what turn configuration does livekit provide

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

i was talking about User and LLM turn

Thanks — that clarification helps. By "User and LLM turn" do you mean one of the following? • Managing conversational turns between a human user and a language model in a chatbot (when the user speaks vs when the LLM responds). • How to capture user audio/video and forward it to an LLM (speech-to-text → model → TTS) within a LiveKit session. • How to coordinate which participant (user vs LLM agent) is publishing media/tracks in a room (e.g., relay-only bot participant, muted/unmuted switching). • Something else—please describe the specific behavior or workflow you want. Tell me which option matches or give a brief example of the flow you need, and I’ll provide targeted guidance. , with interactive elements

• Managing conversational turns between a human user and a language model in a chatbot (when the user speaks vs when the LLM responds). like how do we know user finish talking, when does llm know it can respond

Thanks — I’m responding to your question about how LiveKit detects when a user finishes speaking so an LLM can respond. Short answer: • LiveKit offers three primary ways to detect end-of-turn: VAD (silence-based), STT endpointing (speech-to-text provider signals), and model-based detection (End‑Of‑Utterance transformer). • You can also use manual control (push-to-talk) and explicitly call

session.commit_user_turn()

. How each works and when to use it: • VAD: detects speech vs silence (Silero VAD by default). Good for simple setups but may misfire on short pauses. Configure

min_silence_duration

to tune sensitivity. • STT endpointing: relies on the speech-to-text provider’s end-of-utterance events or punctuation. Use when your STT provides reliable endpoint signals. • Model-based (EOU): a transformer analyzes recent context and predicts end-of-turn, reducing false interruptions and adapting VAD timeouts. Best for conversational, multi-turn dialogues and noisy environments. • Manual: use

turn_detection="manual"

for push-to-talk or when you want explicit control. Practical defaults and tuning: • Transport from highest to lowest priority for detection: realtime model → VAD → STT → manual (fallback order is automatic). • Use

min_endpointing_delay

(default ~0.5s) and

max_endpointing_delay

(default ~6s) to avoid premature or very late turn endings. • If interruptions are a problem, enable the EOU model or increase `min_silence_duration`; if latency is the issue, lower delays or prefer STT-realtime signals. Example integration patterns: • Real-time flow (recommended for natural conversation): capture audio → run VAD + stream to STT → feed transcripts to EOU model → on EOU end signal, stop capture and send transcript to LLM → TTS response published as a bot participant. • Simple flow: VAD-only endpointing triggers STT → send final transcript to LLM → respond. • Push-to-talk: set

turn_detection="manual"

and call

session.commit_user_turn()

when user releases push button. Would you like: • a code example for the EOU model setup and config values, or • guidance for wiring this into your STT/LLM/TTS pipeline (which providers are you using)? Sources: Turn Detection for Voice Agents: VAD, Endpointing, and Model-Based Detectio... | How does end-of-utterance detection work in conversations? | Using a transformer to improve end of turn detection | LiveKit | Agents Framework | livekit/livekit_composite | DeepWiki | Agents (Python) | livekit/livekit_composite | DeepWiki, with interactive elements