I want to implement a bot through livekit to intract with people's. How can implement and I want the avatar should talk and do the lipsink

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

Platform is web Backed currently node but in future we move to python Custom avtar with no or less maintenance cost LLM will give the text and browser will speakout

Thanks — got your requirements: web frontend, Node backend (migrating to Python later), custom low‑maintenance avatar, LLM returns text and browser will speak it. Two important facts first: • Avatar lip‑sync requires the avatar service to receive the audio that the avatar will lip‑sync to. Browser-only speech synthesis cannot be reliably synchronized unless that audio is also sent to the avatar service (server‑side TTS is the typical flow). • You can still keep the browser speaking the LLM text for users, but for proper avatar lip‑sync you should send the same TTS audio (or generate it server‑side) to the avatar provider so the avatar publishes a synced video track into the LiveKit room. Recommended low‑maintenance approaches • Use a managed avatar provider that accepts custom images/models and handles rendering (Hedra, Tavus, bitHuman). They publish a participant video track into LiveKit so your frontend just renders it. • For minimal ops: choose a cloud-managed provider (Tavus) or a provider that supports hosted models (

.imx

) if you want on‑prem later (bitHuman). High‑level implementation plan (Node → later Python) 1. Pick an avatar plugin/provider that fits your custom avatar format. 2. On your Node backend: a. Receive LLM text. b. Generate TTS audio server‑side (16 kHz for many providers). c. Create an AvatarSession (provider plugin) and send the TTS audio to it. d. Start the Agent/AvatarSession so the avatar worker joins the LiveKit room and publishes the video track. 1. On the web frontend: ◦ Render the avatar’s video track like any LiveKit participant (identify avatar by

lk.publish_on_behalf

or use the agent helper hook). ◦ Play browser TTS if you still want local speech; consider muting local playback for users if you prefer avatar audio only (to avoid double audio). 1. When you migrate to Python, the same avatar plugins and flow are supported—swap the backend implementation with minimal architectural change. Quick questions so I can give exact code and config: • Do you want the avatar to be entirely server‑rendered (avatar audio only) or both avatar video+local browser speech (may need sync/duplication handling)? • Any preferred avatar providers from Hedra/Tavus/bitHuman, or should I recommend one based on cost/maintenance tradeoffs? • Which TTS engine do you plan to use (cloud TTS, self‑hosted, or browser-only)? Tell me those and I’ll give a concise Node code example and the frontend rendering snippet. Sources: Hedra Realtime Avatar integration guide | LiveKit Documentation | Video | LiveKit Documentation | examples/avatar_agents | livekit/livekit_composite | DeepWiki | Virtual avatar models overview | LiveKit Documentation | Bringing AI avatars to voice agents | LiveKit, with interactive elements

I am hosting livekit on self hosted servers. Considering this give me the plan. I want complete this my utilising less infra and only one real user and one bot user will be there in session . I also want to implement recording of this session along with monitoring the audio and video of user

Thanks — I’ll give a concise, self‑hosted plan that minimizes infra for a 1:1 (one real user + one bot) session, supports a custom avatar with low maintenance, and includes recording + monitoring. A couple quick confirmations before I give code/config: do you want the avatar video to be server‑rendered (avatar publishes its video track into the LiveKit room) or rendered entirely in the browser (canvas/WebGL) and published from your agent? Server‑rendered is simplest for lip‑sync. Plan (high level) • Topology: single LiveKit server (self‑hosted), one Node.js agent process that runs the bot logic and an avatar worker (either provider plugin or lightweight renderer), and the web client. • Flow: a. User joins LiveKit room from browser. b. Node agent joins same room as bot participant and handles data/RPC from client (receives user text or STT). c. LLM returns text to agent; browser will speak text locally for the user experience. d. For avatar lip‑sync: agent must provide the TTS audio to the avatar worker/service which generates/sends a synced video track into the room (avatar appears as a separate participant). e. Start Room Composite Egress to record the room (user + avatar video + audio) to storage. f. Use client SDK metrics + agent logs + Prometheus metrics for monitoring. Why server audio is needed • Browser-only speech synthesis cannot be reliably lip‑synced by the avatar unless that audio is also provided to the avatar service. So generate or synthesize the TTS audio server‑side (or forward the browser audio back to server) and send it to the avatar worker. Low‑maintenance custom avatar options • Choose a managed avatar plugin that supports custom images/models (Hedra, Tavus, bitHuman). They handle rendering and publish a video track so you avoid building/hosting heavy rendering infra. • If strict self‑hosting with minimal external deps is required, pick bitHuman (supports local models) but expect slightly more maintenance. Minimal infra & deployment • Single VM/container is sufficient: ◦ LiveKit server (Docker container) ◦ Agent service (Node.js) + avatar worker (can be same host/process if plugin allows) ◦ Egress service (run livekit/egress container) or run egress on same host • Ports to allow: TCP 443 (HTTPS), 7880/7881 (LiveKit internal/API), UDP for WebRTC (check your NAT/ICE setup). • Use Docker Compose to keep it simple; containerize agent and egress. Recording (Egress) • Use Room Composite Egress to record MP4 (or separate audio/video). Start egress when the room is created or when participant joins; stop and upload or finalize when the room ends. • Store recordings to local disk or S3-compatible object store. Monitoring & Observability • Client-side: collect WebRTC stats via the LiveKit client SDK (packet loss, RTT, bitrate, frames) and surface in UI. • Server-side: enable LiveKit Prometheus metrics and scrape them. Add basic dashboards for CPU/memory and connection counts. • Agent logs: log STT/LLM/TTS events and errors. Emit health events over a data track for live debugging if needed. Concrete next steps I can provide immediately (choose which you want): • A minimal Docker Compose for LiveKit + Egress + Node agent. • Example Node agent flow: join room, receive user audio/text, get LLM text, generate server TTS, send TTS to avatar plugin (code snippet). • Frontend snippet to render avatar video track and play browser TTS alongside. • Egress start/stop code sample to record the room. Which of the concrete examples above should I generate first? Sources: livekit/livekit_composite | DeepWiki | Agents Framework | livekit/livekit_composite | DeepWiki | Build a healthcare intake assistant with an Anam avatar | LiveKit | Video | LiveKit Documentation | Running LiveKit on AWS | LiveKit, with interactive elements