im have self hosted agent, once the container is deployed, i dont see any logs saying that proccess is intialezed or workers are registered although livekit cloud shows agent worker. however the agent never joins the room

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

dockerfile: # ============================================ # Stage 1: Builder - Install dependencies # ============================================ FROM python:3.13-slim AS builder ENV PYTHONDONTWRITEBYTECODE=1 \ PYTHONUNBUFFERED=1 \ PIP_NO_CACHE_DIR=1 \ PIP_DISABLE_PIP_VERSION_CHECK=1 WORKDIR /app RUN apt-get update && apt-get install -y --no-install-recommends \ build-essential \ libffi-dev \ && rm -rf /var/lib/apt/lists/* COPY requirements.txt . RUN pip install --upgrade pip setuptools wheel && \ pip install --no-cache-dir -r requirements.txt # ============================================ # Stage 2: Runtime # ============================================ FROM python:3.13-slim ENV PYTHONDONTWRITEBYTECODE=1 \ PYTHONUNBUFFERED=1 WORKDIR /app RUN apt-get update && apt-get install -y --no-install-recommends \ curl \ && rm -rf /var/lib/apt/lists/* COPY --from=builder /usr/local/lib/python3.13/site-packages /usr/local/lib/python3.13/site-packages COPY --from=builder /usr/local/bin /usr/local/bin COPY . . CMD ["python", "interview_agent.py", "start"] main section iin interview_agent: if name == "__main__": cli.run_app( WorkerOptions( entrypoint_fnc=entrypoint, agent_name=os.getenv("LIVEKIT_AGENT_NAME", "interview-agent"), num_idle_processes=1, ) ) entrypoint func: async def entrypoint(ctx: JobContext): """Main entrypoint for the interview agent worker.""" logger.info(f"Starting interview agent job: {ctx.job.id}") openai_key = os.getenv("OPENAI_API_KEY") livekit_url = os.getenv("LIVEKIT_URL") if not openai_key: logger.error("Missing OPENAI_API_KEY — agent cannot initialize") return await ctx.connect() metadata = parse_job_metadata(ctx) if not metadata.get("candidate") or not metadata.get("role"): logger.error("Missing required metadata: candidate and role information") return interview_id = metadata.get("interview_id", "unknown") candidate_name = metadata.get("candidate", {}).get("name", "Candidate") duration_minutes = metadata.get("duration_minutes", 20) duration_seconds = duration_minutes * 60 # Start egress recording egress_id = await start_egress_recording(ctx.room.name, interview_id, livekit_url) # Create agent and session interview_agent = create_interview_agent(interview_metadata=metadata) logger.info(f"[AGENT] Starting session: llm={LLM_MODEL} stt={STT_MODEL} tts={TTS_MODEL}") session = AgentSession( vad=silero.VAD.load(), stt=openai.STT(model=STT_MODEL, language=STT_LANGUAGE), llm=openai.LLM(model=LLM_MODEL), tts=openai.TTS(model=TTS_MODEL, voice=TTS_VOICE), min_endpointing_delay=VAD_MIN_ENDPOINTING_DELAY, ) @session.on("user_speech_committed") def on_user_speech(msg: agents.llm.ChatMessage): if msg.content: logger.info(f"[USER] {msg.content}") @session.on("agent_speech_committed") def on_agent_speech(msg: agents.llm.ChatMessage): if msg.content: logger.info(f"[AGENT] {msg.content}") # Register room event listeners BEFORE session.start() to avoid race conditions start_event = asyncio.Event() disconnect_event = asyncio.Event() setup_room_events(ctx, start_event, disconnect_event, candidate_name) # Notify the frontend that the agent is ready to receive the START signal. # If the frontend sent START during agent setup (~7s window), it missed the # data_received listener. The frontend should resend START on AGENT_READY. try: await ctx.room.local_participant.publish_data( json.dumps({"action": "AGENT_READY"}).encode("utf-8"), reliable=True, ) logger.info("Sent AGENT_READY signal to room") except Exception as e: logger.warning(f"Failed to send AGENT_READY signal: {e}") await session.start( agent=interview_agent, room=ctx.room, room_input_options=RoomInputOptions(close_on_disconnect=False), ) await wait_for_start_signal(start_event, candidate_name) await run_interview(session, disconnect_event, candidate_name, duration_seconds) livekit_report = generate_livekit_report(ctx) transcript_entries = extract_transcript_entries(livekit_report or {}) logger.info(f"[CLEANUP] Extracted {len(transcript_entries)} transcript entries") # Disconnect room if still connected (timeout path) try: await ctx.room.disconnect() except Exception: pass run_cleanup(livekit_report, transcript_entries, metadata, interview_id, egress_id) requirements.txt: livekit>=1.0.20 livekit-agents>=1.3.6 livekit-plugins-openai>=1.3.6 livekit-plugins-silero>=1.3.6 livekit-api>=1.1.0 python-dotenv==1.2.1 boto3>=1.42.5 httpx>=0.27.0 livekit url and creds are present

Thanks — this is very helpful information. I’m responding to your report that the container runs with no agent logs, LiveKit Cloud shows a worker, but the agent never joins the room. From what you shared, the most likely cause is a startup-mode mismatch between your code and the Docker CMD. Why this is happening • Your code calls cli.run_app with WorkerOptions (the worker pattern). That pattern expects the process to run as a worker (no top-level

start

subcommand). • Your Dockerfile runs

python interview_agent.py start

. The

start

subcommand is for the AgentServer pattern (server mode), not the WorkerOptions pattern. • Because of that mismatch, the framework never reaches the worker entrypoint correctly, so you see no initialization/job logs even though the container is running. LiveKit Cloud may show a worker record from infrastructure-level signals, but the actual worker job process never joins the room. Two ways to fix this Choose one of the following depending on which pattern you want to use: 1) Keep WorkerOptions (minimal change) • Change your Docker CMD to run the script without the

start

subcommand so the WorkerOptions flow is used: CMD ["python", "interview_agent.py"] • This runs the CLI in worker mode and will allow your

entrypoint

to be used to accept jobs and join rooms. 2) Switch to AgentServer / start mode (code change) • If you prefer using

python ... start

, refactor your code to use the AgentServer pattern instead of WorkerOptions (use the AgentServer constructor and decorate RTC handlers per the AgentServer pattern). • This requires changing how you register the entrypoints and job handlers to match server/start semantics. Extra checks to run after the change • Verify the agent process actually starts inside the container: exec in and run

ps aux

(or

pgrep -a python

) and confirm the command-line matches the Docker CMD you expect. • Increase logging temporarily: run with DEBUG or add at top of `interview_agent.py`: import logging logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger("agent") logger.info("Agent process started") • Confirm required env vars still present inside container (

LIVEKIT_URL

,

LIVEKIT_API_KEY

,

LIVEKIT_API_SECRET

,

OPENAI_API_KEY

). • After changing CMD to

["python","interview_agent.py"]

, watch logs (

docker logs

/

kubectl logs

) for the

Starting interview agent job:

log and

AGENT_READY

or session start logs. If you want, I can: • Provide the exact Dockerfile snippet to replace the CMD. • Walk you through a minimal AgentServer refactor if you prefer to keep

start

. • Look at logs you get after switching CMD to confirm the worker started and accepted a job. Which option would you like to try first — change the Docker CMD to run without

start

, or convert your code to the AgentServer pattern? Sources: Server startup modes | LiveKit Documentation | Server options | LiveKit Documentation

what is the right way to do it? worker pattern or agent server pattern?

Thanks — great question. I’m replying about whether you should use the worker pattern (what you currently have) or the agent server pattern. Short answer: Use the agent server pattern (AgentServer + @server.rtc_session()). It’s the recommended, production-ready approach. Why the agent server pattern is the right choice • It’s designed for production deployments: automatic job dispatch, lifecycle management, graceful shutdown, and better observability (logs, job lifecycle events). • It integrates with LiveKit’s orchestration and load balancing so agents reliably accept jobs and join rooms. • Examples and quickstarts use this pattern, so it’s easiest to maintain and scale. When the worker pattern can make sense • It’s mainly a lower-level/legacy approach or for very custom dispatch logic. • If you need custom worker pooling or experimental behavior you can use it, but you’ll need to manage more details yourself. How to migrate (high-level) 1. Replace your WorkerOptions/agents.run_app code with an AgentServer and a decorated entrypoint: ◦ Create the server:

server = AgentServer()

◦ Use

@server.rtc_session(agent_name="...")

on your entrypoint coroutine. ◦ At the bottom:

if __name__ == "__main__": cli.run_app(server)

1. Keep your existing entrypoint logic (ctx.connect, session start, event listeners). The JobContext APIs remain the same inside the entrypoint. 2. Keep running the container with

CMD ["python", "interview_agent.py", "start"]

(the

start

subcommand is the correct CLI for the AgentServer pattern). Quick example (skeleton)

from livekit.agents import AgentServer, cli, JobContext

server = AgentServer()

@server.rtc_session(agent_name="interview-agent")
async def entrypoint(ctx: JobContext):
    # your existing logic: ctx.connect(), session.start(), etc.
    ...
    
if __name__ == "__main__":
    cli.run_app(server)

Post-migration checks • Ensure

LIVEKIT_URL

,

LIVEKIT_API_KEY

,

LIVEKIT_API_SECRET

, and

OPENAI_API_KEY

are present in the container environment. • Run with DEBUG logging once to confirm the server registers and you see job/room join logs. • Verify the container CMD is

["python", "interview_agent.py", "start"]

so the CLI runs in server mode. If you’d like, I can: • Provide a drop-in refactor of your current main/entrypoint into the AgentServer pattern. • Show the exact Dockerfile CMD change you should make if you decide to stay on the worker pattern instead. Which would you prefer — I can prepare the AgentServer refactor for you now. Sources: Job lifecycle | LiveKit Documentation | An open source stack for real-time multimodal AI | An open source stack for real-time multimodal AI | Simple Call Answering Agent | LiveKit Documentation | Repeater | LiveKit Documentation

please update dockerfile and interview_agent.py. or highlight changes

I don't have the answer you're looking for. You could also try asking your question: • in one of the other Slack channels or • to https://deepwiki.com/livekit/livekit_composite which is trained on all LiveKit source code If you find the answer, please post it here to help others!

Provide a drop-in refactor of your current main/entrypoint into the AgentServer pattern.