Thanks — I’m responding to your two questions: (1) what can cause >3s delays after the user stops speaking, and (2) how TTS audio is played in the room. Short answers • Turn detection (endpointing) is the most common cause of >3s delays. • TTS audio is streamed incrementally — audio frames are published as they’re produced, not after the entire response is synthesized. What commonly causes delays (>3s) • Turn detection settings:

min_endpointing_delay

and

max_endpointing_delay

can add hundreds to thousands of ms while the agent waits to confirm the user stopped speaking (defaults can be up to several seconds for low-confidence endpoints). • Using conservative VAD-only endpointing or high VAD sensitivity extends listening time. • Non-streaming TTS usage or misconfigured chunking: if your TTS provider is invoked in batch mode, playback will wait until the full audio is ready. • Infrastructure issues: CPU throttling (e.g., burstable instances running out of credits), network latency, or cold starts on free/build plans can add seconds. • Misordered pipeline steps or extra processing between STT → LLM → TTS that delays forwarding audio frames. How TTS output is played in the room • The agent pipeline outputs TTS as an AsyncIterable of audio frames; those frames are consumed and published to the agent’s output track incrementally so playback in the room begins as soon as the first chunks arrive. • If you observe full-response waits, it usually means a non-streaming TTS method is being used or the pipeline is buffering frames before publishing. Quick actions to reduce delay 1. Lower

min_endpointing_delay

(try 200–300 ms) if occasional interruptions are acceptable. 2. Use dynamic/semantic turn detection (LiveKit turn detector) to shorten waits while avoiding false cutoffs. 3. Ensure your TTS provider and SDK are used in streaming mode (don’t synthesize entire blobs before publishing). 4. Check VAD sensitivity and switch from VAD-only to STT-based endpointing if helpful. 5. Monitor agent metrics (e.g.,

eou_delay

, endpointing events) and check host CPU/network and cold-start behavior. If you’d like, share: • your AgentSession turn detection settings (values for

min_endpointing_delay

/

max_endpointing_delay

), • whether you use VAD-only or STT endpointing, and • which TTS provider/method you’re calling (streaming vs batch). I can then point out the likeliest culprit and specific settings to change. Sources: Pipeline nodes and hooks | LiveKit Documentation | Using a transformer to improve end of turn detection | LiveKit | Agents Framework | livekit/livekit_composite | DeepWiki | Agent speech and audio | LiveKit Documentation | Build Your First AI Voice Agent in Python: Complete Tutorial | LiveKit, with interactive elements