TanStack
AI v0v0
Getting Started

Overview

TanStack AI is a lightweight, type-safe SDK for building production-ready AI experiences. Its framework-agnostic core provides type-safe tool/function calling, streaming responses, and first-class React and Solid integrations, with adapters for multiple LLM providers — enabling predictable, composable, and testable AI features across any stack.

Key Features

  • ✅ Type-Safe - Full TypeScript support with Zod schema inference

  • ✅ Streaming - Built-in streaming support for real-time responses

  • ✅ Isomorphic Tools - Define once with toolDefinition(), implement with .server() or .client()

  • ✅ Framework Agnostic - Core library works anywhere

  • ✅ Multiple Providers - OpenRouter, OpenAI, Anthropic, Gemini, Ollama, and more

  • ✅ Approval Flow - Built-in support for tool approval workflows

  • ✅ Automatic Execution - Both server and client tools execute automatically

Framework Agnostic

The framework-agnostic core of TanStack AI provides the building blocks for creating AI experiences in any environment, including:

  • Next.js - API routes and App Router

  • TanStack Start - React Start or Solid Start (recommended!)

  • React Native / Expo - Native chat screens with useChat, absolute server URLs, and XHR streaming transports

  • Express - Node.js server

  • React Router v7 - Loaders and actions

    TanStack AI lets you define a tool once and provide environment-specific implementations. Using toolDefinition() to declare the tool's input/output types and the server behavior with .server() (or a client implementation with .client()). These isomorphic tools can be invoked from the AI runtime regardless of framework.

ts
import { chat, toolDefinition } from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'
import { z } from 'zod'

// Define a tool
const getProductsDef = toolDefinition({
  name: 'getProducts',
  inputSchema: z.object({ query: z.string() }),
  outputSchema: z.array(z.object({ id: z.string(), name: z.string() })),
})

// Create server implementation
const getProducts = getProductsDef.server(async ({ query }) => {
  return await db.products.search(query)
})

// Use in AI chat
chat({
  adapter: openaiText('gpt-5.2'),
  messages: [{ role: 'user', content: 'Find products' }],
  tools: [getProducts]
})

Core Packages

The TanStack AI ecosystem consists of several packages:

@tanstack/ai

The core AI library that provides:

  • AI adapter interface for connecting to LLM providers

  • Chat completion and streaming

  • Isomorphic tool/function calling system

  • Agent loop strategies

  • Type-safe tool definitions with toolDefinition()

  • Type-safe Model Options based on adapter & model selection

  • Type-safe content modalities (text, image, audio, video, document) based on model capabilities

@tanstack/ai-client

A framework-agnostic headless client for managing chat state:

  • Message management with full type safety

  • Streaming support

  • Connection adapters (SSE, HTTP stream, custom)

  • Automatic tool execution (server and client)

  • Tool approval flow handling

@tanstack/ai-react

React hooks for TanStack AI:

  • useChat hook for chat interfaces

  • Automatic state management

  • Tool approval flow support

  • Type-safe message handling with InferChatMessages

@tanstack/ai-solid

Solid hooks for TanStack AI:

  • useChat hook for chat interfaces

  • Automatic state management

  • Tool approval flow support

  • Type-safe message handling with InferChatMessages

Adapters

With the help of adapters, TanStack AI can connect to various LLM providers. Available adapters include:

  • @tanstack/ai-openrouter - OpenRouter (300+ models via a single API key — recommended)

  • @tanstack/ai-openai - OpenAI (GPT series)

  • @tanstack/ai-anthropic - Anthropic (Claude)

  • @tanstack/ai-gemini - Google Gemini

  • @tanstack/ai-ollama - Ollama (local models)

  • @tanstack/ai-groq - Groq

  • @tanstack/ai-grok - xAI Grok

  • @tanstack/ai-fal - fal (image & video generation)

Next Steps

Edit on GitHub
Quick Start: React
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