[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-tanstack-ai-corechat-experience":3,"mdc-hnpkno-key":53,"related-repo-tanstack-ai-corechat-experience":12818,"related-org-tanstack-ai-corechat-experience":12911},{"slug":4,"name":5,"fn":6,"description":7,"org":8,"tags":12,"stars":24,"repoUrl":25,"updatedAt":26,"license":27,"forks":28,"topics":29,"repo":48,"sourceUrl":51,"mdContent":52},"ai-corechat-experience","ai-core\u002Fchat-experience","implement chat experiences with TanStack AI","End-to-end chat implementation: server endpoint with chat() and toServerSentEventsResponse(), client-side useChat hook with fetchServerSentEvents(), message rendering with UIMessage parts, multimodal content, thinking\u002Freasoning display. Covers streaming states, connection adapters, and message format conversions. NOT Vercel AI SDK — uses chat() not streamText().\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},"tanstack","TanStack","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Ftanstack.png",[13,17,20,21],{"name":14,"slug":15,"type":16},"LLM","llm","tag",{"name":18,"slug":19,"type":16},"API Development","api-development",{"name":10,"slug":9,"type":16},{"name":22,"slug":23,"type":16},"Frontend","frontend",2884,"https:\u002F\u002Fgithub.com\u002FTanStack\u002Fai","2026-07-30T05:26:11.505241",null,269,[30,31,32,33,34,35,36,37,15,38,39,40,41,42,43,9,44,45,46,47],"ai","ai-agents","ai-sdk","anthropic","chatbot","function-calling","gemini","generative-ai","multimodal","openai","react","solidjs","streaming","svelte","tool-calling","typescript","typescript-sdk","vue",{"repoUrl":25,"stars":24,"forks":28,"topics":49,"description":50},[30,31,32,33,34,35,36,37,15,38,39,40,41,42,43,9,44,45,46,47],"🤖 Type-safe, provider-agnostic TypeScript AI SDK for streaming chat, tool calling, agents, and multimodal apps across OpenAI, Anthropic, Gemini, React, Vue, Svelte, and Solid.","https:\u002F\u002Fgithub.com\u002FTanStack\u002Fai\u002Ftree\u002FHEAD\u002Fpackages\u002Fai\u002Fskills\u002Fai-core\u002Fchat-experience","---\nname: ai-core\u002Fchat-experience\ndescription: >\n End-to-end chat implementation: server endpoint with chat() and\n toServerSentEventsResponse(), client-side useChat hook with\n fetchServerSentEvents(), message rendering with UIMessage parts,\n multimodal content, thinking\u002Freasoning display. Covers streaming\n states, connection adapters, and message format conversions.\n NOT Vercel AI SDK — uses chat() not streamText().\ntype: sub-skill\nlibrary: tanstack-ai\nlibrary_version: '0.10.0'\nsources:\n - 'TanStack\u002Fai:docs\u002Fgetting-started\u002Fquick-start.md'\n - 'TanStack\u002Fai:docs\u002Fchat\u002Fstreaming.md'\n - 'TanStack\u002Fai:docs\u002Fchat\u002Fconnection-adapters.md'\n - 'TanStack\u002Fai:docs\u002Fchat\u002Fthinking-content.md'\n - 'TanStack\u002Fai:docs\u002Fadvanced\u002Fmultimodal-content.md'\n - 'TanStack\u002Fai:docs\u002Fresumable-streams\u002Foverview.md'\n - 'TanStack\u002Fai:docs\u002Fpersistence\u002Fclient-persistence.md'\n---\n\n# Chat Experience\n\nThis skill builds on ai-core. Read it first for critical rules.\n\n## Setup — Minimal Chat App\n\n### Server: API Route (TanStack Start)\n\n```typescript\n\u002F\u002F src\u002Froutes\u002Fapi.chat.ts\nimport { createFileRoute } from '@tanstack\u002Freact-router'\nimport { chat, toServerSentEventsResponse } from '@tanstack\u002Fai'\nimport { openaiText } from '@tanstack\u002Fai-openai'\n\nexport const Route = createFileRoute('\u002Fapi\u002Fchat')({\n server: {\n handlers: {\n POST: async ({ request }) => {\n const abortController = new AbortController()\n const body = await request.json()\n const { messages } = body\n\n const stream = chat({\n adapter: openaiText('gpt-5.5'),\n messages,\n systemPrompts: ['You are a helpful assistant.'],\n abortController,\n })\n\n return toServerSentEventsResponse(stream, { abortController })\n },\n },\n },\n})\n```\n\n### Client: React Component\n\n```typescript\n\u002F\u002F src\u002Froutes\u002Findex.tsx\nimport { useState } from 'react'\nimport { useChat, fetchServerSentEvents } from '@tanstack\u002Fai-react'\nimport type { UIMessage } from '@tanstack\u002Fai-react'\n\nfunction ChatPage() {\n const [input, setInput] = useState('')\n\n const { messages, sendMessage, isLoading, error, stop } = useChat({\n connection: fetchServerSentEvents('\u002Fapi\u002Fchat'),\n })\n\n const handleSubmit = () => {\n if (!input.trim()) return\n sendMessage(input.trim())\n setInput('')\n }\n\n return (\n \u003Cdiv>\n \u003Cdiv>\n {messages.map((message: UIMessage) => (\n \u003Cdiv key={message.id}>\n \u003Cstrong>{message.role}:\u003C\u002Fstrong>\n {message.parts.map((part, i) => {\n if (part.type === 'text') {\n return \u003Cp key={i}>{part.content}\u003C\u002Fp>\n }\n return null\n })}\n \u003C\u002Fdiv>\n ))}\n \u003C\u002Fdiv>\n\n {error && \u003Cdiv>Error: {error.message}\u003C\u002Fdiv>}\n\n \u003Cdiv>\n \u003Cinput\n value={input}\n onChange={(e) => setInput(e.target.value)}\n onKeyDown={(e) => {\n if (e.key === 'Enter' && !e.shiftKey) {\n e.preventDefault()\n handleSubmit()\n }\n }}\n disabled={isLoading}\n placeholder=\"Type a message...\"\n \u002F>\n {isLoading ? (\n \u003Cbutton onClick={stop}>Stop\u003C\u002Fbutton>\n ) : (\n \u003Cbutton onClick={handleSubmit} disabled={!input.trim()}>\n Send\n \u003C\u002Fbutton>\n )}\n \u003C\u002Fdiv>\n \u003C\u002Fdiv>\n )\n}\n```\n\nVue\u002FSolid\u002FSvelte\u002FPreact have identical patterns with different hook imports\n(e.g., `import { useChat } from '@tanstack\u002Fai-solid'`).\n\n## Core Patterns\n\n### 1. Streaming Chat with SSE\n\nServer returns a streaming SSE Response; client parses it automatically.\n\n**Server:**\n\n```typescript\nimport { chat, toServerSentEventsResponse } from '@tanstack\u002Fai'\nimport { anthropicText } from '@tanstack\u002Fai-anthropic'\n\nconst stream = chat({\n adapter: anthropicText('claude-sonnet-4-5'),\n messages,\n modelOptions: {\n temperature: 0.7,\n max_tokens: 2000, \u002F\u002F Anthropic-native key\n },\n systemPrompts: ['You are a helpful assistant.'],\n abortController,\n})\n\nreturn toServerSentEventsResponse(stream, { abortController })\n```\n\nTo make the SSE response resumable (reconnect after a drop\u002Frefresh without\nre-running the provider), pass a delivery-durability adapter:\n`toServerSentEventsResponse(stream, { durability: { adapter: memoryStream(request) } })`\n(`memoryStream` from `@tanstack\u002Fai` is process-local, for dev\u002Ftests) or\n`durableStream(request, { server })` from `@tanstack\u002Fai-durable-stream`\n(Durable Streams protocol, production). Each SSE event gets an opaque\nadapter-owned `id:`; `fetchServerSentEvents` auto-reconnects with\n`Last-Event-ID` and exposes `joinRun(runId)` to replay a run from the start.\nSee `docs\u002Fresumable-streams\u002Foverview.md`.\n\n**Client:**\n\n```typescript\nimport { useChat, fetchServerSentEvents } from '@tanstack\u002Fai-react'\n\nconst { messages, sendMessage, isLoading, error, stop, status } = useChat({\n connection: fetchServerSentEvents('\u002Fapi\u002Fchat'),\n body: { provider: 'anthropic', model: 'claude-sonnet-4-5' },\n onFinish: (message) => {\n console.log('Response complete:', message.id)\n },\n onError: (err) => {\n console.error('Stream error:', err)\n },\n})\n```\n\nThe `body` field is merged into the POST request body alongside `messages`,\nletting the server read `data.provider`, `data.model`, etc.\n\nThe `status` field tracks the chat lifecycle: `'ready'` | `'submitted'` | `'streaming'` | `'error'`.\n\n### 2. Rendering Thinking\u002FReasoning Content\n\nModels with extended thinking (Claude, Gemini) emit `ThinkingPart` in the message parts array.\n\n```typescript\nimport type { UIMessage } from '@tanstack\u002Fai-react'\n\nfunction MessageRenderer({ message }: { message: UIMessage }) {\n return (\n \u003Cdiv>\n {message.parts.map((part, i) => {\n if (part.type === 'thinking') {\n const isComplete = message.parts\n .slice(i + 1)\n .some((p) => p.type === 'text')\n return (\n \u003Cdetails key={i} open={!isComplete}>\n \u003Csummary>{isComplete ? 'Thought process' : 'Thinking...'}\u003C\u002Fsummary>\n \u003Cpre>{part.content}\u003C\u002Fpre>\n \u003C\u002Fdetails>\n )\n }\n\n if (part.type === 'text' && part.content) {\n return \u003Cp key={i}>{part.content}\u003C\u002Fp>\n }\n\n if (part.type === 'tool-call') {\n return (\n \u003Cdiv key={part.id}>\n Tool call: {part.name} ({part.state})\n \u003C\u002Fdiv>\n )\n }\n\n return null\n })}\n \u003C\u002Fdiv>\n )\n}\n```\n\nServer-side, enable thinking via `modelOptions` on the adapter:\n\n```typescript\nimport { geminiText } from '@tanstack\u002Fai-gemini'\n\nconst stream = chat({\n adapter: geminiText('gemini-2.5-flash'),\n messages,\n modelOptions: {\n thinkingConfig: {\n includeThoughts: true,\n thinkingBudget: 100,\n },\n },\n})\n```\n\n### 3. Sending Multimodal Content (Images)\n\nUse `sendMessage` with a `MultimodalContent` object instead of a plain string.\n\n```typescript\nimport { useChat, fetchServerSentEvents } from '@tanstack\u002Fai-react'\nimport type { ContentPart } from '@tanstack\u002Fai'\n\nconst { sendMessage } = useChat({\n connection: fetchServerSentEvents('\u002Fapi\u002Fchat'),\n})\n\nfunction sendImageMessage(text: string, imageBase64: string, mimeType: string) {\n const contentParts: Array\u003CContentPart> = [\n { type: 'text', content: text },\n {\n type: 'image',\n source: { type: 'data', value: imageBase64, mimeType },\n },\n ]\n\n sendMessage({ content: contentParts })\n}\n\nfunction sendImageUrl(text: string, imageUrl: string) {\n const contentParts: Array\u003CContentPart> = [\n { type: 'text', content: text },\n {\n type: 'image',\n source: { type: 'url', value: imageUrl },\n },\n ]\n\n sendMessage({ content: contentParts })\n}\n```\n\nRender image parts in received messages:\n\n```typescript\nif (part.type === 'image') {\n const src =\n part.source.type === 'url'\n ? part.source.value\n : `data:${part.source.mimeType};base64,${part.source.value}`\n return \u003Cimg key={i} src={src} alt=\"Attached image\" \u002F>\n}\n```\n\n### 4. Sending Audio Messages (Browser Recording)\n\nUse `useAudioRecorder` from `@tanstack\u002Fai-react` (or `createAudioRecorder` in Svelte) to capture audio in the browser. The resolved `AudioRecording` includes a ready-to-use `part` that slots directly into `sendMessage`.\n\n```typescript\nimport {\n useAudioRecorder,\n useChat,\n fetchServerSentEvents,\n} from '@tanstack\u002Fai-react'\n\nconst { isRecording, isSupported, start, stop } = useAudioRecorder()\nconst { sendMessage } = useChat({\n connection: fetchServerSentEvents('\u002Fapi\u002Fchat'),\n})\n\nasync function toggle() {\n if (!isRecording) {\n await start()\n return\n }\n const recording = await stop()\n await sendMessage({ content: [recording.part] })\n}\n```\n\n`recording.part` is `{ type: 'audio', source: { type: 'data', value: base64, mimeType } }`. Returns the recorder's native format (`audio\u002Fwebm` or `audio\u002Fmp4`) with no transcoding.\n\n### 5. HTTP Stream Format (Alternative to SSE)\n\nUse `toHttpResponse` + `fetchHttpStream` for newline-delimited JSON instead of SSE.\n\n**Server:**\n\n```typescript\nimport { chat, toHttpResponse } from '@tanstack\u002Fai'\nimport { openaiText } from '@tanstack\u002Fai-openai'\n\nconst stream = chat({\n adapter: openaiText('gpt-5.5'),\n messages,\n abortController,\n})\n\nreturn toHttpResponse(stream, { abortController })\n```\n\n**Client:**\n\n```typescript\nimport { useChat, fetchHttpStream } from '@tanstack\u002Fai-react'\n\nconst { messages, sendMessage } = useChat({\n connection: fetchHttpStream('\u002Fapi\u002Fchat'),\n})\n```\n\nThe only difference is swapping `toServerSentEventsResponse` \u002F `fetchServerSentEvents`\nfor `toHttpResponse` \u002F `fetchHttpStream`. Everything else stays identical.\n\nThis includes resumability: pass the same `durability` adapter to\n`toHttpResponse(stream, { durability: { adapter: memoryStream(request) } })` and\neach NDJSON line becomes an `{ id, chunk }` envelope. `fetchHttpStream`\nauto-reconnects with `Last-Event-ID`, de-dupes the replayed prefix, and exposes\n`joinRun(runId)` — the same guarantees as resumable SSE. The XHR adapters\n(`xhrServerSentEvents` \u002F `xhrHttpStream`) are resumable too.\n\n### 6. MCP Tool Discovery via `chat({ mcp })`\n\nPass `mcp` to let `chat()` own discovery **and** lifecycle for one or more MCP\nclients. Useful when you want minimal boilerplate and don't need to reuse the\nclients across calls.\n\n```typescript\n\u002F\u002F Prop shape:\n\u002F\u002F chat({\n\u002F\u002F ...,\n\u002F\u002F mcp: {\n\u002F\u002F clients: Array\u003CMCPClient | MCPClients>,\n\u002F\u002F connection?: 'close' | 'keep-alive', \u002F\u002F default: 'close'\n\u002F\u002F lazyTools?: boolean,\n\u002F\u002F onDiscoveryError?: (error: unknown, source) => void,\n\u002F\u002F }\n\u002F\u002F })\n```\n\n- **`clients`** — one or more `MCPClient` \u002F `MCPClients` instances.\n- **`connection`** — `'close'` (default) closes each client when the run ends\n (after the agent loop completes and the stream is drained); with\n `'keep-alive'`, `chat()` never closes the clients — the caller owns their\n lifecycle (keep connections warm across requests).\n- **`lazyTools`** — forwarded to `tools({ lazy: true })` so tool schemas are\n sent to the LLM on demand.\n- **`onDiscoveryError`** — throw (or re-throw) to fail the entire call fast;\n return normally to skip that source and continue. Omit to rethrow (fail-fast).\n\n**When to use `mcp` vs. the tools spread:**\n\n| Approach | Use when |\n| ------------------------------------------------------- | ----------------------------------------------------------------------------------------------- |\n| `chat({ mcp: { clients: [...] } })` | You want discovery + lifecycle managed for you, and don't need fully-typed input\u002Foutput schemas |\n| `tools: [...await client.tools([toolDefinition(...)])]` | You want fully-typed MCP tools with Zod input\u002Foutput validation |\n\n**Server-side example:**\n\n```typescript\nimport { createFileRoute } from '@tanstack\u002Freact-router'\nimport { chat, toServerSentEventsResponse } from '@tanstack\u002Fai'\nimport { openaiText } from '@tanstack\u002Fai-openai'\nimport { createMCPClient } from '@tanstack\u002Fai-mcp'\n\nexport const Route = createFileRoute('\u002Fapi\u002Fchat')({\n server: {\n handlers: {\n POST: async ({ request }) => {\n const { messages } = await request.json()\n\n const mcpClient = await createMCPClient({\n transport: { type: 'http', url: 'https:\u002F\u002Fmcp.example.com\u002Fmcp' },\n })\n\n const stream = chat({\n adapter: openaiText('gpt-5.5'),\n messages,\n mcp: {\n clients: [mcpClient],\n connection: 'keep-alive', \u002F\u002F chat() won't close it — reuse across requests\n },\n })\n\n return toServerSentEventsResponse(stream)\n \u002F\u002F connection: 'keep-alive' — chat() never closes mcpClient; it stays open for reuse across runs.\n },\n },\n },\n})\n```\n\n### 7. Queueing Messages Sent While Streaming\n\nBy default, a `sendMessage` call that arrives while a stream is in flight is\n**queued** and sent automatically once the run settles **successfully** —\nthis is a behavior change: such sends used to be silently dropped. Configure\nit with the `queue` option on `useChat`:\n\n```typescript\nimport { useChat, fetchServerSentEvents } from '@tanstack\u002Fai-react'\n\nconst { messages, queue, sendMessage, cancelQueued, isLoading } = useChat({\n connection: fetchServerSentEvents('\u002Fapi\u002Fchat'),\n queue: { whenBusy: 'queue', drain: 'fifo', maxSize: 5, onOverflow: 'reject' },\n})\n```\n\n- **`whenBusy`** — `'queue'` (default) holds the message until a successful\n settle; `'drop'` ignores the send (never appears in `queue`\u002F`messages`);\n `'interrupt'` aborts the current stream and sends immediately (unlike\n `stop()`, does **not** flush already-queued items — they drain after the\n interrupting send **succeeds**).\n- **`drain`** — `'fifo'` (default) sends queued items one at a time in\n order; `'batch'` merges everything queued into a single send once the\n run settles successfully.\n- **`maxSize`** \u002F **`onOverflow`** — cap the queue length; `'reject'`\n (default) silently ignores overflow sends (does not throw),\n `'drop-oldest'` evicts the oldest queued item to make room.\n\nThe top-level `queue` option also accepts a plain `WhenBusy` string\nshorthand (e.g. `queue: 'interrupt'`) or a `QueueStrategy` function for\nper-send action control. Strategy form always drains FIFO; actions are\n`'queue' | 'drop' | 'interrupt'`.\n\n**Drain vs flush:** queued messages auto-send only after a **successful**\nsettle. They are **discarded** on stream error\u002Fabort of the active\ngeneration, `stop()`, `clear()`, `unsubscribe()`, and `reload()`.\n`interrupt` does not flush.\n\n`queue: Array\u003CQueuedMessage>` (`{ id, content, createdAt }`) is separate\nfrom `messages` — render pending sends distinctly and cancel with\n`cancelQueued(id)`:\n\n```typescript\n{queue.map((q) => (\n \u003Cdiv key={q.id}>\n {typeof q.content === 'string' ? q.content : '[attachment]'}\n \u003Cbutton onClick={() => cancelQueued(q.id)}>Cancel\u003C\u002Fbutton>\n \u003C\u002Fdiv>\n))}\n```\n\nOverride the configured policy for a single send with the second argument\nto `sendMessage`:\n\n```typescript\nsendMessage('Never mind, do this instead', { whenBusy: 'interrupt' })\n```\n\n### 8. Browser-Refresh Durability (client persistence)\n\nBy default a `ChatClient` \u002F `useChat` keeps messages in memory only, so a full\npage reload loses the conversation. The optional `persistence` option (a\n`ChatClientPersistence` adapter) fixes this from the client side: it stores one\ncombined record — `{ messages, resume? }` (`ChatPersistedState`) — per chat `id`,\nso a reload restores the transcript **and** rehydrates any pending interrupt \u002F\nrejoins a run that was still streaming. No manual `initialMessages` + `onFinish`\nboilerplate.\n\nThree storage adapters ship from `@tanstack\u002Fai-client`:\n`localStoragePersistence` (survives reloads and browser restarts),\n`sessionStoragePersistence` (scoped to the tab), and `indexedDBPersistence`\n(async, structured-clone storage — no codec needed for `Date`\u002F`Map`\u002Fetc.).\nGive the chat a stable `threadId` so the reload finds the same record.\nPersistence keys on `threadId`; the storage adapters are re-exported from each\nframework package, so a single import works:\n\n```typescript\nimport {\n useChat,\n fetchServerSentEvents,\n localStoragePersistence,\n} from '@tanstack\u002Fai-react'\n\n\u002F\u002F Defaults to the ChatPersistedState shape and a JSON codec, so no type\n\u002F\u002F argument or serialize\u002Fdeserialize is needed. indexedDBPersistence stores via\n\u002F\u002F structured clone (a Date round-trips exactly).\nconst persistence = localStoragePersistence()\n\nfunction Chat() {\n const { messages, sendMessage } = useChat({\n threadId: 'support-chat',\n connection: fetchServerSentEvents('\u002Fapi\u002Fchat'),\n persistence,\n })\n \u002F\u002F ...render messages, call sendMessage(text)\n}\n```\n\n**Keep large transcripts off the client.** `persistence` also accepts `true`\n(server-authoritative): the client caches nothing, and on mount it hydrates the\nthread from the server by `threadId` (transcript plus a cursor to any run still\ngenerating). An adapter is client-authoritative; `true` leaves history on the\nserver and needs a connection with a `hydrate` handler plus a server GET\nendpoint (`reconstructChat`), since the delivery log only holds one run.\n\n**Mid-stream reload rejoin.** If the run was still streaming when the page\nreloaded, the client re-attaches instead of showing a frozen half-reply — but\nonly when the connection is **resumable**: a delivery-durability-backed route\nthat records the stream and exposes a GET replay handler (see\n`docs\u002Fresumable-streams\u002Foverview.md` and Pattern 1's `durability` adapter). Given\nthat, `useChat` finds the persisted in-flight run on load and auto-rejoins it via\n`joinRun`, replaying from the server's log so the reply finishes where it left\noff. No extra client code beyond the resumable connection.\n\n**Every framework, no extra code.** Durability rides the existing `persistence`\noption, so it works identically in `@tanstack\u002Fai-react`, `-solid`, `-vue`,\n`-svelte`, `-angular`, and `-preact` — pass `persistence` (and a stable\n`threadId`, which is the chat's identity) to the framework's `useChat` \u002F\n`createChat` \u002F `injectChat`; nothing is framework-specific.\n\n> **Client vs. server durability.** This is the client (per-browser) half.\n> The authoritative, multi-user, server-side copy is the `withPersistence`\n> middleware — see ai-core\u002Fmiddleware\u002FSKILL.md. The two are independent; use\n> both for instant reload restore plus a durable record of record.\n\n## Common Mistakes\n\n### a. CRITICAL: Using Vercel AI SDK patterns (streamText, generateText)\n\n```typescript\n\u002F\u002F WRONG\nimport { streamText } from 'ai'\nimport { openai } from '@ai-sdk\u002Fopenai'\nconst result = streamText({ model: openai('gpt-5.5'), messages })\n\n\u002F\u002F CORRECT\nimport { chat } from '@tanstack\u002Fai'\nimport { openaiText } from '@tanstack\u002Fai-openai'\nconst stream = chat({ adapter: openaiText('gpt-5.5'), messages })\n```\n\n### b. CRITICAL: Using Vercel createOpenAI() provider pattern\n\n```typescript\n\u002F\u002F WRONG\nimport { createOpenAI } from '@ai-sdk\u002Fopenai'\nconst openai = createOpenAI({ apiKey })\nstreamText({ model: openai('gpt-5.5'), messages })\n\n\u002F\u002F CORRECT\nimport { openaiText } from '@tanstack\u002Fai-openai'\nimport { chat } from '@tanstack\u002Fai'\nchat({ adapter: openaiText('gpt-5.5'), messages })\n```\n\n### c. CRITICAL: Using monolithic openai() instead of openaiText()\n\n```typescript\n\u002F\u002F WRONG\nimport { openai } from '@tanstack\u002Fai-openai'\nchat({ adapter: openai(), model: 'gpt-5.5', messages })\n\n\u002F\u002F CORRECT\nimport { openaiText } from '@tanstack\u002Fai-openai'\nchat({ adapter: openaiText('gpt-5.5'), messages })\n```\n\nThe monolithic `openai()` adapter is deprecated. Use tree-shakeable adapters:\n`openaiText()`, `openaiImage()`, `openaiSpeech()`, etc.\n\n### d. HIGH: Using toResponseStream instead of toServerSentEventsResponse\n\n```typescript\n\u002F\u002F WRONG\nimport { toResponseStream } from '@tanstack\u002Fai'\nreturn toResponseStream(stream, { abortController })\n\n\u002F\u002F CORRECT\nimport { toServerSentEventsResponse } from '@tanstack\u002Fai'\nreturn toServerSentEventsResponse(stream, { abortController })\n```\n\n### e. HIGH: Passing model as separate parameter to chat()\n\n```typescript\n\u002F\u002F WRONG\nchat({ adapter: openaiText(), model: 'gpt-5.5', messages })\n\n\u002F\u002F CORRECT\nchat({ adapter: openaiText('gpt-5.5'), messages })\n```\n\nThe model is passed to the adapter factory, not to `chat()`.\n\n### f. HIGH: Passing sampling options at the root of chat()\n\nSampling options (`temperature`, token limits, `top_p`\u002F`topP`) are **not**\ntop-level fields on `chat()`. They live inside `modelOptions` using the\nprovider's native key.\n\n```typescript\n\u002F\u002F WRONG — temperature\u002FmaxTokens are not root options\nchat({ adapter, messages, temperature: 0.7, maxTokens: 1000 })\n\n\u002F\u002F WRONG — there is no `options` field either\nchat({ adapter, messages, options: { temperature: 0.7, maxTokens: 1000 } })\n\n\u002F\u002F CORRECT — inside modelOptions, provider-native keys (OpenAI shown)\nchat({\n adapter,\n messages,\n modelOptions: { temperature: 0.7, max_output_tokens: 1000 },\n})\n```\n\n`temperature` is universal across providers; token limits use provider-native\nkeys (`max_output_tokens` for OpenAI, `max_tokens` for Anthropic\u002FGrok,\n`maxOutputTokens` for Gemini, `max_completion_tokens` for Groq,\n`maxCompletionTokens` for OpenRouter, and `num_predict` nested under\n`modelOptions.options` for Ollama). See ai-core\u002Fadapter-configuration\u002FSKILL.md.\n\n### g. HIGH: Using providerOptions instead of modelOptions\n\n```typescript\n\u002F\u002F WRONG\nchat({\n adapter,\n messages,\n providerOptions: { responseFormat: { type: 'json_object' } },\n})\n\n\u002F\u002F CORRECT\nchat({\n adapter,\n messages,\n modelOptions: { responseFormat: { type: 'json_object' } },\n})\n```\n\n### h. HIGH: Implementing custom SSE stream instead of using toServerSentEventsResponse\n\n```typescript\n\u002F\u002F WRONG\nconst readable = new ReadableStream({\n async start(controller) {\n const encoder = new TextEncoder()\n for await (const chunk of stream) {\n controller.enqueue(encoder.encode(`data: ${JSON.stringify(chunk)}\\n\\n`))\n }\n controller.enqueue(encoder.encode('data: [DONE]\\n\\n'))\n controller.close()\n },\n})\nreturn new Response(readable, {\n headers: { 'Content-Type': 'text\u002Fevent-stream' },\n})\n\n\u002F\u002F CORRECT\nimport { toServerSentEventsResponse } from '@tanstack\u002Fai'\nreturn toServerSentEventsResponse(stream, { abortController })\n```\n\n`toServerSentEventsResponse` handles SSE formatting, abort signals,\nerror events (RUN_ERROR), and correct headers automatically.\n\n### i. HIGH: Implementing custom onEnd\u002FonFinish callbacks instead of middleware\n\n```typescript\n\u002F\u002F WRONG\nchat({\n adapter,\n messages,\n onEnd: (result) => {\n trackAnalytics(result)\n },\n})\n\n\u002F\u002F CORRECT\nimport type { ChatMiddleware } from '@tanstack\u002Fai'\n\nconst analytics: ChatMiddleware = {\n name: 'analytics',\n onFinish(ctx, info) {\n trackAnalytics({ reason: info.finishReason, iterations: ctx.iteration })\n },\n onUsage(ctx, usage) {\n trackTokens(usage.totalTokens)\n },\n}\n\nchat({ adapter, messages, middleware: [analytics] })\n```\n\n`chat()` has no `onEnd`\u002F`onFinish` option. Use `middleware` for lifecycle events.\nSee also: ai-core\u002Fmiddleware\u002FSKILL.md.\n\n### j. HIGH: Importing from @tanstack\u002Fai-client instead of framework package\n\n```typescript\n\u002F\u002F WRONG\nimport { fetchServerSentEvents } from '@tanstack\u002Fai-client'\nimport { useChat } from '@tanstack\u002Fai-react'\n\n\u002F\u002F CORRECT\nimport { useChat, fetchServerSentEvents } from '@tanstack\u002Fai-react'\n```\n\nFramework packages re-export everything needed from `@tanstack\u002Fai-client`.\nImport from `@tanstack\u002Fai-client` only in vanilla JS (no framework).\n\n### k. MEDIUM: Not handling RUN_ERROR events in streaming context\n\nStreaming errors arrive as `RUN_ERROR` events in the stream, not as thrown\nexceptions. The `useChat` hook surfaces these via the `error` state and\n`onError` callback. 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Generic OpenAI-compatible providers (DeepSeek, Together, Fireworks, etc.) via openaiCompatible({ baseURL, apiKey, models }) from @tanstack\u002Fai-openai\u002Fcompatible. API key env vars: OPENAI_API_KEY, ANTHROPIC_API_KEY, GOOGLE_API_KEY\u002FGEMINI_API_KEY, XAI_API_KEY, GROQ_API_KEY, OPENROUTER_API_KEY, OLLAMA_HOST, BEDROCK_API_KEY (or AWS_BEARER_TOKEN_BEDROCK).\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[12859,12860,12863,12864],{"name":12827,"slug":32,"type":16},{"name":12861,"slug":12862,"type":16},"Configuration","configuration",{"name":14,"slug":15,"type":16},{"name":10,"slug":9,"type":16},"2026-07-16T06:04:17.82075",{"slug":12867,"name":12868,"fn":12869,"description":12870,"org":12871,"tags":12872,"stars":24,"repoUrl":25,"updatedAt":12876},"ai-coreag-ui-protocol","ai-core\u002Fag-ui-protocol","implement TanStack AI streaming protocol","Server-side AG-UI streaming protocol implementation: StreamChunk event types (RUN_STARTED, TEXT_MESSAGE_START\u002FCONTENT\u002FEND, TOOL_CALL_START\u002FARGS\u002FEND, RUN_FINISHED, RUN_ERROR, STEP_STARTED\u002FSTEP_FINISHED, STATE_SNAPSHOT\u002FDELTA, CUSTOM), toServerSentEventsStream() for SSE format, toHttpStream() for NDJSON format. For backends serving AG-UI events without client packages.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[12873,12874,12875],{"name":18,"slug":19,"type":16},{"name":14,"slug":15,"type":16},{"name":10,"slug":9,"type":16},"2026-07-16T06:04:10.093367",{"slug":4,"name":5,"fn":6,"description":7,"org":12878,"tags":12879,"stars":24,"repoUrl":25,"updatedAt":26},{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[12880,12881,12882,12883],{"name":18,"slug":19,"type":16},{"name":22,"slug":23,"type":16},{"name":14,"slug":15,"type":16},{"name":10,"slug":9,"type":16},{"slug":12885,"name":12886,"fn":12887,"description":12888,"org":12889,"tags":12890,"stars":24,"repoUrl":25,"updatedAt":12896},"ai-coreclient-persistence","ai-core\u002Fclient-persistence","implement browser chat persistence for TanStack AI","Browser chat persistence on useChat \u002F ChatClient: localStoragePersistence, sessionStoragePersistence, indexedDBPersistence. Client-authoritative (adapter, full transcript) vs server-authoritative (persistence: true, no client cache). Reload restore, pending interrupts, mid-stream rejoin with delivery durability. Use for SPA reload durability — NOT server history alone. No extra package: the adapters ship in the framework packages.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[12891,12892,12893,12895],{"name":12848,"slug":30,"type":16},{"name":22,"slug":23,"type":16},{"name":12894,"slug":8607,"type":16},"Persistence",{"name":10,"slug":9,"type":16},"2026-07-30T05:53:35.503176",{"slug":12898,"name":12899,"fn":12900,"description":12901,"org":12902,"tags":12903,"stars":24,"repoUrl":25,"updatedAt":12910},"ai-corecustom-backend-integration","ai-core\u002Fcustom-backend-integration","integrate custom backends with TanStack AI","Connect useChat to a non-TanStack-AI backend through custom connection adapters. ConnectConnectionAdapter (single async iterable) vs SubscribeConnectionAdapter (separate subscribe\u002Fsend). Customize fetchServerSentEvents() and fetchHttpStream() with auth headers, custom URLs, and request options. Import from framework package, not @tanstack\u002Fai-client.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[12904,12905,12906,12909],{"name":12848,"slug":30,"type":16},{"name":18,"slug":19,"type":16},{"name":12907,"slug":12908,"type":16},"Backend","backend",{"name":10,"slug":9,"type":16},"2026-07-17T06:06:39.855351",{"items":12912,"total":13052},[12913,12927,12939,12951,12966,12978,12988,12998,13011,13021,13032,13042],{"slug":12914,"name":12914,"fn":12915,"description":12916,"org":12917,"tags":12918,"stars":12924,"repoUrl":12925,"updatedAt":12926},"aggregation","perform data aggregation in TanStack Table","Aggregate TanStack Table columns independently of grouping, including grand totals, caller-selected row totals, multiple keyed aggregations, custom context-based definitions, grouped merges, manual values, and worker constraints.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[12919,12922,12923],{"name":12920,"slug":12921,"type":16},"Data Analysis","data-analysis",{"name":22,"slug":23,"type":16},{"name":10,"slug":9,"type":16},28175,"https:\u002F\u002Fgithub.com\u002FTanStack\u002Ftable","2026-07-30T05:25:59.429787",{"slug":12928,"name":12928,"fn":12929,"description":12930,"org":12931,"tags":12932,"stars":12924,"repoUrl":12925,"updatedAt":12938},"api-not-found","diagnose TanStack Table API errors","Diagnose missing TanStack Table v9 exports, options, state slices, and instance methods. Load before inventing an API when code sees a type error, undefined feature method, absent object key, adapter mismatch, or v8-shaped example.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[12933,12936,12937],{"name":12934,"slug":12935,"type":16},"Debugging","debugging",{"name":22,"slug":23,"type":16},{"name":10,"slug":9,"type":16},"2026-07-30T05:26:05.418735",{"slug":12940,"name":12940,"fn":12941,"description":12942,"org":12943,"tags":12944,"stars":12924,"repoUrl":12925,"updatedAt":12950},"cell-selection","select rectangular cell ranges in tables","Select rectangular cell ranges with cellSelectionFeature: two-corner range state keyed by row and column id, mousedown\u002Fmouseenter handlers, selection edges, render-order resolution under pinning, and autoResetCellSelection. Load when ranges widen unexpectedly after sorting or column reordering, when a drag re-renders the whole table, or when building copy-to-clipboard from a selection.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[12945,12946,12947],{"name":12920,"slug":12921,"type":16},{"name":10,"slug":9,"type":16},{"name":12948,"slug":12949,"type":16},"UI Components","ui-components","2026-07-30T05:25:38.403427",{"slug":12952,"name":12952,"fn":12953,"description":12954,"org":12955,"tags":12956,"stars":12924,"repoUrl":12925,"updatedAt":12965},"client-vs-server","manage TanStack Table data pipelines","Choose client or server ownership for filtering, grouping, sorting, expanding, and pagination in TanStack Table v9. Load for manual* flags, mixed pipelines, server counts, or deciding which dataset each row-model stage receives.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[12957,12960,12961,12964],{"name":12958,"slug":12959,"type":16},"Data Pipeline","data-pipeline",{"name":22,"slug":23,"type":16},{"name":12962,"slug":12963,"type":16},"Performance","performance",{"name":10,"slug":9,"type":16},"2026-07-30T05:25:45.400104",{"slug":12967,"name":12967,"fn":12968,"description":12969,"org":12970,"tags":12971,"stars":12924,"repoUrl":12925,"updatedAt":12977},"column-faceting","build faceted filter UIs","Build faceted filter UIs with columnFacetingFeature, facetedRowModel, facetedUniqueValues, and facetedMinMaxValues. Load for facet counts, numeric ranges, own-filter exclusion, or server-page facet completeness.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[12972,12975,12976],{"name":12973,"slug":12974,"type":16},"Data Visualization","data-visualization",{"name":22,"slug":23,"type":16},{"name":10,"slug":9,"type":16},"2026-07-30T05:25:41.397257",{"slug":12979,"name":12979,"fn":12980,"description":12981,"org":12982,"tags":12983,"stars":12924,"repoUrl":12925,"updatedAt":12987},"column-filtering","implement column filtering in TanStack Table","Filter columns with columnFilteringFeature, filteredRowModel, filterFns, filterMeta, nested-row direction, and manualFiltering. Load for accessor compatibility, controlled filter updaters, fuzzy metadata, or client\u002Fserver ownership.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[12984,12985,12986],{"name":12920,"slug":12921,"type":16},{"name":22,"slug":23,"type":16},{"name":10,"slug":9,"type":16},"2026-07-30T05:25:53.391632",{"slug":12989,"name":12989,"fn":12990,"description":12991,"org":12992,"tags":12993,"stars":12924,"repoUrl":12925,"updatedAt":12997},"column-ordering","manage TanStack Table column ordering","Control TanStack Table v9 leaf columnOrder with stable IDs while accounting for pinning regions, visibility, and groupedColumnMode precedence. Load for drag-and-drop columns or rendered order that differs from state.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[12994,12995,12996],{"name":22,"slug":23,"type":16},{"name":10,"slug":9,"type":16},{"name":12948,"slug":12949,"type":16},"2026-07-30T05:26:03.37801",{"slug":12999,"name":12999,"fn":13000,"description":13001,"org":13002,"tags":13003,"stars":12924,"repoUrl":12925,"updatedAt":13010},"column-pinning","configure column pinning in TanStack Table","Pin columns into logical start, center, and end regions with columnPinningFeature and renderer-owned sticky CSS. Load for RTL offsets, z-index, backgrounds, overflow, widths, gaps, or overlaps.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[13004,13007,13008,13009],{"name":13005,"slug":13006,"type":16},"CSS","css",{"name":22,"slug":23,"type":16},{"name":10,"slug":9,"type":16},{"name":12948,"slug":12949,"type":16},"2026-07-30T05:25:55.377366",{"slug":13012,"name":13012,"fn":13013,"description":13014,"org":13015,"tags":13016,"stars":12924,"repoUrl":12925,"updatedAt":13020},"column-resizing","implement column resizing in TanStack Table","Wire columnResizingFeature, header.getResizeHandler, resize mode and direction, pointer or touch events, and performant CSS-variable updates. Load when resize state changes but widths do not, or large tables resize slowly.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[13017,13018,13019],{"name":22,"slug":23,"type":16},{"name":10,"slug":9,"type":16},{"name":12948,"slug":12949,"type":16},"2026-07-30T05:25:51.400011",{"slug":13022,"name":13022,"fn":13023,"description":13024,"org":13025,"tags":13026,"stars":12924,"repoUrl":12925,"updatedAt":13031},"column-sizing","configure column sizing in TanStack Table","Use columnSizingFeature numeric size, minSize, maxSize, getSize, getStart, getAfter, and total-size APIs in table, grid, or flex CSS. Load for auto or percentage misconceptions and sizing\u002Fpinning layout mismatch.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[13027,13028,13029,13030],{"name":13005,"slug":13006,"type":16},{"name":22,"slug":23,"type":16},{"name":10,"slug":9,"type":16},{"name":12948,"slug":12949,"type":16},"2026-07-30T05:25:48.703799",{"slug":13033,"name":13033,"fn":13034,"description":13035,"org":13036,"tags":13037,"stars":12924,"repoUrl":12925,"updatedAt":13041},"column-visibility","manage column visibility in TanStack Table","Hide columns with columnVisibilityFeature while rendering visibility-aware header, column, and cell collections. Load when hidden columns remain in the DOM, false-versus-absent state is confused, or enableHiding is misunderstood.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[13038,13039,13040],{"name":22,"slug":23,"type":16},{"name":10,"slug":9,"type":16},{"name":12948,"slug":12949,"type":16},"2026-07-30T05:25:47.367943",{"slug":13043,"name":13043,"fn":13044,"description":13045,"org":13046,"tags":13047,"stars":12924,"repoUrl":12925,"updatedAt":13051},"core","build data grids with TanStack Table","Use TanStack Table v9 as a headless data-grid state and row-processing engine. Load for first-table architecture, stable data and columns, row numbering with getDisplayIndex, semantic rendering, framework adapter choice, or deciding what Table owns versus the renderer.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[13048,13049,13050],{"name":12920,"slug":12921,"type":16},{"name":22,"slug":23,"type":16},{"name":12948,"slug":12949,"type":16},"2026-07-30T05:25:52.366295",125]