how does it take for egress recordings to be available for download?

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

so no time frames available for? is it 10mins or more?

You're asking for a concrete estimate (e.g., 10 minutes) for when egress recordings become downloadable. There isn't a fixed SLA, but typical behavior is: • Small/short recordings: available within seconds to a few minutes after egress ends. • Longer recordings: may take longer while the service finalizes packaging and uploads — often still under ~5 minutes for most cases, but can exceed that depending on file size, network/upload speed, and the configured output (S3, GCS, etc.). To help diagnose or give a tighter estimate for your case, could you tell me: • Which output destination are you using (S3, GCS, local, or a stream)? • Example recording duration and whether you’ve seen delays (how long it took)? • Any error events or whether you receive the

egress_ended

webhook?

Can we add ai agent to video rooms?

You're asking if you can add an AI agent to video rooms — yes. LiveKit Agents join rooms as full participants (

ParticipantKind.AGENT

) and can receive live audio, video, and data from users, run STT/LLM/TTS and vision models, then publish synthesized audio (and optionally video/avatars) back into the room. They support real-time multimodal interactions and common use cases like assistants, translators, NPCs, and meeting note-takers. If you want, tell me: • Which language/runtime you prefer (Node.js or Python) • Whether you need vision (camera) processing or audio-only • If you want a prebuilt integration (e.g., OpenAI, Deepgram, ElevenLabs) or a custom model I can then point you to the right quickstart and a minimal example to get started. Sources: Agents (Python) | livekit/livekit_composite | DeepWiki | Vision | LiveKit docs | LiveKit Docs | An open source stack for real-time multimodal AI

its nodejs, i ccreate the room and was wondering if we can add ai agent, that can speak with one participant if needed currently it just shares the users camera, avatars would be cool but not critical, camera processing would be ideal if possible

Thanks — you're asking how to add a Node.js AI agent to your room that can speak with one participant, ideally with camera processing and optional avatars. Yes — you can. Summary and next steps: • Agent type: Use a LiveKit Agent (joins as

ParticipantKind.AGENT

) from Node.js. The agent can subscribe to a single participant’s tracks, run STT/LLM/TTS, and publish synthesized audio back to the room. • Targeting one participant: Subscribe only to that participant’s audio/video tracks (identify them by participant SID or identity) so the agent listens and responds to just that person. • Camera processing: Supported — you can capture video frames from the participant’s video track and send them to vision models (e.g., Gemini Live / OpenAI Realtime) for analysis, then use results in the agent’s responses. • Avatars: Optional. You can publish a video track (avatar output) from the agent instead of or in addition to audio if you want visual presence. • Prebuilt integrations: Use plugins for STT, TTS, and VAD (Deepgram, ElevenLabs, Silero, etc.) to simplify setup, or wire your own model endpoints. • Where to start: Use the Node.js Agent quickstart / Agent Builder to prototype an AgentSession that: a. Joins the room as an agent. b. Subscribes to the chosen participant’s tracks. c. Streams audio/video into your STT/vision/LLM pipeline. d. Converts responses to audio (TTS) and publishes back to the room. If you’d like, I can: • Provide a minimal Node.js example showing how to join as an agent and subscribe to one participant. • Show sample code for extracting video frames and sending them to a vision model. • Recommend plugins for STT/TTS based on your preferred providers. Which would you like next, and which STT/LLM/TTS providers do you plan to use (or do you want recommendations)?

please show me a simple example... groq moonshot llm, deepgram stt and tts

You're asking for a simple Node.js example of a LiveKit Agent that listens to one participant, uses Deepgram for STT, Groq Moonshot as the LLM, and TTS to speak back. Below is a minimal, focused example and setup notes to get you started. Prerequisites • A LiveKit server and API key/secret or LiveKit Cloud credentials. • Deepgram API key. • Groq Moonshot API access/key. • A TTS provider (example uses ElevenLabs; replace with your TTS choice and API key). • Node 18+. Install (example) 1. npm init -y 2. npm install livekit-server-sdk ws node-fetch deepgram-sdk form-data High-level flow 1. Agent joins room as ParticipantKind.AGENT. 2. Agent subscribes to one participant’s audio/video tracks (by identity or SID). 3. Agent forwards audio to Deepgram for STT (streaming or chunked). 4. Send transcript to Groq Moonshot for response. 5. Convert generated text to audio via TTS and publish the audio track back into the room. Minimal example (conceptual — adapt config and error handling for production):

// agent.js
import { LivekitServer, RoomServiceClient, Participant } from "livekit-server-sdk";
import { Deepgram } from "@deepgram/sdk";
import fetch from "node-fetch";
import WebSocket from "ws";
import fs from "fs";
import { spawn } from "child_process"; // for local audio handling if needed

// CONFIG - fill these
const LIVEKIT_API_KEY = process.env.LIVEKIT_API_KEY;
const LIVEKIT_API_SECRET = process.env.LIVEKIT_API_SECRET;
const LIVEKIT_URL = process.env.LIVEKIT_URL; // e.g., <wss://cloud.livekit.io>
const ROOM_NAME = "test-room";
const AGENT_IDENTITY = "ai-agent-node";
const TARGET_PARTICIPANT_IDENTITY = "user-to-respond-to";

const DEEPGRAM_API_KEY = process.env.DG_API_KEY;
const GROQ_API_KEY = process.env.GROQ_API_KEY;
const TTS_API_KEY = process.env.TTS_API_KEY; // ElevenLabs or other

// Initialize SDK clients
const lg = new LivekitServer(LIVEKIT_API_KEY, LIVEKIT_API_SECRET, { url: LIVEKIT_URL });
const deepgram = new Deepgram(DEEPGRAM_API_KEY);

// 1) Create an ephemeral token so the agent can join (server-side)
async function createAgentToken() {
  const at = lg.joinToken(ROOM_NAME, { name: AGENT_IDENTITY, participantType: "agent" });
  return at.toJwt();
}

// 2) Connect as a participant (we'll use WebRTC-less approach conceptually)
// In practice use a WebRTC client to publish an output audio track. For simplicity, this example shows
// server flow for receiving audio via LiveKit egress or subscribing; actual media plumbing requires a WebRTC endpoint.

// Simplified flow: subscribe to the participant's audio via LiveKit WebSocket Events or use Track Egress.
// Here we demonstrate using Deepgram's streaming via WebSocket with PCM chunks you provide.

// Helper: send transcript to Groq Moonshot
async function askGroq(prompt) {
  const res = await fetch("<https://api.groq.ai/v1/streams>", {
    method: "POST",
    headers: {
      "Authorization": `Bearer ${GROQ_API_KEY}`,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({
      model: "moonshot",
      input: prompt,
    }),
  });
  const data = await res.json();
  // adjust based on Groq streaming/response shape
  return data.output || data.text || JSON.stringify(data);
}

// Helper: TTS (ElevenLabs example)
async function ttsGenerate(text) {
  const res = await fetch("<https://api.elevenlabs.io/v1/text-to-speech/YOUR_VOICE_ID>", {
    method: "POST",
    headers: {
      "xi-api-key": TTS_API_KEY,
      "Content-Type": "application/json",
    },
    body: JSON.stringify({ text }),
  });
  const arrayBuffer = await res.arrayBuffer();
  fs.writeFileSync("response.wav", Buffer.from(arrayBuffer));
  return "response.wav";
}

// Main orchestration (conceptual)
async function runAgent() {
  // 1. Create token and instruct your client to join as agent (or create a WebRTC client here)
  const token = await createAgentToken();
  console.log("Agent token (use to join room as agent):", token);

  // 2. Subscribe to track(s) of target participant
  // In a full implementation, your agent's client subscribes to the participant's audio track and receives PCM frames.
  // For demo: assume you receive chunks of audio (PCM or opus) that you forward to Deepgram.

  // Example: using Deepgram live websocket (conceptual)
  const dgSocketUrl = `<wss://api.deepgram.com/v1/listen?encoding=linear16&sample_rate=16000`;>
  const ws = new WebSocket(dgSocketUrl, {
    headers: { Authorization: `Token ${DEEPGRAM_API_KEY}` },
  });

  ws.on("open", () => {
    console.log("Deepgram WS open — stream audio here");
    // send audio bytes as you receive them from the participant's track
  });

  ws.on("message", async (msg) => {
    const parsed = JSON.parse(msg.toString());
    if (parsed.channel && parsed.channel.alternatives) {
      const transcript = parsed.channel.alternatives[0].transcript;
      console.log("Transcript:", transcript);

      // send transcript to Groq for response
      const reply = await askGroq(transcript);
      console.log("Groq reply:", reply);

      // generate TTS audio
      const wavPath = await ttsGenerate(reply);

      // publish wavPath as audio track into LiveKit room (requires a WebRTC or track-publish flow)
      // For example, use a local headless WebRTC client to publish `response.wav` as the agent's audio track.
      // Implementation of publishing is environment-specific; use a browser RTCPeerConnection or a server-side
      // WebRTC implementation to push audio into LiveKit.
    }
  });

  ws.on("close", () => console.log("Deepgram WS closed"));
}

runAgent().catch(console.error);

Notes and next steps • Media plumbing: The example shows orchestration. To actually hear the agent in the room you must publish a microphone-like audio track from the agent. That requires a WebRTC client (browser or server-side) to create and publish an audio track (e.g., play generated WAV into an AudioWorklet or use a server WebRTC library to push PCM to a track). • Subscribing to a single participant: identify them by identity or SID and subscribe to their tracks in your agent client; only forward that audio to Deepgram. • Camera processing: subscribe to the participant's video track, grab frames (e.g., via a MediaStream in a browser agent or using server-side frame extractor if using a headless WebRTC client), then send frames to Groq vision or your chosen model. • Avatars: if you want an avatar video, publish a video track from the agent. That can be an MJPEG or generated frames streamed via a WebRTC video track. • Plugins: use Deepgram for STT (streaming), Groq Moonshot for LLM replies, and ElevenLabs (or another TTS) for audio synthesis as shown. If you want, I can: • Provide a more complete example showing how to publish the generated audio into LiveKit (browser-based agent or server WebRTC example). • Show code to extract video frames and send them to Groq vision. Which of those would you like next?