llm/docs/tools.md

(tools)=

Tools

Many Large Language Models have been trained to execute tools as part of responding to a prompt. LLM supports tool usage with both the command-line interface and the Python API.

How tools work

A tool is effectively a function that the model can request to be executed. Here's how that works:

1. The initial prompt to the model includes a list of available tools, containing their names, descriptions and parameters.
2. The model can choose to call one (or sometimes more than one) of those tools, returning a request for the tool to execute.
3. The code that calls the model - in this case LLM itself - then executes the specified tool with the provided arguments.
4. LLM prompts the model a second time, this time including the output of the tool execution.
5. The model can then use that output to generate its next response.

LLM's implementation of tools

In LLM every tool is a defined as a Python function. The function can take any number of arguments and can return a string or an object that can be converted to a string.

Tool functions should include a docstring that describes what the function does. This docstring will become the description that is passed to the model.

The Python API can accept functions directly. The command-line interface has two ways for tools to be defined: via plugins that implement the {ref}register_tools() plugin hook <plugin-hooks-register-tools>, or directly on the command-line using the --functions argument to specify a block of Python code defining one or more functions - or a path to a Python file containing the same.

You can use tools {ref}with the LLM command-line tool <usage-tools> or {ref}with the Python API <python-api-tools>.