hi

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

{"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 16, 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 2185, 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 16, 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 2185, 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.", "pid": 26, "job_id": "AJ_jBk872METqmN", "room_id": "RM_tM9ogciKNi2k", "timestamp": "2025-12-

Thanks — I’m looking at the

generate_reply timed out waiting for generation_created event

error from your agent’s realtime reply task. I’ll help narrow this down and get you running. Quick summary of what this means • The agent started a realtime generation but never received the model’s

generation_created

event within the timeout window, so

RealtimeError

was raised. • Common causes: realtime LLM provider issues, network/WebSocket problems, incorrect model/config, or server CPU throttling causing long inference delays. Please tell me the following so I can recommend targeted next steps: 1. Which realtime model/provider are you using? (OpenAI Realtime, Azure realtime, another plugin) 2. Have you recently changed API keys, model names, regions, or deployment IDs? 3. Are you running the agent on a cloud VM? If so, what instance type (e.g., t3/t4, c5, m5)? 4. Do you see any network/WebSocket errors or connection drops in your logs around the same timestamp? 5. Can you reproduce this locally with

uv run agent.py console

or does it only happen in your deployed environment? Immediate actions you can try now • Verify the LLM credentials and test the realtime endpoint directly (curl/Postman) to confirm reachability and no rate limits. • If using OpenAI realtime, confirm the model supports realtime (e.g.,

gpt-4o-realtime-preview

) and the plugin config is correct. • Wrap

generate_reply()

in a try/except to handle

RealtimeError

and optionally fall back to a non-realtime model or retry logic. • If on burstable instances (t3/t4), check CPU credits; consider switching to a non-burstable instance if credits are exhausted. • Consider using a

FallbackAdapter

to fall back to a secondary model when realtime fails. Tell me your answers to the 5 questions above and I’ll give concrete commands/config snippets specific to your setup and help narrow whether this is a provider, network, or infra issue.

i am using google realtime in local docker container with cpu

Thanks — since you’re using Google Realtime in a local Docker container on CPU, here are focused steps to troubleshoot and resolve the timeout: What I’m addressing: the

generate_reply timed out waiting for generation_created event

in a Google realtime setup running in Docker on CPU. Quick checks (please confirm) • Is the timeout reproducible every request or intermittent? • How many CPU cores / CPU shares and memory are assigned to the container? • Any network issues or packet loss to Google’s realtime endpoint? Immediate actions to try 1. Increase container CPU resources ◦ Give the container more CPU cores and remove strict CPU limits so local processing (VAD/turn detection, event handling) isn’t delayed. 1. Verify Google realtime credentials and connectivity ◦ Test connectivity from inside the container (curl or a simple websocket test) to the Google realtime endpoint and confirm API key/quota. 1. Review turn detection / STT configuration ◦ If you rely on LiveKit turn detection, either add an STT plugin that provides interim transcripts or disable LiveKit turn detection so the agent doesn’t wait for transcripts that aren’t coming. 1. Enable verbose agent logs ◦ Turn on detailed logging to watch for missing events like

speech_created

,

generation_created

, or websocket reconnects around the timeout timestamp. 1. Add retry/fallback logic ◦ Wrap

generate_reply()

in try/except for

RealtimeError

and retry once or fall back to a non-realtime model to avoid user-facing failures. 1. Test locally outside Docker ◦ Run

uv run agent.py console

(or equivalent) on your host machine to see if the issue is Docker/CPU-constrained. If you prefer, share: • container CPU/memory limits, Docker run command or compose snippet, • a short excerpt of verbose logs around the timeout, and • whether turn detection/STT is enabled — I’ll suggest exact config changes and code snippets. Would you like that? Sources: Gemini Live API plugin | LiveKit docs | Realtime model plugins | LiveKit Docs | Troubleshooting Latency and Timeout Errors with Turn Detection on AWS | Events and error handling | LiveKit docs

what config shoud i have for mu docker pod

Thanks — you’re asking for the Docker/Kubernetes pod config to run a LiveKit agent with Google Realtime on CPU and avoid the generation timeout. Below are concise, practical recommendations and example configs you can adapt. Key recommendations • Give the container dedicated CPU (no very-low limits). Start with 2–4 CPU cores. Increase if you still see timeouts. • Allocate at least 2–4 GB RAM; 8 GB if you run additional services (STT, VAD) in the same pod. • Remove or raise CPU throttling (avoid tiny CPU shares). Don’t use burstable/cloud instance limits for production. • Ensure stable network access to Google Realtime endpoints and no aggressive egress/firewall rules. • Explicitly set turn detection / STT behavior: either provide an STT plugin that returns interim transcripts or disable LiveKit turn detection so the agent doesn’t wait for transcripts that won’t come. • Add retry/fallback logic for

generate_reply()

timeouts and enable verbose logs for

speech_created

/

generation_created

events. Example Dockerfile (minimal)

FROM python:3.11-slim
WORKDIR /app
COPY pyproject.toml poetry.lock /app/
RUN pip install --no-cache-dir -r requirements.txt
COPY . /app
CMD ["uv", "run", "agent.py", "run"]

Example docker-compose service

services:
  agent:
    build: .
    environment:
      - GOOGLE_API_KEY=your_key
      - AGENT_CONFIG=...
    deploy:
      resources:
        limits:
          cpus: '2.0'
          memory: 4G
        reservations:
          cpus: '1.0'
          memory: 2G
    networks:
      - default

Kubernetes pod spec snippet (start point)

apiVersion: v1
kind: Pod
metadata:
  name: livekit-agent
spec:
  containers:
  - name: agent
    image: your-registry/livekit-agent:latest
    env:
      - name: GOOGLE_API_KEY
        value: "<your_key>"
      - name: DISABLE_TURN_DETECTION
        value: "true"   # or set up STT plugin instead
    resources:
      requests:
        cpu: "2000m"
        memory: "2048Mi"
      limits:
        cpu: "4000m"
        memory: "4096Mi"
    readinessProbe:
      httpGet:
        path: /healthz
        port: 8080
      initialDelaySeconds: 10
      periodSeconds: 10
    livenessProbe:
      httpGet:
        path: /healthz
        port: 8080
      initialDelaySeconds: 30
      periodSeconds: 30

Turn detection / STT notes • If using LiveKit turn detection, add an STT plugin that provides interim transcripts; otherwise set

DISABLE_TURN_DETECTION=true

(or the equivalent config in your agent) so the agent won’t wait for interim events. • If you need turn detection + Google realtime, run the STT component in a separate pod with its own CPU/memory resources. Logging & retries • Enable verbose agent logs to capture

speech_created

and

generation_created

timings. • Wrap

generate_reply()

with retry/backoff and a fallback non-realtime model. If you share your current Docker/K8s YAML (or CPU/memory limits you’ve tried), I’ll produce a tuned pod spec for your environment. Would you like that?