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Predicting with LLMs

Agentic Flows

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Manage Models

Model Info

API Reference

Tool Definition

You can define tools with the tool() function and pass them to the model in the operate() call.

Anatomy of a Tool

Follow this standard format to define functions as tools:

import { tool } from "@lmstudio/sdk";
import { z } from "zod";

const exampleTool = tool({
  // The name of the tool
  name: "add",

  // A description of the tool
  description: "Given two numbers a and b. Returns the sum of them.",

  // zod schema of the parameters
  parameters: { a: z.number(), b: z.number() },

  // The implementation of the tool. Just a regular function.
  implementation: ({ a, b }) => a + b,
});

Important: The tool name, description, and the parameter definitions are all passed to the model!

This means that your wording will affect the quality of the generation. Make sure to always provide a clear description of the tool so the model knows how to use it.

Tools with External Effects (like Computer Use or API Calls)

Tools can also have external effects, such as creating files or calling programs and even APIs. By implementing tools with external effects, you can essentially turn your LLMs into autonomous agents that can perform tasks on your local machine.

Example: createFileTool

Tool Definition

import { tool } from "@lmstudio/sdk";
import { existsSync } from "fs";
import { writeFile } from "fs/promises";
import { z } from "zod";

const createFileTool = tool({
  name: "createFile",
  description: "Create a file with the given name and content.",
  parameters: { name: z.string(), content: z.string() },
  implementation: async ({ name, content }) => {
    if (existsSync(name)) {
      return "Error: File already exists.";
    }
    await writeFile(name, content, "utf-8");
    return "File created.";
  },
});

Example code using the createFile tool:

import { LMStudioClient } from "@lmstudio/sdk";
import { createFileTool } from "./createFileTool";

const client = new LMStudioClient();

const model = await client.llm.model("qwen2.5-7b-instruct");
await model.act(
  "Please create a file named output.txt with your understanding of the meaning of life.",
  [createFileTool],
);