# agents **Repository Path**: danielxvcg/agents ## Basic Information - **Project Name**: agents - **Description**: No description available - **Primary Language**: Python - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-09-02 - **Last Updated**: 2025-09-02 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README The LiveKit icon, the name of the repository and some sample code in the background.
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Looking for the JS/TS library? Check out [AgentsJS](https://github.com/livekit/agents-js) ## 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](https://docs.livekit.io/agents/build/dispatch/) 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](https://docs.livekit.io/sip/), allowing your agent to make calls to or receive calls from phones. - **Exchange data with clients**: Use [RPCs](https://docs.livekit.io/home/client/data/rpc/) and other [Data APIs](https://docs.livekit.io/home/client/data/) 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 loc. - **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](https://github.com/livekit/livekit), one of the most widely used WebRTC media servers. ## Installation To install the core Agents library, along with plugins for popular model providers: ```bash pip install "livekit-agents[openai,silero,deepgram,cartesia,turn-detector]~=1.0" ``` ## Docs and guides Documentation on the framework and how to use it can be found [here](https://docs.livekit.io/agents/) ## 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. - Worker: The main process that coordinates job scheduling and launches agents for user sessions. ## Usage ### Simple voice agent --- ```python from livekit.agents import ( Agent, AgentSession, JobContext, RunContext, WorkerOptions, cli, function_tool, ) from livekit.plugins import deepgram, elevenlabs, openai, silero @function_tool async def lookup_weather( context: RunContext, location: str, ): """Used to look up weather information.""" return {"weather": "sunny", "temperature": 70} async def entrypoint(ctx: JobContext): await ctx.connect() agent = Agent( instructions="You are a friendly voice assistant built by LiveKit.", tools=[lookup_weather], ) session = AgentSession( vad=silero.VAD.load(), # any combination of STT, LLM, TTS, or realtime API can be used stt=deepgram.STT(model="nova-3"), llm=openai.LLM(model="gpt-4o-mini"), tts=elevenlabs.TTS(), ) 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(WorkerOptions(entrypoint_fnc=entrypoint)) ``` You'll need the following environment variables for this example: - DEEPGRAM_API_KEY - OPENAI_API_KEY ### Multi-agent handoff --- This code snippet is abbreviated. For the full example, see [multi_agent.py](examples/voice_agents/multi_agent.py) ```python ... 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() async def entrypoint(ctx: JobContext): await ctx.connect() userdata = StoryData() session = AgentSession[StoryData]( vad=silero.VAD.load(), stt=deepgram.STT(model="nova-3"), llm=openai.LLM(model="gpt-4o-mini"), tts=openai.TTS(voice="echo"), 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. ```python @pytest.mark.asyncio async def test_no_availability() -> None: llm = google.LLM() async 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

๐ŸŽ™๏ธ Starter Agent

A starter agent optimized for voice conversations.

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๐Ÿ”„ Multi-user push to talk

Responds to multiple users in the room via push-to-talk.

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๐ŸŽต Background audio

Background ambient and thinking audio to improve realism.

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๐Ÿ› ๏ธ Dynamic tool creation

Creating function tools dynamically.

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โ˜Ž๏ธ Outbound caller

Agent that makes outbound phone calls

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๐Ÿ“‹ Structured output

Using structured output from LLM to guide TTS tone.

Code

๐Ÿ”Œ MCP support

Use tools from MCP servers

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๐Ÿ’ฌ Text-only agent

Skip voice altogether and use the same code for text-only integrations

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๐Ÿ“ Multi-user transcriber

Produce transcriptions from all users in the room

Code

๐ŸŽฅ Video avatars

Add an AI avatar with Tavus, Beyond Presence, and Bithuman

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๐Ÿฝ๏ธ Restaurant ordering and reservations

Full example of an agent that handles calls for a restaurant.

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๐Ÿ‘๏ธ Gemini Live vision

Full example (including iOS app) of Gemini Live agent that can see.

Code

## Running your agent ### Testing in terminal ```shell 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 ```shell 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](https://agents-playground.livekit.io/). ### Running for production ```shell python myagent.py start ``` Runs the agent with production-ready optimizations. ## 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 LiveKit's [Slack community](https://livekit.io/join-slack).
LiveKit Ecosystem
LiveKit SDKsBrowser ยท iOS/macOS/visionOS ยท Android ยท Flutter ยท React Native ยท Rust ยท Node.js ยท Python ยท Unity ยท Unity (WebGL) ยท ESP32
Server APIsNode.js ยท Golang ยท Ruby ยท Java/Kotlin ยท Python ยท Rust ยท PHP (community) ยท .NET (community)
UI ComponentsReact ยท Android Compose ยท SwiftUI ยท Flutter
Agents FrameworksPython ยท Node.js ยท Playground
ServicesLiveKit server ยท Egress ยท Ingress ยท SIP
ResourcesDocs ยท Example apps ยท Cloud ยท Self-hosting ยท CLI