Thanks — this is a great question. I’ll address each of your points and give a recommended pattern plus a short example you can adapt. Acknowledgement • You want the bot to reprompt if the user stays silent ~3 seconds after finishing their turn, and you’re deciding between using VAD events or manual silence detection while avoiding false triggers from STT latency or brief pauses. Short answer / recommendation • Use LiveKit’s built-in VAD/end-of-speech events (not low-level audio-level polling) to detect the end of a user’s turn, then track a timestamp and run a short non-blocking inactivity timer. Only start that timer when you have a clear “turn ended” signal (final STT or VAD stop). This combination avoids most false triggers and keeps logic simple. Why this pattern works • VAD/end-of-speech events provide reliable speech boundaries and integrate with STT and agent state. • A timestamp + background timer separates “end-of-speech detection” from “inactivity monitoring,” which makes it easier to tune reprompt behavior and avoid race conditions with STT latency or agent processing. • Using final transcripts/confidence and skipping reprompts while the agent is thinking/speaking prevents premature or conflicting prompts. Concrete pattern (high-level) 1. Subscribe to the Speech/VAD and STT events you get from LiveKit: ◦

input_speech_started

/

input_speech_stopped

(VAD) OR agent state events like

user_state_changed

to

listening

. ◦ STT final transcript events (e.g.,

FINAL_TRANSCRIPT

) and confidence, if available. 1. When you detect a definitive end-of-turn: ◦ Update

user_last_spoke_time = now()

. ◦ Only treat the turn as finished if you have either a VAD

stopped

event or a final STT transcript (not interim). 1. Launch a non-blocking inactivity monitor task (if not already running) that checks elapsed time since

user_last_spoke_time

. 2. If elapsed time > threshold (e.g., 3s) and the agent is NOT currently

thinking

or

speaking

, send the reprompt: “Are you still there?” 3. Add safeguards: backoff behavior, a max number of reprompts, and cancel/refresh the timer whenever new speech or final transcript arrives. Avoiding false triggers — practical tips • Wait for final STT transcripts (ignore interim partial results) before deciding a turn is complete. • Use STT confidence when available: if final transcript confidence is low, extend endpointing timeout before starting inactivity timer. • Tune VAD endpointing parameters (e.g., min/max endpointing delays) so natural short pauses don’t become “end-of-turn.” • Do not trigger reprompts while the agent is

thinking

or

speaking

. • Add a short grace period (e.g., 0.5s) beyond STT/VAD event before starting your 3s countdown to account for network/STT latency. • Test with real user samples and adjust thresholds (VAD settings, silence threshold) for your user population and language. Minimal example (async Python-style pseudocode) 1. Constants: ◦ SILENCE_THRESHOLD = 3.0 ◦ GRACE_PERIOD = 0.5 1. Key state: ◦ user_last_spoke_time = None ◦ monitor_task = None 1. Event handlers and monitor

def on_vad_speech_started():
    global user_last_spoke_time
    user_last_spoke_time = time.time()
    # ensure monitor_task keeps running or is cancelled and restarted

def on_vad_speech_stopped():
    global user_last_spoke_time, monitor_task
    # consider waiting a tiny grace period to cover STT latency
    user_last_spoke_time = time.time() + GRACE_PERIOD
    if monitor_task is None or monitor_task.done():
        monitor_task = asyncio.create_task(monitor_inactivity())

def on_final_stt(transcript, confidence):
    # treat as definitive end-of-turn; optionally use confidence to extend timeout
    global user_last_spoke_time
    user_last_spoke_time = time.time()
    # optionally adjust endpointing delay if confidence low

async def monitor_inactivity():
    while True:
        await asyncio.sleep(0.25)  # short tick
        if user_last_spoke_time is None:
            continue
        elapsed = time.time() - user_last_spoke_time
        # skip if agent busy
        if agent.state in ("thinking", "speaking"):
            continue
        if elapsed >= SILENCE_THRESHOLD:
            await session.say("Are you still there?")
            # optionally increment reprompt count or disconnect after retries
            break

Other best-practice notes • Clean up monitor_task when the session ends. • Track reprompt count per user/session to avoid repeated annoyance. • For multi-turn dialogs, consider whether you want reprompts only for the user’s first inactivity after their turn, or after each agent prompt. • Log events for tuning: VAD stops, final transcripts, reprompts sent, and user responses after reprompts. If you’d like, I can: • Provide a concrete sample for your language/runtime (Node, Python, or whichever you use). • Suggest exact VAD endpointing parameter values to start with based on typical conversational speech. Would you like a ready-to-run example in Python or Node.js, or guidance on tuning the VAD/STT thresholds for your use case? Sources: examples/voice_agents | livekit/livekit_composite | DeepWiki | How does end-of-utterance detection work in conversations? | How to detect and handle silence during calls | Silero VAD plugin | LiveKit Documentation | Agents Framework | livekit/livekit_composite | DeepWiki, with interactive elements