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+---
+title: 'Stop Waiting on JSON: Stream Structured Output with One Schema'
+published: 2026-05-14
+excerpt: 'Pass a Zod schema to useChat and get a typed `partial` and `final` for free. No more parsePartialJSON glue, no more onChunk wiring. TanStack AI now streams structured output end-to-end across OpenAI, OpenRouter, Grok, Groq, and Ollama.'
+library: ai
+authors:
+  - Alem Tuzlak
+---
+
+![Stop Waiting on JSON](/blog-assets/streaming-structured-output/header.png)
+
+You ask an LLM for a `Person` object. You hit send. The spinner spins. Five seconds. Ten. Twenty. Somewhere on a server in Oregon, the model is happily generating tokens, and your user is staring at a loading state until the very last `}` of the JSON arrives.
+
+That UX is bad and you already know it. The fix, in theory, is "just stream it." The fix, in practice, has been writing 15 lines of glue: an `onChunk` handler, a `useState`, a `parsePartialJSON` call, a manual cast to your `Person` type. Repeat in every project. Hope you got the types right.
+
+This release kills the glue. `useChat({ outputSchema })` now gives you a typed `partial` and `final` straight from the hook. One schema. End to end.
+
+## The old shape
+
+Until now, mixing streaming with `outputSchema` in `@tanstack/ai` looked something like this on the client:
+
+```tsx
+const [partial, setPartial] = useState<Partial<Person>>({})
+const [final, setFinal] = useState<Person | null>(null)
+let raw = ''
+
+useChat({
+  connection: fetchServerSentEvents('/api/extract'),
+  onChunk: (chunk) => {
+    if (chunk.type === 'TEXT_MESSAGE_CONTENT') {
+      raw += chunk.delta
+      setPartial(parsePartialJSON(raw))
+    } else if (chunk.name === 'structured-output.complete') {
+      setFinal(chunk.value.object as Person)
+    }
+  },
+})
+```
+
+This works, but every byte of it is something you didn't want to write:
+
+- You're manually accumulating a string buffer.
+- You're calling `parsePartialJSON` yourself and hoping it tolerates whatever half-JSON the model just emitted.
+- You're casting `chunk.value.object` to `Person` with no real proof it actually matches your schema.
+- You're doing this in every component that wants a typed live preview.
+
+The schema lives on the server. The same schema would happily describe `partial` and `final` on the client. There was no reason for the client to be guessing.
+
+## The new shape
+
+Pass the schema. Get the types back.
+
+```tsx
+import { useChat } from '@tanstack/ai-react'
+import { fetchServerSentEvents } from '@tanstack/ai-client/event-client'
+import { PersonSchema } from './schemas'
+
+const { partial, final, status } = useChat({
+  connection: fetchServerSentEvents('/api/extract'),
+  outputSchema: PersonSchema,
+})
+```
+
+That's the whole thing.
+
+- `partial` is `DeepPartial<Person>` and updates on every streamed delta. The framework parses the partial JSON for you and narrows the type to whatever fields have arrived so far.
+- `final` is `Person | null`. It flips to a fully-typed `Person` the moment the model emits the structured-output completion event.
+- No `parsePartialJSON` import. No `onChunk`. No `useState`. No casts.
+
+The same hook returns the message stream you already use, so partial UI previews and chat transcripts live side-by-side without conflict.
+
+## What changed under the hood
+
+The headline is the hook, but the work that made it possible touches the whole stack.
+
+**A real type for the structured-output stream.** `chat({ outputSchema, stream: true })` now returns a `StructuredOutputStream<T>` that's a proper discriminated union: every regular `StreamChunk` plus a single tagged `StructuredOutputCompleteEvent<T>` carrying a strongly-typed `value.object`. You no longer fight `any` when you destructure or `switch` on the event.
+
+**Tagged variants for the other custom events too.** While we were in there, `ApprovalRequestedEvent` and `ToolInputAvailableEvent` became their own tagged shapes. Tool-calling flows narrow cleanly without a helper.
+
+**Per-chunk debug logging in the structured path.** The streaming structured output path now calls `logger.provider` on every chunk, matching the behavior of plain `chatStream`. Provider-level debugging is no longer a black box just because you opted into a schema.
+
+**Provider coverage.** OpenAI, OpenRouter, Grok, Groq, and Ollama (anything riding the `openai-base`) all go through the same streaming structured output pipeline. The summarize adapter got the same treatment, so structured summaries stream end-to-end too.
+
+**Framework parity.** `useChat({ outputSchema })` works in `@tanstack/ai-react`, `@tanstack/ai-vue`, `@tanstack/ai-solid`, and `@tanstack/ai-svelte`. Same return shape, same types, same behavior.
+
+## Why this is the right shape
+
+There's a temptation, when designing this kind of API, to invent a separate hook: `useStructuredChat`, `useTypedChat`, something parallel. That would have been a mistake. A schema isn't a different mode of chatting, it's just extra information the hook can use.
+
+By folding `outputSchema` into the existing `useChat`, the upgrade path from "I'm using a chat hook" to "I'm using a chat hook with typed streaming output" is _literally one prop_. Your tool wiring, your approval prompts, your transcript state, everything that was already on `useChat` still works exactly the same way. The new behavior only exists for the keys that depend on the schema.
+
+The cost of "one more hook for a slightly different case" is paid in docs, in user confusion, and in the long tail of `useFooButSometimesBar` hooks people inevitably accumulate. We'd rather not.
+
+## Try it
+
+Install the latest:
+
+```bash
+pnpm add @tanstack/ai @tanstack/ai-react @tanstack/ai-openai zod
+```
+
+Define the schema once. Use it on both sides.
+
+```ts
+// schemas.ts
+import { z } from 'zod'
+
+export const PersonSchema = z.object({
+  name: z.string(),
+  age: z.number(),
+  email: z.string().email(),
+})
+```
+
+On the server, hand the schema to `chat` and stream the response as Server-Sent Events:
+
+```ts
+// app/api/extract/route.ts
+import { chat, toServerSentEventsResponse } from '@tanstack/ai'
+import { openaiText } from '@tanstack/ai-openai/adapters'
+import { PersonSchema } from './schemas'
+
+export async function POST(req: Request) {
+  const { messages } = await req.json()
+
+  const stream = chat({
+    adapter: openaiText('gpt-5.2'),
+    messages,
+    outputSchema: PersonSchema,
+    stream: true,
+  })
+
+  return toServerSentEventsResponse(stream)
+}
+```
+
+On the client, point `useChat` at that endpoint and pass the same schema:
+
+```tsx
+// app/extract/page.tsx
+import { useChat } from '@tanstack/ai-react'
+import { fetchServerSentEvents } from '@tanstack/ai-client/event-client'
+import { PersonSchema } from './schemas'
+
+const { partial, final } = useChat({
+  connection: fetchServerSentEvents('/api/extract'),
+  outputSchema: PersonSchema,
+})
+
+return (
+  <form>
+    <input value={partial.name ?? ''} readOnly />
+    <input value={partial.age ?? ''} readOnly />
+    <input value={partial.email ?? ''} readOnly />
+    {final && <p>Done. Extracted a fully-validated Person.</p>}
+  </form>
+)
+```
+
+Read the full guide: [tanstack.com/ai/docs/chat/structured-outputs](https://tanstack.com/ai/docs/chat/structured-outputs).
+
+Drop the glue. Pass the schema. Ship the feature.