livekit-agents

What this skill does

# LiveKit Agents Development for LiveKit Cloud

This skill provides opinionated guidance for building voice AI agents with LiveKit Cloud. It assumes you are using LiveKit Cloud (the recommended path) and encodes *how to approach* agent development, not API specifics. All factual information about APIs, methods, and configurations must come from live documentation.

**This skill is for LiveKit Cloud developers.** If you're self-hosting LiveKit, some recommendations (particularly around LiveKit Inference) won't apply directly.

## MANDATORY: Read This Checklist Before Starting

Before writing ANY code, complete this checklist:

1. **Read this entire skill document** - Do not skip sections even if MCP is available
2. **Ensure LiveKit Cloud project is connected** - You need `LIVEKIT_URL`, `LIVEKIT_API_KEY`, and `LIVEKIT_API_SECRET` from your Cloud project
3. **Set up documentation access** - Use MCP if available, otherwise use web search
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

This checklist applies regardless of whether MCP is available. MCP provides documentation access but does NOT replace the guidance in this skill.

## LiveKit Cloud Setup

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`):
```bash
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 the recommended way to use AI models with 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.

## 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.

## REQUIRED: Use LiveKit MCP Server for Documentation

Before writing any LiveKit code, ensure access to the LiveKit documentation MCP server. This provides current, verified API information and prevents reliance on stale model knowledge.

### Check for MCP Availability

Look for `livekit-docs` MCP tools. If available, use them for all documentation lookups:
- Search documentation before implementing any feature
- Verify API signatures and method parameters
- Look up configuration options and their valid values
- Find working examples for the specific task at hand

### If MCP Is Not Available

If the LiveKit MCP server is not configured, inform the user and recommend installation. Installation instructions for all supported platforms are available at:

**https://docs.livekit.io/intro/mcp-server/**

Fetch the installation instructions appropriate for the user's coding agent from that page.

### Fallback When MCP Unavailable

If MCP cannot be installed in the current session:
1. **Inform the user immediately** that documentation cannot be verified in real-time
2. Use web search to fetch current documentation from docs.livekit.io
3. **Explicitly mark all LiveKit-specific code** with a comment like `# UNVERIFIED: Please check docs.livekit.io for current API`
4. **State clearly** when you cannot verify something: "I cannot verify this API signature against current documentation"
5. Recommend the user verify against https://docs.livekit.io before using the code

## Voice Agent Architecture Principles

Voice AI agents have fundamentally different requirements than text-based agents or traditional software. Internalize these principles:

### Latency Is Critical

Voice conversations are real-time. Users expect responses within hundreds of milliseconds, not seconds. Every architectural decision should consider latency impact:

- Minimize LLM context size to reduce inference time
- Avoid unnecessary tool calls during active conversation
- Prefer streaming responses over batch responses
- Design for the unhappy path (network delays, API timeouts)

### Context Bloat Kills Performance

Large system prompts and extensive tool lists directly increase latency. A voice agent with 50 tools and a 10,000-token system prompt will feel sluggish regardless of model speed.

Design agents with minimal viable context:
- Include only tools relevant to the current conversation phase
- Keep system prompts focused and concise
- Remove tools and context that aren't actively needed

### Users Don't Read, They Listen

Voice interface constraints differ from text:
- Long responses frustrate users—keep outputs concise
- Users cannot scroll back—ensure clarity on first delivery
- Interruptions are normal—design for graceful handling
- Silence feels broken—acknowledge processing when needed

## Workflow Architecture: Handoffs and Tasks

Complex voice agents should not be monolithic. LiveKit Agents supports structured workflows that maintain low latency while handling sophisticated use cases.

### The Problem with Monolithic Agents

A single agent handling an entire conversation flow accumulates:
- Tools for every possible action (bloated tool list)
- Instructions for every conversation phase (bloated context)
- State management for all scenarios (complexity)

This creates latency and reduces reliability.

### Handoffs: Agent-to-Agent Transitions

Handoffs allow one agent to transfer control to another. Use handoffs to:
- Separate distinct conversation phases (greeting → intake → resolution)
- Isolate specialized capabilities (general support → billing specialist)
- Manage context boundaries (each agent has only what it needs)

Design handoffs around natural conversation boundaries where context can be summarized rather than transferred wholesale.

### Tasks: Scoped Operations

Tasks are tightly-scoped prompts designed to achieve a specific outcome. Use tasks for:
- Discrete operations that don't require full agent capabilities
- Situations where a focused prompt outperforms a general-purpose agent
- Reducing context when only a specific capability is needed

Consult the documentation for implementation details on handoffs and tasks.

## REQUIRED: Write Tests for Agent Behavior

Voice agent behavior is code. Every agent implementation MUST include tests. Shipping an agent without tests is shipping untested code.

### Mandatory Testing Workflow

When building or modifying a LiveKit agent:

1. **Create a `tests/` directory** if one doesn't exist
2. **Write at least one test** before considering the implementation complete
3. **Test t