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 comes with a default tool installed, called `llm_version`. You can try that out like this:
```bash
llm -T llm_version "What version of LLM is this?" --td
```
The output should look like this:
```
Tool call: llm_version({})
0.26a0
The installed version of the LLM is 0.26a0.
```
Further tools can be installed using plugins, or you can use the `llm --functions` option to pass tools implemented as PYthon functions directly, as {ref}`described here <usage-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>`.
## Tips for implementing tools
Consult the {ref}`register_tools() plugin hook <plugin-hooks-register-tools>` documentation for examples of how to implement tools in plugins.
If your plugin needs access to API secrets I recommend storing those using `llm keys set api-name` and then reading them using the {ref}`plugin-utilities-get-key` utility function. This avoids secrets being logged to the database as part of tool calls.
<!-- Uncomment when this is true: The [llm-tools-datasette](https://github.com/simonw/llm-tools-datasette) plugin is a good example of this pattern in action. -->
