实时语音 Agent 正在从“演示玩具”走向真正可用，而 LiveKit Agents 可能是目前最完整的开源框架之一。

它不只是把 STT、LLM 和 TTS 串起来，还直接提供：

• WebRTC 实时音视频 • 电话呼入与呼出 • 语义轮次检测，减少抢话 • 原生 MCP 工具调用 • 多 Agent Handoff • 内置测试与 LLM Judge • 支持自托管完整技术栈

开发者可以自由组合不同厂商的语音和大模型，快速构建 AI 客服、电话机器人、会议助手及实时语音应用。

GitHub：

livekit/agents

Source: https://github.com/livekit/agents

Looking for the JS/TS library? Check out AgentsJS

What is Agents?

The Agent Framework is designed for building realtime, programmable participants that run on servers. Use it to create conversational, multi-modal voice agents that can see, hear, and understand.

Features

• Flexible integrations: A comprehensive ecosystem to mix and match the right STT, LLM, TTS, and Realtime API to suit your use case.
• Integrated job scheduling: Built-in task scheduling and distribution with dispatch APIs to connect end users to agents.
• Extensive WebRTC clients: Build client applications using LiveKit’s open-source SDK ecosystem, supporting all major platforms.
• Telephony integration: Works seamlessly with LiveKit’s telephony stack, allowing your agent to make calls to or receive calls from phones.
• Exchange data with clients: Use RPCs and other Data APIs to seamlessly exchange data with clients.
• Semantic turn detection: Uses a transformer model to detect when a user is done with their turn, helps to reduce interruptions.
• MCP support: Native support for MCP. Integrate tools provided by MCP servers with one line of code.
• Builtin test framework: Write tests and use judges to ensure your agent is performing as expected.
• Open-source: Fully open-source, allowing you to run the entire stack on your own servers, including LiveKit server, one of the most widely used WebRTC media servers.

Installation

To install the core Agents library, along with plugins for popular model providers:

pip install "livekit-agents[openai,deepgram,cartesia]"

Docs and guides

Documentation on the framework and how to use it can be found here

Building with AI coding agents

If you’re using an AI coding assistant to build with LiveKit Agents, we recommend the following setup for the best results:

1. Install the LiveKit Docs MCP server — Gives your coding agent access to up-to-date LiveKit documentation, code search across LiveKit repositories, and working examples.

2. Install the LiveKit Agent Skill — Provides your coding agent with architectural guidance and best practices for building voice AI applications, including workflow design, handoffs, tasks, and testing patterns.

   npx skills add livekit/agent-skills --skill livekit-agents
   

The Agent Skill works best alongside the MCP server: the skill teaches your agent how to approach building with LiveKit, while the MCP server provides the current API details to implement it correctly.

Core concepts

• Agent: An LLM-based application with defined instructions.
• AgentSession: A container for agents that manages interactions with end users.
• entrypoint: The starting point for an interactive session, similar to a request handler in a web server.
• AgentServer: The main process that coordinates job scheduling and launches agents for user sessions.

Usage

Simple voice agent

from livekit.agents import (
    Agent,
    AgentServer,
    AgentSession,
    JobContext,
    RunContext,
    cli,
    function_tool,
    inference,
)


@function_tool
async def lookup_weather(
    context: RunContext,
    location: str,
):
    """Used to look up weather information."""

    return {"weather": "sunny", "temperature": 70}


server = AgentServer()


@server.rtc_session()
async def entrypoint(ctx: JobContext):
    session = AgentSession(
        vad=inference.VAD(),
        # any combination of STT, LLM, TTS, or realtime API can be used
        # this example shows LiveKit Inference, a unified API to access different models via LiveKit Cloud
        # to use model provider keys directly, replace with the following:
        # from livekit.plugins import deepgram, openai, cartesia
        # stt=deepgram.STT(model="nova-3"),
        # llm=openai.LLM(model="gpt-4.1-mini"),
        # tts=cartesia.TTS(model="sonic-3", voice="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc"),
        stt=inference.STT("deepgram/nova-3", language="multi"),
        llm=inference.LLM("google/gemma-4-31b-it"),  # low-latency gemma, hosted on LiveKit
        tts=inference.TTS("cartesia/sonic-3", voice="9626c31c-bec5-4cca-baa8-f8ba9e84c8bc"),
    )

    agent = Agent(
        instructions="You are a friendly voice assistant built by LiveKit.",
        tools=[lookup_weather],
    )

    await session.start(agent=agent, room=ctx.room)
    await session.generate_reply(instructions="greet the user and ask about their day")


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

You’ll need the following environment variables for this example:

• LIVEKIT_URL
• LIVEKIT_API_KEY
• LIVEKIT_API_SECRET

Multi-agent handoff

This code snippet is abbreviated. For the full example, see multi_agent.py

...
class IntroAgent(Agent):
    def __init__(self) -> None:
        super().__init__(
            instructions=f"You are a story teller. Your goal is to gather a few pieces of information from the user to make the story personalized and engaging."
            "Ask the user for their name and where they are from"
        )

    async def on_enter(self):
        self.session.generate_reply(instructions="greet the user and gather information")

    @function_tool
    async def information_gathered(
        self,
        context: RunContext,
        name: str,
        location: str,
    ):
        """Called when the user has provided the information needed to make the story personalized and engaging.

        Args:
            name: The name of the user
            location: The location of the user
        """

        context.userdata.name = name
        context.userdata.location = location

        story_agent = StoryAgent(name, location)
        return story_agent, "Let's start the story!"


class StoryAgent(Agent):
    def __init__(self, name: str, location: str) -> None:
        super().__init__(
            instructions=f"You are a storyteller. Use the user's information in order to make the story personalized."
            f"The user's name is {name}, from {location}",
            # override the default model, switching to Realtime API from standard LLMs
            llm=openai.realtime.RealtimeModel(voice="echo"),
            chat_ctx=chat_ctx,
        )

    async def on_enter(self):
        self.session.generate_reply()


@server.rtc_session()
async def entrypoint(ctx: JobContext):
    userdata = StoryData()
    session = AgentSession[StoryData](
        vad=inference.VAD(),
        stt="deepgram/nova-3",
        llm="google/gemma-4-31b-it",  # low-latency gemma, hosted on LiveKit
        tts="cartesia/sonic-3:9626c31c-bec5-4cca-baa8-f8ba9e84c8bc",
        userdata=userdata,
    )

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

Testing

Automated tests are essential for building reliable agents, especially with the non-deterministic behavior of LLMs. LiveKit Agents include native test integration to help you create dependable agents.

@pytest.mark.asyncio
async def test_no_availability() -> None:
    llm = google.LLM()
    async with AgentSession(llm=llm) as sess:
        await sess.start(MyAgent())
        result = await sess.run(
            user_input="Hello, I need to place an order."
        )
        result.expect.skip_next_event_if(type="message", role="assistant")
        result.expect.next_event().is_function_call(name="start_order")
        result.expect.next_event().is_function_call_output()
        await (
            result.expect.next_event()
            .is_message(role="assistant")
            .judge(llm, intent="assistant should be asking the user what they would like")
        )

Examples

For more examples and detailed setup instructions, see the examples directory. For even more examples, see the python-agents-examples repository.

Running your agent

Testing in terminal

python myagent.py console

Runs your agent in terminal mode, enabling local audio input and output for testing. This mode doesn’t require external servers or dependencies and is useful for quickly validating behavior.

Developing with LiveKit clients

python myagent.py dev

Starts the agent server and enables hot reloading when files change. This mode allows each process to host multiple concurrent agents efficiently.

The agent connects to LiveKit Cloud or your self-hosted server. Set the following environment variables:

• LIVEKIT_URL
• LIVEKIT_API_KEY
• LIVEKIT_API_SECRET

You can connect using any LiveKit client SDK or telephony integration. To get started quickly, try the Agents Playground.

Running for production

python myagent.py start

Runs the agent with production-ready optimizations.

License

The Agents framework is licensed under Apache-2.0. The LiveKit turn detection models are licensed under the LiveKit Model License.

Contributing

The Agents framework is under active development in a rapidly evolving field. We welcome and appreciate contributions of any kind, be it feedback, bugfixes, features, new plugins and tools, or better documentation. You can file issues under this repo, open a PR, or chat with us in the LiveKit community.

Development setup

This project uses uv for package management. To install dependencies for development:

uv sync --all-extras --dev

Examples

This project includes many examples in the examples directory. To run them, create the file examples/.env with credentials for LiveKit Server and any necessary model providers (see examples/.env.example), then run:

uv run examples/voice_agents/basic_agent.py dev

For more information, see the examples README.

Tests

Unit tests are in the tests directory and can be run with:

uv run pytest --unit

Integration tests for each plugin require various API credentials and run automatically in GitHub CI for PRs submitted by project maintainers. See the tests workflow for details.

Formatting

This project uses ruff for formatting and linting:

uv run ruff format
uv run ruff check --fix

Documentation

To generate docs locally with pdoc:

uv sync --all-extras --group docs
uv run --active pdoc --skip-errors --html --output-dir=docs livekit