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I have built AI mock interviewer using Livekit I ...
# ask-ai
b
I have built AI mock interviewer using Livekit I wanna implement this There should be a 5–10 second breather time to answer as the agent was not giving enough time to think before answering
t
Thanks for reaching out, Madhav. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
Thanks — this is a great request and totally doable. You're asking how to add a 5–10 second breather so the agent waits longer after the interviewee stops speaking before replying. Below are the simplest, actionable ways to implement that with LiveKit and some short guidance so you can pick the right option for your setup. Quick solution (recommended) • Increase the endpointing delay values on your AgentSession so the agent waits after end-of-speech detection: ◦ Set 
min_endpointing_delay
to 5.0 (minimum wait). ◦ Set 
max_endpointing_delay
to 10.0 (allows a longer wait when confidence in turn-end is low). Examples: 1. Python
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session = AgentSession(
    stt=your_stt_instance,
    llm=your_llm_instance,
    tts=your_tts_instance,
    vad=your_vad_instance,
    min_endpointing_delay=5.0,
    max_endpointing_delay=10.0,
)
1. TypeScript
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const session = new voice.AgentSession({
    stt: yourSttInstance,
    llm: yourLlmInstance,
    tts: yourTtsInstance,
    vad: yourVadInstance,
    minEndpointingDelay: 5.0,
    maxEndpointingDelay: 10.0,
});
How this works • 
min_endpointing_delay
is the minimum pause after end-of-speech before the agent considers the user done and begins a response. • 
max_endpointing_delay
lets the system wait longer if turn-detection confidence is low (so it avoids cutting the user off if they keep speaking softly or hesitate). • These settings work together with VAD and any turn detector you have enabled. Optional improvement (more natural, context-aware) • Enable a turn detector model (e.g., a multilingual turn-detection model) so the agent delays intelligently when it detects hesitations or filler phrases like “uh”/“I think…”. This prevents the agent from responding immediately when a user is still thinking out loud. Example:
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from livekit.plugins.turn_detector import MultilingualModel

session = AgentSession(
    turn_detection=MultilingualModel(),
    vad=your_vad_instance,
    min_endpointing_delay=5.0,
    max_endpointing_delay=10.0,
)
Tuning tips • Start with 5s/10s and test with real users. Some users prefer shorter pauses (3–5s) while others need longer depending on question complexity. • If users speak again within the delay window, ensure your logic keeps the turn open (VAD + turn detector handle this). • If you use a realtime model (e.g., OpenAI Realtime), let me know — turn detection behavior differs slightly and I can give specific adjustments for that flow. Next step • Which language/SDK are you using (TypeScript, Python, or something else)? • Are you using a turn detector already or only VAD now? Tell me that and I’ll give the exact code snippet and small tests you can run to verify the breather works as expected. Sources: External data and RAG | LiveKit Documentation | Turns overview | LiveKit Documentation | How does end-of-utterance detection work in conversations? | examples/voice_agents | livekit/livekit_composite | DeepWiki | Improving voice AI's turn detection with transformers
b
python session = AgentSession( llm=openai.realtime.RealtimeModel( voice=“marin”, temperature=0.8, modalities=[“text”, “audio”], turn_detection=TurnDetection( type=“server_vad”, threshold=0.5, prefix_padding_ms=300, silence_duration_ms=200, create_response=True, ), ), )
t
Thanks — this is helpful. You’re using Python with an OpenAI realtime model and server-side VAD turn detection. To add a 5–10 second breather so the agent waits longer before responding, you can adjust two places: • Increase endpointing delays on the AgentSession (recommended) so the agent waits after detected end-of-speech. • Or increase the silence/timeout values in your turn_detection config — but endpointing delays are clearer and more consistent for a fixed breather window. Below is a focused Python example that shows how to add a 5–10s breather to your existing snippet. 1) Recommended: set 
min_endpointing_delay
and 
max_endpointing_delay
on AgentSession
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session = AgentSession(
    llm=openai.realtime.RealtimeModel(
        voice="marin",
        temperature=0.8,
        modalities=["text", "audio"],
        turn_detection=TurnDetection(
            type="server_vad",
            threshold=0.5,
            prefix_padding_ms=300,
            silence_duration_ms=200,
            create_response=True,
        ),
    ),
    # Wait between 5 and 10 seconds after end-of-speech before responding
    min_endpointing_delay=5.0,
    max_endpointing_delay=10.0,
)
• How this behaves: After the VAD indicates the user stopped speaking, the session will wait at least 5s and up to 10s (if confidence is low) before the agent begins generating audio — giving the interviewee a breather. 2) Alternative (adjust turn_detection silence): lengthen 
silence_duration_ms
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# Example: extend silence duration to 5000 ms for a 5s wait
turn_detection=TurnDetection(
    type="server_vad",
    threshold=0.5,
    prefix_padding_ms=300,
    silence_duration_ms=5000,  # 5000 ms = 5 seconds
    create_response=True,
),
• Note: This makes the VAD consider the turn ended only after that silence window. It can work but mixes endpointing logic with VAD thresholds; using endpointing delay keeps VAD behavior unchanged and adds a post-speech pause. Tuning tips • Start with min=5.0 and max=10.0 and test with real users. If some users speak again within the window, VAD + turn detector will keep the turn open if speech is detected. • If the realtime model or your pipeline has its own auto-cutoff, test to ensure the longer delay doesn’t conflict with other timeouts. • If you want the agent to interrupt earlier when the user clearly continues speaking, keep 
silence_duration_ms
small (like 200ms) and rely on endpointing delays — this preserves responsiveness while adding a thinking pause. Would you like a small test script to simulate a user pause and verify the breather behavior in your environment? If so, tell me whether you prefer a unit-style test or a quick manual test script and I’ll provide it.
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