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Livekit

Build voice AI agents with LiveKit.

Rating
4.9 (125 reviews)
Downloads
4,359 downloads
Version
1.0.0

Overview

Build voice AI agents with LiveKit.

Complete Documentation

View Source →

LiveKit Voice AI Skill

Build production voice agents with LiveKit's open-source platform.

Quick Start

bash
# Install SDK
pip install livekit-agents livekit-plugins-openai livekit-plugins-deepgram livekit-plugins-cartesia

# Or Node.js
npm install @livekit/agents @livekit/agents-plugin-openai

Minimal Agent (Python)

python
from livekit.agents import AgentSession, JobContext, WorkerOptions, cli
from livekit.plugins import deepgram, openai, cartesia

async def entrypoint(ctx: JobContext):
    await ctx.connect()
    
    session = AgentSession(
        stt=deepgram.STT(),
        llm=openai.LLM(model="gpt-4.1-mini"),
        tts=cartesia.TTS(),
    )
    
    session.start(ctx.room)
    await session.say("Hello! How can I help?")

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

Provider Selection

ComponentBudgetQualityLow Latency
STTDeepgram Nova-3AssemblyAIDeepgram Keychain
LLMGPT-4.1 miniClaude SonnetGPT-4.1 mini
TTSCartesia Sonic-3ElevenLabsCartesia Sonic-3

Voice Pipeline vs Realtime

STT-LLM-TTS Pipeline:

  • More control, mix providers
  • Generally cheaper
  • Easier to debug
OpenAI Realtime API:
  • Speech-to-speech, more expressive
  • Higher cost (~$0.10/min)
  • Less control

Environment Variables

bash
LIVEKIT_URL=wss://your-app.livekit.cloud
LIVEKIT_API_KEY=your-api-key
LIVEKIT_API_SECRET=your-api-secret

# Provider keys (if not using LiveKit Inference)
OPENAI_API_KEY=
DEEPGRAM_API_KEY=
CARTESIA_API_KEY=
ELEVENLABS_API_KEY=

Tool Use

python
from livekit.agents import function_tool

@function_tool()
async def get_weather(location: str) -> str:
    """Get current weather for a location."""
    # Your implementation
    return f"Weather in {location}: 72°F, sunny"

session = AgentSession(
    stt=deepgram.STT(),
    llm=openai.LLM(),
    tts=cartesia.TTS(),
    tools=[get_weather],
)

Telephony (SIP)

python
from livekit import api

# Outbound call
await lk_api.sip.create_sip_participant(
    api.CreateSIPParticipantRequest(
        sip_trunk_id="trunk-id",
        sip_call_to="+15551234567",
        room_name="my-room",
    )
)

Deployment

LiveKit Cloud: livekit-server-cli deploy --project my-project

Self-hosted:

bash
docker run -d \
  -p 7880:7880 -p 7881:7881 -p 7882:7882/udp \
  -e LIVEKIT_KEYS="api-key: api-secret" \
  livekit/livekit-server

Cost Estimates

ScenarioMonthly Cost
Dev/testingFree tier
100 hrs/mo voice~$150-250
Production B2B~$300-500
High volumeSelf-host

Common Patterns

Turn Detection

python
session = AgentSession(
    turn_detection=openai.TurnDetection(
        threshold=0.5,
        silence_duration_ms=500,
    ),
    ...
)

Interruption Handling

python
@session.on("user_speech_started")
async def handle_interruption():
    session.stop_speaking()

Multi-Agent Handoff

python
await session.transfer_to(specialist_agent)

References

  • Docs: https://docs.livekit.io/agents/
  • Examples: https://github.com/livekit/agents/tree/main/examples
  • Playground: https://agents-playground.livekit.io

Installation

Terminal bash 

openclaw install livekit
Copied!

💻Code Examples

cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint))

-clirunappworkeroptionsentrypointfncentrypoint.txt
## Provider Selection

| Component | Budget | Quality | Low Latency |
|-----------|--------|---------|-------------|
| **STT** | Deepgram Nova-3 | AssemblyAI | Deepgram Keychain |
| **LLM** | GPT-4.1 mini | Claude Sonnet | GPT-4.1 mini |
| **TTS** | Cartesia Sonic-3 | ElevenLabs | Cartesia Sonic-3 |

## Voice Pipeline vs Realtime

**STT-LLM-TTS Pipeline:**
- More control, mix providers
- Generally cheaper
- Easier to debug

**OpenAI Realtime API:**
- Speech-to-speech, more expressive
- Higher cost (~$0.10/min)
- Less control

## Environment Variables

)

.txt
## Deployment

**LiveKit Cloud:** `livekit-server-cli deploy --project my-project`

**Self-hosted:**

livekit/livekit-server

-livekitlivekit-server.txt
## Cost Estimates

| Scenario | Monthly Cost |
|----------|--------------|
| Dev/testing | Free tier |
| 100 hrs/mo voice | ~$150-250 |
| Production B2B | ~$300-500 |
| High volume | Self-host |

## Common Patterns

### Turn Detection
example.sh
# Install SDK
pip install livekit-agents livekit-plugins-openai livekit-plugins-deepgram livekit-plugins-cartesia

# Or Node.js
npm install @livekit/agents @livekit/agents-plugin-openai
example.py
from livekit.agents import AgentSession, JobContext, WorkerOptions, cli
from livekit.plugins import deepgram, openai, cartesia

async def entrypoint(ctx: JobContext):
    await ctx.connect()
    
    session = AgentSession(
        stt=deepgram.STT(),
        llm=openai.LLM(model="gpt-4.1-mini"),
        tts=cartesia.TTS(),
    )
    
    session.start(ctx.room)
    await session.say("Hello! How can I help?")

if __name__ == "__main__":
    cli.run_app(WorkerOptions(entrypoint_fnc=entrypoint))
example.sh
LIVEKIT_URL=wss://your-app.livekit.cloud
LIVEKIT_API_KEY=your-api-key
LIVEKIT_API_SECRET=your-api-secret

# Provider keys (if not using LiveKit Inference)
OPENAI_API_KEY=
DEEPGRAM_API_KEY=
CARTESIA_API_KEY=
ELEVENLABS_API_KEY=
example.py
from livekit.agents import function_tool

@function_tool()
async def get_weather(location: str) -> str:
    """Get current weather for a location."""
    # Your implementation
    return f"Weather in {location}: 72°F, sunny"

session = AgentSession(
    stt=deepgram.STT(),
    llm=openai.LLM(),
    tts=cartesia.TTS(),
    tools=[get_weather],
)
example.py
from livekit import api

# Outbound call
await lk_api.sip.create_sip_participant(
    api.CreateSIPParticipantRequest(
        sip_trunk_id="trunk-id",
        sip_call_to="+15551234567",
        room_name="my-room",
    )
)
example.sh
docker run -d \
  -p 7880:7880 -p 7881:7881 -p 7882:7882/udp \
  -e LIVEKIT_KEYS="api-key: api-secret" \
  livekit/livekit-server
example.py
session = AgentSession(
    turn_detection=openai.TurnDetection(
        threshold=0.5,
        silence_duration_ms=500,
    ),
    ...
)

Tags

#web_and-frontend-development

Quick Info

Category Development
Model Claude 3.5
Complexity Multi-Agent
Author zoroposkai
Last Updated 3/10/2026
🚀
Optimized for
Claude 3.5
🧠

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openclaw install livekit

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