Thanks — you're asking how to reduce End‑of‑Utterance (EOU) delay. I’ll walk you through the practical options you can use in LiveKit Agents, and what tradeoffs to expect so you can pick the best approach for your use case. Quick summary • The main controls are the endpointing timing parameters and the silence/VAD thresholds. • For safer reductions in EOU delay, use the transformer-based turn detector (it uses semantic context) together with tuned delay settings. • More aggressive lowering of delays reduces latency but increases the chance the agent will interrupt users. What to change (practical steps) 1) Tune endpointing delay parameters 1. Set a lower

min_endpointing_delay

to reduce the wait after a high‑confidence end-of-turn. 2. Optionally lower

max_endpointing_delay

for lower-likelihood end-of-turn cases. Example:

session = AgentSession(
    min_endpointing_delay=0.3,  # faster than default 0.5s
    max_endpointing_delay=4.0,  # faster than default 6.0s
)

• Effect: Faster agent responses. • Risk: Higher chance of interrupting ongoing speech if detection was wrong. 2) Use the transformer-based turn detector (recommended) • Enable the open‑source turn detector plugin (MultilingualModel) that predicts true turn ends using transcription and conversation context. • This model lets you safely reduce endpointing delays with fewer interruptions than VAD alone. Example:

from livekit.plugins.turn_detector import MultilingualModel

session = AgentSession(
    turn_detection=MultilingualModel(),
    stt=inference.STT(language="multi"),
)

• Effect: Enables more aggressive delay settings with much lower interruption rates. • Tradeoff: Additional compute cost and a small integration step. 3) Adjust VAD / silence thresholds • Lower the

min_silence_duration

used by your VAD (Silero VAD is commonly used). Example:

vad = silero.VAD(min_silence_duration=300)  # milliseconds

• Effect: Faster silence detection and earlier endpointing. • Risk: May cut off natural pauses and cause false endpoints. 4) Use a real‑time LLM or co‑located low-latency STT/TTS • If your pipeline supports real‑time LLMs, handle turn detection and response generation server-side to reduce end-to-end latency. • If not using real-time LLMs, ensure STT and TTS are low-latency and ideally co‑located with your agent logic. Performance notes (expected behavior) • The turn detector greatly reduces unintentional interruptions while allowing lower delays. • Real-world defaults:

min_endpointing_delay = 0.5s

,

max_endpointing_delay = 6.0s

. Start by reducing

min_endpointing_delay

in small steps (e.g., 0.5 → 0.3s) and testing with real conversations. How to choose the right balance • If conversational naturalness matters more than occasional latency, keep delays conservative. • If responsiveness is critical (e.g., short interactive prompts), use the turn detector + lower

min_endpointing_delay

. • Always test with representative audio (different speakers, background noise, and languages) to tune thresholds safely. If you want, I can help with any of the following next steps: • Suggest specific values for `min_endpointing_delay`/`max_endpointing_delay` based on your agent’s domain and tolerance for interruptions. • Provide a short test plan and metrics to evaluate false interruptions vs. latency after you change settings. • Walk through integrating the Multilingual turn detector into your current agent pipeline. Which of those would you like to do next, or can you share your current settings and a short description of typical usage (single speaker vs. multi-party, languages, and tolerance for interruptions)? Sources: Agents Framework | livekit/livekit_composite | DeepWiki | Improving voice AI's turn detection with transformers | LiveKit turn detector plugin | LiveKit Documentation | examples/frontdesk | livekit/livekit_composite | DeepWiki