agent joining but does not hearr me there is STT as i can see my diaogue in cahtbox , agent hons but diesnt gheaer or respiknd any idaes livekit cloud andf uinnworld

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

Livekit Cloud , Innworld , ipen ai LLM , deepgram

Thanks — that’s very helpful context. You’re using LiveKit Cloud + Inworld with Deepgram for STT and an OpenAI LLM, and STT shows your speech but the agent doesn’t respond. I’ll help narrow this down and give focused next steps. Quick summary of likely causes • STT succeeds but downstream processing (agent/LLM) isn’t getting the transcript or is erroring. • Agent may be listening to the wrong participant in the room (multi-participant routing). • Noise cancellation settings could be suppressing audio features that trigger the agent (less likely since STT works, but still possible for voice-activation logic). • LLM/runtime issues (token limits, timeouts, or exceptions) may prevent generation of a reply even though transcription exists. Please provide these details so I can give targeted instructions • Sample STT transcript (copy/paste one or two recent lines you saw). • Any agent/LLM logs or error messages you’ve seen after the transcript appears (paste exact messages if possible). • Is the agent configured to listen to a specific participant identity or to “any” participant? If specific, what is that identity and does it match your client’s identity in LiveKit? • Are there multiple human participants in the room during these tests? • Are you running noise cancellation (BVC/BVCTelephony) on the client or on a media pipeline? Have you tried disabling it? • Does the agent ever respond (even intermittently), or is it never producing a reply? • Are you seeing any network errors or timeouts in the service that proxies transcripts to Inworld/OpenAI? Immediate checks and tests you can perform now 1. Test with a full sentence (not a short phrase) so the agent’s activation logic gets a clearer signal. 2. Temporarily disable noise cancellation on your client and test again. 3. Create a test room with only you + the agent (no other participants) and confirm the agent’s participant-binding configuration points to your participant identity. 4. Check the service logs where transcripts are forwarded to Inworld/OpenAI. Look for: ◦ Receipt of the transcript by the agent process ◦ Any exceptions, timeouts, or token errors when calling OpenAI ◦ Any “ignored” or “unassigned participant” warnings 1. If possible, capture a short recording of one test call (audio + transcript + timestamps) and the corresponding agent-side logs for the same timeframe. If you share the STT transcript and any error messages or logs you find, I’ll: • Walk through the exact config checks in LiveKit (how to confirm participant identity binding) • Show what to look for in Inworld event logs to confirm it received the transcript • Provide next steps for OpenAI error types (timeouts, rate limits, token truncation) and how to mitigate them You’re close — STT proves audio capture works, so the next step is confirming the transcript reaches the agent and that the LLM call completes successfully. Share the transcripts/log snippets and I’ll guide you step-by-step.

Questions for LK support: Can BVC noise cancellation on the agent side interfere with the pipeline or block the LLM call? Does

FallbackAdapter

have a timeout for hanging providers, or will it wait indefinitely? 1. Why might

sub=false

appear on the agent's microphone track when

autoSubscribe

is default (true)?

Thanks — this is a great set of focused questions. I’ll respond to each one directly and give practical checks you can run with LiveKit Cloud + Inworld + Deepgram + OpenAI. 1) Can BVC noise cancellation on the agent side interfere with the pipeline or block the LLM call? • No — BVC itself does not directly block or stop the LLM call. BVC runs locally and processes the audio before it’s sent to STT. • However, overly aggressive noise cancellation can degrade or remove speech features that STT needs to produce an accurate transcript. Poor or truncated transcripts can make the agent’s downstream processing (Inworld → OpenAI) produce no reply or an incorrect reply. • Also avoid applying BVC more than once (for example, both client-side and agent-side). Double-processing can corrupt audio and worsen STT results. • Quick checks: ◦ Temporarily disable BVC on the agent and run the same utterances to see if response behavior changes. ◦ Compare transcripts (with and without BVC) for missing words or truncation. ◦ If transcripts look fine but the agent still doesn’t respond, proceed to LLM/log checks (below). 2) Does FallbackAdapter have a timeout for hanging providers, or will it wait indefinitely? • This behavior is not documented in the sources I have. I cannot confirm whether FallbackAdapter enforces a timeout or will wait indefinitely for a hanging provider. • Practical next steps: ◦ Inspect the adapter or proxy logs where provider calls are made; look for timeouts, retries, or long waits. ◦ If you control the adapter code/config, add or verify a provider-call timeout (and a fallback strategy) to avoid indefinite hanging. ◦ If you see the provider call hanging in logs, add a short timeout (e.g., a few seconds) and observe whether the flow fails faster and triggers your fallback path. 3) Why might

sub=false

appear on the agent’s microphone track when

autoSubscribe

is default (true)? •

autoSubscribe=true

enables automatic subscription by default, but

sub=false

means the participant is currently not subscribed to that track. That can happen even with autoSubscribe in place for several reasons: ◦ The client (or application) explicitly called

setTrackSubscribed(false)

for that track. ◦ The server or admin paused/unpublished the track (permissions or admin controls). ◦ A subscription attempt failed due to signaling/connectivity issues and the client did not retry successfully. ◦ The track was created with attributes that prevent subscription, or participant identities don’t match the subscription rules. • Quick checks: ◦ Confirm the client-side code isn’t programmatically unsubscribing that track. ◦ Check room/participant state and permissions for any server-side pause/unpublish. ◦ Reproduce in a minimal test room (agent + your participant only) and watch subscription events to see if and when a subscription attempt is made or rejected. ◦ Inspect client and server logs for subscription failures or errors. ◦ If you use participant identity-based routing, ensure the agent is targeting the correct participant identity. If you’d like, I can walk you through specific commands and log locations to inspect next — for example: • Which exact log lines to watch when the transcript is forwarded to Inworld/OpenAI. • What subscription events to look for in the LiveKit client SDK console. • How to run a reproducible test (single-participant room, BVC off, full-sentence utterance). Tell me which one you want to tackle first and paste any sample transcript lines or error-log snippets you’ve observed; I’ll give targeted steps from there. Sources: Choosing the right noise cancellation for your agent | LiveKit | Troubleshooting STT not picking up utterances | LiveKit | client-sdk-js | livekit/livekit_composite | DeepWiki

Untitled

Thanks — I noticed your last message was empty. I’m here and ready to keep troubleshooting whenever you’re ready. Quick recap of where we left off • You’re on LiveKit Cloud + Inworld with Deepgram STT and an OpenAI LLM. • STT shows your speech in the chatbox, but the agent does not respond. • We discussed BVC (noise cancellation), FallbackAdapter timeout behavior (undocumented), and why a track might show

sub=false

even with

autoSubscribe=true

. How I can help next • I can walk you step-by-step through targeted checks (BVC comparisons, subscription event tracing, adapter/provider call logs, and Inworld/OpenAI call traces). • I can point out exact log lines and SDK events to watch during a test call. • I can draft a minimal reproducible test plan (single room, BVC off, full sentences) you can run to isolate the problem. If you want to continue now, please share one or more of the following (any is helpful): • A recent STT transcript line you saw (copy/paste). • Agent/LLM or adapter logs from the timeframe when you spoke (exact error messages or timestamps). • Whether you’ve tried disabling BVC and what changed. • Whether the room had multiple participants, and how your agent is configured to select which participant to listen to (specific participant identity vs “any”). • Whether

sub=false

was seen on the agent’s microphone track in the LiveKit client console and any subscription events around that time. Pick one item to start with and paste it here — I’ll give precise next steps based on what you share.

livekit-agents[google,openai,deepgram,inworld,silero,turn-detector,noise-cancellation]~=1.4 supabase>=2.0.0 aiohttp~=3.11.0 boto3~=1.35.0 python-dotenv~=1.0.0

Thanks — this is very helpful. You’ve provided your dependency list and confirmed the runtime components; I’ll use that to give focused guidance and next steps tailored to your stack (livekit-agents ~1.4, Supabase, aiohttp, boto3, python-dotenv). Short summary of what I’m addressing • You’re running LiveKit agents (Google, OpenAI, Deepgram, Inworld, Silero, turn-detector, noise-cancellation) and seeing STT transcripts but no agent replies. You asked about BVC noise cancellation, FallbackAdapter timeouts, and why

sub=false

can appear on an agent’s microphone track. What to check now (targeted to your dependencies and components) 1. BVC / noise-cancellation ◦ BVC runs locally in the agent pipeline and will not directly block the OpenAI call. However, overly aggressive noise-cancellation can remove speech elements and produce poor transcripts, which prevents the agent from generating a response. ◦ With your stack, run a quick A/B test: i. Start a test room with only you + the agent. ii. Disable the noise-cancellation module in the agent pipeline (temporarily) and reproduce the utterance. iii. Compare Deepgram transcripts and Inworld events between the two runs. ◦ Look for truncated words or missing intent words in the transcript when BVC is enabled. If transcripts degrade with BVC enabled, keep it off or tune its aggressiveness. 1. FallbackAdapter timeout behavior ◦ The behavior of FallbackAdapter timeouts is not documented in the sources I have. Treat it as unknown for now. ◦ Practical next steps: ▪︎ Inspect the adapter code or logs where provider calls are made (this is likely in the livekit-agents components you listed). ▪︎ Add or confirm a timeout on outbound provider calls (Deepgram → Inworld → OpenAI). For example, ensure your aiohttp client calls include a timeout to avoid indefinite hanging. ▪︎ If you see long waits in logs, add a short timeout (2–10s depending on your use case) and observe whether your fallback path is triggered. 1.

sub=false

on agent microphone track (when autoSubscribe is true) ◦

autoSubscribe=true

is the default but

sub=false

still appears when a subscription was not established or was turned off. Possible causes: ▪︎ Your client or agent code explicitly called

setTrackSubscribed(false)

for that track. ▪︎ The server or an admin paused/unpublished the track. ▪︎ Subscription attempts failed (signaling connectivity issues) and weren’t retried. ▪︎ Participant identity routing mismatches: the agent is configured to listen to a specific identity that doesn’t match your client’s identity. ◦ Focused checks: i. In a minimal room (agent + your client), observe the LiveKit SDK subscription events in the browser/agent logs. Look for subscription success/failure messages. ii. Verify the agent’s participant target configuration matches your client identity. iii. Confirm your client isn’t calling

setTrackSubscribed(false)

anywhere. iv. Check for any server-side or admin actions that may pause/unpublish the track. Specific log locations and what to search for • Agent process logs (livekit-agents components): ◦ Search for receipt of Deepgram STT events, and timestamps showing forwarding to Inworld and OpenAI. ◦ Look for errors/exceptions, timeouts, or stack traces around those forwarding calls. • aiohttp calls: ◦ Ensure outbound calls (to Inworld/OpenAI) include timeouts. Search logs for long-running requests or “timeout”/“connection refused”/“502/504” messages. • LiveKit client logs (browser/agent SDK): ◦ Watch for subscription events and any warnings like “subscription failed,” “track unpublished,” or “participant identity mismatch.” • Deepgram transcripts: ◦ Compare transcripts produced with BVC on vs off. Look for differences in words that carry intent. Quick reproducible test plan (minimal steps) 1. Create a test room with only you and the agent (no other participants). 2. Turn off noise cancellation in the agent pipeline. 3. Speak a full sentence (avoid 1–3 word utterances). 4. Capture: ◦ Deepgram transcript line(s). ◦ Agent process logs for the same timestamp (look for receipt of transcript and calls to Inworld/OpenAI). ◦ LiveKit subscription events for the agent track. 1. Repeat with BVC enabled and compare results. If you want, I can: • Provide the exact log lines and SDK events to watch for (tell me whether your agent runs in Docker or directly on a host). • Show how to add a timeout to aiohttp requests (a small snippet) so provider calls don’t hang indefinitely. Which of the two would you like next? • Exact log/event strings to watch during a test, or • A short aiohttp timeout snippet you can drop into the agent adapter code.

name: "🚀 Deploy: LiveKit Inworld Agent" on: push: branches: [master] paths: - 'apps/fluente-app/livekit-agent-inworld/**' - '!apps/fluente-app/livekit-agent-inworld/**/*.md' - '!apps/fluente-app/livekit-agent-inworld/livekit.toml' workflow_dispatch: inputs: operation: description: 'Operation to run (deploy for updates, update-secrets to rotate secrets)' required: true type: choice options: - deploy - update-secrets default: 'deploy' permissions: contents: read jobs: deploy-agent: runs-on: ubuntu-latest concurrency: group: livekit-inworld-agent-deploy cancel-in-progress: true steps: - uses: actions/checkout@v4 - name: Install LiveKit CLI env: LK_CLI_VERSION: "2.13.2" run: | ARCH=$(dpkg --print-architecture) # amd64 on GHA ubuntu-latest URL="https://github.com/livekit/livekit-cli/releases/download/v${LK_CLI_VERSION}/lk_${LK_CLI_VERSION}_linux_${ARCH}.tar.gz" curl -sSL -o /tmp/lk.tar.gz "$URL" install_dir="$HOME/.local/bin" mkdir -p "$install_dir" tar -xzf /tmp/lk.tar.gz -C "$install_dir" lk chmod +x "$install_dir/lk" echo "$install_dir" >> "$GITHUB_PATH" "$install_dir/lk" --version || true rm -f /tmp/lk.tar.gz - name: Add LiveKit project run: | lk project add fluente \ --url "${{ secrets.LIVEKIT_URL }}" \ --api-key "${{ secrets.LIVEKIT_API_KEY }}" \ --api-secret "${{ secrets.LIVEKIT_API_SECRET }}" \ --default - name: Write secrets file run: | umask 077 { echo "INWORLD_API_KEY=${{ secrets.INWORLD_API_KEY }}" echo "OPENAI_API_KEY=${{ secrets.OPENAI_API_KEY }}" echo "GOOGLE_API_KEY=${{ secrets.GOOGLE_API_KEY }}" echo "GEMINI_API_KEY=${{ secrets.GEMINI_API_KEY }}" echo "DEEPGRAM_API_KEY=${{ secrets.DEEPGRAM_API_KEY }}" echo "SUPABASE_URL=${{ secrets.SUPABASE_URL }}" echo "SUPABASE_SERVICE_ROLE_KEY=${{ secrets.SUPABASE_SERVICE_ROLE_KEY }}" echo "SUPABASE_SERVICE_KEY=${{ secrets.SUPABASE_SERVICE_ROLE_KEY }}" echo "R2_ACCESS_KEY_ID=${{ secrets.R2_ACCESS_KEY_ID }}" echo "R2_SECRET_ACCESS_KEY=${{ secrets.R2_SECRET_ACCESS_KEY }}" echo "R2_ENDPOINT=${{ secrets.R2_ENDPOINT }}" } > /tmp/agent-secrets.env - name: Deploy or update secrets working-directory: apps/fluente-app/livekit-agent-inworld run: | OPERATION="${{ github.event.inputs.operation || 'deploy' }}" if [ "$OPERATION" = "update-secrets" ]; then lk agent update-secrets \ --project fluente \ --secrets-file /tmp/agent-secrets.env else lk agent deploy \ --project fluente \ --secrets-file /tmp/agent-secrets.env fi - name: Verify deployment run: | for i in $(seq 1 30); do STATUS=$(lk agent status --project fluente --id CA_sivAWKvegY3X 2>&1 || true) if echo "$STATUS" | grep -qi "Running"; then echo "Agent is running." echo "$STATUS" exit 0 fi echo "Attempt $i/30 — waiting 10s..." sleep 10 done echo "Agent did not reach Running status within 300s." lk agent status --project fluente --id CA_sivAWKvegY3X exit 1 - name: Cleanup secrets if: always() run: rm -f /tmp/agent-secrets.env

Thanks — I reviewed your GitHub Actions workflow and your earlier details about LiveKit Cloud + Inworld + Deepgram + OpenAI. It looks well-structured. Before I give specific recommendations, can you tell me what problem you’re seeing with this workflow or the deployment? For example: • Is the workflow failing to start or failing during a specific step (Install LiveKit CLI, Add LiveKit project, write secrets, deploy/update-secrets, verify deployment, or cleanup)? • Are you seeing the agent reach a non-Running state in the Verify deployment step, or does the

lk agent status

call return errors? • Are secrets not being applied correctly (wrong values, missing envs, or overwritten)? • Do you see any error logs from the agent after deployment (startup exceptions, provider call failures, timeouts)? • Is the agent deployed but still not responding to transcripts (STT ok, no LLM reply)? If you paste the failing step’s logs or the

lk agent status

output, I’ll point to the exact lines/events to inspect and provide tailored fixes (timeout/sniffing suggestions, secrets handling, and deployment adjustments).

hat's the LK support bot asking for more details. The key issue to share with them: Agent deployed and STT works, but LLM never responds (STT ok, no LLM reply — your option #5). Specifically: Pipeline goes:

STT done → LLM active → hangs indefinitely

— no response, no error Was working yesterday with OpenAI GPT-4.1-mini only (no

GOOGLE_API_KEY

set) • Today

GOOGLE_API_KEY

was added → Gemini became primary LLM → hangs silently

FallbackAdapter

never fails over to OpenAI (no timeout?) We've now swapped priority back to OpenAI-first (PR #1022, merged) and disabled BVC (PR #1023, CI running) • No error logs from the agent —

_on_error

handler never fires, no exceptions in LK Cloud logs •

lk agent status

shows agent as Running

Thanks — this is very clear and I understand the urgency. You have an agent that transcribes audio (Deepgram) but the pipeline hangs at “LLM active” with no response or error after you added a Google API key (Gemini became primary). It worked yesterday with OpenAI-only. Fallback didn’t fail over and the agent shows Running with no exceptions in logs. I’ll give targeted troubleshooting steps and practical changes you can apply immediately. What’s likely happening (quick summary) • Gemini became the primary provider after adding

GOOGLE_API_KEY

. The adapter call to Gemini is hanging or returning no result, and the pipeline never triggers an error path. • The FallbackAdapter behavior is unknown/documented as not having an explicit timeout, so a hanging provider can block the pipeline instead of failing over. • Disabling BVC and reverting to OpenAI-first are good steps to isolate LLM vs audio issues. Immediate actions to diagnose and mitigate (do these in order) 1. Enable detailed/debug logging for the agent process ◦ Turn on debug-level logs for the adapter components (LLM adapter, FallbackAdapter, and network/http client). This will reveal whether a request to Gemini is sent and whether any response/headers are received. ◦ Look specifically for: ▪︎ Outbound request timestamp to Gemini. ▪︎ Any connection/handshake/response events. ▪︎ Any internal adapter state transitions (call started → call finished/failed). ◦ If your agent runs under Docker, set the container env LOG_LEVEL=debug or equivalent and redeploy. 1. Trace the outbound HTTP call and add an explicit timeout ◦ Ensure aiohttp (or your HTTP client) calls to Gemini include a timeout. If calls currently use default behavior, add a short sensible timeout (e.g., 5–15s) while testing. ◦ Example approach: add per-request timeout and a client-level timeout so calls won’t hang indefinitely. ◦ When you add timeouts, confirm the FallbackAdapter triggers the fallback path (Gemini timeout → OpenAI). 1. Confirm provider priority and secret handling ◦ Re-check merged PR #1022 that reorders priority: confirm the effective priority at runtime (environment/config file used by the running agent). Sometimes CI merge doesn’t immediately change deployed runtime config. ◦ Ensure only one effective `GOOGLE_API_KEY`/`OPENAI_API_KEY` value is present in the agent’s runtime secrets file. If both keys exist, provider selection logic may prefer Gemini—verify that behavior matches your intended order. ◦ Verify secrets are present in the agent process you inspected (not just in GitHub Actions) by printing masked secret usage in debug logs (e.g., log which provider is selected, not the key itself). 1. Add a watchdog/fallback guard in the adapter ◦ If FallbackAdapter does not enforce timeouts, add a small wrapper: call the primary provider with a timeout; if it times out or errors, explicitly call the fallback provider. ◦ This can be implemented by catching timeout exceptions from aiohttp and invoking the next provider in the chain. 1. Reproduce with instrumentation ◦ Create a simple test transcript/message and run it through the agent while collecting: ▪︎ Agent debug logs (timestamps). ▪︎ Outbound HTTP logs for requests to Gemini/OpenAI (request start, headers, response, duration). ▪︎ Any Inworld event receipts if Inworld sits between STT and LLM. ◦ Compare behavior when you force OpenAI-first vs Gemini-first. What to look for in logs (examples) • Outbound request sent: ◦ “Calling Gemini endpoint at <url> — start timestamp” • Long-running request: ◦ “No response after 30s for Gemini call” (if you add a timeout you’ll see the timeout exception) • Fallback behavior: ◦ “Primary provider error/time out — switching to fallback: OpenAI” • If you see the outbound request but no response or TLS handshake errors, that points to a networking or provider-side issue. Short-term mitigations you can apply now • Revert to OpenAI-first in runtime config (you already merged PR to prioritize OpenAI; confirm it’s active). This should restore the previous working path immediately. • Keep BVC disabled while debugging. • Add a 10s request timeout for Gemini calls so that the adapter fails fast and triggers fallback. Longer-term recommendations • Add explicit per-provider timeouts and robust retry/failover logic in FallbackAdapter so a single hanging provider won’t stall the pipeline. • Add metrics for provider call latency and failure counts so you can detect silent hangs quickly. • Add an adapter health-check endpoint that probes each LLM provider and flips priority or disables a provider if it’s unresponsive. If you want, I can: • Provide a small aiohttp snippet to add a timeout and fallback catch block you can drop into the agent adapter. • Give exact log strings and locations to search for in livekit-agents 1.4 (tell me whether agent runs in Docker or on a host and I’ll tailor the commands). • Draft a minimal reproducible test script that posts a known prompt and shows the expected debug log flow for primary → fallback. Which of the above would you like next—(A) the aiohttp timeout + fallback snippet, (B) exact debug log lines and commands to inspect your running agent, or (C) a small reproducible test script?