# Generating and Streaming Text

Large language models (LLMs) can generate text in response to a prompt, which can contain instructions and information to process.
For example, you can ask a model to come up with a recipe, draft an email, or summarize a document.

The AI SDK Core provides two functions to generate text and stream it from LLMs:

- [`generateText`](#generatetext): Generates text for a given prompt and model.
- [`streamText`](#streamtext): Streams text from a given prompt and model.

Advanced LLM features such as [tool calling](./tools-and-tool-calling) and [structured data generation](./generating-structured-data) are built on top of text generation.

## `generateText`

You can generate text using the [`generateText`](/docs/reference/ai-sdk-core/generate-text) function. This function is ideal for non-interactive use cases where you need to write text (e.g. drafting email or summarizing web pages) and for agents that use tools.

```tsx
import { generateText } from 'ai';

const { text } = await generateText({
  model: yourModel,
  prompt: 'Write a vegetarian lasagna recipe for 4 people.',
});
```

You can use more [advanced prompts](./prompts) to generate text with more complex instructions and content:

```tsx
import { generateText } from 'ai';

const { text } = await generateText({
  model: yourModel,
  system:
    'You are a professional writer. ' +
    'You write simple, clear, and concise content.',
  prompt: `Summarize the following article in 3-5 sentences: ${article}`,
});
```

The result object of `generateText` contains several promises that resolve when all required data is available:

- `result.text`: The generated text.
- `result.reasoning`: The reasoning text of the model (only available for some models).
- `result.sources`: Sources that have been used as input to generate the response (only available for some models).
- `result.finishReason`: The reason the model finished generating text.
- `result.usage`: The usage of the model during text generation.

### 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:

```ts
import { generateText } from 'ai';

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

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

## `streamText`

Depending on your model and prompt, it can take a large language model (LLM) up to a minute to finish generating its response. This delay can be unacceptable for interactive use cases such as chatbots or real-time applications, where users expect immediate responses.

AI SDK Core provides the [`streamText`](/docs/reference/ai-sdk-core/stream-text) function which simplifies streaming text from LLMs:

```ts
import { streamText } from 'ai';

const result = streamText({
  model: yourModel,
  prompt: 'Invent a new holiday and describe its traditions.',
});

// example: use textStream as an async iterable
for await (const textPart of result.textStream) {
  console.log(textPart);
}
```

<Note>
  `result.textStream` is both a `ReadableStream` and an `AsyncIterable`.
</Note>

<Note type="warning">
  `streamText` immediately starts streaming and suppresses errors to prevent
  server crashes. Use the `onError` callback to log errors.
</Note>

You can use `streamText` on its own or in combination with [AI SDK
UI](/examples/next-pages/basics/streaming-text-generation) and [AI SDK
RSC](/examples/next-app/basics/streaming-text-generation).
The result object contains several helper functions to make the integration into [AI SDK UI](/docs/ai-sdk-ui) easier:

- `result.toDataStreamResponse()`: Creates a data stream HTTP response (with tool calls etc.) that can be used in a Next.js App Router API route.
- `result.pipeDataStreamToResponse()`: Writes data stream delta output to a Node.js response-like object.
- `result.toTextStreamResponse()`: Creates a simple text stream HTTP response.
- `result.pipeTextStreamToResponse()`: Writes text delta output to a Node.js response-like object.

<Note>
  `streamText` is using backpressure and only generates tokens as they are
  requested. You need to consume the stream in order for it to finish.
</Note>

It also provides several promises that resolve when the stream is finished:

- `result.text`: The generated text.
- `result.reasoning`: The reasoning text of the model (only available for some models).
- `result.sources`: Sources that have been used as input to generate the response (only available for some models).
- `result.finishReason`: The reason the model finished generating text.
- `result.usage`: The usage of the model during text generation.

### `onError` callback

`streamText` immediately starts streaming to enable sending data without waiting for the model.
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.

```tsx highlight="6-8"
import { streamText } from 'ai';

const result = streamText({
  model: yourModel,
  prompt: 'Invent a new holiday and describe its traditions.',
  onError({ error }) {
    console.error(error); // your error logging logic here
  },
});
```

### `onChunk` callback

When using `streamText`, you can provide an `onChunk` callback that is triggered for each chunk of the stream.

It receives the following chunk types:

- `text-delta`
- `reasoning`
- `source`
- `tool-call`
- `tool-result`
- `tool-call-streaming-start` (when `toolCallStreaming` is enabled)
- `tool-call-delta` (when `toolCallStreaming` is enabled)

```tsx highlight="6-11"
import { streamText } from 'ai';

const result = streamText({
  model: yourModel,
  prompt: 'Invent a new holiday and describe its traditions.',
  onChunk({ chunk }) {
    // implement your own logic here, e.g.:
    if (chunk.type === 'text-delta') {
      console.log(chunk.text);
    }
  },
});
```

### `onFinish` callback

When using `streamText`, you can provide an `onFinish` callback that is triggered when the stream is finished (
[API Reference](/docs/reference/ai-sdk-core/stream-text#on-finish)
).
It contains the text, usage information, finish reason, messages, and more:

```tsx highlight="6-8"
import { streamText } from 'ai';

const result = streamText({
  model: yourModel,
  prompt: 'Invent a new holiday and describe its traditions.',
  onFinish({ text, finishReason, usage, response }) {
    // your own logic, e.g. for saving the chat history or recording usage

    const messages = response.messages; // messages that were generated
  },
});
```

### `fullStream` property

You can read a stream with all events using the `fullStream` property.
This can be useful if you want to implement your own UI or handle the stream in a different way.
Here is an example of how to use the `fullStream` property:

```tsx
import { streamText } from 'ai';
import { z } from 'zod';

const result = streamText({
  model: yourModel,
  tools: {
    cityAttractions: {
      parameters: z.object({ city: z.string() }),
      execute: async ({ city }) => ({
        attractions: ['attraction1', 'attraction2', 'attraction3'],
      }),
    },
  },
  prompt: 'What are some San Francisco tourist attractions?',
});

for await (const part of result.fullStream) {
  switch (part.type) {
    case 'text-delta': {
      // handle text delta here
      break;
    }
    case 'reasoning': {
      // handle reasoning here
      break;
    }
    case 'source': {
      // handle source here
      break;
    }
    case 'tool-call': {
      switch (part.toolName) {
        case 'cityAttractions': {
          // handle tool call here
          break;
        }
      }
      break;
    }
    case 'tool-result': {
      switch (part.toolName) {
        case 'cityAttractions': {
          // handle tool result here
          break;
        }
      }
      break;
    }
    case 'finish': {
      // handle finish here
      break;
    }
    case 'error': {
      // handle error here
      break;
    }
  }
}
```

### Stream transformation

You can use the `experimental_transform` option to transform the stream.
This is useful for e.g. filtering, changing, or smoothing the text stream.

The transformations are applied before the callbacks are invoked and the promises are resolved.
If you e.g. have a transformation that changes all text to uppercase, the `onFinish` callback will receive the transformed text.

#### Smoothing streams

The AI SDK Core provides a [`smoothStream` function](/docs/reference/ai-sdk-core/smooth-stream) that
can be used to smooth out text streaming.

```tsx highlight="6"
import { smoothStream, streamText } from 'ai';

const result = streamText({
  model,
  prompt,
  experimental_transform: smoothStream(),
});
```

#### Custom transformations

You can also implement your own custom transformations.
The transformation function receives the tools that are available to the model,
and returns a function that is used to transform the stream.
Tools can either be generic or limited to the tools that you are using.

Here is an example of how to implement a custom transformation that converts
all text to uppercase:

```ts
const upperCaseTransform =
  <TOOLS extends ToolSet>() =>
  (options: { tools: TOOLS; stopStream: () => void }) =>
    new TransformStream<TextStreamPart<TOOLS>, TextStreamPart<TOOLS>>({
      transform(chunk, controller) {
        controller.enqueue(
          // for text-delta chunks, convert the text to uppercase:
          chunk.type === 'text-delta'
            ? { ...chunk, textDelta: chunk.textDelta.toUpperCase() }
            : chunk,
        );
      },
    });
```

You can also stop the stream using the `stopStream` function.
This is e.g. useful if you want to stop the stream when model guardrails are violated, e.g. by generating inappropriate content.

When you invoke `stopStream`, it is important to simulate the `step-finish` and `finish` events to guarantee that a well-formed stream is returned
and all callbacks are invoked.

```ts
const stopWordTransform =
  <TOOLS extends ToolSet>() =>
  ({ stopStream }: { stopStream: () => void }) =>
    new TransformStream<TextStreamPart<TOOLS>, TextStreamPart<TOOLS>>({
      // note: this is a simplified transformation for testing;
      // in a real-world version more there would need to be
      // stream buffering and scanning to correctly emit prior text
      // and to detect all STOP occurrences.
      transform(chunk, controller) {
        if (chunk.type !== 'text-delta') {
          controller.enqueue(chunk);
          return;
        }

        if (chunk.textDelta.includes('STOP')) {
          // stop the stream
          stopStream();

          // simulate the step-finish event
          controller.enqueue({
            type: 'step-finish',
            finishReason: 'stop',
            logprobs: undefined,
            usage: {
              completionTokens: NaN,
              promptTokens: NaN,
              totalTokens: NaN,
            },
            request: {},
            response: {
              id: 'response-id',
              modelId: 'mock-model-id',
              timestamp: new Date(0),
            },
            warnings: [],
            isContinued: false,
          });

          // simulate the finish event
          controller.enqueue({
            type: 'finish',
            finishReason: 'stop',
            logprobs: undefined,
            usage: {
              completionTokens: NaN,
              promptTokens: NaN,
              totalTokens: NaN,
            },
            response: {
              id: 'response-id',
              modelId: 'mock-model-id',
              timestamp: new Date(0),
            },
          });

          return;
        }

        controller.enqueue(chunk);
      },
    });
```

#### Multiple transformations

You can also provide multiple transformations. They are applied in the order they are provided.

```tsx highlight="4"
const result = streamText({
  model,
  prompt,
  experimental_transform: [firstTransform, secondTransform],
});
```

## Sources

Some providers such as [Perplexity](/providers/ai-sdk-providers/perplexity#sources) and
[Google Generative AI](/providers/ai-sdk-providers/google-generative-ai#sources) include sources in the response.

Currently sources are limited to web pages that ground the response.
You can access them using the `sources` property of the result.

Each `url` source contains the following properties:

- `id`: The ID of the source.
- `url`: The URL of the source.
- `title`: The optional title of the source.
- `providerMetadata`: Provider metadata for the source.

When you use `generateText`, you can access the sources using the `sources` property:

```ts
const result = await generateText({
  model: google('gemini-2.0-flash-exp', { useSearchGrounding: true }),
  prompt: 'List the top 5 San Francisco news from the past week.',
});

for (const source of result.sources) {
  if (source.sourceType === 'url') {
    console.log('ID:', source.id);
    console.log('Title:', source.title);
    console.log('URL:', source.url);
    console.log('Provider metadata:', source.providerMetadata);
    console.log();
  }
}
```

When you use `streamText`, you can access the sources using the `fullStream` property:

```tsx
const result = streamText({
  model: google('gemini-2.0-flash-exp', { useSearchGrounding: true }),
  prompt: 'List the top 5 San Francisco news from the past week.',
});

for await (const part of result.fullStream) {
  if (part.type === 'source' && part.source.sourceType === 'url') {
    console.log('ID:', part.source.id);
    console.log('Title:', part.source.title);
    console.log('URL:', part.source.url);
    console.log('Provider metadata:', part.source.providerMetadata);
    console.log();
  }
}
```

The sources are also available in the `result.sources` promise.

## Generating Long Text

Most language models have an output limit that is much shorter than their context window.
This means that you cannot generate long text in one go,
but it is possible to add responses back to the input and continue generating
to create longer text.

`generateText` and `streamText` support such continuations for long text generation using the experimental `continueSteps` setting:

```tsx highlight="5-6,9-10"
import { openai } from '@ai-sdk/openai';
import { generateText } from 'ai';

const {
  text, // combined text
  usage, // combined usage of all steps
} = await generateText({
  model: openai('gpt-4o'), // 4096 output tokens
  maxSteps: 5, // enable multi-step calls
  experimental_continueSteps: true,
  prompt:
    'Write a book about Roman history, ' +
    'from the founding of the city of Rome ' +
    'to the fall of the Western Roman Empire. ' +
    'Each chapter MUST HAVE at least 1000 words.',
});
```

<Note>
  When `experimental_continueSteps` is enabled, only full words are streamed in
  `streamText`, and both `generateText` and `streamText` might drop the trailing
  tokens of some calls to prevent whitespace issues.
</Note>

<Note type="warning">
  Some models might not always stop correctly on their own and keep generating
  until `maxSteps` is reached. You can hint the model to stop by e.g. using a
  system message such as "Stop when sufficient information was provided."
</Note>

## Examples

You can see `generateText` and `streamText` in action using various frameworks in the following examples:

### `generateText`

<ExampleLinks
  examples={[
    {
      title: 'Learn to generate text in Node.js',
      link: '/examples/node/generating-text/generate-text',
    },
    {
      title:
        'Learn to generate text in Next.js with Route Handlers (AI SDK UI)',
      link: '/examples/next-pages/basics/generating-text',
    },
    {
      title:
        'Learn to generate text in Next.js with Server Actions (AI SDK RSC)',
      link: '/examples/next-app/basics/generating-text',
    },
  ]}
/>

### `streamText`

<ExampleLinks
  examples={[
    {
      title: 'Learn to stream text in Node.js',
      link: '/examples/node/generating-text/stream-text',
    },
    {
      title: 'Learn to stream text in Next.js with Route Handlers (AI SDK UI)',
      link: '/examples/next-pages/basics/streaming-text-generation',
    },
    {
      title: 'Learn to stream text in Next.js with Server Actions (AI SDK RSC)',
      link: '/examples/next-app/basics/streaming-text-generation',
    },
  ]}
/>


## Navigation

- [Overview](/v4/docs/ai-sdk-core/overview)
- [Generating Text](/v4/docs/ai-sdk-core/generating-text)
- [Generating Structured Data](/v4/docs/ai-sdk-core/generating-structured-data)
- [Tool Calling](/v4/docs/ai-sdk-core/tools-and-tool-calling)
- [Prompt Engineering](/v4/docs/ai-sdk-core/prompt-engineering)
- [Settings](/v4/docs/ai-sdk-core/settings)
- [Embeddings](/v4/docs/ai-sdk-core/embeddings)
- [Image Generation](/v4/docs/ai-sdk-core/image-generation)
- [Transcription](/v4/docs/ai-sdk-core/transcription)
- [Speech](/v4/docs/ai-sdk-core/speech)
- [Language Model Middleware](/v4/docs/ai-sdk-core/middleware)
- [Provider & Model Management](/v4/docs/ai-sdk-core/provider-management)
- [Error Handling](/v4/docs/ai-sdk-core/error-handling)
- [Testing](/v4/docs/ai-sdk-core/testing)
- [Telemetry](/v4/docs/ai-sdk-core/telemetry)


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