Gemini Realtime Agent with Live Vision

This example demonstrates how to start a Gemini Realtime agent that can see video from the call. The session uses Google's realtime model with proactivity enabled and Silero VAD for turn-taking.

Prerequisites

Add a .env in this directory with your LiveKit and Google credentials:

LIVEKIT_URL=your_livekit_url
LIVEKIT_API_KEY=your_api_key
LIVEKIT_API_SECRET=your_api_secret
GOOGLE_API_KEY=your_google_api_key

Install dependencies:

pip install "livekit-agents[silero,google]" python-dotenv

Load environment, logging, and define an AgentServer

Start by importing the required modules and setting up logging. The AgentServer wraps your application and manages the worker lifecycle.

import logging
from dotenv import load_dotenv
from livekit.agents import JobContext, JobProcess, Agent, AgentSession, AgentServer, cli, RoomInputOptions
from livekit.plugins import silero, google

load_dotenv()

logger = logging.getLogger("gemini-live-vision")
logger.setLevel(logging.INFO)

server = AgentServer()

Prewarm VAD for faster connections

Preload the VAD model once per process. This runs before any sessions start and stores the VAD instance in proc.userdata so it can be reused, cutting down on connection latency.

def prewarm(proc: JobProcess):
    proc.userdata["vad"] = silero.VAD.load()

server.setup_fnc = prewarm

Create a simple vision-capable agent

Keep the agent minimal—just add instructions that acknowledge its vision capabilities. The actual video processing comes from the session configuration with RoomInputOptions.

import logging
from dotenv import load_dotenv
from livekit.agents import JobContext, JobProcess, Agent, AgentSession, AgentServer, cli, RoomInputOptions
from livekit.plugins import silero, google

load_dotenv()

logger = logging.getLogger("gemini-live-vision")
logger.setLevel(logging.INFO)

server = AgentServer()

class Assistant(Agent):
    def __init__(self) -> None:
        super().__init__(instructions="You are a helpful voice AI assistant that can see the world around you.")

Define the RTC session entrypoint

Configure the Gemini Realtime model with proactivity and affective dialog enabled. Proactivity lets the model speak when it has something relevant to say. Enable video in RoomInputOptions so the agent receives video frames from the room. After starting and connecting, call generate_reply() to have the agent greet the caller.

import logging
from dotenv import load_dotenv
from livekit.agents import JobContext, JobProcess, Agent, AgentSession, AgentServer, cli, RoomInputOptions
from livekit.plugins import silero, google

load_dotenv()

logger = logging.getLogger("gemini-live-vision")
logger.setLevel(logging.INFO)

server = AgentServer()


def prewarm(proc: JobProcess):
    proc.userdata["vad"] = silero.VAD.load()


server.setup_fnc = prewarm


class Assistant(Agent):
    def __init__(self) -> None:
        super().__init__(instructions="You are a helpful voice AI assistant that can see the world around you.")

@server.rtc_session()
async def entrypoint(ctx: JobContext):
    ctx.log_context_fields = {"room": ctx.room.name}

    session = AgentSession(
        llm=google.beta.realtime.RealtimeModel(
            model="gemini-2.5-flash-native-audio-preview-09-2025",
            proactivity=True,
            enable_affective_dialog=True
        ),
        vad=ctx.proc.userdata["vad"],
    )

    await session.start(
        room=ctx.room,
        agent=Assistant(),
        room_input_options=RoomInputOptions(video_enabled=True),
    )
    await ctx.connect()

    await session.generate_reply()

Run the server

The cli.run_app() function starts the agent server and manages connections to LiveKit.

import logging
from dotenv import load_dotenv
from livekit.agents import JobContext, JobProcess, Agent, AgentSession, AgentServer, cli, RoomInputOptions
from livekit.plugins import silero, google

load_dotenv()

logger = logging.getLogger("gemini-live-vision")
logger.setLevel(logging.INFO)

server = AgentServer()


def prewarm(proc: JobProcess):
    proc.userdata["vad"] = silero.VAD.load()


server.setup_fnc = prewarm


class Assistant(Agent):
    def __init__(self) -> None:
        super().__init__(instructions="You are a helpful voice AI assistant that can see the world around you.")


@server.rtc_session()
async def entrypoint(ctx: JobContext):
    ctx.log_context_fields = {"room": ctx.room.name}

    session = AgentSession(
        llm=google.beta.realtime.RealtimeModel(
            model="gemini-2.5-flash-native-audio-preview-09-2025",
            proactivity=True,
            enable_affective_dialog=True
        ),
        vad=ctx.proc.userdata["vad"],
    )

    await session.start(
        room=ctx.room,
        agent=Assistant(),
        room_input_options=RoomInputOptions(video_enabled=True),
    )
    await ctx.connect()

    await session.generate_reply()

if __name__ == "__main__":
    cli.run_app(server)

Run it

python gemini_live_vision.py console

How it works

The session uses Gemini Realtime as the LLM with proactivity turned on.
RoomInputOptions(video_enabled=True) lets the agent receive video frames.
Silero VAD manages turn-taking for audio.
An initial generate_reply() greets the caller; the model can incorporate vision context in responses.

Full example

import logging
from dotenv import load_dotenv
from livekit.agents import JobContext, JobProcess, Agent, AgentSession, AgentServer, cli, RoomInputOptions
from livekit.plugins import silero, google

load_dotenv()

logger = logging.getLogger("gemini-live-vision")
logger.setLevel(logging.INFO)


class Assistant(Agent):
    def __init__(self) -> None:
        super().__init__(instructions="You are a helpful voice AI assistant that can see the world around you.")


server = AgentServer()


def prewarm(proc: JobProcess):
    proc.userdata["vad"] = silero.VAD.load()


server.setup_fnc = prewarm


@server.rtc_session()
async def entrypoint(ctx: JobContext):
    ctx.log_context_fields = {"room": ctx.room.name}

    session = AgentSession(
        llm=google.beta.realtime.RealtimeModel(
            model="gemini-2.5-flash-native-audio-preview-09-2025",
            proactivity=True,
            enable_affective_dialog=True
        ),
        vad=ctx.proc.userdata["vad"],
    )

    await session.start(
        room=ctx.room,
        agent=Assistant(),
        room_input_options=RoomInputOptions(video_enabled=True),
    )
    await ctx.connect()

    await session.generate_reply()


if __name__ == "__main__":
    cli.run_app(server)