/agents/build/tools

LiveKit docs › Logic & Structure › Tool definition & use › Overview

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Tool definition and use

Let your agents call external tools and more.

Overview

LiveKit Agents has full support for LLM tool use. This feature allows you to create a custom library of tools to extend your agent's context, create interactive experiences, and overcome LLM limitations.

Within a tool, you can:

• Generate agent speech with session.say() or session.generate_reply().
• Call methods on the frontend using RPC.
• Handoff control to another agent as part of a workflow.
• Store and retrieve session data from the context.
• Anything else that a Python function can do.
• Call external APIs or lookup data for RAG.

Tool types

Two types of tools are supported:

• Function tools: Tools that are defined as functions within your agent's code base and can be called by the LLM.
• Provider tools: Tools provided by a specific model provider (e.g. OpenAI, Gemini, etc.) and are executed internally by the provider's model server.

Provider tools

Available in:

• Node.js
• Python

Many LLM providers, including OpenAI, Gemini, and xAI, include built-in server-side tools that are executed entirely within a single API call. Examples include web search, code execution, and file search. These tools, called "provider tools" in LiveKit Agents, can be added to any agent that uses a supported LLM. You can mix and match provider tools with function tools by passing them to the tools parameter on your Agent.

from livekit.plugins import openai  # replace with any supported provider

agent = MyAgent(
    llm=openai.responses.LLM(model="gpt-4.1"),
    tools=[openai.tools.WebSearch()],  # replace with any supported tool
)

Refer to the documentation for each model provider for supported tools and usage details:

• OpenAI: WebSearch, FileSearch, CodeInterpreter.
• Gemini: GoogleSearch, GoogleMaps, URLContext, FileSearch, ToolCodeExecution.
• Anthropic: ComputerUse.
• xAI: WebSearch, XSearch, FileSearch.

Examples

The following additional examples show how to use tools in different ways:

• Use of enum: Example showing how to annotate arguments with enum.

• Dynamic tool creation: Complete example with dynamic tool lists.

• MCP Agent: A voice AI agent with an integrated Model Context Protocol (MCP) client for the LiveKit API.

In this section

Read more about each topic.

| Topic | Description | | Function tool definition | Define function tools with decorators, RunContext, speech in tools, interruptions, dynamic tools, toolsets, and error handling. | | Model Context Protocol (MCP) | Expose tools from MCP servers to your agent (Python only). | | Forwarding to the frontend | Fulfill tool calls via RPC from the client. |

Additional resources

The following articles provide more information about the topics discussed in this guide:

• RPC: Complete documentation on function calling between LiveKit participants.

• Agent speech: More information about precise control over agent speech output.

• Workflows: Read more about handing off control to other agents.

• External data and RAG: Best practices for adding context and taking external actions.

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For the latest version of this document, see https://docs.livekit.io/agents/logic/tools.md.

To explore all LiveKit documentation, see llms.txt.