You're asking for a high-level implementation plan plus the exact LiveKit APIs to build a custom local avatar worker that publishes video into a room using ByteStreams for audio/control, Agent/AvatarSession patterns, and the SDK for publishing tracks. Below is a concise, actionable plan with the LiveKit classes and methods you’ll use and the integration points to implement. High-level plan 1. Prepare credentials and environment • Ensure your LiveKit API keys/tokens are available to the worker process and any avatar/AI services you’ll use. 1. AgentSession (control/LLM/STT/TTS) • Purpose: manage conversation state, LLM calls, STT/TTS orchestration, and control messages. • Key class / usage: ◦ AgentSession — create and configure STT, LLM, and TTS components. ◦ Methods: ▪︎ start(room, agent, room_options) — join room as the agent/controller. ▪︎ input.set_audio_enabled(enabled) / output.set_audio_enabled(enabled) — control audio routing. • Behavior: AgentSession runs the conversation loop, produces audio bytes (TTS) and control events (animation cues, viseme timing, emotions). 1. AvatarSession (avatar rendering & video) • Purpose: manage avatar lifecycle, convert audio/control into rendered video frames, produce video frames to publish. • Key class / usage: ◦ AvatarSession (plugin-specific, e.g., tavus.AvatarSession) — initialize with avatar IDs/persona. ◦ Methods: ▪︎ start(session, room) — start avatar worker and ensure it attaches to the room. ▪︎ set_audio_enabled(enabled) — toggle avatar audio output. • Behavior: Accepts audio stream and control signals, drives avatar rendering engine (local or third-party), outputs a camera-like video source or frame buffer. 1. ByteStreams for audio + control • Purpose: transport raw audio bytes and control messages (visemes, events, timestamps) between AgentSession and AvatarSession or between services and the room. • Key class / usage: ◦ ByteStream — open, send, and receive binary streams tied to room topics. ◦ Methods: ▪︎ stream_bytes(topic) — open a writer/reader stream to send/receive bytes for a topic. ▪︎ send_file(file_path, topic) — (optional) send file via byte stream. ▪︎ register_byte_stream_handler(topic, handler) — register handlers to receive incoming byte streams. • Pattern: ◦ AgentSession writes TTS audio bytes and control payloads into ByteStreams. ◦ AvatarSession registers handlers to consume those ByteStreams and drive rendering. 1. Publishing audio & video tracks (LiveKit SDK) • Purpose: publish the avatar’s audio/video so other participants see/hear the avatar. • Key classes / usage: ◦ LocalVideoTrack — create from a frame source or synthetic renderer. ▪︎ create_video_track(name, source) — make a local video track from your avatar renderer or framebuffer. ◦ LocalAudioTrack — create from generated audio if you want audio published directly. ▪︎ create_audio_track(name, source) ◦ Room / Participant API: ▪︎ publish_track(track, options) — publish the created track(s) into the room. • Synchronization: ◦ Keep timestamps in sync: use the same clock or embed timestamps/control in ByteStreams to align frames with audio events. Concrete integration flow (sequence) 1. Worker process authenticates to LiveKit and joins target room as a participant or service actor. 2. Initialize AgentSession (attach STT/LLM/TTS). Configure output audio disabled if the avatar will render audio itself. 3. Initialize AvatarSession with avatar configuration (persona_id, replica_id). 4. Set up ByteStream topics: ◦ "avatar-audio" — TTS PCM/Opus chunks. ◦ "avatar-control" — JSON control frames (viseme, lip-sync, facial cues, scene commands). 1. AgentSession writes TTS audio into "avatar-audio" ByteStream and control signals into "avatar-control". 2. AvatarSession registers byte stream handlers for those topics, consumes audio/control, and drives its rendering engine. 3. AvatarSession produces rendered frames (e.g., raw RGB or encoded frames) or exposes a virtual camera source. 4. Create a LocalVideoTrack from the renderer output and publish via publish_track. Optionally create LocalAudioTrack for published audio if not relying on raw room audio routing. 5. Ensure track metadata (name, simulcast/resolution) and publishing options are set to match expected consumer behavior. 6. Monitor health (byte stream backpressure, frame rate, CPU) and re-negotiate/publish updates if needed. Exact LiveKit APIs & methods (summary) • AgentSession ◦ Class:

AgentSession

◦ Methods:

start(room, agent, room_options)

,

input.set_audio_enabled(...)

,

output.set_audio_enabled(...)

• AvatarSession (plugin) ◦ Class: e.g.,

tavus.AvatarSession

◦ Methods:

start(session, room)

,

set_audio_enabled(...)

• ByteStream ◦ Class:

ByteStream

◦ Methods:

stream_bytes(topic)

,

send_file(file_path, topic)

,

register_byte_stream_handler(topic, handler)

• Local tracks & publishing ◦ Classes:

LocalVideoTrack

,

LocalAudioTrack

◦ Methods:

create_video_track(name, source)

,

create_audio_track(name, source)

,

publish_track(track, options)

Implementation notes and best practices • Use ByteStreams for high-throughput, low-latency audio/control. Partition topics for audio vs control to avoid interleaving delays. • If using TTS: stream audio chunks rather than batching large files to reduce latency. • For video: prefer providing a frame buffer or virtual camera to the SDK so you can publish a continuous LocalVideoTrack rather than repeatedly re-publishing. • Maintain timestamp alignment: encode timestamps in control messages or use the same realtime clock to sync animations to audio. • Keep the avatar worker single-responsibility: AgentSession handles conversation logic; AvatarSession handles rendering and track publication. • Instrument metrics (RTT, publish lag, CPU/framerate) to react to performance issues. If you’d like, I can: • Provide a minimal, runnable code sketch in your target language (Node, Python, or Go) for the worker showing ByteStream usage, AgentSession <> AvatarSession wiring, and creating/publishing a LocalVideoTrack. • Help design byte-stream message schemas for audio chunks and control frames (visemes, timestamps, emotions). Which language would you prefer the example in, and do you already have an avatar renderer (local frames) or are you using a third-party avatar service?