. For SmallWebRTC, this is correct (SmallWebRTC doesn't need it), but if somehow Daily transport mode is used, the call in `create_daily_first_participant_joined_handler` is **not awaited** (`transport.capture_participant_transcription(participant_id)` -- sync call). The v3 version **awaited** it. If this method is async, dropping the await could mean it never executes.

### 6. Connection Flow Difference

**V3**: Client calls `/connect-webrtc-global-agent` -> server creates Daily room -> bot starts immediately and waits for participant -> client joins room with token.

**V4**: Client calls `/start` -> server returns `sessionId` + ICE config -> client sends SDP offer to `/sessions/{id}/api/offer` -> WebRTC connection established -> bot starts. This is a 2-step process with more failure points.

### Summary: Most Likely Causes of Agent Not Speaking

1. **Dual `on_client_connected` registration** (lines 211-212 in v4 main.py) -- if pipecat overwrites handlers, the first handler (which sets `user_joined`) gets lost, and the greeting/LLMRunFrame may not fire properly.

2. **audio_out_sample_rate mismatch** -- v4 SmallWebRTC outputs at 48kHz vs v3's 16kHz. If TTS generates 16kHz audio but transport expects 48kHz, audio could be played at wrong speed or be silent.

3. **CF Deepgram STT failure** -- if the Cloudflare STT worker is unreachable, user speech never converts to text, so the LLM never generates a response after the initial greeting.

4. **Missing `await` on `capture_participant_transcription`** in the Daily fallback path.

agentId: a2a1550c92ca940f9 (use SendMessage with to: 'a2a1550c92ca940f9' to continue this agent)
<usage>total_tokens: 71236
tool_uses: 17
duration_ms: 85233</usage>

Prompt:
Compare the v3 and v4 bot main/runner implementations. Look for:
1. Check /home/ubuntu/ for any v3 project directories (maya-pipecat-v3, pipecat-v3, etc.)
2. Check git branches with `git branch -a` in /home/ubuntu/pipecat
3. Look for the v3 main bot file (equivalent of src/bot/core/main.py) and compare with the v4 version
4. Specifically look for differences in how Gemini Live sessions are set up, how function calls are handled, and how the pipeline is constructed

The goal is to find what changed between v3 and v4 that could cause the agent to stop speaking.


The comparison found several key differences between v3 and v4. Let me summarize the findings for the user clearly.


Here's what the v3 vs v4 comparison found — 3 potential issues:

**1. Dual `on_client_connected` handler registration (BUG)**
In v4's `main.py`, both `create_client_connected_handler` and `create_first_client_connected_handler` are registered on the **same event** `on_client_connected`. The second likely **overwrites** the first. This means `user_joined=True` may never get set, and the `LLMRunFrame` that kicks the bot into active mode might not fire. In v3, Daily had **separate events** (`on_participant_joined` vs `on_first_participant_joined`) so this wasn't a problem.

**2. Audio sample rate mismatch**
- v3 (Daily): `audio_out_sample_rate=16000` (16kHz)
- v4 (SmallWebRTC): `audio_out_sample_rate=48000` (48kHz)

If TTS generates 16kHz but transport expects 48kHz, audio could stall or go silent after pipeline interruptions like image generation.

**3. STT service changed**
- v3: OpenAI Whisper STT
- v4: Cloudflare Workers proxy to Deepgram Nova-3

If the CF worker goes down, user speech never reaches the LLM → agent appears to stop speaking.

The first one (dual handler registration) is the most likely cause of "agent stops speaking." Let me verify this while you test with Daily.


208-        transport.event_handler("on_participant_left")(create_daily_participant_left_handler(bot_session, session_state, transcript_manager, chunk_state, task))
209-    else:
210:        # SmallWebRTC events: on_client_connected, on_client_disconnected
211:        transport.event_handler("on_client_connected")(create_client_connected_handler(bot_session, task))
212:        transport.event_handler("on_client_connected")(create_first_client_connected_handler(bot_session, rtvi, task, audio_buffer, chunk_state))
213-        transport.event_handler("on_client_disconnected")(create_client_disconnected_handler(bot_session, session_state, transcript_manager, chunk_state, task))
214-    task.event_handler("on_idle_timeout")(create_idle_timeout_handler(bot_session))


Confirmed — line 212 overwrites line 211. Both register on `"on_client_connected"`. Let me check what `create_client_connected_handler` does that gets lost:


157:def create_client_connected_handler(bot_session: "BotSessionState", task):
207:def create_first_client_connected_handler(bot_session: "BotSessionState", rtvi, task, audio_buffer, chunk_state: "ChunkState"):


   157→def create_client_connected_handler(bot_session: "BotSessionState", task):
   158→    """Handle SmallWebRTC client connection (replaces on_participant_joined)."""
   159→    async def on_client_connected(transport, client):
   160→        join_time = time.time()
   161→        participant_id = client.pc_id if hasattr(client, 'pc_id') else "user"
   162→
   163→        bot_session.add_participant(participant_id, "User", "user", join_time)
   164→        bot_session.user_joined = True
   165→        elapsed = join_time - bot_session.session_start_time
   166→        logger.info(f"USER_JOINED: session_id={bot_session.session_id} participant_id={participant_id} elapsed={elapsed:.1f}s transport={bot_session.transport_type}")
   167→
   168→        if bot_session.bot_idle:
   169→            bot_session.bot_idle = False
   170→            await task.queue_frames([LLMRunFrame()])
   171→
   172→        bot_session.cancel_timeout_task()
   173→    return on_client_connected
   174→
   175→
   176→def create_idle_timeout_handler(bot_session: "BotSessionState"):
   177→    async def on_idle_timeout(task):
   178→        logger.info("Bot idle timeout reached - staying in room for potential reconnection")
   179→        bot_session.bot_idle = True
   180→    return on_idle_timeout
   181→
   182→
   183→def create_client_ready_handler():
   184→    async def on_client_ready(rtvi_proc):
   185→        await rtvi_proc.set_bot_ready()
   186→    return on_client_ready
   187→
   188→
   189→def create_app_review_trigger(bot_session: "BotSessionState", rtvi):
   190→    async def trigger_app_review_if_eligible():
   191→        try:
   192→            from src.repository.device_repository import device_repo
   193→
   194→            if bot_session.device_id and bot_session.device_id != 'common' and bot_session.app_review_eligible:
   195→                try:
   196→                    await rtvi.send_server_message({"type": "show_app_review"})
   197→                    await device_repo.update_last_review_time(bot_session.device_id)
   198→                    bot_session.app_review_eligible = False
   199→                    logger.info(f"APP_REVIEW_TRIGGERED: device_id={bot_session.device_id}")
   200→                except Exception as e:
   201→                    logger.error(f"Failed to send app review trigger: {e}")
   202→        except Exception as e:
   203→            logger.error(f"Error triggering app review: {e}")
   204→    return trigger_app_review_if_eligible
   205→
   206→
   207→def create_first_client_connected_handler(bot_session: "BotSessionState", rtvi, task, audio_buffer, chunk_state: "ChunkState"):
   208→    """Handle first SmallWebRTC client connection (replaces on_first_participant_joined)."""
   209→    async def on_first_client_connected(transport, client):
   210→        participant_id = client.pc_id if hasattr(client, 'pc_id') else "user"
   211→        participant = {'id': participant_id}
   212→        await handle_first_participant_joined(
   213→            participant, bot_session, transport, rtvi, task, audio_buffer, chunk_state
   214→        )
   215→    return on_first_client_connected
   216→
   217→
   218→def create_client_disconnected_handler(bot_session: "BotSessionState", session_state, transcript_manager: "TranscriptManager", chunk_state: "ChunkState", task):
   219→    """Handle SmallWebRTC client disconnection — does full cleanup + cancel.
   220→    Combines old on_participant_left (save session) and on_client_disconnected (cancel task)."""
   221→    async def on_client_disconnected(transport, client):
   222→        participant_id = client.pc_id if hasattr(client, 'pc_id') else "user"
   223→        participant = {'id': participant_id}
   224→        await handle_participant_left(
   225→            participant, "disconnected", bot_session, session_state,
   226→            transcript_manager, chunk_state, task
   227→        )
   228→    return on_client_disconnected
   229→
   230→
   231→def create_daily_participant_joined_handler(bot_session: "BotSessionState", task):
   232→    """Handle Daily participant join event."""
   233→    async def on_participant_joined(transport, participant):
   234→        join_time = time.time()
   235→        participant_id = participant.get("id", "user")
   236→        user_name = participant.get("info", {}).get("userName", "User")

def create_client_connected_handler(bot_session: "BotSessionState", task):
    """Handle SmallWebRTC client connection (replaces on_participant_joined)."""
    async def on_client_connected(transport, client):
        join_time = time.time()
        participant_id = client.pc_id if hasattr(client, 'pc_id') else "user"

        bot_session.add_participant(participant_id, "User", "user", join_time)
        bot_session.user_joined = True
        elapsed = join_time - bot_session.session_start_time
        logger.info(f"USER_JOINED: session_id={bot_session.session_id} participant_id={participant_id} elapsed={elapsed:.1f}s transport={bot_session.transport_type}")

        if bot_session.bot_idle:
            bot_session.bot_idle = False
            await task.queue_frames([LLMRunFrame()])

        bot_session.cancel_timeout_task()
    return on_client_connected

def create_idle_timeout_handler(bot_session: "BotSessionState"):
    async def on_idle_timeout(task):
        logger.info("Bot idle timeout reached - staying in room for potential reconnection")
        bot_session.bot_idle = True
    return on_idle_timeout

def create_client_ready_handler():
    async def on_client_ready(rtvi_proc):
        await rtvi_proc.set_bot_ready()
    return on_client_ready

def create_app_review_trigger(bot_session: "BotSessionState", rtvi):
    async def trigger_app_review_if_eligible():
        try:
            from src.repository.device_repository import device_repo

            if bot_session.device_id and bot_session.device_id != 'common' and bot_session.app_review_eligible:
                try:
                    await rtvi.send_server_message({"type": "show_app_review"})
                    await device_repo.update_last_review_time(bot_session.device_id)
                    bot_session.app_review_eligible = False
                    logger.info(f"APP_REVIEW_TRIGGERED: device_id={bot_session.device_id}")
                except Exception as e:
                    logger.error(f"Failed to send app review trigger: {e}")
        except Exception as e:
            logger.error(f"Error triggering app review: {e}")
    return trigger_app_review_if_eligible

def create_first_client_connected_handler(bot_session: "BotSessionState", rtvi, task, audio_buffer, chunk_state: "ChunkState"):
    """Handle first SmallWebRTC client connection (replaces on_first_participant_joined)."""
    async def on_first_client_connected(transport, client):
        participant_id = client.pc_id if hasattr(client, 'pc_id') else "user"
        participant = {'id': participant_id}
        await handle_first_participant_joined(
            participant, bot_session, transport, rtvi, task, audio_buffer, chunk_state
        )
    return on_first_client_connected

def create_client_disconnected_handler(bot_session: "BotSessionState", session_state, transcript_manager: "TranscriptManager", chunk_state: "ChunkState", task):
    """Handle SmallWebRTC client disconnection — does full cleanup + cancel.
    Combines old on_participant_left (save session) and on_client_disconnected (cancel task)."""
    async def on_client_disconnected(transport, client):
        participant_id = client.pc_id if hasattr(client, 'pc_id') else "user"
        participant = {'id': participant_id}
        await handle_participant_left(
            participant, "disconnected", bot_session, session_state,
            transcript_manager, chunk_state, task
        )
    return on_client_disconnected

def create_daily_participant_joined_handler(bot_session: "BotSessionState", task):
    """Handle Daily participant join event."""
    async def on_participant_joined(transport, participant):
        join_time = time.time()
        participant_id = participant.get("id", "user")
        user_name = participant.get("info", {}).get("userName", "User")


There it is. **Line 212 overwrites line 211** because pipecat's `event_handler` decorator replaces the handler. The critical logic in `create_client_connected_handler` (lines 163-172) — setting `user_joined = True`, cancelling timeout, resuming from idle — **never runs**.

And `create_first_client_connected_handler` (line 207-215) does NOT set `user_joined = True` or cancel the timeout.

The fix is to merge the logic from both handlers. Let me check what `handle_first_participant_joined` does:


   290→
   291→    if not bot_session.session_id:
   292→        bot_session.session_id = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{participant_id}"
   293→
   294→    session_id = bot_session.session_id
   295→    device_id = bot_session.device_id
   296→    bot_session.bot_start_time = join_time
   297→
   298→    async def check_review_eligibility_background():
   299→        try:
   300→            from src.repository.device_repository import device_repo
   301→            if device_id and device_id != 'common':
   302→                is_eligible = await device_repo.should_show_app_review(device_id)
   303→                bot_session.app_review_eligible = is_eligible
   304→                logger.debug(f"REVIEW_ELIGIBILITY_CHECKED: device_id={device_id} eligible={is_eligible}")
   305→        except Exception as e:
   306→            logger.error(f"Error checking review eligibility at session start: {e}")
   307→            bot_session.app_review_eligible = False
   308→
   309→    bot_session.track_background_task(asyncio.create_task(check_review_eligibility_background()))
   310→
   311→    # OPTIMIZATION: Skip DB write at session start - write everything at session end
   312→    # Saves ~50% of session writes (~390 writes/min with 500 concurrent sessions)
   313→    # Trade-off: If pod crashes, active sessions won't be saved (~0.1% loss - acceptable)
   314→    bot_session.session_created_at_start = False  # Will be created at end
   315→    logger.info(f"SESSION_STARTED_IN_MEMORY: session_id={session_id} device_id={device_id} transport={bot_session.transport_type}")
   316→
   317→    async def send_session_info():
   318→        await asyncio.sleep(0.5)
   319→        try:
   320→            await rtvi.send_server_message({
   321→                "type": "session_started",
   322→                "data": {
   323→                    "session_id": session_id,
   324→                    "device_id": device_id,
   325→                    "room_name": session_id,
   326→                    "participant_id": participant_id,
   327→                    "started_at": datetime.now().isoformat(),
   328→                    "language": "hi",
   329→                    "voice": "taru"
   330→                }
   331→            })
   332→        except Exception:
   333→            pass
   334→
   335→    bot_session.track_background_task(asyncio.create_task(send_session_info()))
   336→
   337→    try:
   338→        chunk_state.reset(session_id)
   339→        await audio_buffer.start_recording()

    if not bot_session.session_id:
        bot_session.session_id = f"{datetime.now().strftime('%Y%m%d_%H%M%S')}_{participant_id}"

    session_id = bot_session.session_id
    device_id = bot_session.device_id
    bot_session.bot_start_time = join_time

    async def check_review_eligibility_background():
        try:
            from src.repository.device_repository import device_repo
            if device_id and device_id != 'common':
                is_eligible = await device_repo.should_show_app_review(device_id)
                bot_session.app_review_eligible = is_eligible
                logger.debug(f"REVIEW_ELIGIBILITY_CHECKED: device_id={device_id} eligible={is_eligible}")
        except Exception as e:
            logger.error(f"Error checking review eligibility at session start: {e}")
            bot_session.app_review_eligible = False

    bot_session.track_background_task(asyncio.create_task(check_review_eligibility_background()))

    # OPTIMIZATION: Skip DB write at session start - write everything at session end
    # Saves ~50% of session writes (~390 writes/min with 500 concurrent sessions)
    # Trade-off: If pod crashes, active sessions won't be saved (~0.1% loss - acceptable)
    bot_session.session_created_at_start = False  # Will be created at end
    logger.info(f"SESSION_STARTED_IN_MEMORY: session_id={session_id} device_id={device_id} transport={bot_session.transport_type}")

    async def send_session_info():
        await asyncio.sleep(0.5)
        try:
            await rtvi.send_server_message({
                "type": "session_started",
                "data": {
                    "session_id": session_id,
                    "device_id": device_id,
                    "room_name": session_id,
                    "participant_id": participant_id,
                    "started_at": datetime.now().isoformat(),
                    "language": "hi",
                    "voice": "taru"
                }
            })
        except Exception:
            pass

    bot_session.track_background_task(asyncio.create_task(send_session_info()))

    try:
        chunk_state.reset(session_id)
        await audio_buffer.start_recording()


Now I see that `handle_first_participant_joined` does NOT set `user_joined = True`. The fix is simple — merge the two handlers into one:


Edit

def create_first_client_connected_handler(bot_session: "BotSessionState", rtvi, task, audio_buffer, chunk_state: "ChunkState"):
    """Handle first SmallWebRTC client connection (replaces on_first_participant_joined)."""
    async def on_first_client_connected(transport, client):
        participant_id = client.pc_id if hasattr(client, 'pc_id') else "user"
        participant = {'id': participant_id}
        await handle_first_participant_joined(
            participant, bot_session, transport, rtvi, task, audio_buffer, chunk_state
        )
    return on_first_client_connected

def create_first_client_connected_handler(bot_session: "BotSessionState", rtvi, task, audio_buffer, chunk_state: "ChunkState"):
    """Handle first SmallWebRTC client connection (replaces on_first_participant_joined).

    Also includes the logic from create_client_connected_handler since pipecat only
    supports one handler per event — registering both on 'on_client_connected' would
    cause the second to overwrite the first.
    """
    async def on_first_client_connected(transport, client):
        join_time = time.time()
        participant_id = client.pc_id if hasattr(client, 'pc_id') else "user"

        # --- Logic from create_client_connected_handler (must run here) ---
        bot_session.add_participant(participant_id, "User", "user", join_time)
        bot_session.user_joined = True
        elapsed = join_time - bot_session.session_start_time
        logger.info(f"USER_JOINED: session_id={bot_session.session_id} participant_id={participant_id} elapsed={elapsed:.1f}s transport={bot_session.transport_type}")

        if bot_session.bot_idle:
            bot_session.bot_idle = False
            await task.queue_frames([LLMRunFrame()])

        bot_session.cancel_timeout_task()

        # --- First-participant setup ---
        participant = {'id': participant_id}
        await handle_first_participant_joined(
            participant, bot_session, transport, rtvi, task, audio_buffer, chunk_state
        )
    return on_first_client_connected