Atmee avatar plugin for LiveKit Agents

Bring your own LiveKit voice agent; Atmee renders a v1 avatar — a talking head generated from a single portrait — into your room. This plugin targets Atmee's v1 avatars specifically. Works like the other avatar plugins for LiveKit Agents: the avatar joins your room as its own participant, your agent's speech drives its video.

from livekit.plugins import atmee

avatar_id = await atmee.AtmeeAPI().create_avatar(name="Val", image="portrait.jpg")

avatar = atmee.AvatarSession(avatar_id=avatar_id)   # ATMEE_API_KEY in the environment
await avatar.start(session, room=ctx.room)
await session.start(agent=..., room=ctx.room)       # the agent's TTS drives the avatar's video

Installation

pip install livekit-plugins-atmee

Python 3.10+, livekit-agents 1.6.8 or newer.

Prerequisites

• An Atmee API key (sk_atmee_...), in ATMEE_API_KEY. Create one in the Atmee studio at atmee.ai/studio/api-keys. Keep it on your agent's side; it never goes to a browser.
• Your own LiveKit project (LiveKit Cloud or self-hosted): LIVEKIT_URL, LIVEKIT_API_KEY, LIVEKIT_API_SECRET — the same variables your agent already uses. Atmee never sees your LiveKit secret: the plugin mints a token for the avatar participant locally and hands only that token to Atmee.

Usage

1. Create an avatar from a portrait

Once, from a script or your backend (see examples/create_avatar.py):

async with atmee.AtmeeAPI() as api:   # reads ATMEE_API_KEY from the environment
    avatar_id = await api.create_avatar(name="Val", image="portrait.jpg")

All it needs is a name and a single portrait. image may be a local file path, raw image bytes, or an https:// URL Atmee can download; .jpg, .png, and .webp are accepted. A portrait-only avatar is render-ready immediately — nothing to wait for. You can also create and manage avatars in the Atmee studio at atmee.ai; any avatar of your account with a portrait works.

2. Render it in your agent

async def entrypoint(ctx: agents.JobContext):
    await ctx.connect()
    session = AgentSession(stt=..., llm=..., tts=..., vad=...)

    avatar = atmee.AvatarSession(avatar_id=os.environ["ATMEE_AVATAR_ID"])
    await avatar.start(session, room=ctx.room)        # before session.start

    await session.start(agent=MyAgent(), room=ctx.room)

start() returns as soon as Atmee's rendering worker acknowledged the start (about a second); the avatar's video and audio tracks appear in the room a few seconds later, published by the participant atmee-avatar-agent. Your agent publishes no audio of its own — the avatar speaks for it, lips in sync.

Options on AvatarSession:

avatar.session_id and avatar.session_info hold the Atmee session after start(); avatar.on("avatar_disconnected", ...) fires if the avatar participant leaves while your agent is still running.

Avatar versions

Atmee avatars come in generations, and the plugin states which one it renders:

• v1 — a talking-head avatar generated from one portrait, lip-synced to your agent's speech. This is what the plugin renders today, and the default everywhere, so existing code needs no change.
• v2 — Atmee's next avatar generation. It is not yet available through the plugin: AvatarSession(..., avatar_version="v2") and create_avatar(..., avatar_version="v2") raise ValueError right away, before any request is sent.

from livekit.plugins import atmee

avatar = atmee.AvatarSession(avatar_id=..., avatar_version="v1")  # explicit is fine
avatar.avatar_version                     # "v1"
avatar.session_info.avatar_version        # "v1", after start()
atmee.SUPPORTED_AVATAR_VERSIONS           # frozenset({"v1"})

atmee.AvatarVersion is the Literal["v1", "v2"] type of the argument; AtmeeAPI.create_avatar() takes the same avatar_version and reports it as AvatarInfo.version. The version is plugin-side surface: it never appears in the requests sent to the Atmee API, and it is unrelated to AvatarInfo.kind (conversational | render_only), which says whether an avatar has a voice and persona.

Lifecycle and billing

Billing starts when the avatar sees your agent in the room and runs by the minute. It stops when

• your agent leaves the room or the room closes (the rendering worker notices and reports the end itself — a crashed agent stops being billed within seconds),
• aclose() runs — registered automatically as a job shutdown callback; it ends the session and removes the avatar participant from your room,
• the rendering worker itself dies or loses contact: it proves it is alive once a minute, and a session silent for a few minutes is closed and billed only up to the last proof,
• or the session reaches max_duration_seconds.

Errors

All API failures raise atmee.AtmeeException with status_code, code (the API's error code, e.g. insufficient_credits, avatar_not_renderable, missing_publish_on_behalf) and message. Two subclasses are worth handling: AtmeeNoCapacityError (every rendering worker is busy; check retry_after and try again later) and AtmeeAvatarNotReadyError (the avatar has no portrait yet).

Examples

• examples/agent.py — a Deepgram + OpenAI + Silero agent with an Atmee avatar; run with python examples/agent.py dev and open the LiveKit Agents Playground.

• examples/gpt_live_1_agent.py — the same agent using OpenAI gpt-live-1 (LiveKit's native GPTLiveModel) instead of the STT/LLM/TTS pipeline. Runs delegation="client" by default — gpt-live-1 answers itself, no backend round-trip, lowest latency, backchannels while you talk — with expressive full-duplex instructions. GPT_LIVE_DELEGATION=responses switches to a backend reasoning model (gpt-5.6-luna): smarter and able to speak unprompted, but slower on every turn. Needs livekit-agents[openai] and an OpenAI key with gpt-live-1 access.

• examples/dispatch.py — helper for the gpt-live-1 example: prints a viewer URL, waits until you've joined, then dispatches the avatar into that room (viewer-first). Run the worker in one terminal and this in another.

• examples/create_avatar.py — create an avatar from a portrait and print its id.

• tests/test_live_agent.py — a manual live smoke test: a real GPT voice agent (OpenAI LLM + TTS, silero VAD) with an Atmee avatar in a real LiveKit room, asserting the avatar joins, publishes video, and that its agent-driven audio flows. Skipped unless RUN_LIVE_ATMEE_AGENT=1 and the credentials are set:

  RUN_LIVE_ATMEE_AGENT=1 ATMEE_API_KEY=… ATMEE_AVATAR_ID=… \
  LIVEKIT_URL=… LIVEKIT_API_KEY=… LIVEKIT_API_SECRET=… OPENAI_API_KEY=… \
  uv run --group dev --with "livekit-agents[openai,silero]" \
      pytest tests/test_live_agent.py -m live -s
  

How it works

your agent (livekit-agents) ── plugin ──► Atmee API        (X-Api-Key, https)
        │                                     │
        │  audio over lk.audio_stream          ▼
        └──────────────► your LiveKit room ◄── Atmee rendering worker
                                              joins as atmee-avatar-agent,
                                              publishes video + audio

1. AvatarSession.start() mints a LiveKit access token for the avatar participant with your LiveKit credentials: identity atmee-avatar-agent, kind: agent, a roomJoin grant for your room, and the attribute lk.publish_on_behalf set to your agent's identity. Atmee receives only that token, never your secret.
2. It calls POST /v1/avatars/{avatarId}/avatar_sessions with your LiveKit URL and the token. Atmee reserves a session, starts a rendering worker, and answers as soon as the worker acknowledged the start.
3. The worker joins your room with the token, waits for your agent, receives its audio over the LiveKit data stream (lk.audio_stream, 16 kHz PCM, the standard DataStreamAudioOutput), and publishes lip-synced video and audio on behalf of your agent.
4. When your agent leaves or the room closes, the worker notices from inside the room and reports the end to Atmee, which stops billing. aclose() removes the avatar participant and ends the session explicitly as well.

Development

uv venv && uv pip install -e . --group dev
uv run pytest -q
uv run ruff check . && uv run ruff format --check . && uv run mypy

Releases are published to PyPI from v* tags by .github/workflows/publish.yml (PyPI trusted publishing).

License

Apache-2.0.