Generating Structured Data

While text generation can be useful, your use case will likely call for generating structured data. For example, you might want to extract information from text, classify data, or generate synthetic data.

Many language models are capable of generating structured data, often defined as using "JSON modes" or "tools". However, you need to manually provide schemas and then validate the generated data as LLMs can produce incorrect or incomplete structured data.

The AI SDK standardises structured object generation across model providers with the generateObject and streamObject functions. You can use both functions with different output strategies, e.g. array, object, enum, or no-schema, and with different generation modes, e.g. auto, tool, or json. You can use Zod schemas, Valibot, or JSON schemas to specify the shape of the data that you want, and the AI model will generate data that conforms to that structure.

Generate Object

The generateObject generates structured data from a prompt. The schema is also used to validate the generated data, ensuring type safety and correctness.

import { generateObject } from 'ai';
import { z } from 'zod';

const { object } = await generateObject({
  model: 'openai/gpt-4.1',
  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.',
});

Accessing response headers & body

Sometimes you need access to the full response from the model provider, e.g. to access some provider-specific headers or body content.

You can access the raw response headers and body using the response property:

import { generateObject } from 'ai';

const result = await generateObject({
  // ...
});

console.log(JSON.stringify(result.response.headers, null, 2));
console.log(JSON.stringify(result.response.body, null, 2));

Stream Object

Given the added complexity of returning structured data, model response time can be unacceptable for your interactive use case. With the streamObject function, you can stream the model's response as it is generated.

import { streamObject } from 'ai';

const { partialObjectStream } = streamObject({
  // ...
});

// use partialObjectStream as an async iterable
for await (const partialObject of partialObjectStream) {
  console.log(partialObject);
}

You can use streamObject to stream generated UIs in combination with React Server Components (see Generative UI)) or the useObject hook.

onError callback

streamObject immediately starts streaming. Errors become part of the stream and are not thrown to prevent e.g. servers from crashing.

To log errors, you can provide an onError callback that is triggered when an error occurs.

import { streamObject } from 'ai';

const result = streamObject({
  // ...
  onError({ error }) {
    console.error(error); // your error logging logic here
  },
});

Output Strategy

You can use both functions with different output strategies, e.g. array, object, enum, or no-schema.

Object

The default output strategy is object, which returns the generated data as an object. You don't need to specify the output strategy if you want to use the default.

Array

If you want to generate an array of objects, you can set the output strategy to array. When you use the array output strategy, the schema specifies the shape of an array element. With streamObject, you can also stream the generated array elements using elementStream.

import { streamObject } from 'ai';
import { z } from 'zod';

const { elementStream } = streamObject({
  model: 'openai/gpt-4.1',
  output: 'array',
  schema: z.object({
    name: z.string(),
    class: z
      .string()
      .describe('Character class, e.g. warrior, mage, or thief.'),
    description: z.string(),
  }),
  prompt: 'Generate 3 hero descriptions for a fantasy role playing game.',
});

for await (const hero of elementStream) {
  console.log(hero);
}

Enum

If you want to generate a specific enum value, e.g. for classification tasks, you can set the output strategy to enum and provide a list of possible values in the enum parameter.

import { generateObject } from 'ai';

const { object } = await generateObject({
  model: 'openai/gpt-4.1',
  output: 'enum',
  enum: ['action', 'comedy', 'drama', 'horror', 'sci-fi'],
  prompt:
    'Classify the genre of this movie plot: ' +
    '"A group of astronauts travel through a wormhole in search of a ' +
    'new habitable planet for humanity."',
});

No Schema

In some cases, you might not want to use a schema, for example when the data is a dynamic user request. You can use the output setting to set the output format to no-schema in those cases and omit the schema parameter.

import { generateObject } from 'ai';

const { object } = await generateObject({
  model: 'openai/gpt-4.1',
  output: 'no-schema',
  prompt: 'Generate a lasagna recipe.',
});

Schema Name and Description

You can optionally specify a name and description for the schema. These are used by some providers for additional LLM guidance, e.g. via tool or schema name.

import { generateObject } from 'ai';
import { z } from 'zod';

const { object } = await generateObject({
  model: 'openai/gpt-4.1',
  schemaName: 'Recipe',
  schemaDescription: 'A recipe for a dish.',
  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 lasagna recipe.',
});

Accessing Reasoning

You can access the reasoning used by the language model to generate the object via the reasoning property on the result. This property contains a string with the model's thought process, if available.

import { OpenAIResponsesProviderOptions } from '@ai-sdk/openai';
import { generateObject } from 'ai';
import { z } from 'zod';

const result = await generateObject({
  model: 'openai/gpt-5',
  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.',
  providerOptions: {
    openai: {
      strictJsonSchema: true,
      reasoningSummary: 'detailed',
    } satisfies OpenAIResponsesProviderOptions,
  },
});

console.log(result.reasoning);

Error Handling

When generateObject cannot generate a valid object, it throws a AI_NoObjectGeneratedError.

This error occurs when the AI provider fails to generate a parsable object that conforms to the schema. It can arise due to the following reasons:

• The model failed to generate a response.
• The model generated a response that could not be parsed.
• The model generated a response that could not be validated against the schema.

The error preserves the following information to help you log the issue:

• text: The text that was generated by the model. This can be the raw text or the tool call text, depending on the object generation mode.
• response: Metadata about the language model response, including response id, timestamp, and model.
• usage: Request token usage.
• cause: The cause of the error (e.g. a JSON parsing error). You can use this for more detailed error handling.

import { generateObject, NoObjectGeneratedError } from 'ai';

try {
  await generateObject({ model, schema, prompt });
} catch (error) {
  if (NoObjectGeneratedError.isInstance(error)) {
    console.log('NoObjectGeneratedError');
    console.log('Cause:', error.cause);
    console.log('Text:', error.text);
    console.log('Response:', error.response);
    console.log('Usage:', error.usage);
  }
}

Repairing Invalid or Malformed JSON

Sometimes the model will generate invalid or malformed JSON. You can use the repairText function to attempt to repair the JSON.

It receives the error, either a JSONParseError or a TypeValidationError, and the text that was generated by the model. You can then attempt to repair the text and return the repaired text.

import { generateObject } from 'ai';

const { object } = await generateObject({
  model,
  schema,
  prompt,
  experimental_repairText: async ({ text, error }) => {
    // example: add a closing brace to the text
    return text + '}';
  },
});

Structured outputs with generateText and streamText

You can generate structured data with generateText and streamText by using the output setting.

generateText

// output is a structured object that matches the schema:
const { output } = await generateText({
  // ...
  output: Output.object({
    schema: z.object({
      name: z.string(),
      age: z.number().nullable().describe('Age of the person.'),
      contact: z.object({
        type: z.literal('email'),
        value: z.string(),
      }),
      occupation: z.object({
        type: z.literal('employed'),
        company: z.string(),
        position: z.string(),
      }),
    }),
  }),
  prompt: 'Generate an example person for testing.',
});

streamText

// partialOutputStream contains generated partial objects:
const { partialOutputStream } = await streamText({
  // ...
  output: Output.object({
    schema: z.object({
      name: z.string(),
      age: z.number().nullable().describe('Age of the person.'),
      contact: z.object({
        type: z.literal('email'),
        value: z.string(),
      }),
      occupation: z.object({
        type: z.literal('employed'),
        company: z.string(),
        position: z.string(),
      }),
    }),
  }),
  prompt: 'Generate an example person for testing.',
});

Output Types

The AI SDK supports multiple ways of specifying the expected structure of generated data via the Output object. You can select from various strategies for structured/text generation and validation.

Output.text()

Use Output.text() to generate plain text from a model. This option doesn't enforce any schema on the result: you simply receive the model's text as a string.

const { output } = await generateText({
  // ...
  output: Output.text(),
  prompt: 'Tell me a joke.',
});
// output will be a string (the joke)

Output.object()

Use Output.object({ schema }) to generate a structured object based on a schema (for example, a Zod schema). The output is type-validated to ensure the returned result matches the schema.

const { output } = await generateText({
  // ...
  output: Output.object({
    schema: z.object({
      name: z.string(),
      age: z.number().nullable(),
      labels: z.array(z.string()),
    }),
  }),
  prompt: 'Generate information for a test user.',
});
// output will be an object matching the schema above

Partial outputs (e.g. with streamText) are also validated against your provided schema, as much as possible.

Output.array()

Use Output.array({ element }) to specify that you expect an array of typed objects from the model, where each element should conform to a schema.

const { output } = await generateText({
  // ...
  output: Output.array({
    element: z.object({
      location: z.string(),
      temperature: z.number(),
      condition: z.string(),
    }),
  }),
  prompt: 'List the weather for San Francisco and Paris.',
});
// output will be an array of objects like:
// [
//   { location: 'San Francisco', temperature: 70, condition: 'Sunny' },
//   { location: 'Paris', temperature: 65, condition: 'Cloudy' },
// ]

With Output.array, the model is guided to return an object with an elements property that is an array; the SDK then extracts and validates this array for you.

For more advanced validation or different structures, see the Output API reference.

Output.choice()

Use Output.choice({ options }) when you expect the model to choose from a specific set of string options, such as for classification or fixed-enum answers.

const { output } = await generateText({
  // ...
  output: Output.choice({
    options: ['sunny', 'rainy', 'snowy'],
  }),
  prompt: 'Is the weather sunny, rainy, or snowy today?',
});
// output will be one of: 'sunny', 'rainy', or 'snowy'

You can provide any set of string options, and the output will always be a single string value that matches one of the specified options. The SDK validates that the result matches one of your options, and will throw if the model returns something invalid.

This is especially useful for making classification-style generations or forcing valid values for API compatibility.

More Examples

You can see generateObject and streamObject in action using various frameworks in the following examples:

generateObject

streamObject