The Developer’s Blueprint for Multimodal AI Agents with LiveKit

livekit/agents

Think of this framework as the "connective tissue" between high-end AI brains (like LLMs) and the real-world plumbing of low-latency video and audio.

Traditionally, building a voice assistant required stitching together three very different worlds

WebRTC
For low-latency streaming (notoriously difficult to manage at scale).

STT/TTS
Converting speech to text and back again.

LLM Orchestration
Handling context, interruptions, and function calling.

LiveKit Agents simplifies this by providing a unified framework. It treats an AI agent like a first-class participant in a video call. The agent "joins" the room, listens to the audio tracks, and publishes its own audio/video tracks back to the users.

Low Latency by Design
It uses WebRTC, which is significantly faster than standard HTTP-based polling.

VAD (Voice Activity Detection)
It includes built-in logic to know exactly when a user starts and stops talking.

Interruptibility
This is the "holy grail" of voice AI. The framework makes it easy to stop the agent's speech the moment the user interrupts.

Multi-modal
It’s not just for voice; you can build agents that "see" through video tracks or manipulate data in real-time.

You’ll need a LiveKit server instance. You can run one locally via Docker or use their cloud managed service.

# Install the agent framework
pip install livekit-agents livekit-plugins-openai livekit-plugins-silero

Here is a high-level example of how you define an agent. This agent uses OpenAI for its brain and Silero for voice detection.

from livekit.agents import JobContext, WorkerOptions, cli, llm
from livekit.plugins import openai, silero

async def entrypoint(ctx: JobContext):
    # Define the "brain" and "voice" of your agent
    initial_ctx = llm.ChatContext().append(
        role="system",
        text="You are a helpful assistant. Keep your responses concise."
    )

    # Initialize the Voice Assistant logic
    agent = VoiceAssistant(
        vad=silero.VAD.load(),
        stt=openai.STT(),
        llm=openai.LLM(),
        tts=openai.TTS(),
        chat_ctx=initial_ctx,
    )

    # Connect to the LiveKit room
    await ctx.connect()
    
    # Start the assistant in the room
    agent.start(ctx.room)
    
    # Say hello!
    await agent.say("Hey there! How can I help you today?", allow_interruptions=True)

if __name__ == "__main__":
    cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint))

You typically deploy these agents as Workers. When a user joins a room, the LiveKit server sends a webhook to your worker, which then "spawns" an agent instance to join that specific room.

When building with this, pay close attention to VAD sensitivity. If it's too sensitive, the agent will stop talking every time the user coughs. If it's not sensitive enough, the user will feel like they can't get a word in edgewise. LiveKit allows you to tune these parameters easily!