AI SDK

The AI SDK is a TypeScript toolkit designed to help you build AI-powered applications using popular frameworks like Next.js, React, Svelte, Vue and runtimes like Node.js.

To learn more about how to use the AI SDK, check out our API Reference and Documentation.

Installation

You will need Node.js 18+ and pnpm installed on your local development machine.

npm install ai

Usage

AI SDK Core

The AI SDK Core module provides a unified API to interact with model providers like OpenAI, Anthropic, Google, and more.

You will then install the model provider of your choice.

npm install @ai-sdk/openai

@/index.ts (Node.js Runtime)

import { generateText } from 'ai';
import { openai } from '@ai-sdk/openai'; // Ensure OPENAI_API_KEY environment variable is set

const { text } = await generateText({
  model: openai('gpt-4o'),
  system: 'You are a friendly assistant!',
  prompt: 'Why is the sky blue?',
});

console.log(text);

AI SDK UI

The AI SDK UI module provides a set of hooks that help you build chatbots and generative user interfaces. These hooks are framework agnostic, so they can be used in Next.js, React, Svelte, and Vue.

You need to install the package for your framework:

npm install @ai-sdk/react

@/app/page.tsx (Next.js App Router)

'use client';

import { useChat } from '@ai-sdk/react';

export default function Page() {
  const { messages, input, handleSubmit, handleInputChange, status } =
    useChat();

  return (
    <div>
      {messages.map(message => (
        <div key={message.id}>
          <strong>{`${message.role}: `}</strong>
          {message.parts.map((part, index) => {
            switch (part.type) {
              case 'text':
                return <span key={index}>{part.text}</span>;

              // other cases can handle images, tool calls, etc
            }
          })}
        </div>
      ))}

      <form onSubmit={handleSubmit}>
        <input
          value={input}
          placeholder="Send a message..."
          onChange={handleInputChange}
          disabled={status !== 'ready'}
        />
      </form>
    </div>
  );
}

@/app/api/chat/route.ts (Next.js App Router)

import { streamText } from 'ai';
import { openai } from '@ai-sdk/openai';

export async function POST(req: Request) {
  const { messages } = await req.json();

  const result = streamText({
    model: openai('gpt-4o'),
    system: 'You are a helpful assistant.',
    messages,
  });

  return result.toUIMessageStreamResponse();
}

Templates

We've built templates that include AI SDK integrations for different use cases, providers, and frameworks. You can use these templates to get started with your AI-powered application.

Community

The AI SDK community can be found on GitHub Discussions where you can ask questions, voice ideas, and share your projects with other people.

With over 2 million weekly downloads, the AI SDK is the leading open-source AI application toolkit for TypeScript and JavaScript. Its unified provider API allows you to use any language model and enables powerful integrations into leading web frameworks.

Building applications with TypeScript means building applications for the web. Today, we are releasing AI SDK 5, the first AI framework with a fully typed and highly customizable chat integration for React, Svelte, Vue and Angular.

AI SDK 5 introduces:

• Redesigned Chat
• Agentic Loop Control
• Speech Generation & Transcription
• Tool Improvements
• V2 Specifications
• Global Provider
• Access Raw Responses
• Zod 4 Support

Overview

The AI SDK standardizes integrating artificial intelligence (AI) models across supported providers. This enables developers to focus on building great AI applications, not waste time on technical details.

For example, here’s how you can generate text with various models using the AI SDK:

To effectively leverage the AI SDK, it helps to familiarize yourself with the following concepts:

Generative Artificial Intelligence

Generative artificial intelligence refers to models that predict and generate various types of outputs (such as text, images, or audio) based on what’s statistically likely, pulling from patterns they’ve learned from their training data. For example:

• Given a photo, a generative model can generate a caption.
• Given an audio file, a generative model can generate a transcription.
• Given a text description, a generative model can generate an image.

Large Language Models

A large language model (LLM) is a subset of generative models focused primarily on text. An LLM takes a sequence of words as input and aims to predict the most likely sequence to follow. It assigns probabilities to potential next sequences and then selects one. The model continues to generate sequences until it meets a specified stopping criterion.

LLMs learn by training on massive collections of written text, which means they will be better suited to some use cases than others. For example, a model trained on GitHub data would understand the probabilities of sequences in source code particularly well.

However, it's crucial to understand LLMs' limitations. When asked about less known or absent information, like the birthday of a personal relative, LLMs might "hallucinate" or make up information. It's essential to consider how well-represented the information you need is in the model.

Embedding Models

An embedding model is used to convert complex data (like words or images) into a dense vector (a list of numbers) representation, known as an embedding. Unlike generative models, embedding models do not generate new text or data. Instead, they provide representations of semantic and syntactic relationships between entities that can be used as input for other models or other natural language processing tasks.

In the next section, you will learn about the difference between models providers and models, and which ones are available in the AI SDK.