Thanks — I see you’ve confirmed the agent connects (WebRTC negotiation succeeds and

ontrack

fires), the agent works locally, and in production you get no visible STT/LLM/TTS activity (“still nothing”). I’ll focus on what we can check next and ask a few targeted questions to narrow this down. What I’m responding to • The production agent connects (ICE relay), tracks are attached (

ontrack

), and the process registers with LiveKit Cloud, but there are no transcription or LLM/TTS events visible in the logs. • You also have repeated memory-usage warnings in the worker logs. Quick checklist of the highest-impact things to verify • Use Agent Observability in the LiveKit Cloud dashboard to inspect a production session trace. This will show audio playback, transcripts (if any), turn traces, and cross-service logs so we can see where the pipeline stops. • Confirm whether the agent is publishing text-only responses (audio output disabled). If so, make sure the frontend is listening to the

lk.transcription

stream instead of expecting an audio track. • Verify the STT provider is receiving audio in production: ◦ Ensure the Deepgram API key is present in the production environment (

DEEPGRAM_API_KEY

or your equivalent). ◦ Check whether any STT events (e.g., transcribed user input) appear in logs or Agent Observability. • Check for noise-suppression or telephony plugins enabled in production (e.g., BVCTelephony, krisp). These can suppress valid speech and leave you with tracks but no detectable utterances. • Confirm VAD/turn detection is ready: ◦ Ensure the VAD model is loaded/initialized on service start (pre-warm if possible). If VAD never detects voice, STT/LLM won’t be triggered. • Test with longer utterances in production (full sentences). Short 1–3 word utterances are often missed by STT. • Confirm the agent is subscribed to the correct participant audio track. In multi-party rooms, the agent may need a fixed participant identity via RoomIO to process the intended speaker. • Review memory warnings. While not a proven cause of STT failure, sustained high memory usage could impact service stability or processing throughput. Targeted questions to narrow it down 1. When you open Agent Observability for a failing prod session, do you see any audio waveform or transcript entries at all? If yes, where does the pipeline stop (e.g., transcript present but no LLM/TTS)? 2. Is audio output intentionally enabled in production? Or are you relying on text responses only? 3. Do you have any noise-cancellation or telephony processing enabled in the production build that isn’t present locally? 4. Can you confirm the Deepgram credentials/config used in prod match the working local setup (same model/keys)? Any environment differences? 5. Are participants in the room using the same client code in prod as locally (same capture settings, same permission to publish audio)? 6. Can you reproduce the issue with a single participant and the agent in the same room (to eliminate multi-party subscription issues)? If you can share answers to the questions above (or a session ID/room ID you viewed in Agent Observability), I’ll walk through the next diagnostic steps and concrete checks to pinpoint where audio is being dropped or ignored. Short-term tests you can run right now • Open Agent Observability for a recent prod session and play the audio timeline. • Speak a long sentence into the prod client and watch for STT events or trace entries. • Temporarily disable any noise-suppression/telephony plugins in prod and retest. • Ensure the frontend subscribes to

lk.transcription

if audio output is disabled. I’m here to help walk through the Agent Observability output or interpret logs you paste from the dashboard — send the session trace or answers to the questions above and we’ll dig into the next steps. Sources: Deepgram STT plugin guide | LiveKit Documentation | OpenAI and LiveKit partner to turn Advanced Voice into an API | An open source stack for real-time multimodal AI | Text and transcriptions | LiveKit Documentation | Streamline troubleshooting with Agent Observability

Thanks — that helps. I understand you run

lk agent logs --id CA_XX

and see nothing when joining the room from your Expo app. Let’s narrow down why the agent isn’t emitting logs for that session. What I’m responding to • You see successful WebRTC negotiation in the client logs but no LiveKit agent logs appear when joining from Expo. • You used

lk agent logs --id CA_XX

and got no output correlated with the join. Immediate checks and actions (quick, high-impact) • Confirm the agent with ID CA_XX is the same worker registered in the Cloud that should handle this room. The worker must be the one that registered (the logs you shared earlier show a different agent id format like CAW_kezeV2gXdbRE); make sure the IDs match the agent you expect to observe. • Ensure the agent process is running and registered at the time you join the room. The agent must be active and assigned to that room for logs to stream. • Reproduce while tailing broader logs: run the agent-logs command before joining the room and keep it open while you perform the join from Expo — logs may only appear if the agent receives room assignment after you start tailing. • Increase logging verbosity for the agent (if configurable) or enable debug-level logs for the agent process, then reproduce the join. That often reveals why the session wasn’t assigned or why speech events aren’t emitted. • Confirm token permissions and routing: ◦ Verify the token you use from Expo grants the identity/permissions the agent expects (e.g., publish audio). ◦ If you're using RoomIO or a fixed participant mapping, ensure the agent is configured to process that participant identity. Targeted diagnostic steps to run now 1. Start tailing logs: run

lk agent logs --id CA_XX

and leave it running. 2. Immediately join the room from Expo and speak a long sentence. 3. If nothing appears, check the Cloud dashboard (Agent Observability) for active sessions and which worker/node handled the room. Does the session show assigned worker/node? If yes, is it CA_XX or another ID? 4. If Agent Observability shows the session but a different worker handled it, the CA_XX agent won’t log anything for that session — confirm your deployment’s worker routing and region. 5. If Agent Observability shows no session at all, confirm the client actually joined (client logs showing

connected

and

ontrack

help) and the token used targeted the same region/cluster as your worker. Possible root causes aligned with what you described • You may be tailing the wrong agent id (the room was handled by a different worker). • The agent may not have received an assignment for that room (worker not registered or not available at join time). • Token/participant identity mismatch caused the agent to ignore that participant’s audio. • The agent’s logging verbosity is low and only logs on certain events (increase verbosity to capture join/assign events). Short checklist to reply with so I can help further • Can you confirm the exact agent ID returned in the worker’s registration log (the one that printed “registered worker”)? Paste that ID (or the registration log line). • After you run

lk agent logs --id CA_XX

and join, do you see absolutely no lines, or only unrelated lines (e.g., memory warnings from startup)? If unrelated lines appear, paste a snippet. • What token are you using in Expo — is it a room token for the same region/cluster where the agent is deployed? • Can you open Agent Observability for the time window when you join and tell me whether a session appears and which worker/node handled it? If you send the agent registration line and a short snippet of what you see when tailing logs, I’ll point out the exact next commands and checks to get the agent to produce logs for your Expo join.

Totally understood — here’s a short, actionable list to add more server-side logs so you can see joins from your Expo app. 1) Start the agent with debug logging • Node:

pnpm run dev --log-level=debug

• Python:

uv run agent.py dev --log-level=DEBUG

Use the

dev

command (not

start

) to get human-readable, verbose logs and hot reloads. 2) Add contextual fields in your agent entrypoint so each log shows room/worker info • Python example:

@server.rtc_session()
async def entrypoint(ctx: JobContext):
    ctx.log_context_fields = {"room": ctx.room.name, "worker_id": ctx.worker_id}
    await ctx.connect()

This makes it easy to correlate logs with the room join. 3) Log the key lifecycle events (on assign, connect, ontrack, on_disconnect) • Add explicit logs at: ◦ when the job is assigned/registered ◦ after

ctx.connect()

(connected) ◦ on

ontrack

events (track id / publisher) ◦ on STT/TTS callbacks and errors 4) Tail agent logs while reproducing • Run:

lk agent logs --id <AGENT_ID>

Start tailing, then immediately join from Expo. If you still see nothing, confirm the agent ID exactly matches the registered worker ID printed at startup (paste that line if you want me to check). 5) Quick checks if logs still don’t appear • Ensure the worker is registered at time of join (look for the “registered worker” line). • Confirm the session is assigned to that worker in Agent Observability; if a different node handled it, your agent won’t emit logs for that session. • Temporarily increase log verbosity for STT/turn-detection modules if available. If you want, paste the exact agent registration line (the “registered worker” log) and the command you use to start the agent — I’ll point out the exact command to tail the right logs. Sources: Server options | LiveKit Documentation

Thanks — I hear you. Short and direct: here’s exactly how to add server-side logs to see why the LLM response is empty even though transcription appears. 1) Start agent with DEBUG logs (use dev for readable output) • Node:

pnpm run dev --log-level=debug

• Python:

uv run agent.py dev --log-level=DEBUG

Keep this terminal open while you reproduce the join. 2) Add a few minimal debug prints in the agent flow (these will show up in deploy logs) • Log when the job is assigned / entrypoint runs:

@server.rtc_session()
async def entrypoint(ctx: JobContext):
    print(f"DEBUG: agent assigned room={ctx.room.name} worker={ctx.worker_id}")
    await ctx.connect()

• Log when STT produces the transcript and when you call the LLM:

async def on_user_turn_completed(self, chat_ctx):
    print("DEBUG: on_user_turn_completed — chat_ctx:", chat_ctx)
    print("DEBUG: calling llm_node now")
    await self.generate_reply()   # or call your llm invocation

• Log inside the LLM node wrapper:

async def llm_node(self, chat_ctx, tools, model_settings=None):
    print("DEBUG: llm_node entered — chat_ctx length:", len(chat_ctx.items))
    res = await super().llm_node(chat_ctx, tools, model_settings)
    print("DEBUG: llm_node returned:", bool(res) )
    return res

3) Tail the right logs while reproducing • Use the exact agent/job ID you saw in the dashboard/registration:

lk agent logs --id AJ_UYY --follow

Start tailing, then immediately join from Expo and speak a long sentence. 4) What to look for (quick checklist) • "DEBUG: agent assigned..." — confirms the agent instance started for that room. • Transcript printed in "on_user_turn_completed" — confirms STT delivered text to the session. • "DEBUG: calling llm_node now" and "DEBUG: llm_node entered" — confirms the LLM path is invoked. • Any exceptions or error lines right after LLM call — indicates API/key/rate-limit or exception preventing response. • If you see transcript but never see LLM debug lines, the code never calls generate_reply()/llm_node. 5) If LLM call runs but returns no text • Log the LLM response object (status, tokens, error) right after the API call. • Confirm the model/credentials used in prod are valid and not empty strings. 6) Quick repro tips to force a visible path • Speak a long, clear sentence (short utterances can be missed). • Temporarily remove noise-suppression in prod build. • Force the agent to always call the LLM for testing, e.g., call generate_reply() unconditionally on every user turn. If you run the above and paste the few DEBUG lines you see (or paste the first 20 lines after you reproduce), I’ll point to the exact failing step. Sources: sip | livekit/livekit_composite | DeepWiki | Simple Content Filter | LiveKit Documentation | Context Variables | LiveKit Documentation | Server options | LiveKit Documentation | Streamline troubleshooting with Agent Observability

I’m sorry — I hear your frustration, and I’ll keep this short and direct so we can fix it. What I’ll do: give the minimum, highest-impact steps to get clear server logs and pinpoint the failure both locally and in Cloud. Do these three things now (in order) 1) Run the agent with DEBUG and tail logs • Start dev with debug and keep the terminal open: ◦ Node:

pnpm run dev --log-level=debug

• Python:

uv run agent.py dev --log-level=DEBUG

• Immediately reproduce the failure (join the room and speak). • Tail the cloud agent logs for the job id:

lk agent logs --id AJ_UYY --follow

• Copy the first 30–50 lines you see after reproducing and paste them here. 2) Add three tiny, guaranteed DEBUG prints (so we know which stage fails) • Entrypoint assign:

print(f"DEBUG: assigned room={ctx.room.name} worker={ctx.worker_id}")

• After transcript arrives / turn completes:

print("DEBUG: on_user_turn_completed — transcript:", chat_ctx.items[-1].content if chat_ctx.items else None)

• Immediately before and after calling the LLM:

print("DEBUG: calling LLM")
  # call LLM...
  print("DEBUG: LLM returned:", response_text)

Add these, deploy, reproduce, and paste the new DEBUG lines you get. 3) Quick credential & routing checks (one-liners) • Confirm Deepgram/OpenAI/Eleven keys are non-empty in production env. • Confirm the session in Cloud Dashboard shows this exact worker/job ID (AJ_UYY) — if a different worker handled it, tail that worker’s logs. • Turn off any noise-suppression in prod temporarily and test with a single, long sentence. If you do steps 1–3 and paste: • The 30–50 log lines from step 1 after reproduce, and • The DEBUG lines from step 2, I will immediately point to the failing call and next fix (no long text). If you prefer, I’ll tell you exactly which single line to change in your code — paste the small log output and I’ll respond with the one-line fix.

You’re right to focus on the llm_node — if transcripts arrive but the llm_node never returns text, we only need a few focused checks. Short and actionable: 1) Confirm llm_node is actually invoked • Add one guaranteed debug print at the top of the llm_node function:

print("DEBUG: llm_node entered — chat_ctx items:", len(chat_ctx.items))

Deploy and reproduce. If you do NOT see this line in cloud logs for that job, the node isn’t being called. 2) If llm_node is entered, log the LLM call and response • Right before calling the LLM:

print("DEBUG: calling LLM — model:", envServer.AZURE_OPENAI_DEPLOYMENT)

• Right after the call:

print("DEBUG: LLM returned — ok:", bool(response), "len:", len(response_text or ""))
print("DEBUG: LLM raw response:", repr(response_text)[:1000])

This will show whether the API returned an empty string, an error, or nothing. 3) Check for exceptions or silent failures • Wrap the LLM call and print any exception:

try:
    response_text = await call_llm(...)
    print("DEBUG: LLM succeeded")
except Exception as e:
    print("ERROR: LLM call failed:", type(e).__name__, str(e))
    raise

4) Verify credentials & endpoint in cloud • Confirm Azure OpenAI env vars in production are non-empty: ◦ AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_API_KEY, AZURE_OPENAI_DEPLOYMENT, OPENAI_API_VERSION • If any are missing or incorrect, the call may silently fail or throw an exception you need to capture (see step 3). 5) Quick test to isolate • Replace the real LLM call temporarily with a dummy response to confirm downstream pipeline works:

print("DEBUG: returning dummy LLM response for test")
return "TEST RESPONSE"

If this yields a response in the session, the problem is the LLM call/config; if it still fails, the problem is downstream (reply generation or routing). 6) Tail the correct cloud logs while reproducing • Run:

lk agent logs --id AJ_UYY --follow

Start tailing, then reproduce. Paste the first ~30 relevant DEBUG/ERROR lines here if you want immediate diagnosis. One quick clarifying question • When you inspect the cloud trace for that session, do you see any LLM attempt entries (like an LLM request/response or error) at all, or is it completely absent? Answer yes/no. Do the three small changes (enter print in llm_node, logging around the call, and the exception wrapper), reproduce, and paste the debug lines if you want me to pinpoint the failing step. I’ll give the single-line fix next.

Thanks — clear and short. You confirmed: llm_node is called, but

lk.response.text

is always empty. Here are the minimal, highest-value checks and exact debug snippets to run now. 1) Confirm the LLM call result object • Immediately after your LLM call, log the full response object and any fields:

res = await call_llm(...)   # whatever your call returns
print("DEBUG: raw_llm_response:", repr(res)[:2000])
# If res is an object/dict, also:
print("DEBUG: raw_keys:", list(res.keys()) if hasattr(res, "keys") else None)

Why: some SDKs return structured objects where the text is nested (e.g.,

res.choices[0].text

or

res.content

).

lk.response.text

may remain empty if you read the wrong field. 2) Log every candidate text location • Dump common places LLM text might be:

text_candidates = []
# examples — adapt to your SDK return shape
if isinstance(res, dict):
    text_candidates.append(res.get("text"))
    text_candidates.append(res.get("content"))
    choices = res.get("choices") or []
else:
    choices = getattr(res, "choices", None) or []
if choices:
    for i, c in enumerate(choices):
        text_candidates.append(c.get("text") if isinstance(c, dict) else getattr(c, "text", None))
        text_candidates.append(c.get("message", {}).get("content") if isinstance(c, dict) else None)
print("DEBUG: text_candidates:", text_candidates)

Why: this shows if the model returned text somewhere you’re not pulling from. 3) Check for streaming / async usage mismatch • If you use a streaming API or async iterator, ensure you consume the stream fully and assemble text:

# pseudo
async for chunk in res.stream():
    collected += chunk
print("DEBUG: collected_stream_len:", len(collected))

Why: empty final text can happen if you never drain the stream. 4) Catch and log exceptions or status codes

try:
    res = await call_llm(...)
    print("DEBUG: LLM call completed, status/ok:", getattr(res, "status", None))
except Exception as e:
    print("ERROR: llm call exception:", type(e).__name__, str(e))
    raise

Why: silent failures sometimes appear as empty outputs but an exception will show here. 5) Validate credentials & endpoint at runtime

print("DEBUG: AZURE_OPENAI_ENDPOINT:", bool(os.getenv("AZURE_OPENAI_ENDPOINT")))
print("DEBUG: AZURE_OPENAI_API_KEY present:", bool(os.getenv("AZURE_OPENAI_API_KEY")))
print("DEBUG: AZURE_OPENAI_DEPLOYMENT:", os.getenv("AZURE_OPENAI_DEPLOYMENT"))

Why: misconfigured/empty env vars can let the client object exist but return empty responses. 6) Quick isolate: force a test response • Replace the LLM call temporarily:

print("DEBUG: returning DUMMY_RESPONSE")
response_text = "DUMMY RESPONSE"
# assign to lk.response.text the same way your code normally does

Why: confirms downstream plumbing works—if this shows up, problem is LLM call/config. If it still doesn’t, problem is reply serialization. 7) One-line final check to map to LiveKit response • After you compute response_text, log before setting:

print("DEBUG: final response_text len:", len(response_text or ""), "repr:", repr(response_text)[:500])

Why: proves that the variable you intend to push into

lk.response.text

actually contains data. If you run 1–7 and paste only the small debug outputs showing: • the

raw_llm_response

line and • the

text_candidates

line and • the

final response_text

line I’ll tell you the single line to change to fix it.

You’re right — Node.js only. Short, exact steps to debug the empty

lk.response.text

in your Node agent (cloud). Do these now and paste the three debug lines I ask for at the end. 1) Run with debug logging • Start the agent in dev with debug:

pnpm run dev --log-level=debug

• Tail the cloud job logs while reproducing:

lk agent logs --id AJ_UYY --follow

2) Add minimal debug logs around the llm call (Node) • Immediately at the top of your

llm_node

implementation:

console.log("DEBUG: llm_node entered — chat_ctx.items.length=", chat_ctx?.items?.length);

• Right before calling your LLM client:

console.log("DEBUG: calling LLM — deployment=", process.env.AZURE_OPENAI_DEPLOYMENT);

• Right after receiving the LLM result, dump the raw object and common text fields:

console.log("DEBUG: raw_llm_response =", JSON.stringify(rawRes, null, 2).slice(0, 2000));
const candidates = [];
if (rawRes?.choices) rawRes.choices.forEach((c, i) => candidates.push({i, text: c?.text ?? c?.message?.content}));
if (rawRes?.content) candidates.push({content: rawRes.content});
console.log("DEBUG: text_candidates =", JSON.stringify(candidates));

• Log final assembled response just before assigning to `lk.response.text`:

console.log("DEBUG: final response_text len=", (responseText || "").length, "repr=", (responseText || "").slice(0,500));

3) Wrap the LLM call to catch and log any exceptions or status info

try {
  const rawRes = await llmClient.call(...);
  console.log("DEBUG: llm call completed, status:", rawRes?.status || "no-status");
} catch (err) {
  console.error("ERROR: llm call failed:", err && err.message, err);
  throw err;
}

4) Check streaming vs non-streaming usage • If you use a streaming API, ensure you drain/collect chunks:

let collected = "";
for await (const chunk of rawRes.stream()) collected += chunk;
console.log("DEBUG: collected_stream_len=", collected.length);

5) Quick env sanity logs (one-liners)

console.log("DEBUG: AZURE_OPENAI_API_KEY present=", !!process.env.AZURE_OPENAI_API_KEY);
console.log("DEBUG: AZURE_OPENAI_ENDPOINT present=", !!process.env.AZURE_OPENAI_ENDPOINT);
console.log("DEBUG: AZURE_OPENAI_DEPLOYMENT=", process.env.AZURE_OPENAI_DEPLOYMENT);

6) Fast isolate test • Temporarily replace real call with a dummy response to confirm plumbing:

const responseText = "DUMMY RESPONSE";
console.log("DEBUG: returning dummy response:", responseText);

If dummy appears in

lk.response.text

, the issue is LLM call/config. If dummy still doesn’t appear, the issue is downstream serialization/routing. What I need you to paste here (only these lines) • The raw_llm_response line (from step 2) • The text_candidates line (from step 2) • The final response_text len/repr line (from step 2) With those three lines I’ll tell you the single exact code change to fix it. If you prefer, paste the raw_llm_response only and I’ll extract the text field for you.

Thanks — here’s a concise summary of your conversation, questions, and context. What you reported • Your LiveKit voice agent works locally but fails in the cloud; no visible agent responses. • WebRTC connects and

ontrack

fires; you can see sessions, turns, and audio in the Cloud Dashboard. • Transcription arrives (chat context contains user message), but

lk.response.text

is always an empty string in the deployed/cloud agent. • You tried tailing logs with

lk agent logs --id ...

and initially saw nothing; later confirmed LLM node calls appear but return no text. • Your agent is Node.js and deployed on LiveKit Cloud. You use Azure OpenAI for the LLM and Deepgram for STT. Your primary questions / goals • Why is the LLM node running but not producing a response (empty

lk.response.text

)? • How to add concise, effective server-side logs to pinpoint the failure both locally and in Cloud? • How to confirm whether the problem is LLM call/config, streaming handling, or downstream reply serialization/routing? Key facts from the session • Agent receives audio and produces transcripts (chat_ctx.items shows user text). •

llm_node

is invoked in production, but

lk.response.text

remains empty. • No explicit LLM errors were reported in the logs you shared. • You’re running Node.js agent code in Cloud and want focused, minimal debugging steps. Immediate, high-value debugging steps you should run now (Node.js, cloud) 1) Start the agent with debug logs and tail the job: • Start:

pnpm run dev --log-level=debug

• Tail:

lk agent logs --id AJ_UYY --follow

2) Add three tiny, guaranteed debug logs in your

llm_node

(exact snippets to paste back): • At entry:

console.log("DEBUG: llm_node entered — chat_ctx.items.length=", chat_ctx?.items?.length);

• Right after LLM call, dump raw response (limit to 2000 chars):

console.log("DEBUG: raw_llm_response =", JSON.stringify(rawRes, null, 2).slice(0, 2000));

• Final assembled response before assigning to LiveKit:

console.log("DEBUG: final response_text len=", (responseText || "").length, "repr=", (responseText || "").slice(0,500));

3) Wrap the LLM call to catch/expose exceptions:

try {
     const rawRes = await llmClient.call(...);
     console.log("DEBUG: llm call completed, status:", rawRes?.status || "no-status");
   } catch (err) {
     console.error("ERROR: llm call failed:", err && err.message, err);
     throw err;
   }

4) Quick isolates: • Temporarily return a hard-coded

"DUMMY RESPONSE"

to verify downstream plumbing. • Ensure environment vars exist in Cloud:

AZURE_OPENAI_API_KEY

,

AZURE_OPENAI_ENDPOINT

,

AZURE_OPENAI_DEPLOYMENT

. What I need from you (minimal) • Paste these three debug lines from the cloud logs after reproducing: 1) the

raw_llm_response

line 2) the

text_candidates

or equivalent line if you add it 3) the

final response_text

line • If you prefer, paste only the

raw_llm_response

and I’ll tell you exactly which field to use. If those three lines show: • No text anywhere in raw response → the LLM call/config is wrong (creds, endpoint, deployment, streaming handling). • Text present in an unexpected field → I’ll tell you the single line to change to pull the correct field. • Response object empty but no exception → add streaming collector or enable SDK debug to surface status. I’ll respond with the single, exact code change once you paste the requested debug lines.