create a skills.md for livekit using python sdk

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

{"message": "process exiting", "level": "INFO", "name": "livekit.agents", "reason": "", "pid": 44567, "job_id": "AJ_eEQ4C8FKLRaH", "room_id": "RM_qn5xv7GHFCFw", "timestamp": "2026-03-11T124039.430618+00:00"}, when do we get this logs, what is the reason?

Thanks — I’m responding to your question about the log: {"message":"process exiting","level":"INFO","name":"livekit.agents","reason":"","pid":44567,"job_id":"AJ_eEQ4C8FKLRaH","room_id":"RM_qn5xv7GHFCFw","timestamp":"2026-03-11T124039.430618+00:00"} What this means • This log indicates an agent process is shutting down — it’s emitted during the shutdown phase of an agent job lifecycle. • The

reason

field is empty here, so the log is a generic, informational confirmation that the agent process (pid 44567) handling job AJ_eEQ4C8FKLRaH for room RM_qn5xv7GHFCFw has exited. When you typically see this log • After a session completes because all standard and SIP participants have left the room. • When your agent code explicitly shuts the session (for example, calling

agent_session.shutdown()

or

agent_session.aclose()

). • During rolling deployments or instance drains, when older agent instances are gracefully terminated after active sessions end. • When the server scales down or restarts and idle/completed processes are cleaned up. Common reasons behind the exit • Normal session completion (room closed or last participant left). • Explicit programmatic shutdown from your agent logic. • Deployment/rollout events that drain and stop old agent processes. • Resource cleanup or autoscaling behavior. • Unexpected termination is possible, but would normally be accompanied by earlier error or exception logs. How to investigate further • Correlate timestamps with: ◦ Any preceding error or exception logs from the same pid/job — unexpected failures will usually log errors before the exit. ◦ Events showing the session closed or participants leaving the room. ◦ Deployment or rollout activity that matches the timestamp. • Check for an

AgentSession

close event or any calls to

shutdown()

in your agent entrypoint around the same time. • If you have observability/insights enabled, review agent job lifecycle traces for that job_id to see what led to shutdown. If you’d like, I can: • Help you parse related logs if you paste earlier lines around that timestamp. • Suggest specific log filters or queries to run if you tell me what logging/observability system you’re using. • Describe how to add more detailed shutdown reasons into logs from your agent code so future exits include a clear cause. Which would you prefer? Sources: Job lifecycle | LiveKit Documentation | Deployment reliability on LiveKit Cloud | LiveKit | Python Agents: Handling exceptions in your entrypoint | LiveKit | Why is my Agent insights data missing? | LiveKit