aiwrapper

AIWrapper

A universal AI wrapper for JavaScript & TypeScript.

Use LLMs from anywhere—servers, browsers and web-apps. AIWrapper works in anything that runs JavaScript.

:warning: It's in early WIP stage and the API may change.

Features

• Generate plain text or JSON objects with a simple API
• Use different LLM providers: OpenAI, Anthropic, Groq, DeepSeek, Ollama, OpenRouter and any OpenAI-compatible services
• Output objects based on needed schemas
• Swap models quickly or chain different models together
• Use it with JavaScript or TypeScript from anywhere

Installation

Install with npm or import in Deno by URL.

NPM

npm install aiwrapper

Deno

import * as aiwrapper from "https://deno.land/x/aiwrapper/mod.ts";

Quick Start

Generate Text

import { Lang } from "aiwrapper";

const lang = Lang.openai({ apiKey: "YOUR KEY" });
const result = await lang.ask("Say hi!");
console.log(result);

Lang (LLM) Examples

Initialize a Model

import { Lang } from "aiwrapper";

const lang = Lang.openai({ apiKey: "YOUR KEY" }); // or Lang.anthropic

Connect to Custom OpenAI-compatible APIs

import { Lang } from "aiwrapper";

// Connect to a custom OpenAI-compatible API
const lang = Lang.openaiLike({
  apiKey: "YOUR KEY", // Optional - not needed for APIs without authentication
  model: "model-name",
  baseURL: "https://your-custom-api.example.com/v1",
  systemPrompt: "Optional system prompt",
  
  // Optional headers for authentication or other purposes
  headers: {
    "X-Custom-Header": "custom-value",
    "Authorization": "Basic dXNlcm5hbWU6cGFzc3dvcmQ=" // Alternative auth method example
  },
  
  // Additional properties to include in the request body
  bodyProperties: {
    temperature: 0.7,
    presence_penalty: 0.6,
    frequency_penalty: 0.1
  }
});

// Use it just like any other LLM provider
const result = await lang.ask("Hello!");
console.log(result);

Use OpenRouter (Access 100+ Models)

import { Lang } from "aiwrapper";

// Basic OpenRouter usage
const lang = Lang.openrouter({
  apiKey: "YOUR_OPENROUTER_API_KEY",
  model: "openai/gpt-4o", // Or any model from OpenRouter's catalog
});

// With optional site information for rankings
const langWithSiteInfo = Lang.openrouter({
  apiKey: "YOUR_OPENROUTER_API_KEY",
  model: "anthropic/claude-3.5-sonnet",
  siteUrl: "https://your-app.com", // Optional: appears on OpenRouter leaderboards
  siteName: "Your App Name", // Optional: appears on OpenRouter leaderboards
  systemPrompt: "You are a helpful assistant.",
  maxTokens: 4000,
});

const result = await langWithSiteInfo.ask("Explain quantum computing in simple terms");
console.log(result.answer);

Stream Results

await lang.ask('Hello, AI!', streamingResult => {
  console.log(streamingResult.answer);
});

Use Templates

// In most cases - a prompt template should be just a function that returns a string
function getPrompt(product) {
  return `You are a naming consultant for new companies. What is a good name for a company that makes ${product}?     
Write just the name. Nothing else aside from the name - no extra comments or characters that are not part of the name.`;
}

const prompt = getPrompt("colorful socks");

await lang.ask(prompt, streamingResult => { 
  console.log(streamingResult.answer);
});

Getting Objects from LLMs

async function askForCompanyNames() {
  // We can ask for an object with a particular schema. In that case - an array with company names as strings.
  
  const product = "colorful socks";
  const numberOfNames = 3;
  
  const result = await lang.askForObject({
    instructions: [
      `You are a naming consultant for new companies. What is a good name for a company that makes ${product}?`,
      `Return ${numberOfNames} names.`
    ],
    objectExamples: [
      ["Name A", "Name B", "Name C"]
    ]
  }, streamingResult => { 
    console.log(streamingResult.answer);
  });
  
  return result.answerObj;
}

const names = await askForCompanyNames();

Chaining Prompts

async function askForStoriesBehindTheNames() {
  // We can use an answer in other prompts. Here we ask to come up with stories for all of the names we've got.
  const names = await askForCompanyNames();
  const stories = [];

  for (const name of names) {
    const story = await lang.askForObject({
      instructions: [
        `You are a professional writer and a storyteller.`,
        `Look at the name "${name}" carefully and reason step-by-step about the meaning of the name and what is the potential story behind it.`,
        `Write a short story. Don't include any comments or characters that are not part of the story.`,
      ],
      objectExamples: [
        {
          "name": "Name A",
          "reasoning": "Reasoning about Name A",
          "story": "Story about Name A"
        }
      ]
    }, streamingResult => { 
      console.log(streamingResult.answer);
    });

    stories.push(story);
  }

  return stories;
}

const namesWithStories = await askForStoriesBehindTheNames();

Getting Complex Objects

// When you work with complex objects it's better to define them as classes or types.
class Task {
  constructor(name, description, tasks) {
    this.name = name;
    this.description = description;
    this.tasks = tasks;
  }
}

async function getTask() {
  // In this case we represent the schema. You may also treat it 
  // as a few shot example.
  const exampleTask = new Task("Root Task", "This is the task that has subtasks", [
    new Task("Task A1", "This is task A1", []),
    new Task("Task A2", "This is task A2", []),
  ]);

  const taskPrompt = {
    instructions: [
      "Reflect on the objective and tasks (from the Objective section) step by step. Ensure that you understand them; identify any ambiguities or gaps in information. The Context section offers relevant information. Feel free to add critique or insights about the objective.",
      "Create a tree of tasks. If the task is complex, break it down into subtasks, following the KISS principle. Each task should have a clear, actionable title, and a reasoning. If there are ambiguities or gaps in information, start by posing follow-up questions.",
    ],
    outputExamples: [
      exampleTask,
    ],
    content: {
      "Objective":
        "Make me $1 000 000 in 3 years. I have $10000 to spare and can live without income for 18 months. I only want to do it by starting a business. Be my CEO.",
      "Context": "I'm a software developer and a digital nomad",
    },
  };

  const result = await lang.askForObject(taskPrompt, res => { 
    console.log(res.answer);
  });

  
  return result.answerObject
}

const task = await getTask();