Generating Structured Data

While text generation is useful, many applications need structured, typed data. The AI SDK Core provides built-in support for generating schema-validated structured data through the output property on generateText and streamText.

Why Structured Data?

Structured data generation is essential for:

Data extraction: Extract information from unstructured text
Classification: Categorize content into predefined types
Synthetic data: Generate test data or examples
Form filling: Generate structured responses for forms
API responses: Create typed data for downstream systems

Basic Usage

Use Output.object() with a schema to generate structured data:

import { generateText, Output } from 'ai';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';

const { output } = await generateText({
  model: openai('gpt-5'),
  output: Output.object({
    schema: z.object({
      recipe: z.object({
        name: z.string(),
        ingredients: z.array(
          z.object({ 
            name: z.string(), 
            amount: z.string() 
          })
        ),
        steps: z.array(z.string()),
      }),
    }),
  }),
  prompt: 'Generate a lasagna recipe.',
});

// output is fully typed and validated
console.log(output.recipe.name);
console.log(output.recipe.ingredients);

Output Types

The AI SDK supports multiple output formats through the Output object:

Output.object()

Generate a structured object matching a schema:

import { generateText, Output } from 'ai';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';

const { output } = await generateText({
  model: openai('gpt-5'),
  output: Output.object({
    schema: z.object({
      name: z.string(),
      age: z.number(),
      email: z.string().email(),
      interests: z.array(z.string()),
    }),
  }),
  prompt: 'Generate a user profile for a software engineer.',
});

// Fully typed
console.log(output.name);      // string
console.log(output.age);       // number
console.log(output.interests); // string[]

Output.array()

Generate an array of structured elements:

import { generateText, Output } from 'ai';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';

const { output } = await generateText({
  model: openai('gpt-5'),
  output: Output.array({
    element: z.object({
      city: z.string(),
      temperature: z.number(),
      condition: z.string(),
    }),
  }),
  prompt: 'List weather for San Francisco, London, and Tokyo.',
});

// output is an array of weather objects
for (const weather of output) {
  console.log(`${weather.city}: ${weather.temperature}°F, ${weather.condition}`);
}

Streaming Arrays

When streaming arrays, use elementStream to receive complete elements as they’re generated:

import { streamText, Output } from 'ai';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';

const { elementStream } = streamText({
  model: openai('gpt-5'),
  output: Output.array({
    element: z.object({
      name: z.string(),
      class: z.string(),
      description: z.string(),
    }),
  }),
  prompt: 'Generate 3 fantasy RPG characters.',
});

for await (const character of elementStream) {
  console.log('Character:', character.name);
  console.log('Class:', character.class);
  console.log('Description:', character.description);
  console.log('---');
}

Output.choice()

Choose from a fixed set of options:

import { generateText, Output } from 'ai';
import { openai } from '@ai-sdk/openai';

const { output } = await generateText({
  model: openai('gpt-5'),
  output: Output.choice({
    options: ['positive', 'negative', 'neutral'],
  }),
  prompt: 'Classify the sentiment: "This product exceeded my expectations!"',
});

console.log(output); // 'positive' | 'negative' | 'neutral'

Output.json()

Generate arbitrary JSON without schema validation:

import { generateText, Output } from 'ai';
import { openai } from '@ai-sdk/openai';

const { output } = await generateText({
  model: openai('gpt-5'),
  output: Output.json(),
  prompt: 'Generate a JSON object with city weather data for multiple cities.',
});

// output is any valid JSON
console.log(output);
// {
//   "San Francisco": { "temperature": 65, "condition": "Foggy" },
//   "New York": { "temperature": 72, "condition": "Sunny" }
// }

Output.json() only validates that the response is valid JSON. For type safety, use Output.object() or Output.array().

Output.text()

Generate plain text (this is the default when no output is specified):

import { generateText, Output } from 'ai';
import { openai } from '@ai-sdk/openai';

const { output } = await generateText({
  model: openai('gpt-5'),
  output: Output.text(),
  prompt: 'Write a haiku about programming.',
});

console.log(output); // string

Schema Definitions

The AI SDK supports multiple schema libraries:

Zod Schemas

import { z } from 'zod';
import { generateText, Output } from 'ai';
import { openai } from '@ai-sdk/openai';

const { output } = await generateText({
  model: openai('gpt-5'),
  output: Output.object({
    schema: z.object({
      title: z.string(),
      tags: z.array(z.string()),
      published: z.boolean(),
    }),
  }),
  prompt: 'Generate blog post metadata',
});

JSON Schema

import { generateText, Output, jsonSchema } from 'ai';
import { openai } from '@ai-sdk/openai';

const { output } = await generateText({
  model: openai('gpt-5'),
  output: Output.object({
    schema: jsonSchema({
      type: 'object',
      properties: {
        name: { type: 'string' },
        age: { type: 'number' },
      },
      required: ['name', 'age'],
    }),
  }),
  prompt: 'Generate a person profile',
});

Property Descriptions

Add descriptions to schema properties to guide the model:

import { z } from 'zod';
import { generateText, Output } from 'ai';
import { openai } from '@ai-sdk/openai';

const { output } = await generateText({
  model: openai('gpt-5'),
  output: Output.object({
    schema: z.object({
      name: z.string().describe('The name of the recipe'),
      servings: z.number().describe('Number of servings (1-12)'),
      ingredients: z.array(
        z.object({
          name: z.string(),
          amount: z.string().describe('Amount in grams or ml'),
        })
      ).describe('List of ingredients with amounts'),
      steps: z.array(z.string()).describe('Step-by-step cooking instructions'),
    }),
  }),
  prompt: 'Generate a pasta recipe for 4 people.',
});

Descriptions help:

Clarify ambiguous property names
Specify expected formats
Provide context for nested structures

Output Name and Description

Provide a name and description for the output to improve generation quality:

import { generateText, Output } from 'ai';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';

const { output } = await generateText({
  model: openai('gpt-5'),
  output: Output.object({
    name: 'Recipe',
    description: 'A complete recipe with ingredients and steps',
    schema: z.object({
      name: z.string(),
      ingredients: z.array(z.object({ name: z.string(), amount: z.string() })),
      steps: z.array(z.string()),
    }),
  }),
  prompt: 'Generate a recipe for chocolate cake.',
});

Streaming Structured Data

Stream structured data as it’s generated using streamText:

import { streamText, Output } from 'ai';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';

const { partialOutputStream } = streamText({
  model: openai('gpt-5'),
  output: Output.object({
    schema: z.object({
      recipe: z.object({
        name: z.string(),
        ingredients: z.array(
          z.object({ name: z.string(), amount: z.string() })
        ),
        steps: z.array(z.string()),
      }),
    }),
  }),
  prompt: 'Generate a lasagna recipe.',
});

// Stream partial objects as they're built
for await (const partialObject of partialOutputStream) {
  console.log(partialObject);
  // { recipe: { name: "Lasagna" } }
  // { recipe: { name: "Lasagna", ingredients: [{ name: "pasta" }] } }
  // ...
}

Partial outputs cannot be validated against the schema since they’re incomplete. Validation happens on the final output.

Combining with Tools

Structured output can be combined with tool calling:

import { generateText, Output, tool, stepCountIs } from 'ai';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';

const { output } = await generateText({
  model: openai('gpt-5'),
  tools: {
    weather: tool({
      description: 'Get weather for a location',
      inputSchema: z.object({ location: z.string() }),
      execute: async ({ location }) => {
        return { temperature: 72, condition: 'sunny' };
      },
    }),
  },
  output: Output.object({
    schema: z.object({
      summary: z.string(),
      recommendation: z.string(),
    }),
  }),
  stopWhen: stepCountIs(5),
  prompt: 'What should I wear in San Francisco today?',
});

console.log(output.summary);
console.log(output.recommendation);

Generating structured output counts as a step. Configure stopWhen to allow enough steps for both tool execution and output generation.

Error Handling

When generateText cannot generate valid structured data, it throws NoObjectGeneratedError:

import { generateText, Output, NoObjectGeneratedError } from 'ai';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';

try {
  const { output } = await generateText({
    model: openai('gpt-5'),
    output: Output.object({
      schema: z.object({
        name: z.string(),
        age: z.number(),
      }),
    }),
    prompt: 'Generate a person',
  });
} catch (error) {
  if (NoObjectGeneratedError.isInstance(error)) {
    console.log('Failed to generate object');
    console.log('Cause:', error.cause);
    console.log('Text:', error.text);
    console.log('Usage:', error.usage);
  }
}

For streaming, use the onError callback:

import { streamText, Output } from 'ai';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';

const result = streamText({
  model: openai('gpt-5'),
  output: Output.object({
    schema: z.object({ name: z.string() }),
  }),
  prompt: 'Generate data',
  onError({ error }) {
    console.error('Stream error:', error);
  },
});

Examples

Data Extraction

import { generateText, Output } from 'ai';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';

const { output } = await generateText({
  model: openai('gpt-5'),
  output: Output.object({
    schema: z.object({
      company: z.string(),
      position: z.string(),
      location: z.string(),
      salary: z.string().optional(),
      requirements: z.array(z.string()),
    }),
  }),
  prompt: `Extract job posting details from:
  
  "We're hiring a Senior Software Engineer in San Francisco. 
  Requirements: 5+ years experience, TypeScript, React. 
  Competitive salary."`,
});

console.log(output);

Content Classification

import { generateText, Output } from 'ai';
import { openai } from '@ai-sdk/openai';
import { z } from 'zod';

const { output } = await generateText({
  model: openai('gpt-5'),
  output: Output.object({
    schema: z.object({
      category: z.enum(['technology', 'business', 'health', 'entertainment']),
      confidence: z.number().min(0).max(1),
      keywords: z.array(z.string()),
    }),
  }),
  prompt: 'Classify this article: "New AI model achieves breakthrough in natural language understanding"',
});

console.log(`Category: ${output.category}`);
console.log(`Confidence: ${output.confidence}`);

Next Steps

Tool Calling

Combine structured output with tools

Prompt Engineering

Tips for better structured data generation

Settings

Configure generation parameters

Getting Started

Foundations

AI SDK Core

Agents

AI SDK UI

AI SDK RSC

Advanced

Troubleshooting

Generating Structured Data

Generating Structured Data

Why Structured Data?

Basic Usage

Output Types

Output.object()

Output.array()

Streaming Arrays

Output.choice()

Output.json()

Output.text()

Schema Definitions

Zod Schemas

JSON Schema

Property Descriptions

Output Name and Description

Streaming Structured Data

Combining with Tools

Error Handling

Examples

Data Extraction

Content Classification

Next Steps

Tool Calling

Prompt Engineering

Settings

Build docs developers (and LLMs) love

Getting Started

Foundations

AI SDK Core

Agents

AI SDK UI

AI SDK RSC

Advanced

Troubleshooting

Documentation Index

​Generating Structured Data

​Why Structured Data?

​Basic Usage

​Output Types

​Output.object()

​Output.array()

​Streaming Arrays

​Output.choice()

​Output.json()

​Output.text()

​Schema Definitions

​Zod Schemas

​JSON Schema

​Property Descriptions

​Output Name and Description

​Streaming Structured Data

​Combining with Tools

​Error Handling

​Examples

​Data Extraction

​Content Classification

​Next Steps

Tool Calling

Prompt Engineering

Settings

Build docs developers (and LLMs) love

Generating Structured Data

Why Structured Data?

Basic Usage

Output Types

Output.object()

Output.array()

Streaming Arrays

Output.choice()

Output.json()

Output.text()

Schema Definitions

Zod Schemas

JSON Schema

Property Descriptions

Output Name and Description

Streaming Structured Data

Combining with Tools

Error Handling

Examples

Data Extraction

Content Classification

Next Steps