/livekit-skills
Build voice AI agents with LiveKit Agents SDK. Use when the user asks to "build a voice agent", "create a LiveKit agent", "add voice AI", "implement handoffs", "structure agent workflows", or is working with LiveKit Agents SDK. Covers both LiveKit Cloud and self-hosted
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Build voice AI agents with LiveKit Agents SDK. Use when the user asks to "build a voice agent", "create a LiveKit agent", "add voice AI", "implement handoffs", "structure agent workflows", or is working with LiveKit Agents SDK. Covers both LiveKit Cloud and self-hosted
SKILL.md
livekit-skills.SKILL.mdname: livekit-skills
description: 'Build voice AI agents with LiveKit Agents SDK. Use when the user asks to "build a voice agent", "create a LiveKit agent", "add voice AI", "implement handoffs", "structure agent workflows", or is working with LiveKit Agents SDK. Covers both LiveKit Cloud and self-hosted deployments using lk CLI.'
license: MIT
metadata:
author: livekit
version: "0.3.1"
LiveKit Voice Agent Development
This skill provides guidance for building voice AI agents with the LiveKit Agents SDK. It covers both LiveKit Cloud and self-hosted deployments, using the `lk` CLI for documentation access and project management. All factual information about APIs, methods, and configurations must come from live documentation.
MANDATORY: Read This Checklist Before Starting
Before writing ANY code, complete this checklist:
1. **Read this entire skill document** - Do not skip sections 2. **Set up LiveKit credentials** (Cloud project or self-hosted server) - You need `LIVEKIT_URL`, `LIVEKIT_API_KEY`, and `LIVEKIT_API_SECRET` 3. **Set up documentation access** - Install `lk` CLI for `lk docs` commands 4. **Plan to write tests** - Every agent implementation MUST include tests (see testing section below) 5. **Verify all APIs against live docs** - Never rely on model memory for LiveKit APIs
Setup
LiveKit Cloud
LiveKit Cloud is the fastest way to get a voice agent running. It provides:
- Managed infrastructure (no servers to deploy)
- **LiveKit Inference** for AI models (no separate API keys needed)
- Built-in noise cancellation, turn detection, and other voice features
- Simple credential management
Connect to Your Cloud Project
1. Sign up at [cloud.livekit.io](https://cloud.livekit.io) if you haven't already 2. Create a project (or use an existing one) 3. Get your credentials from the project settings:
- `LIVEKIT_URL` - Your project's WebSocket URL (e.g., `wss://your-project.livekit.cloud`)
- `LIVEKIT_API_KEY` - API key for authentication
- `LIVEKIT_API_SECRET` - API secret for authentication
4. Set these as environment variables (typically in `.env.local`):
LIVEKIT_URL=wss://your-project.livekit.cloud
LIVEKIT_API_KEY=your-api-key
LIVEKIT_API_SECRET=your-api-secret
The LiveKit CLI can automate credential setup. Consult the CLI documentation for current commands.
Use LiveKit Inference for AI Models
LiveKit Inference is one option for AI model access when using LiveKit Cloud. It provides access to leading AI model providers—all through your LiveKit credentials with no separate API keys needed.
Benefits of LiveKit Inference:
- No separate API keys to manage for each AI provider
- Billing consolidated through your LiveKit Cloud account
- Optimized for voice AI workloads
Consult the documentation for available models, supported providers, and current usage patterns. The documentation always has the most up-to-date information.
Self-Hosted Setup
Self-hosting removes Cloud tier limits on deployments and concurrency. You control scaling directly.
Local development
Install and run the LiveKit server:
- macOS: `brew install livekit`
- Linux: `curl -sSL https://get.livekit.io | bash`
Start in dev mode:
livekit-server --dev
Default credentials: API key `devkey`, API secret `secret`.
Set environment variables:
LIVEKIT_URL=ws://localhost:7880
LIVEKIT_API_KEY=devkey
LIVEKIT_API_SECRET=secret
Production deployment
Deploy `livekit-server` via Docker, Kubernetes, or VMs on any provider (Hetzner, AWS, GCP, etc.). Consult `lk docs get-page /home/self-hosting` or see `references/self-hosting.md` for details. Agent servers run as regular processes managed by your infra tooling.
Using Your Own Model Providers
When self-hosting or when you prefer your own API keys over LiveKit Inference, configure model providers directly via environment variables:
# STT (Speech-to-Text)
DEEPGRAM_API_KEY=your-key
# LLM
OPENAI_API_KEY=your-key
# TTS (Text-to-Speech)
ELEVEN_API_KEY=your-key
# or
CARTESIA_API_KEY=your-key
The Agents SDK has plugins for all major providers. Pass model identifiers directly:
**Node.js / TypeScript:**
import { voice } from "@livekit/agents";
const session = new voice.AgentSession({
stt: "deepgram/nova-3:multi",
llm: "openai/gpt-4.1-mini",
tts: "cartesia/sonic-3:voice-id", // or "elevenlabs/..."
});**Python:**
session = AgentSession(
stt="deepgram/nova-3",
llm="openai/gpt-4.1-mini",
tts="elevenlabs/...", # or "cartesia/sonic-3:voice-id"
)Consult `lk docs search "plugins"` for the full list of supported providers.
Project Templates
Initialize a new agent project with the CLI:
**Backend agents:**
lk agent init my-agent --template agent-starter-python
lk agent init my-agent --template agent-starter-node
**Frontend apps (React/Next.js, React Native, Swift, Flutter, Android):**
lk agent init my-frontend --template agent-starter-react
lk agent init my-frontend --template agent-starter-react-native
Omit `--template` to see all available templates interactively.
Critical Rule: Never Trust Model Memory for LiveKit APIs
LiveKit Agents is a fast-evolving SDK. Model training data is outdated the moment it's created. When working with LiveKit:
- **Never assume** API signatures, method names, or configuration options from memory
- **Never guess** SDK behavior or default values
- **Always verify** against live documentation before writing code
- **Always cite** the documentation source when implementing features
This rule applies even when confident about an API. Verify anyway.
Use LiveKit CLI for Documentation
Before writing any LiveKit code, use the `lk docs` CLI commands for current, verified API information. This prevents reliance on stale model knowledge.
Search documentation
lk docs search "voice agent quickstart"
lk docs search "handoffs and tasks"
Fetch specific
Read more
name: livekit-skills description: 'Build voice AI agents with LiveKit Agents SDK. Use when the user asks to "build a voice agent", "create a LiveKit agent", "add voice AI", "implement handoffs", "structure agent workflows", or is working with LiveKit Agents SDK. Covers both LiveKit Cloud and self-hosted deployments using lk CLI.' license: MIT metadata: author: livekit version: "0.3.1"
LiveKit Voice Agent Development
This skill provides guidance for building voice AI agents with the LiveKit Agents SDK. It covers both LiveKit Cloud and self-hosted deployments, using the `lk` CLI for documentation access and project management. All factual information about APIs, methods, and configurations must come from live documentation.
MANDATORY: Read This Checklist Before Starting
Before writing ANY code, complete this checklist:
1. **Read this entire skill document** - Do not skip sections 2. **Set up LiveKit credentials** (Cloud project or self-hosted server) - You need `LIVEKIT_URL`, `LIVEKIT_API_KEY`, and `LIVEKIT_API_SECRET` 3. **Set up documentation access** - Install `lk` CLI for `lk docs` commands 4. **Plan to write tests** - Every agent implementation MUST include tests (see testing section below) 5. **Verify all APIs against live docs** - Never rely on model memory for LiveKit APIs
Setup
LiveKit Cloud
LiveKit Cloud is the fastest way to get a voice agent running. It provides:
- Managed infrastructure (no servers to deploy)
- **LiveKit Inference** for AI models (no separate API keys needed)
- Built-in noise cancellation, turn detection, and other voice features
- Simple credential management
Connect to Your Cloud Project
1. Sign up at [cloud.livekit.io](https://cloud.livekit.io) if you haven't already 2. Create a project (or use an existing one) 3. Get your credentials from the project settings:
- `LIVEKIT_URL` - Your project's WebSocket URL (e.g., `wss://your-project.livekit.cloud`)
- `LIVEKIT_API_KEY` - API key for authentication
- `LIVEKIT_API_SECRET` - API secret for authentication
4. Set these as environment variables (typically in `.env.local`):
LIVEKIT_URL=wss://your-project.livekit.cloud LIVEKIT_API_KEY=your-api-key LIVEKIT_API_SECRET=your-api-secret
The LiveKit CLI can automate credential setup. Consult the CLI documentation for current commands.
Use LiveKit Inference for AI Models
LiveKit Inference is one option for AI model access when using LiveKit Cloud. It provides access to leading AI model providers—all through your LiveKit credentials with no separate API keys needed.
Benefits of LiveKit Inference:
- No separate API keys to manage for each AI provider
- Billing consolidated through your LiveKit Cloud account
- Optimized for voice AI workloads
Consult the documentation for available models, supported providers, and current usage patterns. The documentation always has the most up-to-date information.
Self-Hosted Setup
Self-hosting removes Cloud tier limits on deployments and concurrency. You control scaling directly.
Local development
Install and run the LiveKit server:
- macOS: `brew install livekit`
- Linux: `curl -sSL https://get.livekit.io | bash`
Start in dev mode:
livekit-server --dev
Default credentials: API key `devkey`, API secret `secret`.
Set environment variables:
LIVEKIT_URL=ws://localhost:7880 LIVEKIT_API_KEY=devkey LIVEKIT_API_SECRET=secret
Production deployment
Deploy `livekit-server` via Docker, Kubernetes, or VMs on any provider (Hetzner, AWS, GCP, etc.). Consult `lk docs get-page /home/self-hosting` or see `references/self-hosting.md` for details. Agent servers run as regular processes managed by your infra tooling.
Using Your Own Model Providers
When self-hosting or when you prefer your own API keys over LiveKit Inference, configure model providers directly via environment variables:
# STT (Speech-to-Text) DEEPGRAM_API_KEY=your-key # LLM OPENAI_API_KEY=your-key # TTS (Text-to-Speech) ELEVEN_API_KEY=your-key # or CARTESIA_API_KEY=your-key
The Agents SDK has plugins for all major providers. Pass model identifiers directly:
**Node.js / TypeScript:**
import { voice } from "@livekit/agents";
const session = new voice.AgentSession({
stt: "deepgram/nova-3:multi",
llm: "openai/gpt-4.1-mini",
tts: "cartesia/sonic-3:voice-id", // or "elevenlabs/..."
});**Python:**
session = AgentSession(
stt="deepgram/nova-3",
llm="openai/gpt-4.1-mini",
tts="elevenlabs/...", # or "cartesia/sonic-3:voice-id"
)Consult `lk docs search "plugins"` for the full list of supported providers.
Project Templates
Initialize a new agent project with the CLI:
**Backend agents:**
lk agent init my-agent --template agent-starter-python lk agent init my-agent --template agent-starter-node
**Frontend apps (React/Next.js, React Native, Swift, Flutter, Android):**
lk agent init my-frontend --template agent-starter-react lk agent init my-frontend --template agent-starter-react-native
Omit `--template` to see all available templates interactively.
Critical Rule: Never Trust Model Memory for LiveKit APIs
LiveKit Agents is a fast-evolving SDK. Model training data is outdated the moment it's created. When working with LiveKit:
- **Never assume** API signatures, method names, or configuration options from memory
- **Never guess** SDK behavior or default values
- **Always verify** against live documentation before writing code
- **Always cite** the documentation source when implementing features
This rule applies even when confident about an API. Verify anyway.
Use LiveKit CLI for Documentation
Before writing any LiveKit code, use the `lk docs` CLI commands for current, verified API information. This prevents reliance on stale model knowledge.
Search documentation
lk docs search "voice agent quickstart" lk docs search "handoffs and tasks"
Fetch specific
Showing the first part of this file.
Battle-tested Claude Code, OpenAI Codex, Cursor configs, plugins, hooks and agents with Kimi, MiniMax and GLM API support.
Repo: fcakyon/claude-codex-settings
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