[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-tanstack-ai-corestructured-outputs":3,"mdc--wj20t-key":52,"related-org-tanstack-ai-corestructured-outputs":10338,"related-repo-tanstack-ai-corestructured-outputs":10482},{"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":47,"sourceUrl":50,"mdContent":51},"ai-corestructured-outputs","ai-core\u002Fstructured-outputs","generate type-safe structured JSON outputs","Type-safe JSON schema responses from LLMs using outputSchema on chat() and useChat(). Supports Zod, ArkType, and Valibot schemas. The adapter handles provider-specific strategies transparently — never configure structured output at the provider level. Pass stream:true alongside outputSchema for incremental JSON deltas + a terminal validated object via the `structured-output.complete` event. Every assistant turn in useChat carries its own typed `StructuredOutputPart` on `messages[i].parts`, so multi-turn structured chats preserve history automatically — partial\u002Ffinal derive from the latest assistant turn's part. convertSchemaToJsonSchema() for manual schema conversion.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},"tanstack","TanStack","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Ftanstack.png",[13,17,20,23],{"name":14,"slug":15,"type":16},"TypeScript","typescript","tag",{"name":18,"slug":19,"type":16},"JSON","json",{"name":21,"slug":22,"type":16},"AI SDK","ai-sdk",{"name":10,"slug":9,"type":16},2884,"https:\u002F\u002Fgithub.com\u002FTanStack\u002Fai","2026-07-16T06:04:17.136221",null,269,[30,31,22,32,33,34,35,36,37,38,39,40,41,42,43,9,44,15,45,46],"ai","ai-agents","anthropic","chatbot","function-calling","gemini","generative-ai","llm","multimodal","openai","react","solidjs","streaming","svelte","tool-calling","typescript-sdk","vue",{"repoUrl":25,"stars":24,"forks":28,"topics":48,"description":49},[30,31,22,32,33,34,35,36,37,38,39,40,41,42,43,9,44,15,45,46],"🤖 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\u002Fstructured-outputs","---\nname: ai-core\u002Fstructured-outputs\ndescription: >\n Type-safe JSON schema responses from LLMs using outputSchema on chat()\n and useChat(). Supports Zod, ArkType, and Valibot schemas. The adapter\n handles provider-specific strategies transparently — never configure\n structured output at the provider level. Pass stream:true alongside\n outputSchema for incremental JSON deltas + a terminal validated object\n via the `structured-output.complete` event. Every assistant turn in\n useChat carries its own typed `StructuredOutputPart` on\n `messages[i].parts`, so multi-turn structured chats preserve history\n automatically — partial\u002Ffinal derive from the latest assistant turn's\n part. convertSchemaToJsonSchema() for manual schema conversion.\ntype: sub-skill\nlibrary: tanstack-ai\nlibrary_version: '0.10.0'\nsources:\n - 'TanStack\u002Fai:docs\u002Fstructured-outputs\u002Foverview.md'\n - 'TanStack\u002Fai:docs\u002Fstructured-outputs\u002Fone-shot.md'\n - 'TanStack\u002Fai:docs\u002Fstructured-outputs\u002Fstreaming.md'\n - 'TanStack\u002Fai:docs\u002Fstructured-outputs\u002Fmulti-turn.md'\n - 'TanStack\u002Fai:docs\u002Fstructured-outputs\u002Fwith-tools.md'\n---\n\n# Structured Outputs\n\n> **Dependency note:** This skill builds on ai-core. Read it first for critical rules. The `useChat` patterns below build on ai-core\u002Fchat-experience — read that for the base hook surface, then come back here for the structured-output specifics.\n\n## Setup\n\n```typescript\nimport { chat } from '@tanstack\u002Fai'\nimport { openaiText } from '@tanstack\u002Fai-openai'\nimport { z } from 'zod'\n\nconst person = await chat({\n adapter: openaiText('gpt-5.2'),\n messages: [{ role: 'user', content: 'John Doe, 30' }],\n outputSchema: z.object({\n name: z.string(),\n age: z.number(),\n }),\n})\n\nperson.name \u002F\u002F string — fully typed, no cast\nperson.age \u002F\u002F number\n```\n\nWhen `outputSchema` is provided, `chat()` returns `Promise\u003CInferSchemaType\u003CTSchema>>` instead of `AsyncIterable\u003CStreamChunk>`. The result is fully typed.\n\nAdding `stream: true` switches the return to `StructuredOutputStream\u003CInferSchemaType\u003CTSchema>>` — incremental JSON deltas plus a terminal validated object. See **Pattern 3** below for direct iteration, **Pattern 4** for the `useChat` shape on the client, and **Pattern 5** for multi-turn structured chats.\n\n## Decision: which pattern fits\n\n| Building this | Use |\n| ---------------------------------------------------------------------------------------------- | ---------------------------------------------------------------- |\n| One prompt in → one typed object out (script, server endpoint, CLI) | Pattern 1 (basic) or 2 (nested) |\n| A UI that fills in field by field as the model streams (progressive form, live card) | Pattern 4 — `useChat({ outputSchema })` |\n| Direct iteration of the stream in Node or tests | Pattern 3 — async iterable |\n| Users iterate on a structured object across multiple turns (recipe builder, ticket refinement) | Pattern 5 — multi-turn structured chat |\n| Tools that gather info, then return a typed object | Combine any of the above with `tools` — see ai-core\u002Ftool-calling |\n\n## Core Patterns\n\n### Pattern 1: Basic structured output with Zod\n\n```typescript\nimport { chat } from '@tanstack\u002Fai'\nimport { openaiText } from '@tanstack\u002Fai-openai'\nimport { z } from 'zod'\n\nconst PersonSchema = z.object({\n name: z.string().meta({ description: \"The person's full name\" }),\n age: z.number().meta({ description: \"The person's age in years\" }),\n email: z.string().email().meta({ description: 'Email address' }),\n})\n\n\u002F\u002F chat() returns Promise\u003C{ name: string; age: number; email: string }>\nconst person = await chat({\n adapter: openaiText('gpt-5.2'),\n messages: [\n {\n role: 'user',\n content:\n 'Extract the person info: John Doe is 30 years old, email john@example.com',\n },\n ],\n outputSchema: PersonSchema,\n})\n\nconsole.log(person.name) \u002F\u002F \"John Doe\"\nconsole.log(person.age) \u002F\u002F 30\nconsole.log(person.email) \u002F\u002F \"john@example.com\"\n```\n\n### Pattern 2: Complex nested schemas\n\n```typescript\nimport { chat } from '@tanstack\u002Fai'\nimport { anthropicText } from '@tanstack\u002Fai-anthropic'\nimport { z } from 'zod'\n\nconst CompanySchema = z.object({\n name: z.string(),\n founded: z.number().meta({ description: 'Year the company was founded' }),\n headquarters: z.object({\n city: z.string(),\n country: z.string(),\n address: z.string().optional(),\n }),\n employees: z.array(\n z.object({\n name: z.string(),\n role: z.string(),\n department: z.string(),\n }),\n ),\n financials: z\n .object({\n revenue: z\n .number()\n .meta({ description: 'Annual revenue in millions USD' }),\n profitable: z.boolean(),\n })\n .optional(),\n})\n\nconst company = await chat({\n adapter: anthropicText('claude-sonnet-4-5'),\n messages: [\n {\n role: 'user',\n content: 'Extract company info from this article: ...',\n },\n ],\n outputSchema: CompanySchema,\n})\n\n\u002F\u002F Full type safety on nested properties\nconsole.log(company.headquarters.city)\nconsole.log(company.employees[0].role)\nconsole.log(company.financials?.revenue)\n```\n\n### Pattern 3: Direct stream iteration\n\nPass `stream: true` alongside `outputSchema` to get an async iterable of standard streaming chunks plus a terminal validated object. Use this when you're a single process end-to-end — Node script, CLI, test, or a server endpoint that responds with one JSON blob. For the in-browser progressive-UI case, jump to Pattern 4 instead.\n\n```typescript\nimport { chat } from '@tanstack\u002Fai'\nimport { openaiText } from '@tanstack\u002Fai-openai'\nimport { z } from 'zod'\n\nconst PersonSchema = z.object({\n name: z.string(),\n age: z.number(),\n email: z.string().email(),\n})\n\nconst stream = chat({\n adapter: openaiText('gpt-5.2'),\n messages: [\n { role: 'user', content: 'Extract: John Doe is 30, john@example.com' },\n ],\n outputSchema: PersonSchema,\n stream: true,\n})\n\nfor await (const chunk of stream) {\n if (chunk.type === 'CUSTOM' && chunk.name === 'structured-output.complete') {\n \u002F\u002F Terminal event. `chunk.value.object` is fully validated and typed\n \u002F\u002F against the schema you passed in — no helper or cast required.\n chunk.value.object.name \u002F\u002F string\n chunk.value.object.age \u002F\u002F number\n chunk.value.reasoning \u002F\u002F string | undefined (thinking models only)\n }\n}\n```\n\nThe terminal event is a `CUSTOM` chunk: `{ type: 'CUSTOM', name: 'structured-output.complete', value: { object: T, raw: string, reasoning?: string } }`. The return type of `chat({ outputSchema, stream: true })` carries `T` through, so a plain discriminated narrow (`chunk.type === 'CUSTOM' && chunk.name === 'structured-output.complete'`) is enough — no type guard helper.\n\n**Adapter coverage for streaming:**\n\n| Adapter | `outputSchema` + `stream: true` |\n| --------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- |\n| `@tanstack\u002Fai-openai` (Responses + Chat Completions) | **Native combined mode (#605)** — schema wired into the regular `chatStream` call alongside `tools`; engine harvests JSON, no finalization round-trip |\n| `@tanstack\u002Fai-anthropic` (Claude 4.5+ only) | **Native combined mode (#605)** — `output_config.format` + `tools` in one beta Messages call. Older Claude models fall back |\n| `@tanstack\u002Fai-gemini` (Gemini 3.x only) | **Native combined mode (#605)** — `responseSchema` + `tools` in one `generateContentStream`. Gemini 2.x falls back |\n| `@tanstack\u002Fai-grok` (Grok 4 family only) | **Native combined mode (#605)** — `response_format: json_schema` + `tools`. Grok 2 \u002F 3 fall back |\n| `@tanstack\u002Fai-openrouter` | Native single-request stream (legacy `structuredOutputStream` path; per-call combined-mode lookup is a follow-up) |\n| `@tanstack\u002Fai-groq` | Legacy `structuredOutputStream` only (no tools — Groq's API rejects schema + tools + stream) |\n| All other adapters (ollama, older Claude, Gemini 2.x, Grok 2\u002F3) | Fallback: runs non-streaming `structuredOutput`, emits one `structured-output.complete` event |\n\n**Native combined mode vs fallback** is signaled by the adapter's\noptional `supportsCombinedToolsAndSchema(modelOptions)` method. When\nit returns `true`, the engine wires the JSON Schema into the regular\n`chatStream` call and harvests the final-turn text — middleware sees\nthe run through `beforeModel` \u002F `modelStream` as usual, and the\n`'structuredOutput'` middleware phase does **not** fire. When it\nreturns `false` (or is omitted), the engine takes the legacy\nfinalization path: agent loop, then a separate `structuredOutput` \u002F\n`structuredOutputStream` call with `'structuredOutput'` phase tagging.\n\nConsumer code is identical across providers — always read the final object off `structured-output.complete`.\n\n### Pattern 4: useChat with outputSchema (progressive UI)\n\nPass `outputSchema` to `useChat` and you get a `partial` field that fills in as JSON streams in, plus a `final` field that snaps to the validated object on the terminal event. No `onChunk` ceremony, no manual JSON accumulation, no `parsePartialJSON` calls.\n\n**Server** (same as Pattern 3, just behind an SSE endpoint):\n\n```typescript\n\u002F\u002F app\u002Fapi\u002Fextract-person\u002Froute.ts (or your framework's equivalent)\nimport { chat, toServerSentEventsResponse } from '@tanstack\u002Fai'\nimport { openaiText } from '@tanstack\u002Fai-openai'\nimport { z } from 'zod'\n\nconst PersonSchema = z.object({\n name: z.string(),\n age: z.number(),\n email: z.string().email(),\n})\n\nexport async function POST(request: Request) {\n const { messages } = await request.json()\n const stream = chat({\n adapter: openaiText('gpt-5.2'),\n messages,\n outputSchema: PersonSchema,\n stream: true,\n })\n return toServerSentEventsResponse(stream)\n}\n```\n\n**Client:**\n\n```tsx\nimport { useChat, fetchServerSentEvents } from '@tanstack\u002Fai-react'\nimport { z } from 'zod'\n\nconst PersonSchema = z.object({\n name: z.string(),\n age: z.number(),\n email: z.string().email(),\n})\n\nfunction PersonExtractor() {\n const { sendMessage, isLoading, partial, final } = useChat({\n connection: fetchServerSentEvents('\u002Fapi\u002Fextract-person'),\n outputSchema: PersonSchema,\n })\n\n return (\n \u003Cdiv>\n \u003Cbutton\n disabled={isLoading}\n onClick={() => sendMessage('Extract: John Doe, 30, john@example.com')}\n >\n Extract\n \u003C\u002Fbutton>\n {\u002F* `partial` fills in field by field while streaming. *\u002F}\n \u003Cp>Name: {partial.name ?? '…'}\u003C\u002Fp>\n \u003Cp>Age: {partial.age ?? '…'}\u003C\u002Fp>\n \u003Cp>Email: {partial.email ?? '…'}\u003C\u002Fp>\n {final && \u003Cpre>Validated: {JSON.stringify(final, null, 2)}\u003C\u002Fpre>}\n \u003C\u002Fdiv>\n )\n}\n```\n\n- `partial` is `DeepPartial\u003Cz.infer\u003Ctypeof PersonSchema>>` — every property optional, every nested array element optional. Updated from `TEXT_MESSAGE_CONTENT` deltas.\n- `final` is `z.infer\u003Ctypeof PersonSchema> | null` — populated when `structured-output.complete` arrives.\n- `outputSchema` is for client-side type inference only. **Validation runs on the server** against the schema you pass to `chat({ outputSchema })` there.\n- Same shape works for non-streaming adapters: the fallback path emits one whole-JSON `TEXT_MESSAGE_CONTENT` then the terminal event, so `partial` populates and `final` snaps in the same render tick — same consumer code as the native-streaming providers, just without an intermediate field-by-field reveal.\n\n### Pattern 5: Multi-turn structured chat\n\nEvery assistant turn produced by `useChat({ outputSchema })` carries its own typed `StructuredOutputPart` on `messages[i].parts`. Old turns stay renderable; new turns produce new parts; history is preserved without manual state plumbing. This is what makes the recipe-builder shape (\"now make it vegan\") work.\n\n```tsx\nimport { useChat, fetchServerSentEvents } from '@tanstack\u002Fai-react'\nimport type { StructuredOutputPart } from '@tanstack\u002Fai-client'\nimport { z } from 'zod'\n\nconst RecipeSchema = z.object({\n title: z.string(),\n cuisine: z.string(),\n servings: z.number(),\n ingredients: z.array(z.object({ item: z.string(), amount: z.string() })),\n steps: z.array(z.string()),\n})\ntype Recipe = z.infer\u003Ctypeof RecipeSchema>\ntype RecipePart = StructuredOutputPart\u003CRecipe>\n\nfunction RecipeBuilder() {\n const { messages, sendMessage } = useChat({\n outputSchema: RecipeSchema,\n connection: fetchServerSentEvents('\u002Fapi\u002Frecipes'),\n })\n\n return (\n \u003Cdiv>\n {messages.map((m) => {\n if (m.role === 'user') {\n const text = m.parts\n .filter((p) => p.type === 'text')\n .map((p) => p.content)\n .join('')\n return \u003CUserBubble key={m.id} text={text} \u002F>\n }\n if (m.role === 'assistant') {\n \u002F\u002F `data` is `Recipe` because the schema generic flows from\n \u002F\u002F `useChat({ outputSchema })` through `messages` to the part.\n const part = m.parts.find(\n (p): p is RecipePart => p.type === 'structured-output',\n )\n if (!part) return null\n return \u003CRecipeCard key={m.id} part={part} \u002F>\n }\n return null\n })}\n \u003Cbutton onClick={() => sendMessage('pasta for two')}>Cook\u003C\u002Fbutton>\n \u003Cbutton onClick={() => sendMessage('now make it vegan')}>Modify\u003C\u002Fbutton>\n \u003C\u002Fdiv>\n )\n}\n\nfunction RecipeCard({ part }: { part: RecipePart }) {\n \u002F\u002F `data` lands on complete, `partial` fills in while streaming.\n \u002F\u002F Both are typed against the schema. No casts.\n const recipe = part.data ?? part.partial ?? ({} as Partial\u003CRecipe>)\n return \u003Ch3>{recipe.title ?? 'Plating up…'}\u003C\u002Fh3>\n}\n```\n\nKey behaviors:\n\n- **Per-turn parts.** Each `sendMessage()` produces a new assistant message with its own `StructuredOutputPart`. The previous turn's part is untouched — `messages.map(...)` renders the whole history.\n- **Typed by schema.** `messages[i].parts.find(p => p.type === 'structured-output').data` is typed as `Recipe` (no cast, no `unknown`). Works because `useChat\u003CTSchema>` threads `InferSchemaType\u003CTSchema>` down through `UIMessage\u003CTTools, TData>` → `MessagePart\u003CTTools, TData>` → `StructuredOutputPart\u003CTData>`. **In `@tanstack\u002Fai` core** the message types are single-generic (`UIMessage\u003CTData>`); the tools generic lives in `@tanstack\u002Fai-client` and the framework hook packages — import from your framework package or `ai-client`, not from `@tanstack\u002Fai`.\n- **`partial` \u002F `final` are derived.** The hook-level `partial` and `final` are NOT singleton state — they're derived from the latest assistant message's part (the one after the most recent user message). Between `sendMessage()` and the first chunk, `partial` reads `{}` and `final` reads `null` because no new assistant turn exists yet.\n- **Round-trip preserves history.** When the client sends turn N+1, each prior assistant turn's `structured-output` part is serialized back as `{ role: 'assistant', content: \u003Cpart.raw> }` so the model sees its own prior structured response. Streaming \u002F errored parts are dropped from the round-trip.\n\n## Common Mistakes\n\n### HIGH: Filtering `TextPart`s out of `useChat` renderers when using `outputSchema`\n\nEarlier versions of the library routed structured-output JSON deltas through `TextPart`, so renderers had to filter them out:\n\n```tsx\n\u002F\u002F OBSOLETE — this guard was needed only because JSON used to land in a TextPart\nconst last = messages.at(-1)\nlast?.parts.map((part) => {\n if (part.type === 'text') return null \u002F\u002F ❌ hides the structured JSON\n \u002F\u002F ...\n})\n```\n\nThat hack is **gone**. With `outputSchema` set, `TEXT_MESSAGE_CONTENT` deltas now route into a dedicated `StructuredOutputPart` (with `raw`, `partial`, `data`, `status`, optional `errorMessage`). Render the structured part directly; let real `TextPart`s through.\n\n```tsx\n\u002F\u002F CORRECT — find the structured-output part directly; let actual TextParts render\nlast?.parts.map((part, i) => {\n if (part.type === 'thinking')\n return \u003CReasoningView key={i} text={part.content} \u002F>\n if (part.type === 'tool-call') return \u003CToolCallView key={i} part={part} \u002F>\n if (part.type === 'structured-output')\n return \u003CRecipeCard key={i} part={part} \u002F>\n if (part.type === 'text') return \u003Cp key={i}>{part.content}\u003C\u002Fp> \u002F\u002F ← real text, not JSON\n return null\n})\n```\n\nIf you still have an `if (part.type === 'text') return null` line in a structured-output renderer specifically for \"hiding the JSON,\" delete it.\n\nSource: PR #577 — structured-output became a typed UIMessage part.\n\n### HIGH: Treating `partial` \u002F `final` as sticky state across turns\n\n`partial` and `final` are **derived from the latest assistant message's `structured-output` part**, not a sticky hook-level slot. In a multi-turn chat:\n\n- Between `sendMessage()` and the first chunk, `partial` reads `{}` and `final` reads `null` (no assistant message after the latest user yet).\n- Once the latest turn completes, `partial === final`. Earlier turns' data is NOT in `partial` \u002F `final` — it lives on the prior assistant messages' parts.\n\nTo render history, walk `messages` directly (see Pattern 5). Use `partial` \u002F `final` for a sticky summary of the **most recent** turn only.\n\n```tsx\n\u002F\u002F WRONG — `final` only reflects the latest turn; earlier recipes vanish from this view\n{final && \u003CRecipeCard recipe={final} \u002F>}\n\n\u002F\u002F CORRECT for history — walk messages, render every assistant's structured-output part\n{messages.map((m) =>\n m.role === 'assistant'\n ? m.parts.find((p) => p.type === 'structured-output')\n ? \u003CRecipeCard key={m.id} part={...} \u002F>\n : null\n : null\n)}\n```\n\nSource: PR #577 — partial\u002Ffinal derive from the latest assistant turn's part.\n\n### HIGH: Parsing streaming JSON deltas yourself\n\nWhen iterating `chat({ outputSchema, stream: true })` directly (Pattern 3), the `TEXT_MESSAGE_CONTENT` chunks contain _partial_ JSON fragments — they are not valid JSON until the stream completes. Always read the validated object from the terminal `structured-output.complete` event. Validation runs once, on the complete payload.\n\n```typescript\n\u002F\u002F WRONG -- partial JSON, throws SyntaxError mid-stream, no schema validation\nfor await (const chunk of stream) {\n if (chunk.type === 'TEXT_MESSAGE_CONTENT') {\n const obj = JSON.parse(chunk.delta) \u002F\u002F ❌ partial, invalid\n }\n}\n\n\u002F\u002F CORRECT -- trust the terminal event\nfor await (const chunk of stream) {\n if (chunk.type === 'CUSTOM' && chunk.name === 'structured-output.complete') {\n const result = chunk.value.object \u002F\u002F ✅ typed and validated\n }\n}\n```\n\nIf you need progressive parsed state in a non-React environment, use a partial-JSON parser on the accumulated raw string at render time — but do NOT treat the result as schema-validated; only the terminal event is. In `useChat`, this is already done for you (`partial` field on Pattern 4).\n\nSource: maintainer interview\n\n### HIGH: Trying to implement provider-specific structured output strategies\n\nThe adapter already handles provider differences (OpenAI uses `response_format`, Anthropic uses tool-based extraction, Gemini uses `responseSchema`). Never configure this yourself.\n\n```typescript\n\u002F\u002F WRONG -- do not set provider-specific response format\nchat({\n adapter,\n messages,\n modelOptions: {\n responseFormat: { type: 'json_schema', json_schema: mySchema },\n },\n})\n\n\u002F\u002F CORRECT -- just pass outputSchema, the adapter handles the rest\nchat({\n adapter,\n messages,\n outputSchema: z.object({ name: z.string(), age: z.number() }),\n})\n```\n\nThere is no scenario where you need to know the provider's strategy. Just pass `outputSchema` to `chat()`.\n\nSource: maintainer interview\n\n### HIGH: Passing raw objects instead of using the project's schema library\n\nAgents often generate raw JSON Schema objects or plain TypeScript types instead\nof using the schema validation library already in the project (Zod, ArkType,\nValibot). Always check what the project uses and match it.\n\n```typescript\n\u002F\u002F WRONG -- raw object, no runtime validation, no type inference\nchat({\n adapter,\n messages,\n outputSchema: {\n type: 'object',\n properties: {\n name: { type: 'string' },\n age: { type: 'number' },\n },\n required: ['name', 'age'],\n additionalProperties: false,\n },\n})\n\n\u002F\u002F CORRECT -- use the project's schema library (e.g. Zod)\nimport { z } from 'zod'\n\nchat({\n adapter,\n messages,\n outputSchema: z.object({\n name: z.string(),\n age: z.number(),\n }),\n})\n```\n\nUsing the project's schema library gives you runtime validation, TypeScript\ntype inference on the result, and correct JSON Schema conversion automatically.\nCheck `package.json` for `zod`, `arktype`, or `valibot` and use whichever is\nalready installed.\n\nSource: maintainer interview\n\n## Middleware coverage\n\nThe final structured-output adapter call runs through the same middleware\npipeline as the agent loop. `onChunk` observes chunks attributed to\n`ctx.phase === 'structuredOutput'`; `onUsage` fires for the final call's\ntokens; `onFinish` fires once at the end of the whole `chat()` invocation,\nafter the structured-output result is available.\n\nFor schema-aware middleware (e.g., transforming the JSON Schema before the\nprovider call, stripping system prompts), use the dedicated\n`onStructuredOutputConfig` hook. See [middleware skill](..\u002Fmiddleware\u002FSKILL.md).\n\n## Cross-References\n\n- See also: **ai-core\u002Fchat-experience\u002FSKILL.md** — Base `useChat` surface; the structured-output additions documented here layer on top.\n- See also: **ai-core\u002Fadapter-configuration\u002FSKILL.md** — Adapter handles structured-output strategy transparently.\n- See also: **ai-core\u002Ftool-calling\u002FSKILL.md** — Combine `tools` with `outputSchema` for an agent loop that runs tools first and returns a typed object. 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Use chat() not streamText(), openaiText() not createOpenAI(), toServerSentEventsResponse() not manual SSE, middleware hooks not onEnd callbacks.\n",{"slug":9,"name":10,"logoUrl":11,"githubOrg":10},[10506,10509,10511],{"name":10507,"slug":10508,"type":16},"Agents","agents",{"name":10510,"slug":30,"type":16},"AI",{"name":10512,"slug":10513,"type":16},"Middleware","middleware","2026-07-30T05:26:10.404565",{"slug":10516,"name":10517,"fn":10518,"description":10519,"org":10520,"tags":10521,"stars":24,"repoUrl":25,"updatedAt":10529},"ai-coreadapter-configuration","ai-core\u002Fadapter-configuration","configure AI provider adapters","Provider adapter selection and configuration: openaiText, anthropicText, geminiText, ollamaText, grokText, groqText, openRouterText, bedrockText, openaiCompatible. Per-model type safety with modelOptions, reasoning\u002Fthinking configuration, runtime adapter switching, extendAdapter() for custom models, createModel(). 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},[10522,10523,10526,10528],{"name":21,"slug":22,"type":16},{"name":10524,"slug":10525,"type":16},"Configuration","configuration",{"name":10527,"slug":37,"type":16},"LLM",{"name":10,"slug":9,"type":16},"2026-07-16T06:04:17.82075",{"slug":10531,"name":10532,"fn":10533,"description":10534,"org":10535,"tags":10536,"stars":24,"repoUrl":25,"updatedAt":10542},"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},[10537,10540,10541],{"name":10538,"slug":10539,"type":16},"API Development","api-development",{"name":10527,"slug":37,"type":16},{"name":10,"slug":9,"type":16},"2026-07-16T06:04:10.093367",{"slug":10544,"name":10545,"fn":10546,"description":10547,"org":10548,"tags":10549,"stars":24,"repoUrl":25,"updatedAt":10554},"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},[10550,10551,10552,10553],{"name":10538,"slug":10539,"type":16},{"name":10350,"slug":10351,"type":16},{"name":10527,"slug":37,"type":16},{"name":10,"slug":9,"type":16},"2026-07-30T05:26:11.505241",{"slug":10556,"name":10557,"fn":10558,"description":10559,"org":10560,"tags":10561,"stars":24,"repoUrl":25,"updatedAt":10568},"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},[10562,10563,10564,10567],{"name":10510,"slug":30,"type":16},{"name":10350,"slug":10351,"type":16},{"name":10565,"slug":10566,"type":16},"Persistence","persistence",{"name":10,"slug":9,"type":16},"2026-07-30T05:53:35.503176",{"slug":10570,"name":10571,"fn":10572,"description":10573,"org":10574,"tags":10575,"stars":24,"repoUrl":25,"updatedAt":10582},"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},[10576,10577,10578,10581],{"name":10510,"slug":30,"type":16},{"name":10538,"slug":10539,"type":16},{"name":10579,"slug":10580,"type":16},"Backend","backend",{"name":10,"slug":9,"type":16},"2026-07-17T06:06:39.855351"]