Build, run, and observe AI agents

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from dotenv import load_dotenv
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from livekit import agents, rtc
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from livekit.agents import AgentServer,AgentSession, Agent, room_io
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from livekit.plugins import noise_cancellation, silero
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from livekit.plugins.turn_detector.multilingual import MultilingualModel
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load_dotenv(".env.local")
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class Assistant(Agent):
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    def __init__(self) -> None:
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        super().__init__(
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            instructions="""You are a helpful voice AI assistant.
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            You eagerly assist users with their questions by providing information from your extensive knowledge.
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            Your responses are concise, to the point, and without any complex formatting or punctuation including emojis, asterisks, or other symbols.
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            You are curious, friendly, and have a sense of humor.""",
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        )
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server = AgentServer()
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@server.rtc_session(agent_name="my-agent")
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async def my_agent(ctx: agents.JobContext):
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    session = AgentSession(
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        stt="deepgram/nova-3:multi",
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        llm="openai/gpt-4.1-mini",
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        tts="cartesia/sonic-3:9626c31c-bec5-4cca-baa8-f8ba9e84c8bc",
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        vad=silero.VAD.load(),
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        turn_detection=MultilingualModel(),
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    )
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    await session.start(
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        room=ctx.room,
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        agent=Assistant(),
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        room_options=room_io.RoomOptions(
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            audio_input=room_io.AudioInputOptions(
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                noise_cancellation=lambda params: noise_cancellation.BVCTelephony() if params.participant.kind == rtc.ParticipantKind.PARTICIPANT_KIND_SIP else noise_cancellation.BVC(),
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            ),
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        ),
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    )
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    await session.generate_reply(
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        instructions="Greet the user and offer your assistance."
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    )
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if __name__ == "__main__":
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    agents.cli.run_app(server)