tanstack-ai

What this skill does

# 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

```bash
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)

```ts
// 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

```tsx
// 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

```ts
// 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)

```env
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

```ts
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

```ts
// 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