lukasa1993/tanstack-skills-extracted/tanstack-ai-core-structured-outputs

import { chat } from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'
import { z } from 'zod'
const person = await chat({
  adapter: openaiText('gpt-5.2'),
  messages: [{ role: 'user', content: 'John Doe, 30' }],
  threadId: 'thread-1',
  outputSchema: z.object({
    name: z.string(),
    age: z.number(),
  }),
})
person.name // string — fully typed, no cast
person.age // number

import { chat } from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'
import { z } from 'zod'
const PersonSchema = z.object({
  name: z.string().meta({ description: "The person's full name" }),
  age: z.number().meta({ description: "The person's age in years" }),
  email: z.string().email().meta({ description: 'Email address' }),
})
// chat() returns Promise<{ name: string; age: number; email: string }>
const person = await chat({
  adapter: openaiText('gpt-5.2'),
  messages: [
    {
      role: 'user',
      content:
        'Extract the person info: John Doe is 30 years old, email john@example.com',
    },
  ],
  threadId: 'thread-1',
  outputSchema: PersonSchema,
})
console.log(person.name) // "John Doe"
console.log(person.age) // 30
console.log(person.email) // "john@example.com"

import { chat } from '@tanstack/ai'
import { anthropicText } from '@tanstack/ai-anthropic'
import { z } from 'zod'
const CompanySchema = z.object({
  name: z.string(),
  founded: z.number().meta({ description: 'Year the company was founded' }),
  headquarters: z.object({
    city: z.string(),
    country: z.string(),
    address: z.string().optional(),
  }),
  employees: z.array(
    z.object({
      name: z.string(),
      role: z.string(),
      department: z.string(),
    }),
  ),
  financials: z
    .object({
      revenue: z
        .number()
        .meta({ description: 'Annual revenue in millions USD' }),
      profitable: z.boolean(),
    })
    .optional(),
})
const company = await chat({
  adapter: anthropicText('claude-sonnet-4-5'),
  messages: [
    {
      role: 'user',
      content: 'Extract company info from this article: ...',
    },
  ],
  threadId: 'thread-1',
  outputSchema: CompanySchema,
})
// Full type safety on nested properties
console.log(company.headquarters.city)
console.log(company.employees[0]?.role)
console.log(company.financials?.revenue)

import { chat } from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'
import { z } from 'zod'
 
const PersonSchema = z.object({
  name: z.string(),
  age: z.number(),
  email: z.string().email(),
})
 
const stream = chat({
  adapter: openaiText('gpt-5.2'),
  messages: [
    { role: 'user', content: 'Extract: John Doe is 30, john@example.com' },
  ],
  threadId: 'thread-1',
  outputSchema: PersonSchema,
  stream: true,
})
 
for await (const chunk of stream) {
  if (chunk.type === 'CUSTOM' && chunk.name === 'structured-output.complete') {
    // Terminal event. `chunk.value.object` is complete and typed against the
    // schema you passed in. Validate it in the consumer when required.
    chunk.value.object.name // string
    chunk.value.object.age // number
    chunk.value.reasoning // string | undefined (thinking models only)
  }
}

// app/api/extract-person/route.ts (or your framework's equivalent)
import { chat, toServerSentEventsResponse } from '@tanstack/ai'
import { openaiText } from '@tanstack/ai-openai'
import { z } from 'zod'
const PersonSchema = z.object({
  name: z.string(),
  age: z.number(),
  email: z.string().email(),
})