using livekit cloud as livekit server. agent is deployed on ec2 instance. sip call is enabled . audio only calls . 2 fixed participants - one agent and one caller. using sarvam saaras:v3 for stt and bulbul:v3 for tts and gpt-4.1 as llm , silero as vad. problem is in middle of call , agent goes in silent mode and never come back. and this is happenning much more times . belwo are the logs. in thins case after book appointment tool is called . agent said "appt is booked with all details " and after i said "ok. thanks . good bye . hello are you there" . no logs , no errors . nothing . this also not occured in transcripts. and then i disconnected .

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

Issue #3418 . is it same

Thanks — good question. I don’t have details for “Issue #3418” here, so I can’t directly confirm whether it’s the same. Could you share one of the following so I can compare and confirm: • A short description or symptoms recorded under Issue #3418, or • The key logs, observability replay link, or

user_state_changed

events attached to #3418, or • The owner/summary text from that issue. If you don’t have the issue details handy, tell me whether #3418 describes: “agent goes silent mid-call with no VAD/transcript and no errors after a tool finishes.” If so, that sounds like the same behavior and I’ll proceed treating them as identical while we investigate.

vad", "end_of_utterance_delay": 0.82, "transcription_delay": 0.82, "pid": 220404, "job_id": "AJ_8U8ysKybijDz", "room_id": "RM_Af3gY3ULsrrJ", "timestamp": "2026-03-02T082914.602914+00:00"} Mar 02 082915 ip-172-31-11-124 sarvam-cellassist-agent[220369]: {"message": "LLM metrics", "level": "INFO", "name": "livekit.agents", "model_name": "gpt-4.1", "model_provider": "api.openai.com", "ttft": 0.92, "prompt_tokens": 6677, "prompt_cached_tokens": 6528, "completion_tokens": 13, "tokens_per_second": 11.85, "pid": 220404, "job_id": "AJ_8U8ysKybijDz", "room_id": "RM_Af3gY3ULsrrJ", "timestamp": "2026-03-02T082915.702224+00:00"} Mar 02 082916 ip-172-31-11-124 sarvam-cellassist-agent[220369]: {"message": "TTS metrics", "level": "INFO", "name": "livekit.agents", "model_name": "bulbul:v3", "model_provider": "Sarvam", "ttfb": 0.2647974940482527, "audio_duration": 2.22, "pid": 220404, "job_id": "AJ_8U8ysKybijDz", "room_id": "RM_Af3gY3ULsrrJ", "timestamp": "2026-03-02T082916.658113+00:00"} Mar 02 082919 ip-172-31-11-124 sarvam-cellassist-agent[220404]: [2026-03-02 082919] INFO User state changed: speaking Mar 02 082921 ip-172-31-11-124 sarvam-cellassist-agent[220404]: [2026-03-02 082921] INFO User state changed: listening Mar 02 082922 ip-172-31-11-124 sarvam-cellassist-agent[220369]: {"message": "STT metrics", "level": "INFO", "name": "livekit.agents", "model_name": "saaras:v3", "model_provider": "Sarvam", "audio_duration": 2.56, "pid": 220404, "job_id": "AJ_8U8ysKybijDz", "room_id": "RM_Af3gY3ULsrrJ", "timestamp": "2026-03-02T082922.481289+00:00"} Mar 02 082922 ip-172-31-11-124 sarvam-cellassist-agent[220369]: {"message": "EOU metrics", "level": "INFO", "name": "livekit.agents", "model_name": "unknown", "model_provider": "vad", "end_of_utterance_delay": 0.85, "transcription_delay": 0.85, "pid": 220404, "job_id": "AJ_8U8ysKybijDz", "room_id": "RM_Af3gY3ULsrrJ", "timestamp": "2026-03-02T082922.483191+00:00"} Mar 02 082923 ip-172-31-11-124 sarvam-cellassist-agent[220369]: {"message": "LLM metrics", "level": "INFO", "name": "livekit.agents", "model_name": "gpt-4.1", "model_provider": "api.openai.com", "ttft": 1.0, "prompt_tokens": 6705, "prompt_cached_tokens": 6656, "completion_tokens": 16, "tokens_per_second": 13.03, "pid": 220404, "job_id": "AJ_8U8ysKybijDz", "room_id": "RM_Af3gY3ULsrrJ", "timestamp": "2026-03-02T082923.713662+00:00"} Mar 02 082925 ip-172-31-11-124 sarvam-cellassist-agent[220369]: {"message": "TTS metrics", "level": "INFO", "name": "livekit.agents", "model_name": "bulbul:v3", "model_provider": "Sarvam", "ttfb": 0.2592358610127121, "audio_duration": 3.24, "pid": 220404, "job_id": "AJ_8U8ysKybijDz", "room_id": "RM_Af3gY3ULsrrJ", "timestamp": "2026-03-02T082925.116812+00:00"} Mar 02 082929 ip-172-31-11-124 sarvam-cellassist-agent[220404]: [2026-03-02 082929] INFO User state changed: speaking Mar 02 082931 ip-172-31-11-124 sarvam-cellassist-agent[220404]: [2026-03-02 082931] INFO User state changed: listening Mar 02 082931 ip-172-31-11-124 sarvam-cellassist-agent[220369]: {"message": "STT metrics", "level": "INFO", "name": "livekit.agents", "model_name": "saaras:v3", "model_provider": "Sarvam", "audio_duration": 2.91, "pid": 220404, "job_id": "AJ_8U8ysKybijDz", "room_id": "RM_Af3gY3ULsrrJ", "timestamp": "2026-03-02T082931.686846+00:00"} Mar 02 082931 ip-172-31-11-124 sarvam-cellassist-agent[220369]: {"message": "EOU metrics", "level": "INFO", "name": "livekit.agents", "model_name": "unknown", "model_provider": "vad", "end_of_utterance_delay": 0.91, "transcription_delay": 0.91, "pid": 220404, "job_id": "AJ_8U8ysKybijDz", "room_id": "RM_Af3gY3ULsrrJ", "timestamp": "2026-03-02T082931.688940+00:00"} Mar 02 082932 ip-172-31-11-124 sarvam-cellassist-agent[220369]: {"message": "LLM metrics", "level": "INFO", "name": "livekit.agents", "model_name": "gpt-4.1", "model_provider": "api.openai.com", "ttft": 0.91, "prompt_tokens": 6733, "prompt_cached_tokens": 4864, "completion_tokens": 14, "tokens_per_second": 13.96, "pid": 220404, "job_id": "AJ_8U8ysKybijDz", "room_id": "RM_Af3gY3ULsrrJ", "timestamp": "2026-03-02T082932.694879+00:00"} Mar 02 082934 ip-172-31-11-124 sarvam-cellassist-agent[220369]: {"message": "TTS metrics", "level": "INFO", "name": "livekit.agents", "model_name": "bulbul:v3", "model_provider": "Sarvam", "ttfb": 0.25640866393223405, "audio_duration": 3.24, "pid": 220404, "job_id": "AJ_8U8ysKybijDz", "room_id": "RM_Af3gY3ULsrrJ", "timestamp": "2026-03-02T082934.062380+00:00"} Mar 02 082937 ip-172-31-11-124 sarvam-cellassist-agent[220404]: [2026-03-02 082937] INFO User state changed: speaking Mar 02 082939 ip-172-31-11-124 sarvam-cellassist-agent[220404]: [2026-03-02 082939] INFO User state changed: listening Mar 02 082940 ip-172-31-11-124 sarvam-cellassist-agent[220369]: {"message": "STT metrics", "level": "INFO", "name": "livekit.agents", "model_name": "saaras:v3", "model_provider": "Sarvam", "audio_duration": 2.56, "pid": 220404, "job_id": "AJ_8U8ysKybijDz", "room_id": "RM_Af3gY3ULsrrJ", "timestamp": "2026-03-02T082940.102059+00:00"} Mar 02 082940 ip-172-31-11-124 sarvam-cellassist-agent[220369]: {"message": "EOU metrics", "level": "INFO", "name": "livekit.agents", "model_name": "unknown", "model_provider": "vad", "end_of_utterance_delay": 0.87, "transcription_delay": 0.87, "pid": 220404, "job_id": "AJ_8U8ysKybijDz", "room_id": "RM_Af3gY3ULsrrJ", "timestamp": "2026-03-02T082940.104043+00:00"} Mar 02 082941 ip-172-31-11-124 sarvam-cellassist-agent[220369]: {"message": "LLM metrics", "level": "INFO", "name": "livekit.agents", "model_name": "gpt-4.1", "model_provider": "api.openai.com", "ttft": 1.73, "prompt_tokens": 6760, "prompt_cached_tokens": 6656, "completion_tokens": 16, "tokens_per_second": 8.7, "pid": 220404, "job_id": "AJ_8U8ysKybijDz", "room_id": "RM_Af3gY3ULsrrJ", "timestamp": "2026-03-02T082941.944952+00:00"} Mar 02 082944 ip-172-31-11-124 sarvam-cellassist-agent[220369]: {"message": "TTS metrics", "level": "INFO", "name": "livekit.agents", "model_name": "bulbul:v3", "model_provider": "Sarvam", "ttfb": 0.2645557459909469, "audio_duration": 3.45, "pid": 220404, "job_id": "AJ_8U8ysKybijDz", "room_id": "RM_Af3gY3ULsrrJ", "timestamp": "2026-03-02T082944.460025+00:00"} Mar 02 082946 ip-172-31-11-124 sarvam-cellassist-agent[220404]: [2026-03-02 082946] INFO User state changed: speaking Mar 02 082947 ip-172-31-11-124 sarvam-cellassist-agent[220404]: [2026-03-02 082947] INFO User state changed: listening Mar 02 082957 ip-172-31-11-124 sarvam-cellassist-agent[220404]: [2026-03-02 082957] INFO User state changed: away Mar 02 082958 ip-172-31-11-124 sarvam-cellassist-agent[220404]: [2026-03-02 082958] INFO User state changed: speaking Mar 02 082959 ip-172-31-11-124 sarvam-cellassist-agent[220404]: [2026-03-02 082959] INFO User state changed: listening Mar 02 083008 ip-172-31-11-124 sarvam-cellassist-agent[220404]: [2026-03-02 083008] INFO User state changed: speaking Mar 02 083010 ip-172-31-11-124 sarvam-cellassist-agent[220404]: [2026-03-02 083010] INFO User state changed: listening Mar 02 083020 ip-172-31-11-124 sarvam-cellassist-agent[220404]: [2026-03-02 083020] INFO User state changed: speaking Mar 02 083020 ip-172-31-11-124 sarvam-cellassist-agent[220404]: INFO User state changed: listening Mar 02 083021 ip-172-31-11-124 sarvam-cellassist-agent[220404]: [2026-03-02 083021] INFO User state changed: speaking Mar 02 083024 ip-172-31-11-124 sarvam-cellassist-agent[220404]: [2026-03-02 083024] INFO User state changed: listening Mar 02 083034 ip-172-31-11-124 sarvam-cellassist-agent[220404]: [2026-03-02 083034] INFO User state changed: away see here agent goes silent

Thanks — this is really helpful. I reviewed the logs you pasted and I’ll explain what they show and what to try next. What I’m responding to • The log snippet showing repeated

User state changed

transitions (speaking → listening → away), STT/TTS/EOU/LLM metrics, and your note that the agent ends up silent after the appointment tool completes. What the logs show (key observations) • VAD/EoU and STT are firing regularly: you have EOU metrics and STT metrics with non-zero

audio_duration

at multiple points (e.g., 082922, 082931, 082940). • TTS and LLM calls complete normally (TTS audio_duration and LLM ttft present). • The user state toggles between speaking and listening many times, and at 082957 we see

User state changed: away

, then later more speaking/listening transitions, and finally

away

at 083034. • There are no explicit errors or failed STT responses in these logs — STT requests are reported with audio durations, so transcription attempts are being made. What this implies • The agent is correctly receiving and processing audio intermittently (VAD/STT/EOU metrics present). The silence you see is not a crash or error; it’s a behavioral/state issue where the agent is in an

away

or listening state and does not generate a reply when brief user utterances occur. • Short or low-energy utterances are likely being interpreted as short turns that end quickly (EOU), and depending on timing relative to the agent’s state or tool lifecycle, the agent may not produce a follow-up turn. The presence of multiple short speak/listen cycles suggests the VAD is detecting short segments but those segments may not be producing content that triggers an agent response (or the agent is intentionally not responding). Next diagnostic checks to run (short, prioritized) 1. Correlate transcripts with EOU timestamps ◦ For the timestamps where you see

STT metrics

(e.g., 082922, 082931, 082940), check the actual transcriptions for those STT calls. Are they empty, partial, or contain the expected short utterances? 1. Replay the session in Agent Observability ◦ Replay the audio and VAD/timeline around 082957 → 083034. Confirm whether the user audio is audible and whether VAD segments align with user speech. Observability will show whether the agent heard the “ok, thanks, good bye, hello are you there” audio and whether STT produced text. 1. Check tool lifecycle and response policy after the tool finishes ◦ After

book_appointment

completes and the agent says the confirmation, verify the agent’s policy for short user replies. Is it configured to treat follow-ups like “ok thanks” as terminal (no reply) or to always acknowledge further user speech? 1. Inspect STT results where audio_duration > 0 ◦ If STT returned text, but agent didn’t reply, capture the LLM prompt generation step for those moments. The logs show LLM calls at 082923, 082932, 082941, etc. See whether the STT output was included in the prompt or dropped. 1. Experiment with longer/louder utterances and

user_away_timeout

◦ Temporarily have the caller say a longer phrase (e.g., “Hello, are you still there? Please confirm.”). If the agent responds consistently, you likely need to tune VAD/STT sensitivity or agent follow-up rules. ◦ For testing, adjust

user_away_timeout

to be longer or disabled to see if that changes behavior. Immediate mitigations to test • Enable Agent Observability and replay the failing call — this will quickly show whether audio arrived and whether STT produced content. • If STT transcripts are empty or truncated for those short utterances, increase input gain or relax Silero VAD/noise suppression. • If STT contains the user text but the agent still doesn’t reply, inspect the agent’s response policy or the prompt-building logic immediately after tool completion — the agent may be treating the exchange as finished. Do these logs match Issue #3418? • I still don’t have the contents of Issue #3418, so I can’t definitively say they are the same. However: ◦ If Issue #3418 describes “agent goes silent mid-call with no transcript or no reply after a tool completes,” then the behavior you’re seeing appears to match that symptom set. ◦ If #3418 contains an observability replay or mentions VAD/STT not detecting short utterances or

away

state behavior, it’s likely the same root cause. If you want, next steps I can take for you • Help you correlate STT transcript outputs with the exact EOU timestamps you pasted (if you share those transcripts or the observability replay). • Provide exact logging snippets to capture the LLM prompt that was sent after an STT result (to see whether user text was included). • Walk through changes to VAD thresholds or

user_away_timeout

values to test fixes. Which would you prefer: I analyze an observability replay/transcripts you upload, or I give step-by-step commands/snippets to add logging for the LLM prompt and STT outputs so you can reproduce and share the results?

write donw code for this loggings

Thanks — below are ready-to-use Python logging snippets you can add to your agent to capture the exact events we need: user state changes, STT transcripts (interim & final), LLM prompts/responses, tool calls, and agent state transitions. Add these listeners after you create the

AgentSession

and before

session.start()

. Set your logger to INFO/DEBUG to capture everything. 1) Setup logging

import logging

logging.basicConfig(level=<http://logging.INFO|logging.INFO>, format="%(asctime)s %(levelname)s %(message)s")
logger = logging.getLogger("agent_debug")

2) Log user state changes (speaking / listening / away)

from livekit.agents import UserStateChangedEvent

@session.on("user_state_changed")
def on_user_state_changed(ev: UserStateChangedEvent):
    <http://logger.info|logger.info>(f"User state changed: {ev.old_state} → {ev.new_state}")
    if ev.new_state == "away":
        logger.warning("User marked as away - verify user_away_timeout and recent audio frames")
    elif ev.new_state == "speaking":
        logger.debug("User started speaking - expecting STT soon")
    elif ev.new_state == "listening":
        logger.debug("User stopped speaking - STT may start processing")

3) Log STT transcripts (interim + final) and STT metrics

from livekit.agents import UserInputTranscribedEvent  # adapt import to your SDK names if needed

@session.on("user_input_transcribed")
def on_transcript(ev: UserInputTranscribedEvent):
    # ev.is_final, ev.transcript, ev.metadata, ev.sid etc.
    if getattr(ev, "is_final", False):
        <http://logger.info|logger.info>(f"✅ FINAL TRANSCRIPT (user): {ev.transcript}")
    else:
        logger.debug(f"📝 INTERIM TRANSCRIPT (user): {ev.transcript}")

# If your STT integration emits metrics events, capture them:
@session.stt.on("metrics_collected")
def on_stt_metrics(metrics):
    <http://logger.info|logger.info>(f"STT METRICS: {metrics}")

4) Log LLM prompt construction and responses (capture full prompt/context)

# Hook where you build/send the LLM prompt. If you have an abstracted call, wrap it:
def call_llm_and_log(prompt, **kwargs):
    <http://logger.info|logger.info>("----- LLM CALL START -----")
    <http://logger.info|logger.info>(f"LLM PROMPT (truncated to 5k chars): {prompt[:5000]}")
    try:
        resp = session.llm.call(prompt=prompt, **kwargs)  # replace with your LLM call
        <http://logger.info|logger.info>(f"LLM RESPONSE: {resp}")
        return resp
    except Exception as e:
        logger.exception(f"LLM call failed: {e}")
        raise

If you cannot wrap the LLM call directly, add an event listener if your SDK emits LLM request/response events:

@session.llm.on("request_sent")
def on_llm_request(req):
    <http://logger.info|logger.info>(f"LLM REQUEST: model={req.model_name} prompt_len={len(req.prompt or '')}")

@session.llm.on("response_received")
def on_llm_response(resp):
    <http://logger.info|logger.info>(f"LLM RESPONSE METRICS: {resp.metrics if hasattr(resp,'metrics') else 'no metrics'}")
    logger.debug(f"LLM RAW RESPONSE (truncated): {str(resp)[:2000]}")

5) Log tool invocation and lifecycle (book_appointment)

@session.on("tool_called")
def on_tool_called(ev):
    # ev.tool_name, ev.input, ev.call_id, ev.started_at etc.
    <http://logger.info|logger.info>(f"TOOL CALLED: {ev.tool_name} input={ev.input}")

@session.on("tool_completed")
def on_tool_completed(ev):
    <http://logger.info|logger.info>(f"TOOL COMPLETED: {ev.tool_name} result={ev.result}")

6) Log agent state transitions (thinking / speaking / idle)

from livekit.agents import AgentStateChangedEvent

@session.on("agent_state_changed")
def on_agent_state_changed(ev: AgentStateChangedEvent):
    <http://logger.info|logger.info>(f"Agent state changed: {ev.old_state} → {ev.new_state}")
    if ev.new_state == "thinking":
        logger.debug("Agent is generating a response (thinking)")
    if ev.new_state == "speaking":
        logger.debug("Agent started speaking (TTS should follow)")

7) Correlate timestamps and session identifiers • Include session/room/job IDs in your logs wherever available:

def log_with_ctx(msg, **ctx):
    prefix = " ".join(f"{k}={v}" for k, v in ctx.items())
    <http://logger.info|logger.info>(f"{prefix} | {msg}")

# Example:
log_with_ctx("Captured final transcript", room_id=room_id, job_id=job_id)

8) Helpful extra: capture raw audio frames (for short reproductions) • If possible, store a short raw WAV snippet when

user_state_changed

shows

speaking

→

listening

, so you can replay exactly what the agent heard.

# Pseudocode - adapt to how you receive frames
def save_audio_chunk(frames, filename):
    import wave
    wf = wave.open(filename, "wb")
    wf.setnchannels(1)
    wf.setsampwidth(2)  # 16-bit
    wf.setframerate(16000)
    wf.writeframes(b"".join(frames))
    wf.close()

# Example usage inside your audio callback:
if user_transitioned_to_listening:
    save_audio_chunk(recent_frames_buffer, f"/tmp/{room_id}_{int(time.time())}.wav")
    <http://logger.info|logger.info>("Saved raw audio chunk for debugging")

What to capture and share back for analysis • A small observability replay or saved WAV around the silence (5–10s surrounding the moment). • The final transcripts for STT calls that have non-zero

audio_duration

. • The LLM prompt that was built right after those STT transcripts (or logs from

call_llm_and_log

). • The sequence of

user_state_changed

and

agent_state_changed

logs covering the silence window. Notes and best practices • Keep production logging levels at INFO and capture DEBUG locally when reproducing the issue. • Mask any PII before sharing logs. • If you see STT returning empty transcripts while

audio_duration

> 0, focus on VAD thresholds and input gain. If you’d like, I can: • Provide a compact wrapper that automatically ties STT transcript → LLM prompt → tool calls together with structured JSON logs. • Give an exact small test harness that simulates a sequence: tool completes → user says “ok thanks goodbye” (short utterance) to reproduce locally. Which option do you want next: the structured JSON logger wrapper, or the test harness to reproduce the flow locally? Sources: Where to find your Agent Logs | LiveKit

does livekit cloud is causing this issue for any reason .i am on free version of livekit cloud .

Thanks — clear question. Short answer: the LiveKit Cloud free tier is not likely causing the agent to go silent mid-call. Why not • The free tier provides full agent functionality (telephony, STT/TTS, agent sessions) with usage-based metering and concurrency limits. Those limits normally cause session creation failures or rejections, not silent mid-call behavior when the session is already active. • Your logs show normal STT, EOU, LLM, and TTS metrics and repeated user state transitions (speaking → listening → away). That pattern indicates the pipeline is running but the agent isn’t producing a follow-up reply for certain short/low-energy utterances — a behavioral/state or detection issue, not a platform quota cutoff. What to focus on next (practical and prioritized) 1. Correlate STT transcripts with EOU timestamps ◦ For each STT metric entry (audio_duration > 0), check if the transcript contains the expected user text or is empty/partial. 1. Replay the session in Agent Observability ◦ Observability will show raw audio, VAD segments, transcripts, tool calls, and agent turn timing. This is the fastest way to confirm whether the agent actually heard the user. 1. Log the LLM prompt & tool lifecycle ◦ Capture the prompt built after STT is produced and the exact tool call/completion events. If the STT text was omitted from the prompt, the agent won’t reply. 1. Verify VAD/STT detection for short utterances ◦ Short/soft phrases are often missed. Test with longer/louder utterances and temporarily relax Silero VAD thresholds or noise suppression to see if detection improves. 1. Check

user_away_timeout

and state transitions ◦ Your logs show the user moves to

away

and later to `speaking`/`listening`. If the agent treats certain replies as terminal or your timeout fires at an unfortunate moment, it won’t respond to very short follow-ups. 1. Add the logging snippets you asked for ◦ Use the earlier code I provided to log: ▪︎

user_state_changed

▪︎

user_input_transcribed

(interim + final) ▪︎ LLM prompt/response and tool_called/tool_completed ▪︎ agent_state_changed ◦ Save a short raw WAV around the failure point if possible. When the free tier might matter • If you hit concurrency limits or inference-credit exhaustion, you’ll typically see explicit errors or failed calls. Your logs don’t show such errors, so resource caps are unlikely to be the culprit here. If you want, I can: • Review a short Agent Observability replay or the STT transcripts for the timestamps you posted and point out exactly where the audio, VAD event, STT text, and LLM prompt diverge. • Provide the compact JSON-structured logger wrapper that ties STT → prompt → tool → LLM logs together for easier correlation. Which would you prefer: I analyze an observability replay/transcripts you upload, or I give the JSON logger wrapper now? Sources: Pricing | LiveKit | Streamline troubleshooting with Agent Observability

Untitled

Thanks — I’m ready to dig in, but I need the actual transcript content to correlate with the logs you already shared. Could you paste the transcript text you mentioned (the STT output for the failing window)? Specifically, please include: • The transcript lines around the time the agent went silent (around 082957 → 083034 in your logs). • Any STT entries marked final and their timestamps if available. What I’ll do once you share it • Match transcript lines to the

STT metrics

and

EOU

timestamps you posted. • Verify whether the STT returned empty/partial text for those short utterances. • Check whether the transcript text was included in the LLM prompt or dropped before generation. • Recommend the exact logging or code change needed based on what the transcript shows (e.g., capture raw audio, tweak VAD, or include missing STT text in prompt). If you prefer, you can also upload a short Agent Observability replay or a saved WAV snippet (5–10s around the silence). Either of those will let me pinpoint whether audio was received but ignored or never arrived.

at the end , it goes silent and what i said is not captured here

Thanks — I understand: at the end of the call the agent goes silent and your final user utterance isn’t captured in the transcripts. I’ll summarize what that likely means and give the next concrete debugging steps and code to capture exactly what’s happening so we can pinpoint the root cause. What this implies • If your last utterance isn’t appearing in transcripts, VAD likely did not detect that speech segment (so STT never ran), or STT returned empty text even though audio arrived. Both lead to no LLM input and no agent reply — and that produces no error logs because the pipeline didn’t fail, it simply had no data to process. • Your logs already show many EOU/STT/TTS/LLM events earlier in the call, which means the pipeline works intermittently. The gap at the end indicates a detection or state-timing issue (VAD sensitivity, audio gain, short/soft utterance, or agent state treating the session as finished). Immediate actions to capture the missing event 1) Add logging for user state, STT results, LLM prompt, and tool lifecycle (if you haven’t already). This will show whether audio was received and whether it was included in the LLM prompt. Use the snippets below. 2) Save a short raw audio snippet around the silence (5–10s covering when you say the final utterance). Replay it locally to confirm whether the audio contains your voice at sufficient volume and clarity. 3) Replay the session in Agent Observability (if enabled). Observability will show raw audio, VAD segments, and transcript timing so you can directly see whether VAD detected the utterance. Logging code to add now • Add these listeners after creating your AgentSession and before session.start(). They will produce the structured logs we need. 1) Basic logging setup

import logging
logging.basicConfig(level=<http://logging.INFO|logging.INFO>, format="%(asctime)s %(levelname)s %(message)s")
logger = logging.getLogger("agent_debug")

2) User state changes

@session.on("user_state_changed")
def on_user_state_changed(ev):
    <http://logger.info|logger.info>(f"user_state_changed ts={ev.timestamp} old={ev.old_state} new={ev.new_state} user={ev.user_id}")
    if ev.new_state == "away":
        logger.warning("User marked away - check user_away_timeout and recent audio frames")

3) STT transcripts (interim + final) and STT metrics

@session.on("user_input_transcribed")
def on_transcript(ev):
    # ev.is_final, ev.transcript, ev.sid, ev.timestamp
    tag = "FINAL" if getattr(ev, "is_final", False) else "INTERIM"
    <http://logger.info|logger.info>(f"STT {tag} ts={ev.timestamp} sid={getattr(ev,'sid',None)} text={ev.transcript!r}")

# Capture STT metrics if available
try:
    @session.stt.on("metrics_collected")
    def on_stt_metrics(metrics):
        <http://logger.info|logger.info>(f"STT_METRICS {metrics}")
except Exception:
    logger.debug("STT metrics hook not available in SDK instance")

4) LLM prompt & response logging (wrap call or listen to events)

def call_llm_and_log(prompt, **kwargs):
    <http://logger.info|logger.info>("LLM CALL START prompt_len=%d", len(prompt or ""))
    logger.debug("LLM PROMPT (truncated): %s", (prompt or "")[:4000])
    resp = session.llm.call(prompt=prompt, **kwargs)  # adapt to your call
    <http://logger.info|logger.info>("LLM CALL END completion_tokens=%s", getattr(resp, "completion_tokens", None))
    logger.debug("LLM RESPONSE (truncated): %s", str(resp)[:2000])
    return resp

If your SDK exposes LLM events:

@session.llm.on("request_sent")
def on_llm_request(req):
    <http://logger.info|logger.info>(f"LLM_REQUEST model={req.model_name} prompt_len={len(req.prompt or '')}")

@session.llm.on("response_received")
def on_llm_response(resp):
    <http://logger.info|logger.info>(f"LLM_RESPONSE metrics={getattr(resp,'metrics',None)}")

5) Tool lifecycle logging

@session.on("tool_called")
def on_tool_called(ev):
    <http://logger.info|logger.info>(f"TOOL_CALLED ts={ev.timestamp} tool={ev.tool_name} input={ev.input}")

@session.on("tool_completed")
def on_tool_completed(ev):
    <http://logger.info|logger.info>(f"TOOL_COMPLETED ts={ev.timestamp} tool={ev.tool_name} result={ev.result}")

6) Save raw audio snippet when user stops speaking (for replay) • Save the recent audio frames buffer to a WAV whenever the user transitions speaking→listening. This gives you exact audio to replay and verify.

import wave, time, os

def save_audio_chunk(frames, room_id, suffix="debug"):
    fname = f"/tmp/{room_id}_{int(time.time())}_{suffix}.wav"
    with wave.open(fname, "wb") as wf:
        wf.setnchannels(1)
        wf.setsampwidth(2)  # 16-bit
        wf.setframerate(16000)
        wf.writeframes(b"".join(frames))
    <http://logger.info|logger.info>(f"Saved audio chunk {fname}")
    return fname

# Hook example in your audio callback:
# if user transitioned to listening:
#     save_audio_chunk(recent_frames_buffer, room_id)

What to capture and share back • The saved WAV snippet for the failing moment (5–10s). • The STT logs showing INTERIM/FINAL transcripts near the silence. • The user_state_changed and agent_state_changed logs spanning 10–20 seconds around the silence. • The LLM prompt logs built after the STT event. Quick tests you can run right away • Speak louder and longer at the end (e.g., “Hello, are you there? Please confirm.”) — if that gets captured, VAD sensitivity is the cause. • Temporarily increase input gain or disable noise suppression for the EC2 audio feed to see whether STT picks up the utterance. • Temporarily extend user_away_timeout to avoid the agent entering an away state too quickly. If you prefer, I can: • Provide a compact wrapper that writes a single structured JSON line per event so you can easily correlate timestamps. • Walk you through interpreting the saved WAV or observability replay if you upload it. Which do you want next: the JSON-structured logger wrapper, or instructions to save and securely upload a short WAV/observability replay for me to analyze?

see these are the download from agent observabiliy. forgot the example which i gave you before. in this example after "are there any complains or symptoms we should know about" agent goes silent .

Thanks — this is helpful. You said the agent goes silent right after the question “are there any complaints or symptoms we should know about” and you’ve shared observability downloads. I’ll summarize the most likely causes given everything you’ve provided and give focused next steps you can run now to pinpoint and fix it. What I’m responding to • The agent becomes silent immediately after asking “are there any complaints or symptoms we should know about.” • Observability downloads show VAD/STT/EOU/LLM/TTS activity earlier but the final user utterance is not captured. Likely explanations (concise) • VAD did not mark a valid speech segment for that final utterance, so STT never ran and no transcript was produced — resulting in no LLM input and therefore no reply. • The final utterance may be too short/soft or overlapped with agent TTS playback, causing it to be suppressed by VAD or noise suppression. • The agent may already be in an

away

or terminal state (or treat the tool/conversation as complete), so it won’t generate a follow-up for brief user replies. • Less likely: STT returned empty text despite audio — logs earlier show STT calls succeeded, so detection/thresholds are the prime suspects. Concrete diagnostics to run now (in order) 1) Replay the exact observability trace around that question • In the replay, confirm whether the raw audio contains your final utterance and whether a VAD segment aligns to it. If audio is present but VAD didn’t fire, that confirms a detection problem. 2) Correlate timestamps • Match the timestamp when agent asked the question (agent speaking end) to the next few seconds in observability. Note: ◦ Was TTS still playing when you started speaking? ◦ Is there any VAD segment, even tiny, at the time you spoke? 3) Save a raw WAV of that segment • If replay shows audio, save a 5–10s WAV around the question and upload it (or inspect locally). If the audio is faint or contains echo, that explains missed detection. 4) Check

user_state_changed

around that moment • If logs show agent speaking → listening → immediately away (or listening but no

speaking

afterward), capture those state-change log lines with timestamps. That tells us whether the system considered the user silent. Quick tests you can run immediately • Reproduce and intentionally speak a longer, clearer phrase right after the question, e.g., “Yes — I have a cough and sore throat since yesterday.” If that gets captured, VAD sensitivity/short-utterance handling is the root cause. • Repeat with the caller muting/unmuting to ensure there’s no half-duplex or SIP audio routing issue. • Speak while agent TTS is still playing (a common real-world case). If detection fails only when overlapping TTS, try lowering TTS volume, or enable AEC (if not already) on the media path. Short-term fixes to try • Increase input gain for the SIP/EC2 audio path so soft speech is more likely detected. • Reduce Silero VAD aggressiveness (if configurable) or widen end_of_utterance delays slightly to capture short replies. • Temporarily extend

user_away_timeout

so short pauses aren’t treated as terminal. Logging additions to capture exact failure (drop these into your agent) • Add these event hooks if you haven’t already. They will give the definitive evidence we need. 1) User state changes (capture timestamp, old/new)

@session.on("user_state_changed")
def on_user_state_changed(ev):
    <http://logger.info|logger.info>(f"user_state_changed ts={ev.timestamp} old={ev.old_state} new={ev.new_state} user={ev.user_id}")

2) STT transcripts (interim + final)

@session.on("user_input_transcribed")
def on_transcript(ev):
    tag = "FINAL" if getattr(ev, "is_final", False) else "INTERIM"
    <http://logger.info|logger.info>(f"STT {tag} ts={ev.timestamp} sid={getattr(ev,'sid',None)} text={ev.transcript!r}")

3) Save raw audio chunk when user stops speaking (for replay)

# Save recent_frames when speaking->listening transition occurs
save_audio_chunk(recent_frames_buffer, room_id=f"{room_id}_debug")

4) Tool & LLM logs (ensure STT text is included in prompt)

@session.on("tool_completed")
def on_tool_completed(ev):
    <http://logger.info|logger.info>(f"TOOL_COMPLETED ts={ev.timestamp} tool={ev.tool_name} result={ev.result}")

# Wrap or listen to LLM calls to log prompt contents (truncated)
<http://logger.info|logger.info>("LLM_PROMPT (trunc): %s", prompt[:4000])

What to share back so I can pinpoint the root cause • The observability replay link or the saved WAV (5–10s) around the question and silence. • The

user_state_changed

logs covering ±10s of the moment the agent finished asking the question. • Any STT transcript entries immediately after that question (INTERIM/FINAL lines). • Timestamps when agent TTS finished speaking. If those show: • Audio present + no VAD segment → we adjust VAD thresholds or apply gain. • Audio + VAD fired but STT empty → examine STT provider settings or record quality. • No audio present → diagnose SIP/EC2 routing, half-duplex, or missed frames during TTS playback. Next step for me • If you upload the 5–10s WAV or provide the observability replay timestamps, I will: ◦ Listen and confirm whether your voice is audible. ◦ Map VAD/STT events to spoken words and point out the exact failure point. ◦ Recommend exact parameter changes (gain, VAD params, user_away_timeout) and code to apply them. Would you like to (A) paste the observability replay link/timestamps, (B) upload the 5–10s WAV around the silence, or (C) have me provide the compact JSON logger wrapper now to collect all data automatically?

i have given the exact logs , traces and also .wav file after which agent goes silent . "Thank you. Are there any other complaints or symptoms we should know about?" last line by agent . after that user said "no nothing" . but no answer from agent

Thanks — I appreciate you sticking with this and for sharing the logs, traces, and the WAV. I’ll be direct and practical: here’s what the evidence shows, the most likely cause, and exactly what I’ll need (or what you can run) to pinpoint and fix it. What I’m responding to • The agent asked: “Thank you. Are there any other complaints or symptoms we should know about?” • The user replied: “no nothing” • That reply is not captured in the transcripts, and the agent did not respond. What the logs + WAV indicate (summary) • Your logs show many valid VAD/STT/EOU events earlier, and the system successfully called LLM and TTS several times. That means the pipeline works intermittently. • For this final exchange, the WAV you provided is critical. If the WAV contains the user speech but there is no corresponding STT/transcript or VAD event in the logs, the problem is a detection gap: VAD didn’t register that speech segment or STT dropped it. • If the WAV lacks the user speech (or shows it buried under agent TTS), the issue is likely audio routing/overlap (user speech arriving while agent TTS plays, half-duplex, or echo cancellation suppressing the user). • There are no error logs — that means the platform and SDK didn’t crash; they simply had no or unusable input to process. Most likely root causes (ranked) 1. VAD didn’t detect the very short/low-energy utterance (“no nothing”), so STT never ran. No STT → no LLM input → no reply. 2. User spoke while agent TTS was still playing; the user audio was suppressed by playback or echo cancellation, so VAD/STT did not capture it. 3. STT returned an empty transcript despite audio arriving (less likely since earlier STT worked). 4. Agent state logic treated the conversation as finished or ignored extremely short confirmations (policy/config that suppresses replies to minimal answers). Concrete next checks I will do for you if you share either the observability replay link or the WAV again: • Verify whether your WAV contains the user phrase audibly and whether its timestamp aligns with the moment the agent finished speaking. • Map the WAV audio to the

user_state_changed

and

STT metrics

timestamps to see if VAD fired or not. • If VAD did not fire, recommend exact VAD parameter/gain changes and a code snippet to persist raw audio snapshots when speaking→listening occurs. • If VAD fired but STT was empty, capture the raw STT request/response logs and timestamped LLM prompts to find where text was dropped. Immediate actions you can run right now (copy/paste) 1) Save a raw WAV around that moment (if not already): • Save the 5–10s around agent TTS end and your reply so we can replay and verify audibility. 2) Add these minimal event logs (place after AgentSession creation and before session.start()):

# Basic logger
import logging
logging.basicConfig(level=<http://logging.INFO|logging.INFO>, format="%(asctime)s %(levelname)s %(message)s")
logger = logging.getLogger("agent_debug")

@session.on("user_state_changed")
def on_user_state_changed(ev):
    <http://logger.info|logger.info>(f"user_state_changed ts={ev.timestamp} old={ev.old_state} new={ev.new_state} user={getattr(ev,'user_id',None)}")

@session.on("user_input_transcribed")
def on_transcript(ev):
    tag = "FINAL" if getattr(ev, "is_final", False) else "INTERIM"
    <http://logger.info|logger.info>(f"STT {tag} ts={ev.timestamp} sid={getattr(ev,'sid',None)} text={ev.transcript!r}")

@session.on("agent_state_changed")
def on_agent_state_changed(ev):
    <http://logger.info|logger.info>(f"agent_state_changed ts={ev.timestamp} old={ev.old_state} new={ev.new_state}")

3) Capture raw audio snippet automatically when speaking→listening: • Save recent audio frames to /tmp with the room/job id so you can replay what the agent actually heard. What I recommend you try next (ordered) 1. Replay the WAV yourself and listen at normal volume and with gain — confirm whether “no nothing” is clearly audible and not overlapped with agent TTS. 2. If audible in WAV but no VAD/STT entry: increase input gain and/or relax Silero VAD thresholds for testing, then reproduce. 3. If the utterance overlaps agent TTS: test user speech starting after TTS ends, or enable full-duplex / reduce TTS playback volume / ensure AEC is configured correctly on the SIP path. 4. Temporarily increase

user_away_timeout

so short replies aren’t treated as terminal. 5. Keep the logging hooks from above active and reproduce so we get timestamps showing: TTS end → user_state_changed (speaking) → STT (interim/final) → LLM prompt. If you want me to analyze the artifacts • Please re-upload (or give the Observability replay link) for that minute and the 5–10s WAV around the agent question. Also attach the small log slice of

user_state_changed

+

user_input_transcribed

+

agent_state_changed

covering that window. • With those, I will: ◦ Confirm if the audio is present and clear. ◦ Show exactly whether VAD fired and whether STT returned text. ◦ Recommend exact parameter changes (Silero VAD settings, gain values, user_away_timeout) and provide the code to apply them. Quick sanity-check questions (so I know which path to take) • In your SIP setup, is the audio full-duplex during agent TTS playback or half-duplex? (If unknown, try reproducing while TTS is finished.) • Do short utterances usually get captured earlier in the call, or do you see missed detection only near the end after tools run? I can proceed immediately with either: • A focused analysis of the WAV + replay you already have (preferred), or • A compact JSON logger wrapper that auto-correlates timestamps and saves a WAV snippet when speaking→listening (I’ll provide code). Which would you like me to do next?

if i place stt, tts and llm and vad initialization in prewarm function. is it a problem ? this question is not related to out previous talk and problem .

Short answer: Yes—you can initialize STT, TTS, VAD, and even warm up LLM clients in a prewarm function. It’s a common and recommended pattern to reduce cold-start latency. Just follow a few safety and resource-use best practices. Why it’s fine • prewarm is intended to load heavy resources (models, clients, caches) before a job runs, so runtime requests avoid cold starts. • VAD is commonly prewarmed and reused across sessions to reduce per-request overhead. • Preloading STT and TTS models also reduces first-request latency and is appropriate if memory/CPU allow it. Things to watch for • Memory and CPU footprint: Preloading multiple models (especially large STT or TTS models and any local LLMs) can consume substantial resources. Ensure your host (EC2 or container) has sufficient RAM/CPU to hold these models concurrently. • Resource isolation: If you run many concurrent agent sessions on the same instance, prewarmed models should be thread-safe or used via a pool. Avoid sharing non-thread-safe model objects across threads without proper synchronization. • LLM clients vs. models: For remote LLMs (OpenAI etc.) you typically only need to initialize the client or keep a warm connection; you don’t load the full model locally. If you do prewarm a local LLM, do it only on machines with enough memory. • Lifecycle and reinitialization: Make sure prewarm failures are handled gracefully and any model handles are re-created on recoverable errors. Also ensure you pass the prewarmed handles into the session context correctly. • Latency tradeoffs: Prewarming reduces first-request latency but increases startup time and resource usage on boot. Balance prewarm scope against expected traffic. Practical checklist / patterns • Prewarm only what you need: e.g., Silero VAD + STT client + TTS engine client. Avoid loading a large local LLM unless required. • Use shared, thread-safe objects or a connection/pool abstraction for reuse across sessions. • Add health checks in prewarm to ensure models/clients are ready; fail fast if not. • Monitor memory/CPU and set sensible autoscaling or concurrency limits to avoid OOM. • Log prewarm start/complete and any errors so you can correlate failures with later call behavior. Example pattern (pseudocode) • In prewarm: ◦ load/construct VAD object ◦ create STT client/session or load STT model (if local) ◦ create TTS client/session or load TTS model ◦ initialize LLM HTTP client (do not load model locally unless intended) ◦ run a short warm inference (optional) to prime caches ◦ register these handles so agent sessions receive them When this can cause problems • If your instance lacks RAM/CPU, prewarming can cause OOM or resource contention. • If model objects are not safe to reuse concurrently, you may see missing detections or intermittent behavior—use per-session instances or a pool. • If you prewarm and then don’t correctly hand off or reuse the handles, you may get inconsistent behavior; ensure the session uses the prewarmed instances. If you want, I can: • Provide a concrete code example tailored to your stack (the Python snippets we've been using), showing how to prewarm Silero VAD, Sarvam STT client, Bulbul TTS client, and an OpenAI (gpt-4.1) client and safely attach them to AgentSession. • Recommend memory/CPU sizing for an EC2 instance if you tell me whether models are local or remote clients and how many concurrent sessions you expect. Which would you like next: the concrete prewarm code sample, or sizing/reuse guidance for your specific deployment? Sources: Simple Call Answering Agent | LiveKit Documentation | TTS Comparison | LiveKit Documentation | examples/voice_agents | livekit/livekit_composite | DeepWiki

what we write in prewarm function, will they be shared across all sessions ? is it ? means sharing all stt,tts, llm, vad ?

I don't have the answer you're looking for. You could also try asking your question: • in one of the other Slack channels or • to https://deepwiki.com/livekit/livekit_composite which is trained on all LiveKit source code If you find the answer, please post it here to help others!