1. Starts session with LangGraph integration.
2. Trigger any tool call
3. Observe tool output streamed.
...
- Sample code snippet, or a GitHub Gist link -

@tool
def get_weather(city: str) -> str:
    """Get current weather for a city."""
    weather = random.choice(["sunny", "cloudy", "rainy", "snowy"])
    temp = random.randint(-5, 35)
    return f"{city}: {weather}, {temp}C"


@tool
def get_time() -> str:
    """Get the current time."""
    return datetime.now().strftime("%H:%M")


# -- LangGraph agent --

model = ChatGoogleGenerativeAI(model="gemini-2.5-flash")
tools = [get_weather, get_time]

graph = create_agent(
    model=model,
    tools=tools,
    system_prompt="You are a helpful voice assistant. Keep responses to 1-2 sentences.",
)

# -- LiveKit voice agent --

server = AgentServer()


@server.rtc_session()
async def voice_session(ctx: agents.JobContext):
    session = AgentSession(
        stt=openai.STT(model="gpt-4o-transcribe"),
        tts=openai.TTS(voice="marin"),
        llm=LLMAdapter(graph=graph, stream_mode="messages"),
        vad=silero.VAD.load(),
    )

    await session.start(
        room=ctx.room,
        agent=Agent(instructions=""),
    )


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

livekit-agents==1.4.1