there is a latency on voice agent startup, when th...
# ask-ai
a
there is a latency on voice agent startup, when the user connects via voice the initial greeting i.e. say works perfectly but however i have noticed a initial latency when getting the first response from the LLM, the furthermore responses are fast due to streaming but what will be the cause of this behaviour.
t
Thanks for reaching out, Aditya. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
Thanks — I’m looking at the initial LLM-response latency you described (greeting plays immediately, but the first LLM reply is slow while later replies stream fast). That behavior is common; here are the likely causes and the most effective mitigations. Likely causes • Cold start of VAD or local audio models: the VAD/STT model may be loading on the first connection, delaying turn detection and transcription. • LLM connection/model cold start: the agent may be establishing the LLM connection (DNS/TLS/auth) or loading model context on first request. • No preemptive/pipelined work on the first turn: prefetching, prewarming, or streaming pipelines typically kick in after the first interaction, so the first response is handled more synchronously. • Deployment cold start: if the agent process was scaled-to-zero, the container must boot and initialize before handling the request. • First STT/TTF (time-to-first-token) overhead from provider side: the initial STT or LLM request may incur extra latency before streaming begins. Recommended quick mitigations • Prewarm on startup: initialize VAD, STT/LLM clients and authenticate at agent startup so models/connections are ready before the first user speaks. • Keep a warm instance: run at least one always-on replica or use provisioned capacity so you avoid container cold starts. • Reuse persistent connections: ensure LLM/STT/TTS clients keep persistent sessions rather than creating new sockets per request. • Enable/verify streaming & preemptive generation: allow the agent to start generation earlier and stream tokens back as soon as they’re available. • Monitor TTFT and metrics: track Time-To-First-Token and related metrics to measure impact of each mitigation. If you want, tell me: • how your agent is deployed (serverless / always-on / autoscaled), • which STT/LLM providers you use, • and whether you already initialize clients in a startup/prewarm step— and I’ll give precise steps you can apply to your setup. Sources: LLM Metrics | LiveKit docs | LLM Output Replacement | LiveKit docs | Gemini Realtime Agent with Live Vision | LiveKit docs | Agent speech and audio | LiveKit docs | Deploying to production | LiveKit Docs
a
how do i do preemptive generation?
t
You're asking how to enable preemptive generation — below are concise steps and examples for Python and Node.js, plus requirements and tips. What to enable • Turn detection (VAD) must be active. • Initialize/stay connected to STT/LLM/TTS clients at startup. • Use a standard (non-realtime) LLM client. Python example
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session = AgentSession(
    stt=deepgram.STT(),
    llm=openai.LLM(),
    tts=elevenlabs.TTS(model="eleven_multilingual_v2"),
    vad=ctx.proc.userdata["vad"],
    preemptive_generation=True,
)
Node.js/TypeScript example
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const session = new voice.AgentSession({
  stt: new assemblyai.STT(),
  llm: new openai.LLM(),
  tts: new cartesia.TTS(),
  vad: proc.userData.vad,
  voiceOptions: {
    preemptiveGeneration: true,
  },
});
How it works (brief) • On interim transcripts, the agent triggers LLM inference early. • A draft response is stored; if final transcript matches, reuse it; otherwise regenerate. • Streaming still applies for subsequent tokens. Trade-offs & tips • Extra cost/compute if drafts are discarded when user continues speaking. • Prewarm VAD and LLM clients at startup to avoid load/cold-start delays. • Monitor Time-To-First-Token to measure improvements. • Ensure your STT returns interim transcripts quickly and turn detection is tuned. If you want, tell me: • which language (Python/Node) and LLM/STT providers you use, and • whether you already prewarm clients— and I’ll give exact code changes and tuning suggestions. Sources: Pipeline Translator Agent | LiveKit docs | examples/voice_agents | livekit/livekit_composite | DeepWiki | Agent speech and audio | LiveKit docs