LiveAvatar × LiveKit Agents starter (Python)

Minimal Python boilerplate for driving a HeyGen LiveAvatar session with a LiveKit Agents voice pipeline. Talk into your browser, the agent thinks, the avatar lip-syncs the response.

Quickstart

uv venv && source .venv/bin/activate
uv pip install -e .
python src/worker.py download-files
cp .env.example .env.local                 # then fill in your API keys
python src/liveavatar_hosted_demo.py       # talks to a sample avatar in your default browser

This runs the LiveAvatar-hosted demo (Flow 1) — fastest path, no LiveKit Cloud worker deploy required. For the production-shaped path (your LK Cloud project, lk agent deploy), see the BYO LiveKit demo.

Two demos, two ops models

There are two integration modes for connecting a LiveKit agent to LiveAvatar. They differ in who owns the LiveKit room, which in turn determines how you ship the agent in production.

Both demos share the same agent code (src/agent.py, src/worker.py, src/pipeline.py, src/avatar_ws.py). Only the orchestration entrypoint and deploy story differ.

Project layout

src/
  liveavatar_hosted_demo.py   Flow 1 entrypoint (LiveAvatar hosts the room)
  byo_livekit_demo.py         Flow 2 entrypoint (you host the room)
  worker.py                   AgentServer worker — used by both demos
  agent.py                    LiveAvatarAgent (instructions + tts_node override)
  pipeline.py                 STT/LLM/TTS session + observability wiring
  avatar_ws.py                WebSocket bridge to the LiveAvatar media server
  liveavatar_client.py        async client for the LiveAvatar HTTP API
viewer/
  index.html                  vanilla-JS LiveKit viewer (auto-connects via query string)
docs/
  liveavatar-hosted-demo.md   Flow 1 walkthrough
  byo-livekit-demo.md         Flow 2 walkthrough
Dockerfile                    LK Cloud agent worker image (Flow 2 deploy)
livekit.toml                  `lk agent` deploy manifest
.env.example
pyproject.toml

Prerequisites

Accounts

• LiveAvatar — https://app.liveavatar.com (API key + at least one avatar)
• LiveKit Cloud — https://cloud.livekit.io (any project; used for the inference gateway, billed to your project)

Runtime

• Python ≥ 3.10 (3.13 tested)
• uv for dependency management (or pip)

Python dependencies (declared in pyproject.toml):

• livekit-agents[silero,turn-detector] ~= 1.5 — agent framework, Silero VAD plugin, multilingual turn detector
• livekit-plugins-ai-coustics ~= 0.2 — input noise cancellation
• httpx — async HTTP client for the LiveAvatar API
• websockets — WebSocket client for the LiveAvatar media-server bridge
• python-dotenv — .env.local loading
• audioop-lts (Python ≥ 3.13 only) — drop-in replacement for the stdlib audioop module (removed in 3.13), used for resample / mono mixdown

Why a LiveKit Cloud project? The voice pipeline plugins (inference.STT/LLM/TTS) call LiveKit's hosted inference gateway.

⚠️ Both demos consume LiveKit credits on your LK Cloud project. Even the LiveAvatar-hosted demo, where LiveAvatar owns the room, routes STT / LLM / TTS through your project's inference gateway. Plan accordingly.

Common setup

# 1. Install dependencies (using uv)
uv venv
source .venv/bin/activate
uv pip install -e .

# 2. Download the turn-detector + VAD model weights.
#    REQUIRED before either demo. Skipping this will make the agent crash
#    mid-session the first time it tries to detect a turn.
python src/worker.py download-files

# 3. Configure environment
cp .env.example .env.local
# fill in: LIVEAVATAR_API_KEY, LIVEKIT_API_KEY, LIVEKIT_API_SECRET
# (the BYO demo also needs LIVEKIT_URL)
# AVATAR_ID is pre-filled with a public sample — swap once you have your own.

Useful environment variables

Running the demos

Once the common setup is done, head to the per-demo walkthrough:

• docs/liveavatar-hosted-demo.md — fastest path; no LK Cloud deploy required, runs entirely on your machine.
• docs/byo-livekit-demo.md — production-shaped path; deploys the worker to LiveKit Cloud and dispatches into a room you own.

Customizing

• System prompt / personality — edit instructions= in LiveAvatarAgent (src/agent.py).
• STT / LLM / TTS models — change inference.STT/LLM/TTS(...) in pipeline.build_session. Available models: https://docs.livekit.io/agents/models/
• Tools (function calling) — add @function_tool methods to LiveAvatarAgent. See the comment in agent.py for an example.
• Avatar appearance — change AVATAR_ID in .env.local.