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Okeysir198
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livekit-agent-tools

Design and implement LiveKit agent tools with the @function_tool decorator.

2|4|Updated Nov 21, 2025
Search Tags:#multi-agent#livekit#RunContext#agent-tools#development-guide#tool-patterns#function_tool
One-click install
npx skills add https://github.com/Okeysir198/P20251122-claude-skills --skill livekit-agent-tools
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: livekit-agent-tools
Source: https://github.com/Okeysir198/P20251122-claude-skills/tree/main/skills-reference/livekit-agent-tools
Command: npx skills add https://github.com/Okeysir198/P20251122-claude-skills --skill livekit-agent-tools

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a practical, end-to-end guide for building robust LiveKit voice agent tools using the @function_tool decorator. It helps developers create well-scoped tools, document parameters clearly, and implement safe, production-ready patterns for real-time voice tasks.

Core Features & Use Cases

  • Clear tool design patterns: single-responsibility functions, precise tool descriptions, and meaningful return values that guide LLM usage.
  • RunContext and state management: access to userdata, RunContext, and session orchestration for multi-agent workflows.
  • Production readiness: error handling, logging, monitoring, and testing strategies for reliable agent behavior.
  • Multi-agent patterns: agent handoff, shared state, dynamic tool creation, and context-preserving transfers.

Quick Start

Read the companion assets (agent-template.py) and references (references/*) to implement a basic LiveKit tool, then experiment with a simple two-agent workflow by following the patterns shown in the repository.

Frequently Asked Questions about livekit-agent-tools

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build LiveKit agent tools using the function_tool decorator?▼

You design LiveKit agent tools by applying the @function_tool decorator to single-responsibility Python functions, ensuring precise descriptions and meaningful return values that guide LLM usage in real-time voice tasks.

How does RunContext handle state in multi-agent LiveKit workflows?▼

RunContext handles state in multi-agent LiveKit workflows by providing access to userdata and enabling session orchestration for agent handoffs, shared state, and context-preserving transfers.

Can I use LiveKit agent tools for multi-agent workflows with dynamic tooling?▼

Yes, you can use LiveKit agent tools for multi-agent workflows by implementing agent handoffs, sharing state via RunContext, and creating dynamic tools that preserve context during transfers.

What patterns should I follow for error handling and testing LiveKit voice agents?▼

For error handling and testing LiveKit voice agents, follow production readiness patterns that include robust error management, logging, monitoring, and testing strategies to ensure reliable agent behavior.

Do I need Python to implement interruption patterns for LiveKit agent tools?▼

Yes, you need a standard Python environment to implement interruption patterns for LiveKit agent tools, utilizing provided templates and references for hands-on learning and production-ready deployment.

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