ERRORlivekit.agentsprocess did not exit in time, killing process I keep getting this error, cutting of my agent mid speech, leaving dead air doesnt hang up line im using twlio sip

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

live kit cloud... but why does it just stop with no real error indicating whats the cause

Thanks — I hear your frustration. You’re on LiveKit Cloud and the agent is being killed mid‑speech with only the log line

process did not exit in time, killing process

. I’ll explain why that happens and give clear, actionable fixes. Why it appears to stop with “no real error” • That log is emitted when the agent process receives a termination signal (SIGTERM) and does not exit within the allowed shutdown/drain window. The host then forcefully kills it — so the runtime reason is a timed forced kill, not an exception inside your code, which is why you don’t see a richer error trace. • Common triggers on LiveKit Cloud: a lifecycle event (deploy, scale, restart), health‑probe failures, or an orchestrator enforcing a short termination grace period. When the process is killed while TTS is queued, you get dead air and the call stays open unless the room is explicitly deleted. • Your code calls

self.session.shutdown()

without awaiting and schedules long background work after that. That pattern increases the chance the process is terminated before speech finishes or transfer logic completes. Immediate code changes to prevent abrupt kill • Drain on shutdown and await it: ◦ Replace

self.session.shutdown()

with

await self.session.shutdown(drain=True)

so queued speech finishes. • Start/await transfer work in a predictable order: ◦ Option A (background transfer, then shutdown): create the transfer task first, then await shutdown so the process stays alive long enough.

asyncio.create_task(transfer_caller(...))
    await self.session.shutdown(drain=True)

• Option B (complete transfer before shutdown): await

transfer_caller(...)

(or at least wait for the key steps) and then shutdown with drain. • Don’t call shutdown synchronously; always

await

it to ensure the runtime completes the drain logic. Room and call cleanup (necessary for Twilio SIP) • After moving the participant and finishing the session, explicitly delete the LiveKit room via the server API (

deleteRoom

) so Twilio SIP call ends cleanly. Otherwise Twilio may keep the call open. Orchestration/hosting checks to verify • On LiveKit Cloud, confirm whether a deployment/auto‑restart or scaling event coincides with the kills. • If you see regular restarts or health probe failures, share logs around the pod/process termination (30–60s before the kill). Those will show whether a SIGTERM came from the platform. • If termination is caused by a short platform grace period, awaiting

shutdown(drain=True)

plus keeping important transfer work started before calling shutdown will usually avoid kills. Logging/diagnostics to gather next • Add these logs to capture the shutdown flow: ◦ Log immediately before and after

await self.session.say(...)

◦ Log when you create the transfer task, when it starts, and when it completes ◦ Log immediately before

await self.session.shutdown(drain=True)

and on return • Share the log snippet that contains: ◦ the

process did not exit in time, killing process

line ◦ 30–60s before that (to see SIGTERM and any lifecycle events) If you want, I can: • Provide the exact minimal revised snippet for your handler to guarantee draining and safe transfer ordering. • Review the logs you collect and point to the precise cause. Which would you prefer — a quick code snippet first, or help collecting the logs to pinpoint whether LiveKit Cloud sent the termination?

{"level": "INFO", "name": "livekit.agents", "message": "process exiting", "reason": "", "pid": 157, "job_id": "AJ_Hue2XmLnPTfT", "room_id": "RM_Du7cTTunKkEE", "timestamp": "2025-12-19T010204.511811+00:00"} how do i force it to stop this process exitiing

Thanks — I see the log line "process exiting" for job AJ_Hue2XmLnPTfT in room RM_Du7cTTunKkEE. You asked how to force the process to stop exiting (prevent abrupt termination mid‑speech). Below are concise, actionable steps to keep the agent from being killed and ensure clean call teardown. What’s happening • The platform sent a shutdown (SIGTERM) and the process is exiting while speech or background work is still pending. LiveKit Cloud will force‑kill after the drain window if the agent doesn’t finish draining, so you only see "process exiting" and then a timed kill. Immediate fixes (code changes) 1. Always drain on shutdown and await it:

await session.shutdown(drain=True)

1. Ensure speech finishes before shutdown:

handle = session.say("Please hold while I transfer you.")
await handle.wait_for_playout()
await session.shutdown(drain=True)

1. Don’t return from your entrypoint or let the process exit while transfers/speech are pending. Either: • Start background work before initiating shutdown:

asyncio.create_task(transfer_caller(...))
await session.shutdown(drain=True)

or • Await the transfer to finish, then shutdown:

await transfer_caller(...)
await session.shutdown(drain=True)

1. Avoid

session.aclose()

or calling

session.shutdown()

without awaiting — those exit immediately. Call cleanup (Twilio SIP) • After finishing the session, explicitly delete the room via the server API to ensure Twilio call ends:

await api_client.room.delete_room(api.DeleteRoomRequest(room=ctx.job.room.name))

Orchestration / LiveKit Cloud checks • Confirm whether a deployment/scale event or health probe coincides with the kill. • If LiveKit Cloud triggers termination, ensure your agent completes draining within the platform grace period. Do not let the entrypoint return early. Logging to help diagnose • Add logs immediately before/after

say()

, when transfer task starts/completes, and before/after

await session.shutdown(drain=True)

. • Share 30–60s of logs before the "process exiting" line if you want me to inspect for SIGTERM or platform events. If you want, tell me: • Do you want the transfer to finish before process exit or run fully in background? • I’ll provide the minimal handler snippet matching your preferred flow and show where to add logs. Sources: Job lifecycle | LiveKit docs | Agent speech and audio | LiveKit docs | Events and error handling | LiveKit docs

figured it out... mmight be a bug in the code tho

Nice — glad you got it working. Would you like to share the bug details or the code change you made? I can review it quickly and suggest fixes or confirm it's a bug.

sure, can livekit live stream a background audio?

Yes — LiveKit can stream background audio during a session. What it can do • Publish a separate ambient/audio track (looped or one‑off) concurrently with agent speech. • Play "thinking" or ambient sounds while the agent processes, and play on‑demand clips. • Support common audio formats (MP3, WAV, AAC, FLAC, OGG, Opus, WebM, MP4) via FFmpeg or optimized decoding. • Deliver low‑latency audio to connected clients through LiveKit’s WebRTC stack. How to use it (Python) 1. Create and start the player after you join the room:

background_audio = BackgroundAudioPlayer()
await background_audio.start(room=ctx.room, agent_session=session)

1. Play a clip or loop ambient audio:

await background_audio.play("/path/to/ambient.mp3")   # play one shot
# or configure ambient/loop at construction

1. Ensure you stop and clean up when done:

await background_audio.aclose()

Useful notes • Use

wait_for_playout()

/

session.shutdown(drain=True)

patterns for coordinated shutdown so background audio and TTS finish cleanly. • Thinking-sounds feature is currently only available in Python. • Example: see the Background Audio Python recipe in the Agents repo for a complete pattern. If you want, tell me whether you need: • continuous looped ambient audio, or • short thinking/processing sounds, or • a streamed music/background feed — and I’ll provide a minimal example tailored to that use case. Sources: Agent speech and audio | LiveKit docs | OpenAI and LiveKit partner to turn Advanced Voice into an API