tanstack-ai

TanStack AI (Provider-Agnostic LLM SDK)

Status: Production Ready ✅ Last Updated: 2025-12-09 Dependencies: Node.js 18+, TypeScript 5+; React 18+ for @tanstack/ai-react; Solid 1.8+ for @tanstack/ai-solid Latest Versions: @tanstack/ai@latest (alpha), @tanstack/ai-react@latest, @tanstack/ai-client@latest, adapters: @tanstack/ai-openai@latest @tanstack/ai-anthropic@latest @tanstack/ai-gemini@latest @tanstack/ai-ollama@latest

Quick Start (7 Minutes)

1) Install core + adapter

pnpm add @tanstack/ai @tanstack/ai-react @tanstack/ai-openai
# swap adapters as needed: @tanstack/ai-anthropic @tanstack/ai-gemini @tanstack/ai-ollama
pnpm add zod              # recommended for tool schemas

Why this matters:

• Core is framework-agnostic; React binding just wraps the headless client. citeturn1search3
• Adapters abstract provider quirks so you can change models without rewriting code. citeturn1search3

2) Ship a streaming chat endpoint (Next.js or TanStack Start)

// app/api/chat/route.ts (Next.js) or src/routes/api/chat.ts (TanStack Start)
import { chat, toStreamResponse } from '@tanstack/ai'
import { openai } from '@tanstack/ai-openai'
import { tools } from '@/tools/definitions' // definitions only

export async function POST(request: Request) {
  const { messages, conversationId } = await request.json()
  const stream = chat({
    adapter: openai(),
    messages,
    model: 'gpt-4o',
    tools,
  })
  return toStreamResponse(stream)
}

CRITICAL:

• Pass tool definitions to the server so the LLM can request them; implementations live in their runtimes. citeturn0search7
• Always stream; chunked responses keep UIs responsive and reduce token waste. citeturn0search1

3) Wire the client with useChat + SSE

// components/Chat.tsx
import { useChat, fetchServerSentEvents } from '@tanstack/ai-react'
import { clientTools } from '@tanstack/ai-client'
import { updateUIDef } from '@/tools/definitions'

const updateUI = updateUIDef.client(({ message }) => {
  alert(message)
  return { success: true }
})

export function Chat() {
  const tools = clientTools(updateUI)
  const { messages, sendMessage, isLoading, approval } = useChat({
    connection: fetchServerSentEvents('/api/chat'),
    tools,
  })

  return (
    <form onSubmit={e => { e.preventDefault(); sendMessage(e.currentTarget.prompt.value) }}>
      <textarea name="prompt" disabled={isLoading} />
      {approval?.pending && (
        <button type="button" onClick={() => approval.approve()}>
          Approve tool
        </button>
      )}
    </form>
  )
}

CRITICAL:

• Use fetchServerSentEvents (or matching adapter) to mirror the streaming response. citeturn0search0
• Keep client tool names identical to definitions to avoid “tool not found” errors. citeturn0search7

The 4-Step Setup Process

Step 1: Choose provider + model safely

• Add the correct adapter and set the matching API key (OPENAI_API_KEY, ANTHROPIC_API_KEY, GEMINI_API_KEY, or Ollama host).
• Prefer per-model option typing from adapters to avoid invalid options (e.g., vision-only fields). citeturn1search3

Step 2: Define tools once, implement per runtime

// tools/definitions.ts
import { z, toolDefinition } from '@tanstack/ai'

export const getWeatherDef = toolDefinition({
  name: 'getWeather',
  description: 'Get current weather for a city',
  inputSchema: z.object({ city: z.string() }),
  needsApproval: true,
})

export const getWeather = getWeatherDef.server(async ({ city }) => {
  const data = await fetch(`https://api.weather.gov/points?q=${city}`).then(r => r.json())
  return { summary: data.properties?.relativeLocation?.properties?.city ?? city }
})

export const showToast = getWeatherDef.client(({ city }) => {
  console.log(`Showing toast for ${city}`)
  return { acknowledged: true }
})

Key Points:

• needsApproval: true forces explicit user approval for sensitive actions. citeturn0search1
• Keep tools single-purpose and idempotent; return structured objects instead of throwing errors. citeturn0search1

Step 3: Create connection adapter + chat options

• Server: toStreamResponse(stream) for HTTP streaming; toServerSentEventsStream helper for Server-Sent Events. citeturn0search3turn0search4
• Client: fetchServerSentEvents('/api/chat') or a custom adapter for websockets if needed. citeturn0search0
• Configure agentLoopStrategy (e.g., maxIterations(8)) to cap tool recursion. citeturn1search4

Step 4: Add observability + guardrails

• Log tool executions and stream chunks for debugging; alpha exposes hooks while devtools are in progress. citeturn0search1
• Validate inputs with Zod; fail fast and return typed error objects.
• Enforce timeouts on external API calls inside tools to prevent stuck agent loops.

Critical Rules

Always Do

✅ Stream responses; avoid waiting for full completions. citeturn0search1 ✅ Pass definitions to the server and implementations to the correct runtime. citeturn0search7 ✅ Use Zod schemas for tool inputs/outputs to keep type safety across providers. citeturn0search1 ✅ Cap agent loops with maxIterations to prevent runaway tool calls. citeturn1search4 ✅ Require needsApproval for destructive or billing-sensitive tools. citeturn0search1

Never Do

❌ Mix provider adapters in a single request—instantiate one adapter per call. ❌ Throw raw errors from tools; return structured error payloads. ❌ Send client tool implementations to the server (definitions only). ❌ Hardcode model capabilities; rely on adapter typings for per-model options. citeturn0search1 ❌ Skip API key checks; fail fast with helpful messages on the server. citeturn0search1

Known Issues Prevention

This skill prevents 3 documented issues:

Issue #1: “tool not found” / silent tool failures

Why it happens: Definitions aren’t passed to chat(); only implementations exist locally. Prevention: Export definitions separately and include them in the server tools array; keep names stable. citeturn0search7

Issue #2: Streaming stalls in the UI

Why it happens: Mismatch between server response type and client adapter (HTTP chunked vs SSE). Prevention: Use toStreamResponse on the server + fetchServerSentEvents (or matching adapter) on the client. citeturn0search1turn0search0

Issue #3: Model option validation errors

Why it happens: Provider-specific options (e.g., vision params) sent to unsupported models. Prevention: Use adapter-provided types; rely on per-model option typing to surface invalid fields at compile time. citeturn1search3

Configuration Files Reference

.env.local (Full Example)

OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=
GEMINI_API_KEY=
OLLAMA_HOST=http://localhost:11434
AI_STREAM_STRATEGY=immediate

Why these settings:

• Keep non-active providers empty to avoid accidental multi-provider calls.
• AI_STREAM_STRATEGY is read by the sample client to pick chunk strategies (immediate vs buffered).

Common Patterns

Pattern 1: Agentic cycle with bounded tools

import { chat, maxIterations } from '@tanstack/ai'
import { openai } from '@tanstack/ai-openai'

const stream = chat({
  adapter: openai(),
  messages,
  tools,
  agentLoopStrategy: maxIterations(8), // hard cap
})

When to use: Any flow where the LLM could recurse across tools (search → summarize → fetch detail). citeturn1search4

Pattern 2: Hybrid server + client tools

// server: data fetch
const fetchUser = fetchUserDef.server(async ({ id }) => db.user.find(id))

// client: UI update
const highlightUser = highlightUserDef.client(({ id }) => {
  document.querySelector(`#user-${id}`)?.classList.add('ring')
  return { highlighted: true }
})

chat({ tools: [fetchUser, highlightUser] })

When to use: When the mode