Getting this error during conversation with agent:

The engine is currently overloaded. Please try again later.', 'type': 'server_error', 'param': None, 'code': None}}", status_code=503, retryable=True, request_id=req_f89dce8008bd4636b8235428b3cfddba, body={'message': 'The engine is currently overloaded. Please try again later.', 'type': 'server_error', 'param': None, 'code': None}

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.

agent is hosted on my own machine using a python script. 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", DEFAULT_DURATION_MINUTES) 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) if name == "__main__": cli.run_app( WorkerOptions( entrypoint_fnc=entrypoint, agent_name=os.getenv("LIVEKIT_AGENT_NAME", "interview-agent"), num_idle_processes=1, ) )

Thanks — this is helpful. I’m responding to your request for concrete guidance to handle the 503 "The engine is currently overloaded" error in your self-hosted Python interview agent and to reduce interruptions during live conversations. Short answer / what to do first • Add retry logic with exponential backoff around LLM calls (these errors are retryable). • Add a fallback provider (or FallbackAdapter) so requests route to a backup LLM when the primary is overloaded. • Add basic observability (counts/alerts) so you can detect repeated overloads and adjust. Recommended changes (practical, in-order) 1) Wrap LLM calls with retry + exponential backoff • Identify where the agent makes synchronous requests to the LLM (likely inside the

openai.LLM(...)

calls used by AgentSession or in the session handlers). • Implement retries with backoff for transient server errors (HTTP 5xx, especially 503). Example strategy: a. Try immediately. b. On 5xx error, wait 1s, retry. c. On next failure, wait 2s, retry. d. On next failure, wait 4s, retry. e. Stop after N attempts (3–5) and surface a graceful error (or trigger fallback). • Make sure to treat 4xx errors as non-retryable. • If you use an HTTP client library, use its retry/backoff utilities or implement a small wrapper that catches exceptions and checks status_code and retryable flag. 2) Use a fallback provider (FallbackAdapter) • Configure a secondary LLM provider in your agent stack so the agent routes requests to it when the primary returns overload errors. • If you’re using LiveKit’s adapter pattern, add or enable the FallbackAdapter to your LLM configuration so a failing primary triggers the fallback automatically. • Ensure the fallback provider has appropriate API keys, model names, and quotas. 3) Ensure session-level resilience • If the agent session makes multiple internal LLM calls per user turn, apply retries at the point of each LLM request. • Consider caching small inference results where possible to avoid repeated identical requests. • If a single LLM call is critical and retrying still fails, return a short, friendly spoken message to the candidate (e.g., “I’m having trouble answering right now — please hold while I try again”), then retry in the background. 4) Limit request rate / add queuing • If your agent can receive quick successive user inputs, add a short debounce or queue to avoid spiking the LLM with concurrent requests. • For example, serialize LLM requests per session or allow a limited concurrency (N requests at once). 5) Monitoring & alerting • Record counts of: ◦ LLM requests ◦ 5xx responses (especially 503) ◦ Retries attempted ◦ Failures after retries / fallback invoked • Add logs that include request_id and timestamps so you can correlate to provider status pages. Concrete implementation hints for your code • Where you create

openai.LLM(model=LLM_MODEL)

or in the lower-level call site, wrap the call that sends the prompt in a retry helper. Pseudocode example:

def retry_with_backoff(fn, retries=4, initial_delay=1.0):
    delay = initial_delay
    for attempt in range(retries):
        try:
            return fn()
        except ProviderServerError as e:  # catch provider 5xx or client wrapper that exposes status
            if attempt == retries - 1:
                raise
            time.sleep(delay)
            delay *= 2

• If the OpenAI client you use already surfaces

retryable=True

or raises a specific exception for 503, use that to decide when to retry. • If your LLM calls are async, use

asyncio.sleep(delay)

inside an async retry helper. Questions to narrow recommendations • Do you instantiate the LLM client directly (the

openai

library) or is the LLM call abstracted by LiveKit agent libraries that call the provider? In other words: where would you add the retry wrapper—inside your code or inside the adapter configuration? • Do you already have a secondary LLM provider/API key available for fallback, or would you prefer guidance on picking a fallback option? • Is it acceptable for the agent to delay responses briefly (a few seconds) while retrying, or do you need immediate fallback behavior? If you tell me whether your LLM calls are invoked directly from your code or via an agent adapter layer and whether you have a fallback provider ready, I’ll provide specific sample code for async retries and a sample FallbackAdapter configuration tailored to your setup. , with interactive elements