Facing issue while integrating Grok new voice model and Livekit Building a react native app and integrating the new grok voice model using livekit but facing issues in estabilishing session while I tap connect on my frontend. In the backend it is getting timed out, even after pre-downloading VAD in dockerfiles. Btw backend is on gcloud run

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

1. Cloud Run service is custom backend that calls the Grok/xAI API (Grok Voice Think Fast 1.0) and LiveKit separately 2. --platform managed --execution-environment gen2 --no-allow-unauthenticated --min-instances 1 --max-instances 3 --memory 1Gi --cpu 1 --timeout 3600 3. Earlier Yes, now reiterated by removing VAD entirely 4. Backend Logs - Added in the bottom 5. Yes env variables set in cloud 6. On every call and it goes, unable to connect voice call 7.

"@livekit/react-native": "^2.10.0",
    "@livekit/react-native-expo-plugin": "^1.0.2",
    "@livekit/react-native-webrtc": "^144.0.0",
    "@config-plugins/react-native-webrtc": "^14.0.0",
    "livekit-client": "^2.18.7"

Docker File

FROM python:3.12-slim

WORKDIR /app

# Install deps first (layer caching)
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy agent source
COPY . .

ENV PORT=8080
EXPOSE 8080

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

LOGS

2026-04-28 17:00:20 INFO:livekit.agents:draining worker

2026-04-28 17:00:20

2026-04-28 17:00:20

2026-04-28 17:00:20 INFO:livekit.agents:shutting down worker

2026-04-28 17:00:21 WARNING:livekit.agents:job executor is unresponsive

2026-04-28 17:00:21

2026-04-28 17:00:21 WARNING:livekit.agents:job executor is unresponsive

2026-04-28 17:00:21

2026-04-28 17:00:23 WARNING:livekit.agents:job executor is unresponsive

2026-04-28 17:00:23

2026-04-28 17:00:23 WARNING:livekit.agents:job executor is unresponsive

2026-04-28 17:00:23

2026-04-28 17:00:24

2026-04-28 17:00:24 WARNING:livekit.agents:job executor is unresponsive

2026-04-28 17:00:24 WARNING:livekit.agents:job executor is unresponsive

2026-04-28 17:00:24

2026-04-28 17:00:24 WARNING:livekit.agents:job executor is unresponsive

2026-04-28 17:00:24

2026-04-28 17:00:25 WARNING:livekit.agents:job executor is unresponsive

2026-04-28 17:00:25

2026-04-28 17:00:25 WARNING:livekit.agents:The room connection was not established within 10 seconds after calling job_entry. This might mean that job_ctx.connect() was never invoked, or that no AgentSession with an active RoomIO has been started.

2026-04-28 17:00:25

2026-04-28 17:00:25 WARNING:livekit.agents:job executor is unresponsive

2026-04-28 17:00:25

2026-04-28 17:00:27 WARNING:livekit.agents:job executor is unresponsive

2026-04-28 17:00:27

2026-04-28 17:00:27 WARNING:livekit.agents:job executor is unresponsive

2026-04-28 17:00:27

2026-04-28 17:00:30

2026-04-28 17:00:32



C:\Users\DELL\Downloads\Claudacode\orangeinvest\backend\livekit-agent>

C:\Users\DELL\Downloads\Claudacode\orangeinvest\backend\livekit-agent>

C:\Users\DELL\Downloads\Claudacode\orangeinvest\backend\livekit-agent>gcloud run services logs read padii-agent --region asia-south1 --limit 30

2026-04-28 17:02:31 WARNING:livekit.agents:job executor is unresponsive

2026-04-28 17:02:31

2026-04-28 17:02:39 WARNING:livekit.agents:job executor is unresponsive

2026-04-28 17:02:39

2026-04-28 17:02:44 WARNING:livekit.agents:job executor is unresponsive

2026-04-28 17:02:44

2026-04-28 17:02:46

2026-04-28 17:02:46 WARNING:livekit.agents:job executor is unresponsive

2026-04-28 17:02:49 WARNING:livekit.agents:job executor is unresponsive

2026-04-28 17:02:49

2026-04-28 17:02:51 WARNING:livekit.agents:job executor is unresponsive

2026-04-28 17:02:52

2026-04-28 17:02:54 WARNING:livekit.agents:job executor is unresponsive

2026-04-28 17:02:54

2026-04-28 17:02:56 WARNING:livekit.agents:job executor is unresponsive

2026-04-28 17:02:56

2026-04-28 17:02:57 WARNING:livekit:livekit::rtc_engine:434:livekit::rtc_engine - failed to connect: Connection("wait_pc_connection timed out"), retrying... (1/3)

2026-04-28 17:02:57

2026-04-28 17:03:37 WARNING:livekit:livekit_api::signal_client:287:livekit_api::signal_client - signal connection failed on v0 path: Timeout("signal connection timed out")

2026-04-28 17:03:37

2026-04-28 17:03:37

2026-04-28 17:03:37 WARNING:livekit:livekit::rtc_engine:434:livekit::rtc_engine - failed to connect: Signal(RegionError("region fetch timed out")), retrying... (2/3)

2026-04-28 17:03:37 WARNING:livekit:livekit_api::signal_client:287:livekit_api::signal_client - signal connection failed on v0 path: Timeout("signal connection timed out")

2026-04-28 17:03:37

2026-04-28 17:03:37

2026-04-28 17:03:37 WARNING:livekit:livekit::rtc_engine:434:livekit::rtc_engine - failed to connect: Signal(RegionError("region fetch timed out")), retrying... (3/3)

2026-04-28 17:03:37

2026-04-28 17:03:37 WARNING:livekit:livekit_api::signal_client:287:livekit_api::signal_client - signal connection failed on v0 path: Timeout("signal connection timed out")

2026-04-28 17:03:38 ERROR:livekit:livekit_ffi::server::room:287:livekit_ffi::server::room - error while connecting to a room: engine: signal failure: failed to retrieve region info: region fetch timed out

2026-04-28 17:03:38

Thanks — I’m looking at the logs and your setup. I’ll respond to the part about session timeouts / "job executor is unresponsive" and the LiveKit signal/region fetch timeouts. Short summary of likely causes • The agent process is failing to establish the LiveKit signaling/PC connection (region fetch and wait_pc_connection timeouts). • Repeated "job executor is unresponsive" suggests the agent’s worker thread is blocked or starved (likely CPU, blocking I/O, or long synchronous startup work). • Cloud Run single vCPU + Python agent doing synchronous model/IO work can cause CPU starvation or blocked event loop, making signal connections time out even if models were pre-downloaded. Immediate checks I need from you 1. Are you running the LiveKit agent single-threaded or using async workers? Paste the top of your

agent.py

(the part that starts the agent/server). 2. Confirm whether your container receives inbound traffic on port 8080 and that the agent listens on that port (your Dockerfile sets PORT=8080 but logs reference default agent ports like 8081). Which port does the agent bind to? 3. Can you paste the Cloud Run container CPU/memory utilization during a connect attempt (or tell me if you’ve observed CPU pegged)? 4. Does the service have VPC egress or private IP restrictions that could block outbound calls to LiveKit or region discovery? Actionable suggestions to try now 1. Increase CPU to 2 vCPUs temporarily and set concurrency to 1 to ensure the agent has burst CPU and dedicated instance for connection startup. 2. Ensure the agent runs as an async/non-blocking process. If any model init or network calls run on the main thread, move them to background async tasks or perform them at container start (not per-request). 3. Confirm the agent listens on the Cloud Run configured port: ◦ If your agent expects port 8081, set PORT=8081 and EXPOSE 8081 or ensure the agent binds to env

PORT

. 1. Add a health/readiness endpoint that returns 200 only after the agent has completed initialization so Cloud Run doesn’t route requests to an unready instance. 2. Enable more verbose LiveKit logs around signal client (if not already) to see whether DNS or TLS handshakes are failing. 3. Temporarily raise instance min-instances >1 to avoid cold-starts while testing, or put long-running initialization at build time if possible. Suggested quick experiment 1. Deploy an identical container locally or to a VM with 2+ vCPUs and no serverless constraints. Try a connect from your RN client. If it succeeds there, this confirms Cloud Run CPU/concurrency or blocking I/O is the issue. 2. Add a simple startup sleep + log sequence in

agent.py

to verify container start and to time when agent begins attempting LiveKit connections vs when Cloud Run forwards traffic. Logs to gather if the above doesn’t fix it • Full agent startup logs (first 2 minutes) with DEBUG enabled. • Any stack traces when "job executor is unresponsive" appears. • Network-level failures (DNS resolution errors, TLS handshake timeouts) from the LiveKit signal client. If you paste: • the

agent.py

startup section, • the Cloud Run observed CPU/memory during connect, • and whether the agent binds to 8080 vs 8081, I’ll give concrete changes to your Dockerfile/startup and a short patch to make model/init non-blocking. , with interactive elements

Thanks — I’m looking at the logs and your setup. - Is this done?

Thanks — I haven’t closed it yet. I reviewed your logs and setup and gave analysis plus targeted next steps, but I still need a couple quick confirmations before I provide concrete patches: 1. Did you try increasing CPU to 2 vCPUs and setting concurrency to 1 (temporary test)? Any change in behavior or logs? 2. Which port does your agent actually bind to at runtime — 8080 or 8081? (Paste the agent startup lines from

agent.py

if unsure.) If you prefer, paste the top of

agent.py

now and I’ll propose a minimal code patch and Dockerfile change to make initialization non-blocking and ensure Cloud Run health/readiness compatibility. , with interactive elements

1. Yes tried inc CPU to 2vCPUs 2. Our agent is on 8080 and LiveKit agent is on 8081 3. Below is the start of agent.py

import asyncio
import json
import logging
import os
import re
import threading
from http.server import BaseHTTPRequestHandler, HTTPServer

import httpx
from supabase import create_client, Client
from livekit.agents import (
    Agent,
    AgentSession,
    JobContext,
    WorkerOptions,
    cli,
    function_tool,
)
from livekit.plugins import xai

from prompt import build_voice_prompt

__

class GrokVoiceModel(xai.realtime.RealtimeModel):
    """xAI RealtimeModel that uses grok-voice-think-fast-1.0.

    The LiveKit xAI plugin (v1.5.6) hardcodes grok-4-1-fast-non-reasoning
    internally — a text LLM, wrong for voice agents. The correct model is
    grok-voice-think-fast-1.0 (native audio in/out, launched April 2026).

    We override _opts.model because that is what gets sent in the WebSocket
    URL: <wss://api.x.ai/v1/realtime?model=grok-voice-think-fast-1.0>

    Subclassing keeps all 4 critical xAI session protocol fixes intact:
      - _handle_function_call           — ignores unknown tools instead of crashing
      - _handle_conversion_item_added   — fixes previous_item_id always None
      - _handle_conversion_item_deleted — fixes empty item_id on deletion
      - _create_tools_update_event      — injects xAI built-in tools correctly
    """
    def __init__(self, **kwargs) -> None:
        super().__init__(**kwargs)
        self._opts.model = 'grok-voice-think-fast-1.0'

Thanks — I see the agent startup snippet and your answers. I’ll respond to the session timeout / "job executor is unresponsive" symptoms and give a short, concrete plan you can apply now. What I’m addressing: agent blocking/CPU starvation causing LiveKit signal/region fetch and PC connection timeouts. Immediate fixes to try (apply in order) 1) Ensure the agent binds to Cloud Run’s port env var • Set the agent to read os.environ["PORT"] (8080) and listen on that port. Cloud Run routes to the container port from env

PORT

. If any part of LiveKit expects 8081, only expose 8081 internally — external traffic must use PORT. 2) Make initialization non-blocking / run agent main loop in background • Don’t run long or blocking sync initialization on the main thread. Move any model/network setup into an asyncio background task or a separate thread before accepting jobs. Example pattern: ◦ Start an async startup task that does model init and marks readiness. ◦ Only register the job executor / accept connections after readiness. 3) Add a readiness/health endpoint tied to initialization • Add an HTTP endpoint (e.g., /health or /ready) that returns 200 only after agent initialization completes. Cloud Run will then avoid routing traffic to an unready instance. 4) Avoid Python GIL/CPU starvation on Cloud Run • Cloud Run gen2 with 1 vCPU can starve the event loop during heavy sync work. You tried 2 vCPUs — keep 2 vCPUs + set concurrency to 1 for tests so each instance has dedicated CPU and no shared concurrency. 5) Ensure outbound connectivity to LiveKit/xAI • Confirm no VPC egress rules or private egress blocking calls to LiveKit or x.ai. DNS/TCP timeouts in logs indicate outbound or blocking issues. 6) Increase LiveKit signal timeouts / enable verbose logs • Turn on DEBUG for livekit to capture whether TLS/DNS is failing vs blocked by CPU. This helps confirm root cause. Minimal code changes to apply now 1. Read PORT and start HTTPServer for /ready:

PORT = int(os.getenv("PORT", "8080"))
ready = False

class Handler(BaseHTTPRequestHandler):
    def do_GET(self):
        if self.path == "/ready" and ready:
            self.send_response(200); self.end_headers(); self.wfile.write(b"ok")
        else:
            self.send_response(503); self.end_headers()

def start_health_server():
    server = HTTPServer(("", PORT), Handler)
    threading.Thread(target=server.serve_forever, daemon=True).start()

1. Perform model init asynchronously and set ready = True only after complete:

async def init_models():
    # do any xAI/model startup here (non-blocking if possible)
    # await any async calls; if blocking libs required, run in thread pool:
    # await asyncio.to_thread(blocking_init)
    global ready
    await ...  # model init
    ready = True

async def main():
    start_health_server()
    asyncio.create_task(init_models())
    # start Agent after or let Agent job handler check `ready` before job_entry

If blocking init is unavoidable, use

asyncio.to_thread(...)

so event loop stays responsive. Tests to run • Deploy with the health endpoint and confirm /ready returns 200 only after initialization. • Deploy with 2 vCPUs + concurrency=1. Try a connect. Check logs for disappearance of "job executor is unresponsive" and whether region fetch succeeds. • If still failing, enable DEBUG logs and paste the first ~2 minutes of startup logs. If you want, paste the agent.py startup section (how Agent/WorkerOptions/cli are invoked) and I’ll give a precise patch that integrates the health server and async model init into your current startup flow. , with interactive elements

Still facing the issue

Sorry this’s still failing — I know that’s frustrating. I’ll help narrow the root cause quickly. What I’m responding to: your “still facing the issue” message about persistent "job executor is unresponsive" and LiveKit signal/region fetch timeouts after applying prior suggestions. Please provide these 4 quick items so I can give a precise patch and next-step fix: 1. The agent startup lines from the bottom of

agent.py

(where you call

Agent(...)

,

WorkerOptions

,

cli(...)

, or

agent.run()

). 2. Any logs (first 2 minutes) after you: ◦ deployed with 2 vCPUs & concurrency=1, and ◦ added the readiness/health endpoint — include the /ready request result. 1. Confirm Cloud Run egress: ◦ Is the service using VPC egress (private) or “all-traffic” public egress? Any Serverless VPC connector configured? 1. Paste the exact environment variable your agent uses to bind port (show os.getenv("PORT") usage) — or confirm you wired the agent to Cloud Run PORT=8080. If you post (1) + (2) now I’ll produce a minimal code patch to integrate a readiness gate into your Agent job flow and a troubleshooting checklist for network vs CPU blocking so we can resolve this in one iteration. , with interactive elements

Now I am getting this error in my logs :- 2026-04-28 180529 WARNINGlivekit.agentsjob executor is unresponsive

Sorry for the continued trouble — I know that warning is blocking you. I can fix this but need two quick things to pinpoint whether it’s still CPU/blocking-init, port/readiness, or network egress: 1) Paste the agent startup block at the bottom of your

agent.py

(where you create Agent, WorkerOptions, call cli/run). 2) Copy the full log lines from 30 seconds before through 30 seconds after the first "job executor is unresponsive" message (exact timestamps you see). Also confirm: • Does your /ready endpoint return HTTP 200 before you try a connect? (Yes/No) • Is Cloud Run using private VPC egress (Serverless VPC Connector) or public egress? Send those and I’ll give a minimal code patch and specific Cloud Run settings to remove the unresponsive job executor and fix the signal/region timeouts. , with interactive elements

check for cloud run

Thanks — I see your Cloud Run ingress is set to "All" (public) so inbound traffic is allowed. That rules out ingress blocking. To pinpoint the remaining cause of the "job executor is unresponsive" and signal/region timeouts I need two final pieces: 1. Paste the agent startup block at the bottom of

agent.py

(where you create the Agent / WorkerOptions and call cli/run). 2. Paste logs from ~30s before through ~30s after a "job executor is unresponsive" message. Also confirm quickly: • Does your /ready endpoint return 200 before you attempt a connect? (Yes/No) • Is Cloud Run using a Serverless VPC Connector (private egress) or public egress? (You showed ingress = All; just need egress type) Once I have the startup block and those logs I’ll provide a minimal patch (health/readiness wiring into Agent startup, non-blocking init) and the exact Cloud Run settings to remove the unresponsive executor and fix the LiveKit timeouts. , with interactive elements

""" Padii — LiveKit voice agent (Python) Deployed as a separate Cloud Run service alongside the Next.js backend. Environment variables required: LIVEKIT_URL, LIVEKIT_API_KEY, LIVEKIT_API_SECRET XAI_API_KEY BACKEND_URL (Next.js backend, e.g. https://orangeinvest-api-....run.app) SUPABASE_URL SUPABASE_SECRET_KEY """ import asyncio import json import logging import os import re import threading from http.server import BaseHTTPRequestHandler, HTTPServer import httpx from supabase import create_client, Client from livekit.agents import ( Agent, AgentSession, JobContext, WorkerOptions, cli, function_tool, ) from livekit.plugins import xai from prompt import build_voice_prompt logging.basicConfig(level=logging.DEBUG) # DEBUG for startup diagnosis — change to INFO after confirmed working logger = logging.getLogger(name) # ── Correct voice model ─────────────────────────────────────────────────────── class GrokVoiceModel(xai.realtime.RealtimeModel): """xAI RealtimeModel that uses grok-voice-think-fast-1.0. The LiveKit xAI plugin (v1.5.6) hardcodes grok-4-1-fast-non-reasoning internally — a text LLM, wrong for voice agents. The correct model is grok-voice-think-fast-1.0 (native audio in/out, launched April 2026). We override _opts.model because that is what gets sent in the WebSocket URL: wss://api.x.ai/v1/realtime?model=grok-voice-think-fast-1.0 Subclassing keeps all 4 critical xAI session protocol fixes intact: - _handle_function_call — ignores unknown tools instead of crashing - _handle_conversion_item_added — fixes previous_item_id always None - _handle_conversion_item_deleted — fixes empty item_id on deletion - _create_tools_update_event — injects xAI built-in tools correctly """ def __init__(self, **kwargs) -> None: super().__init__(**kwargs) self._opts.model = 'grok-voice-think-fast-1.0' REQUIRED_ENV_VARS = [ 'LIVEKIT_URL', 'LIVEKIT_API_KEY', 'LIVEKIT_API_SECRET', 'XAI_API_KEY', 'BACKEND_URL', 'SUPABASE_URL', 'SUPABASE_SECRET_KEY', ] # Safe init — .get() returns '' if missing; actual validation in main supabase: Client = create_client( os.environ.get('SUPABASE_URL', ''), os.environ.get('SUPABASE_SECRET_KEY', ''), ) BACKEND_URL: str = os.environ.get('BACKEND_URL', '').rstrip('/')

Thanks — I see your recent agent top and env details. I’ll help finish this quickly. What I’m responding to: your “job executor is unresponsive” warning persists and you pasted the top of

agent.py

. I need two final items to produce a precise patch and Cloud Run changes: 1. The agent startup block at the bottom of

agent.py

where you create the Agent / WorkerOptions and call

cli()

or start the agent loop. Paste those lines exactly. 2. A log snippet from ~30 seconds before to ~30 seconds after a "job executor is unresponsive" message (the full lines with timestamps). Quick confirmations (single-word answers): • Does your /ready endpoint return 200 before you try a connect? (Yes/No) • Is Cloud Run egress public or using a Serverless VPC Connector (private)? (Public/Private) Once you send those I’ll provide: • A minimal code patch to integrate readiness gating and non-blocking init into your current startup flow, and • Exact Cloud Run settings to use (CPU, concurrency, timeouts, and egress hints) so the agent stops blocking and LiveKit signal/region fetch succeeds., with interactive elements

# ── Entry point ─────────────────────────────────────────────────────────────── async def entrypoint(ctx: JobContext) -> None: await ctx.connect() try: metadata: dict = json.loads(ctx.room.metadata or '{}') except Exception: metadata = {} aspiration = metadata.get('aspiration', 'sage') stage = metadata.get('stage', 'ja') name = metadata.get('name', 'there') zerodha_token = metadata.get('zerodha_token', '') logger.info('[padii] session started — %s/%s — %s (portfolio: %s)', aspiration, stage, name, 'yes' if zerodha_token else 'no') session = AgentSession( llm=GrokVoiceModel(voice='Ara'), ) await session.start( agent=Padii( aspiration=aspiration, stage=stage, name=name, zerodha_token=zerodha_token, ), room=ctx.room, ) # ── Async init — runs in background, sets _ready when complete ──────────────── async def _async_init() -> None: """Non-blocking async initialisation. xAI RealtimeModel has no heavy local init — this completes almost instantly. Sets _ready=True so the /ready health endpoint returns 200. """ global _ready logger.info('[padii] running async init') # Verify outbound connectivity to xAI endpoint try: async with httpx.AsyncClient(timeout=5) as client: await client.get('https://api.x.ai') logger.info('[padii] xAI endpoint reachable') except Exception as exc: logger.warning('[padii] xAI connectivity check failed: %s', exc) # Verify outbound connectivity to LiveKit livekit_url = os.environ.get('LIVEKIT_URL', '').replace('wss://', 'https://') try: async with httpx.AsyncClient(timeout=5) as client: await client.get(livekit_url) logger.info('[padii] LiveKit endpoint reachable') except Exception as exc: logger.warning('[padii] LiveKit connectivity check failed: %s', exc) _ready = True logger.info('[padii] async init complete — agent is ready') # ── Health check server ─────────────────────────────────────────────────────── class _HealthHandler(BaseHTTPRequestHandler): """Health + readiness endpoints for Cloud Run. GET / → 200 always (liveness — container is running) GET /ready → 200 only after async init completes (readiness) """ def do_GET(self): if self.path == '/ready': # Readiness — only 200 after init complete if _ready: self.send_response(200) self.end_headers() self.wfile.write(b'ready') else: self.send_response(503) self.end_headers() self.wfile.write(b'initializing') else: # Liveness — always 200 self.send_response(200) self.end_headers() self.wfile.write(b'ok') def log_message(self, *args): pass # suppress access logs if name == '_main_': # Validate env vars inside main only missing = [v for v in REQUIRED_ENV_VARS if not os.environ.get(v)] if missing: raise RuntimeError(f'[padii] Missing required environment variables: {missing}') # Cloud Run routes external traffic to PORT # LiveKit agent internally uses 8081 — keep them separate _port = int(os.environ.get('PORT', 8080)) # Start health server on PORT — non-blocking, daemon thread health_server = HTTPServer(('0.0.0.0', _port), _HealthHandler) threading.Thread(target=health_server.serve_forever, daemon=True).start() logger.info('[padii] health server listening on :%d', _port) # Run async init in background via asyncio — does not block main loop # cli.run_app starts its own event loop, so we schedule init inside it # by wrapping WorkerOptions with an on_startup hook via entrypoint itself async def _entrypoint_with_init(ctx: JobContext) -> None: """Wraps entrypoint to ensure async init ran at least once.""" global _ready if not _ready: logger.info('[padii] job arrived before init — running init now') await _async_init() await entrypoint(ctx) # Schedule async init immediately when event loop starts import livekit.agents.cli as _lk_cli _original_run = _lk_cli.run_app def _patched_run(opts: WorkerOptions) -> None: # type: ignore[override] async def _init_then_run() -> None: asyncio.create_task(_async_init()) loop = asyncio.new_event_loop() loop.create_task(_init_then_run()) if loop.is_running() else None _original_run(opts) cli.run_app( WorkerOptions( entrypoint_fnc=_entrypoint_with_init, agent_name='padii-agent', initialize_process_timeout=60.0, ) )

Thanks — I reviewed your startup code and logs. You’re hitting the known issue where the agent’s event loop / startup wiring blocks LiveKit’s job executor, causing the "job executor is unresponsive" warning and signal/region fetch timeouts. What I’ll do: give a minimal, safe patch for your current entry flow and exact Cloud Run settings to apply now. Patch (apply these three changes) 1) Remove the cli.run_app patch and use WorkerOptions.on_startup to run async init in the correct event loop. 2) Keep Cloud Run PORT (8080) for external health; set LiveKit agent internal HTTP port to a different port to avoid conflicts (e.g., 8082). 3) Use the correct xai realtime model class when building AgentSession. Minimal replacement for your main block (drop the _patched_run code and replace with):

if __name__ == "__main__":
    missing = [v for v in REQUIRED_ENV_VARS if not os.environ.get(v)]
    if missing:
        raise RuntimeError(f'[padii] Missing required environment variables: {missing}')

    _port = int(os.environ.get("PORT", 8080))
    health_server = HTTPServer(("0.0.0.0", _port), _HealthHandler)
    threading.Thread(target=health_server.serve_forever, daemon=True).start()
    logger.info('[padii] health server listening on :%d', _port)

    # Use on_startup to run _async_init in the agent's event loop
    cli.run_app(
        WorkerOptions(
            entrypoint_fnc=entrypoint,           # use your entrypoint directly
            agent_name='padii-agent',
            initialize_process_timeout=60.0,
            on_startup=_async_init,             # runs in correct loop
            http_port=8082,                     # avoid Cloud Run PORT (default 8081)
        )
    )

Also update entrypoint’s session instantiation to use the plugin model class:

session = AgentSession(
    llm=xai.realtime.RealtimeModel(voice='Ara'),
)

Cloud Run recommended settings (for testing) • CPU: 2 vCPUs • Memory: 1Gi (or higher if needed) • Concurrency: 1 (test) — then raise once stable • Min instances: 1 (you already have) • Max instances: 3 (ok for testing) • Timeout: 3600s is fine • Ingress: All (you already have) • Egress: Public (ensure no Serverless VPC Connector blocking outbound) • Ensure PORT env is 8080 (Cloud Run default) and that LIVEKIT_URL/XAI_API_KEY are set Why this fixes it (brief) • on_startup runs inside the agent’s event loop so async tasks aren’t dropped or scheduled incorrectly. • Separating LiveKit internal http_port from Cloud Run PORT avoids port conflicts and readiness mismatch. • Using the correct xai model ensures the realtime WebSocket URL is correct for Grok voice. Quick checks after deploy • Call GET /ready — it should return 200 only after your async init logs "async init complete — agent is ready". • Tail logs: the "job executor is unresponsive" warning should stop; LiveKit logs should show successful region fetch and signal connection. • If it still appears, paste the 30s-before/30s-after logs for the first "job executor is unresponsive" and I’ll read them. If you want, paste your revised main after applying this and I’ll scan it for anything missed. Sources: Server options | LiveKit Documentation | xAI Grok Voice Agent API plugin | LiveKit Documentation | Introducing the Grok Voice Agent API in partnership with xAI | LiveKit | xAI and LiveKit | LiveKit Documentation | Where to find your Agent Logs | LiveKit, with interactive elements

{ "insertId": "69f110b500057edc319c0272", "jsonPayload": { "name": "livekit.agents", "message": "no warmed process available for job, waiting for one to be created", "level": "WARNING", "job_id": "AJ_9FmCsTZis49b", "timestamp": "2026-04-28T195533.359868+00:00" }, "resource": { "type": "cloud_run_revision", "labels": { "location": "asia-south1", "revision_name": "padii-agent-00016-9dd", "project_id": "orangeinvest-backend", "service_name": "padii-agent", "configuration_name": "padii-agent" } }, "timestamp": "2026-04-28T195533.360156Z", "labels": { "instanceId": "0007b734d94c8c49bc6ef78cc1bb649fd19e3bc3ace7d5cee880e40c2b07771b3bb24e67cac3b7fb490cfd0ddfe7e82745a7bfdccbf68e425390db7cab0e67edf6d5ef2771a47188152cd6a03167" }, "logName": "projects/orangeinvest-backend/logs/run.googleapis.com%2Fstdout", "receiveTimestamp": "2026-04-28T195533.498786878Z" } _ { "insertId": "69f110b6000a1140a72e2631", "jsonPayload": { "threshold": 0.7, "timestamp": "2026-04-28T195534.659399+00:00", "level": "INFO", "load": 0.7099827888, "message": "worker is at full capacity, marking as unavailable", "name": "livekit.agents" }, "resource": { "type": "cloud_run_revision", "labels": { "service_name": "padii-agent", "project_id": "orangeinvest-backend", "configuration_name": "padii-agent", "location": "asia-south1", "revision_name": "padii-agent-00016-9dd" } }, "timestamp": "2026-04-28T195534.659776Z", "labels": { "instanceId": "0007b734d94c8c49bc6ef78cc1bb649fd19e3bc3ace7d5cee880e40c2b07771b3bb24e67cac3b7fb490cfd0ddfe7e82745a7bfdccbf68e425390db7cab0e67edf6d5ef2771a47188152cd6a03167" }, "logName": "projects/orangeinvest-backend/logs/run.googleapis.com%2Fstdout", "receiveTimestamp": "2026-04-28T195534.832274132Z" } _ { "insertId": "69f110b5000270f0d5a2d62a", "jsonPayload": { "job_id": "AJ_9FmCsTZis49b", "enable_recording": false, "agent_name": "padii-agent", "name": "livekit.agents", "dispatch_id": "AD_zWAgUvQt4LFs", "room": "padii-3a0caac7-b200-4e4b-be3c-07f48f372157", "room_id": "RM_rmSZ9x77yjSv", "resuming": false, "level": "INFO", "message": "received job request", "timestamp": "2026-04-28T195533.159725+00:00" }, "resource": { "type": "cloud_run_revision", "labels": { "configuration_name": "padii-agent", "service_name": "padii-agent", "project_id": "orangeinvest-backend", "revision_name": "padii-agent-00016-9dd", "location": "asia-south1" } }, "timestamp": "2026-04-28T195533.159984Z", "labels": { "instanceId": "0007b734d94c8c49bc6ef78cc1bb649fd19e3bc3ace7d5cee880e40c2b07771b3bb24e67cac3b7fb490cfd0ddfe7e82745a7bfdccbf68e425390db7cab0e67edf6d5ef2771a47188152cd6a03167" }, "logName": "projects/orangeinvest-backend/logs/run.googleapis.com%2Fstdout", "receiveTimestamp": "2026-04-28T195533.166061314Z" }

Thanks — I reviewed the new logs showing "no warmed process available" and "worker is at full capacity." That confirms the agent is hitting cold-start / process-capacity limits on Cloud Run rather than a pure network/port issue. What I recommend now (apply in order) 1) Add pre-warm + keep idle workers • Configure the agent to pre-load required models and keep 1–2 idle job processes so incoming jobs can be handled immediately. Minimal pattern (conceptual; adapt to your code):

from livekit.agents import JobProcess, AgentServer

def prewarm(proc: JobProcess):
    # load any heavy models / VAD / TTS into process-local userdata
    proc.userdata["vad"] = load_vad_model()
    proc.userdata["tts"] = load_tts_model()

server = AgentServer(
    setup_fnc=prewarm,
    num_idle_processes=2,        # keep 2 warm processes
    load_threshold=0.5,         # mark unavailable earlier to reduce overload
    drain_timeout=300,
)

Set WorkerOptions/AgentServer so the agent starts with those idle processes. 2) Ensure on_startup runs in correct event loop • Use WorkerOptions.on_startup=_async_init (do NOT patch cli.run_app). That runs _async_init in the agent loop and avoids dropped tasks. 3) Cloud Run resource & instance tuning (test settings) • CPU: 2 vCPUs for tests (or at least 1 with no CPU throttling). • Memory: 2Gi if you run TTS/STT models. • Concurrency: set to 1 for testing, then raise once stable. • Min instances: >=1 (you already have). Consider min-instances=1–2 to reduce cold starts. • Timeout: keep 3600s while diagnosing. • Egress: public (no Serverless VPC Connector blocking outbound). • Ensure PORT=8080 for Cloud Run; set agent internal http_port to different port (e.g., 8082) to avoid conflicts. 4) Deployment checklist • Confirm prewarm code runs at process start (look for model load logs). • Confirm /ready returns 200 only after _async_init completes. • Confirm logs show "warm processes created" or similar and no "no warmed process available" messages. 5) Quick debugging steps if warnings persist • Increase num_idle_processes to 3 and min-instances to 2; redeploy and retry one connect. • Tail logs during a connect attempt and paste the 30s-before/30s-after logs if you still see the warnings. • If you see "worker is at full capacity" with load ~0.7 repeatedly, lower load_threshold to 0.5 and increase num_idle_processes. If you want, paste the small section where you call AgentServer or WorkerOptions now and I’ll produce an exact minimal patch to insert prewarm and num_idle_processes into your current startup flow. , with interactive elements

1. Earlier u asked me to remove the Pre-warm 🥲

You’re right — sorry for the confusing back-and-forth. Short answer: keep pre-warm, but implement it correctly (don’t patch cli.run_app). Below is a small, clear plan with two safe options depending on your livekit-agents version, plus what I need from you next. Why pre-warm is needed (one line) • Cloud Run cold starts + model load time cause the "no warmed process available" / "worker is at full capacity" warnings; warm 1–2 worker processes with models loaded to avoid that. Two safe ways to add pre-warm Option A — If your livekit-agents supports AgentServer/JobProcess • Use a setup_fnc that runs in each worker process (blocking loads okay there):

from livekit.agents import AgentServer, JobProcess

def prewarm(proc: JobProcess):
    proc.userdata["vad"] = load_vad_model()   # placeholder: implement actual load
    proc.userdata["tts"] = load_tts_model()

server = AgentServer(
    setup_fnc=prewarm,
    num_idle_processes=2,
    load_threshold=0.5,
    drain_timeout=300,
)
# Wire server into your agent start per SDK docs

Option B — If AgentServer is NOT available (fallback) • Use WorkerOptions.on_startup (runs inside the agent event loop) or WorkerOptions.setup_fnc if available. Perform blocking loads on the worker process via asyncio.to_thread so the event loop stays responsive:

async def setup_fnc():
    # run blocking loads in a thread so the loop remains responsive
    vad = await asyncio.to_thread(load_vad_model)
    tts = await asyncio.to_thread(load_tts_model)
    # store loaded objects in a place worker code can access (e.g., global or proc-local)

cli.run_app(
    WorkerOptions(
        entrypoint_fnc=entrypoint,
        agent_name="padii-agent",
        initialize_process_timeout=60.0,
        on_startup=setup_fnc,   # run inside the correct event loop
        http_port=8082,         # avoid Cloud Run PORT=8080 conflict
    )
)

Important: do NOT patch cli.run_app or create your own event loop — that was the earlier cause of task drop / executor unresponsive. Cloud Run settings to use while testing • CPU: 2 vCPUs (or enable no-throttling); Memory: 2Gi if running TTS/STT • Concurrency: 1 (test), then increase when stable • Min instances: 1–2 (helps reduce cold starts) • PORT env: 8080 (Cloud Run) — set agent internal http_port to different port (e.g., 8082) Placeholders • load_vad_model / load_tts_model are placeholders — replace with your actual model-loading calls. Next actions I need from you (pick any) • Tell me your livekit-agents Python package version (I’ll give the exact API-compatible patch). • Or paste the prewarm function you added (I’ll validate it and show exactly where to attach it). • Also confirm whether you set num_idle_processes >=1 and min-instances >=1 after re-adding pre-warm. If you send the package version now I’ll return a one-file patch tailored to your SDK (either AgentServer usage or WorkerOptions-based setup_fnc) you can drop into your agent and redeploy. , with interactive elements

It is showing me 2 assistants, one is showing self hosted to which my calls are routing. Other is Livekit cloud hosted and I have xai api key also in advanced tab of it's console. Which one is correct ? How to delete the other?

Thanks — good question. I’ll keep this short. Which one is correct? • The correct agent is whichever one your dispatch configuration (the LiveKit URL, API key/secret, and dispatch rules or token generation) points to. The XAI API key in the LiveKit Cloud console only controls that cloud-hosted agent’s access to xAI, not which agent your calls route to. How to tell which agent is handling your calls 1. Check the agent_name used in your dispatch logic (token generation, SIP/disptach rules, or agent routing). That name must match the agent you want to receive jobs. 2. In the LiveKit Cloud dashboard (Agents) or with the CLI (

lk agent status

/

lk agents list

), look for active agents and their agent_name and connection timestamps. 3. Tail logs: ◦ For your self‑hosted agent: check your Cloud Run logs (you already have them). ◦ For cloud-hosted agent: check the LiveKit Cloud agent logs in the dashboard. How to remove the unwanted agent • LiveKit Cloud-hosted agent: delete it from the LiveKit dashboard or use the CLI (

lk agent delete <agent-id>

). • Self-hosted agent: stop the service (stop the Cloud Run service or remove the process). Also remove any dispatch rules or tokens that reference its agent_name. How to avoid future confusion • Use distinct agent_name values (e.g., padii-selfhost vs padii-cloud). • Separate projects for self-hosted vs cloud-hosted environments if possible. • Ensure your token/disptach generation uses the intended LIVEKIT_URL and API key/secret. If you want, tell me: • the agent_name you expect to use, and • whether you want to keep the cloud-hosted or the self-hosted agent. I’ll give the exact CLI commands and dashboard steps to delete the other one and confirm routing. Sources: Self-hosted deployments | LiveKit Documentation | Pricing | LiveKit | EndCallTool | LiveKit Documentation | Avoiding crosstalk when multiple developers share a LiveKit project | LiveK... | Agent deployment overview | LiveKit Documentation, with interactive elements

What would be ideal case for my use case?

Good question — here’s a concise recommendation and next steps tailored to your Grok voice + LiveKit setup. Short recommendation • Use self-hosted (Cloud Run) for this project. It gives the control you need to pre-warm models, tune CPU/memory, manage readiness, and avoid the cold-start issues causing "no warmed process available" and "job executor is unresponsive." Keep the LiveKit cloud-hosted agent disabled to avoid routing confusion. Why self-hosted is ideal for you • Deterministic low-latency: you can pre-load VAD/TTS and keep idle workers to avoid cold starts. • Resource control: set 2 vCPUs and >=2Gi RAM for reliable audio/model inference. • Debuggability: full logs and ability to adjust readiness/health to match Cloud Run. • Routing certainty: you control LIVEKIT_URL/API key and agent_name used by your token/dispatch logic. When to use LiveKit cloud-hosted instead • Use the cloud-hosted agent only if you prefer fully managed scaling and don’t need custom pre-warm/model init. It’s convenient but not ideal for heavy or latency-sensitive model loads. Immediate actions I recommend 1. Keep and stabilize the self-hosted Cloud Run agent: ◦ CPU: 2 vCPUs (test), Memory: 2Gi ◦ Concurrency: 1 while testing, raise later ◦ Min instances: 1–2, Max per your load ◦ Timeout: keep long during tests (you have 3600s) ◦ Ensure PORT=8080 (Cloud Run) and set agent internal http_port to 8082 ◦ Implement pre-warm (num_idle_processes >=1–2) and load VAD/TTS in setup_fnc or on_startup (use asyncio.to_thread for blocking loads) 1. Disable/delete the cloud-hosted agent to avoid misrouting (optional — see safety note below). 2. Use a unique agent_name (e.g., padii-selfhost) and ensure your token/dispatch logic points to that agent_name and LIVEKIT_URL. Safety note before deleting anything • Verify which agent is actually receiving calls (check agent_name in dispatch/token generation and the dashboard). Confirm the cloud-hosted agent ID in the LiveKit dashboard before deletion to avoid accidental downtime. If you want, I can: • Give the exact Cloud Run deploy flags and a one-file patch to add pre-warm and on_startup wiring, OR • Provide the exact CLI/dashboard steps to delete the cloud-hosted agent — tell me the cloud agent’s name or ID and confirm you want it removed. Which would you like?, with interactive elements

I am still issues with my code. Is there any setup I need to do on livekit dashboard or agents

Thanks — I’ll keep this short and actionable. What I’m answering: whether you need any LiveKit dashboard/agent setup and next steps to fix your code. Short answers • No extra manual setup is required in the LiveKit dashboard for a self‑hosted agent — agents register automatically when they connect using your LIVEKIT_URL + API key/secret. • You do need to ensure your agent_name, LIVEKIT_URL, and API keys used by your token/dispatch logic match the agent you want to handle jobs. • Keep pre-warm enabled (load models into warm worker processes) and run pre-warm / init via WorkerOptions.on_startup or AgentServer setup_fnc — do NOT patch cli.run_app or create your own event loop. Immediate checklist — run through these now 1. Confirm agent routing ◦ Verify the agent_name used when creating tokens/dispatch points to your self‑hosted agent. 1. Confirm envs in Cloud Run ◦ LIVEKIT_URL, LIVEKIT_API_KEY, LIVEKIT_API_SECRET, XAI_API_KEY present and correct. 1. Ensure readiness & port separation ◦ Cloud Run PORT=8080 for /ready. ◦ Set LiveKit agent internal http_port to a different port (e.g., 8082). 1. Implement pre-warm correctly ◦ If AgentServer available: use setup_fnc to load VAD/TTS and set num_idle_processes=1–2. ◦ If not: use WorkerOptions.on_startup and run blocking loads via asyncio.to_thread so the event loop stays responsive. 1. Cloud Run test settings ◦ CPU: 2 vCPUs (test), Memory: 2Gi, Concurrency: 1, Min instances: 1–2, Timeout: long (you have 3600s). 1. Verify after deploy ◦ /ready returns 200 after "_async init complete — agent is ready" log. ◦ No "no warmed process available" or "job executor is unresponsive" logs. ◦ LiveKit logs show successful signal/region fetch. If it still fails, send two things and I’ll give an exact patch: 1. The exact agent_name you expect to use and which agent (self-hosted or cloud) you want to keep. 2. The 30s-before / 30s-after logs around the next "job executor is unresponsive" (or the latest "no warmed process available") message. I’ll then produce a one-file patch (pre-warm + on_startup + http_port set) and the precise Cloud Run deploy flags to remove the issue. Sources: Server lifecycle | LiveKit Documentation | Agent deployment overview | LiveKit Documentation | Agents on LiveKit Cloud | Deploy voice AI on global infrastructure | LiveKi... | Build Your First AI Voice Agent in Python: Complete Tutorial | LiveKit | Server options | LiveKit Documentation, with interactive elements

2026-04-28 201630 INFOlivekit.agentsdraining worker 2026-04-28 201630 2026-04-28 201641 2026-04-28 201641,808 WARNING timed out waiting for idle processes to initialize 2026-04-28 201642 2026-04-28 201645 2026-04-28 201645,339 INFO registered worker 2026-04-28 201645 2026-04-28 201645 2026-04-28 201645,709 INFO received job request 2026-04-28 201645 2026-04-28 201645 2026-04-28 201645 2026-04-28 201645,808 WARNING no warmed process available for job, waiting for one to be created 2026-04-28 201648 2026-04-28 201648,208 INFO worker is at full capacity, marking as unavailable 2026-04-28 201648 2026-04-28 202236 2026-04-28 202236,809 INFO initializing process 2026-04-28 202236 2026-04-28 202258 2026-04-28 202258,408 INFO killing process 2026-04-28 202258 2026-04-28 202258 2026-04-28 202258,409 INFO sending SIGUSR1 signal to process 2026-04-28 202258 2026-04-28 202259 2026-04-28 202259,208 WARNING Failed to get memory info for process 2026-04-28 202259 Traceback (most recent call last): File "/usr/local/lib/python3.12/site-packages/livekit/agents/ipc/supervised_proc.py", line 491, in _memory_monitor_task memory_info = process.memory_info() ^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/psutil/_common.py", line 377, in wrapper raise err from None File "/usr/local/lib/python3.12/site-packages/psutil/_common.py", line 375, in wrapper return fun(self) ^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/psutil/__init__.py", line 1138, in memory_info return self._proc.memory_info() ^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/psutil/_pslinux.py", line 1605, in wrapper raise NoSuchProcess(pid, name) from err psutil.NoSuchProcess: process no longer exists (pid=26) 2026-04-28 202259 2026-04-28 202300 2026-04-28 202259,522 ERROR error initializing process 2026-04-28 202300 Traceback (most recent call last): File "/usr/local/lib/python3.12/asyncio/tasks.py", line 520, in wait_for return await fut ^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/livekit/agents/ipc/channel.py", line 47, in arecv_message return _read_message(await dplx.recv_bytes(), messages) ^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/site-packages/livekit/agents/utils/aio/duplex_unix.py", line 35, in recv_bytes len_bytes = await self._reader.readexactly(4) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/asyncio/streams.py", line 752, in readexactly await self._wait_for_data('readexactly') File "/usr/local/lib/python3.12/asyncio/streams.py", line 545, in _wait_for_data await self._waiter 2026-04-28 202300 asyncio.exceptions.CancelledError 2026-04-28 202300 The above exception was the direct cause of the following exception: 2026-04-28 202300 Traceback (most recent call last): File "/usr/local/lib/python3.12/site-packages/livekit/agents/ipc/proc_pool.py", line 209, in _proc_spawn_task await proc.initialize() File "/usr/local/lib/python3.12/site-packages/livekit/agents/ipc/supervised_proc.py", line 238, in initialize init_res = await asyncio.wait_for( ^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/asyncio/tasks.py", line 519, in wait_for async with timeouts.timeout(timeout): ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/asyncio/timeouts.py", line 115, in aexit raise TimeoutError from exc_val 2026-04-28 202300 TimeoutError 2026-04-28 202300 2026-04-28 202310 2026-04-28 202310,015 ERROR process did not ack shutdown in time, killing process 2026-04-28 202310 C:\Users\DELL\Downloads\Claudacode\orangeinvest\backend\livekit-agent>

Thanks — I can help get this fixed. I’m seeing process init timeouts, killed child processes, and psutil NoSuchProcess errors — those point to worker processes failing during pre-warm/initialization (cold start + timed shutdown). I need a few exact pieces so I can give a precise patch you can deploy immediately. Please paste these four items: 1. The exact code you now run to start the agent (the full main / cli.run_app or AgentServer/WorkerOptions call including all options: initialize_process_timeout, prewarm_timeout, num_idle_processes, load_threshold, http_port, on_startup, setup_fnc). 2. The current pre-warm function (the code that loads VAD/TT S/models) or say “none” if you removed it. 3. Your failing Cloud Run revision settings for that deployment: CPU, memory, concurrency, min-instances, max-instances, and whether egress is public or via Serverless VPC Connector. 4. requirements.txt (or pip freeze) so I can confirm

psutil

and any blocking libs. Send those and I’ll return: • A one-file minimal patch (safe to drop in) that ◦ runs pre-warm in worker processes correctly, ◦ uses WorkerOptions.on_startup (no cli.run_app patching), ◦ sets num_idle_processes and increases initialize/process timeouts, ◦ and ensures graceful process monitoring so psutil errors stop. • Exact Cloud Run deploy flags (CPU/memory/min-instances/concurrency) to use with that patch. Sources: Understand and Improve Agent Latency | LiveKit | Server options | LiveKit Documentation | Deployment reliability on LiveKit Cloud | LiveKit, with interactive elements