.

Thanks for reaching out, Aditya. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.

99_nNHmuKJURtbK", "level": "INFO", "name": "livekit_agent", "room": "koelai-testing-inbound-_08975012399_nNHmuKJURtbK", "pid": 113, "job_id": "AJ_wwgWncQpzfeh", "room_id": "RM_aqpyYKn7TB3V", "timestamp": "2026-02-27T112146.251271+00:00"} {"message": "🕒 Waiting for participant to map SIP attributes...", "level": "INFO", "name": "livekit_agent", "room": "koelai-testing-inbound-_08975012399_nNHmuKJURtbK", "pid": 113, "job_id": "AJ_wwgWncQpzfeh", "room_id": "RM_aqpyYKn7TB3V", "timestamp": "2026-02-27T112146.251600+00:00"} {"message": "received server content but no active generation.", "level": "WARNING", "name": "livekit.plugins.google", "room": "koelai-testing-inbound-_08975012399_nNHmuKJURtbK", "pid": 113, "job_id": "AJ_wwgWncQpzfeh", "room_id": "RM_aqpyYKn7TB3V", "timestamp": "2026-02-27T112146.638722+00:00"} {"message": "received server content but no active generation.", "level": "WARNING", "name": "livekit.plugins.google", "room": "koelai-testing-inbound-_08975012399_nNHmuKJURtbK", "pid": 113, "job_id": "AJ_wwgWncQpzfeh", "room_id": "RM_aqpyYKn7TB3V", "timestamp": "2026-02-27T112146.639236+00:00"} {"message": "received server content but no active generation.", "level": "WARNING", "name": "livekit.plugins.google", "room": "koelai-testing-inbound-_08975012399_nNHmuKJURtbK", "pid": 113, "job_id": "AJ_wwgWncQpzfeh", "room_id": "RM_aqpyYKn7TB3V", "timestamp": "2026-02-27T112146.639766+00:00"} {"message": "no active generation to report metrics for", "level": "WARNING", "name": "livekit.plugins.google", "room": "koelai-testing-inbound-_08975012399_nNHmuKJURtbK", "pid": 113, "job_id": "AJ_wwgWncQpzfeh", "room_id": "RM_aqpyYKn7TB3V", "timestamp": "2026-02-27T112146.640092+00:00"} {"message": "process initialized", "level": "INFO", "name": "livekit.agents", "pid": 260, "elapsed_time": 1.36, "timestamp": "2026-02-27T112147.391273+00:00"} {"message": "process memory usage is high", "level": "WARNING", "name": "livekit.agents", "memory_usage_mb": 554.90625, "memory_warn_mb": 500, "memory_limit_mb": 0, "pid": 113, "job_id": "AJ_wwgWncQpzfeh", "room_id": "RM_aqpyYKn7TB3V", "timestamp": "2026-02-27T112150.459502+00:00"} {"message": "Error in _realtime_reply_task\nTraceback (most recent call last):\n File \"/usr/local/lib/python3.11/site-packages/livekit/agents/utils/log.py\", line 17, in async_fn_logs\n return await fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.11/site-packages/livekit/agents/voice/agent_activity.py\", line 2310, in _realtime_reply_task\n generation_ev = await self._rt_session.generate_reply(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nlivekit.agents.llm.realtime.RealtimeError: generate_reply timed out waiting for generation_created event.", "level": "ERROR", "name": "livekit.agents", "exc_info": "Traceback (most recent call last):\n File \"/usr/local/lib/python3.11/site-packages/livekit/agents/utils/log.py\", line 17, in async_fn_logs\n return await fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.11/site-packages/livekit/agents/voice/agent_activity.py\", line 2310, in _realtime_reply_task\n generation_ev = await self._rt_session.generate_reply(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nlivekit.agents.llm.realtime.RealtimeError: generate_reply timed out waiting for generation_created event.", "room": "koelai-testing-inbound-_08975012399_nNHmuKJURtbK", "pid": 113, "job_id": "AJ_wwgWncQpzfeh", "room_id": "RM_aqpyYKn7TB3V", "timestamp": "2026-02-27T112151.253155+00:00"} {"message": "process memory usage is high", "level": "WARNING", "name": "livekit.agents", "memory_usage_mb": 561.24609375, "memory_warn_mb": 500, "memory_limit_mb": 0, "pid": 113, "job_id": "AJ_wwgWncQpzfeh", "room_id": "RM_aqpyYKn7TB3V", "timestamp": "2026-02-27T112155.462547+00:00"} {"message": "process memory usage is high", "level": "WARNING", "name": "livekit.agents", "memory_usage_mb": 559.5625, "memory_warn_mb": 500, "memory_limit_mb": 0, "pid": 113, "job_id": "AJ_wwgWncQpzfeh", "room_id": "RM_aqpyYKn7TB3V", "timestamp": "2026-02-27T112200.464727+00:00"} {"message": "RealtimeModel metrics", "level": "INFO", "name": "livekit.agents", "model_name": "gemini-live-2.5-flash-native-audio", "model_provider": "Vertex AI", "ttft": -1.0, "input_tokens": 809, "cached_input_tokens": 0, "input_text_tokens": 787, "input_cached_text_tokens": 0, "input_image_tokens": 0, "input_cached_image_tokens": 0, "input_audio_tokens": 22, "input_cached_audio_tokens": 0, "output_tokens": 3, "output_text_tokens": 3, "output_audio_tokens": 0, "output_image_tokens": 0, "total_tokens": 812, "tokens_per_second": 175.83, "room": "koelai-testing-inbound-_08975012399_nNHmuKJU

Thanks — I can see the logs you pasted. I’ll address the three main problems shown in those entries (high memory, Gemini realtime generation timeouts, and “received server content but no active generation”), explain likely causes, and give targeted next steps you can try. Acknowledging what you sent • You provided logs from job_id

AJ_wwgWncQpzfeh

in room

RM_aqpyYKn7TB3V

. Key messages: repeated “process memory usage is high” warnings (~550–560 MB), “generate_reply timed out waiting for generation_created event,” and multiple “received server content but no active generation” warnings from the Google plugin. What’s happening (short summary) • The agent process is using high memory and warning above the 500 MB threshold. • A call to the Gemini realtime integration’s

generate_reply()

timed out waiting for the model to emit a generation event. • The Google plugin is sending content but the agent has no active generation session to attach it to — a state desync/race or prior failed generation likely caused this. Concrete actions you can take (ordered, prioritized) 1. Reduce CPU/memory pressure quickly ◦ Restart the affected agent process to clear memory while you investigate (short-term mitigation). ◦ If running on burstable instances (t3/t4), switch to a non-burstable compute instance (for example, c5/c6 or m5) to avoid CPU throttling that can indirectly worsen memory issues. 1. Prewarm and reuse large models ◦ Ensure large models/components (VAD, LLM) are loaded once per process using your

prewarm

or process

userdata

pattern so you don’t repeatedly allocate memory per session. 1. Add robust timeout and retry handling around

generate_reply()

◦ Wrap

generate_reply()

in try/except and implement: ▪︎ A reasonable timeout and 1–2 retries with backoff. ▪︎ A fallback behavior if timeout persists (e.g., shorter prompt, alternate model, or return a polite “sorry, I’m having trouble” response). ◦ When a timeout occurs, explicitly reset the Realtime session state so stray server streams don’t remain unmatched. 1. Prevent / handle desynced server content ◦ Ensure you only call

generate_reply()

when the agent state is appropriate (e.g., check that session is `listening`/`thinking`). ◦ Serialize or queue calls to

generate_reply()

to avoid concurrent/duplicate requests. ◦ On a generation failure or timeout, send a session reset/cleanup to discard any in-flight generation context. 1. Inspect connectivity and credentials to Vertex AI (Gemini) ◦ Confirm the agent can reach Google Vertex AI and that credentials (e.g.,

GOOGLE_APPLICATION_CREDENTIALS

) are valid. Network or auth problems can cause the model to never emit a generation_created event. 1. Observe model metrics to pinpoint latency ◦ Track

ttft

(time-to-first-token),

tokens_per_second

, and output token counts. The logs already show

ttft: -1.0

in one metric line, which indicates the model didn’t return tokens in expected time—use this to tune timeouts and sizing. Debug checklist (quick things to capture and share if you want me to dig deeper) • Exact instance type / cloud VM family (e.g., t3.large vs c6i.large). • Whether you prewarm models (yes/no) and how

generate_reply()

is invoked (concurrent or single-threaded). • Recent changes: plugin version, model selection (Gemini variant), or networking rules. • A brief sequence of events that reproduces the issue (steps to reproduce). • Any network or auth errors for Google Vertex AI in your environment. What I can help with next • Review your

generate_reply()

usage pattern and suggest concrete retry/reset code (if you paste the relevant code snippet). • Suggest specific prewarm patterns for your agent process configuration. • Help interpret additional logs (connection traces, agent_state_changed events) if you paste them. If you share the instance type, how you call

generate_reply()

, and whether you prewarm the LLM, I’ll give tailored code/config changes to reduce the memory and timeout issues. Sources: Troubleshooting Latency and Timeout Errors with Turn Detection on AWS | Server options | LiveKit Documentation | examples/voice_agents | livekit/livekit_composite | DeepWiki | Gemini Live API plugin | LiveKit Documentation | Gemini Realtime Agent with Live Vision | LiveKit Documentation