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: Read this entire skill document - Do not skip sections even if MCP is available Ensure LiveKit Cloud project is connected - You need LIVEKIT_URL , LIVEKIT_API_KEY , and LIVEKIT_API_SECRET from your Cloud project Set up documentation access - Use MCP if available, otherwise use web search Plan to write tests - Every agent implementation MUST include tests (see testing section below) 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 Sign up at cloud.livekit.io if you haven't already Create a project (or use an existing one) 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 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 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: Inform the user immediately that documentation cannot be verified in real-time Use web search to fetch current documentation from docs.livekit.io Explicitly mark all LiveKit-specific code with a comment like

UNVERIFIED: Please check docs.livekit.io for current API

State clearly

when you cannot verify something: "I cannot verify this API signature against current documentation"

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:

Create a

tests/

directory

if one doesn't exist

Write at least one test

before considering the implementation complete

Test the core behavior

the user requested

Run the tests

to verify they pass

Test-Driven Development Process

When modifying agent behavior—instructions, tool descriptions, workflows—begin by writing tests for the desired behavior:

Define what the agent should do in specific scenarios

Write test cases that verify this behavior

Implement the feature

Iterate until tests pass

This approach prevents shipping agents that "seem to work" but fail in production.

What Every Agent Test Should Cover

At minimum, write tests for:

Basic conversation flow

Agent responds appropriately to a greeting

Tool invocation

(if tools exist): Tools are called with correct parameters

Error handling

Agent handles unexpected input gracefully

Focus tests on:

Tool invocation

Does the agent call the right tools with correct parameters?

Response quality

Does the agent produce appropriate responses for given inputs?

Workflow transitions

Do handoffs and tasks trigger correctly?

Edge cases

How does the agent handle unexpected input, interruptions, silence? Test Implementation Pattern Use LiveKit's testing framework. Consult the testing documentation via MCP for current patterns: search: "livekit agents testing" The framework supports: Simulated user input Verification of agent responses Tool call assertions Workflow transition testing Why This Is Non-Negotiable Agents that "seem to work" in manual testing frequently fail in production: Prompt changes silently break behavior Tool descriptions affect when tools are called Model updates change response patterns Tests catch these issues before users do. Skipping Tests If a user explicitly requests no tests, proceed without them but inform them: "I've built the agent without tests as requested. I strongly recommend adding tests before deploying to production. Voice agents are difficult to verify manually and tests prevent silent regressions." Common Mistakes to Avoid Overloading the Initial Agent Starting with one agent that "does everything" and adding tools/instructions over time. Instead, design workflow structure upfront, even if initial implementation is simple. Ignoring Latency Until It's a Problem Latency issues compound. An agent that feels "a bit slow" in development becomes unusable in production with real network conditions. Measure and optimize latency continuously. Copying Examples Without Understanding Examples in documentation demonstrate specific patterns. Copying code without understanding its purpose leads to bloated, poorly-structured agents. Understand what each component does before including it. Skipping Tests Because "It's Just Prompts" Agent behavior is code. Prompt changes affect behavior as much as code changes. Test agent behavior with the same rigor as traditional software. Never deliver an agent implementation without at least one test file. Assuming Model Knowledge Is Current Reiterating the critical rule: never trust model memory for LiveKit APIs. The SDK evolves faster than model training cycles. Verify everything. When to Consult Documentation Always consult documentation for: API method signatures and parameters Configuration options and their valid values SDK version-specific features or changes Deployment and infrastructure setup Model provider integration details CLI commands and flags This skill provides guidance on: Architectural approach and design principles Workflow structure decisions Testing strategy Common pitfalls to avoid The distinction matters: this skill tells you how to think about building voice agents. The documentation tells you how to implement specific features. Feedback Loop When using LiveKit documentation via MCP, note any gaps, outdated information, or confusing content. Reporting documentation issues helps improve the ecosystem for all developers. Summary Building effective voice agents with LiveKit Cloud requires: Use LiveKit Cloud + LiveKit Inference as the foundation—it's the fastest path to production Verify everything against live documentation—never trust model memory Minimize latency at every architectural decision point Structure workflows using handoffs and tasks to manage complexity Test behavior before and after changes—never ship without tests Keep context minimal —only include what's needed for the current phase These principles remain valid regardless of SDK version or API changes. For all implementation specifics, consult the LiveKit documentation via MCP.