Generating 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: Generates text for a given prompt and model.
streamText: Streams text from a given prompt and model.

Advanced LLM features such as tool calling and structured data generation are built on top of text generation.

`generateText`

You can generate text using the generateText 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.

import { generateText } from 'ai';

const { text } = await generateText({
  model: "anthropic/claude-sonnet-5.5",
  prompt: 'Write a vegetarian lasagna recipe for 4 people.',
});

You can use more advanced prompts to generate text with more complex instructions and content:

import { generateText } from 'ai';

const { text } = await generateText({
  model: "anthropic/claude-sonnet-5.5",
  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.content: The content that was generated in the last step.
result.text: The generated text.
result.reasoning: The full reasoning that the model has generated in the last step.
result.reasoningText: The reasoning text of the model (only available for some models).
result.files: The files that were generated in the last step.
result.sources: Sources that have been used as references in the last step (only available for some models).
result.toolCalls: The tool calls that were made in the last step.
result.toolResults: The results of the tool calls from the last step.
result.finishReason: The reason the model finished generating text.
result.usage: The usage of the model during the final step of text generation.
result.totalUsage: The total usage across all steps (for multi-step generations).
result.warnings: Warnings from the model provider (e.g. unsupported settings).
result.request: Additional request information.
result.response: Additional response information, including response messages and body.
result.providerMetadata: Additional provider-specific metadata.
result.steps: Details for all steps, useful for getting information about intermediate steps.
result.experimental_output: The generated structured output using the experimental_output specification.

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 { 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 function which simplifies streaming text from LLMs:

import { streamText } from 'ai';

const result = streamText({
  model: "anthropic/claude-sonnet-5.5",
  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);
}

result.textStream is both a ReadableStream and an AsyncIterable.

streamText immediately starts streaming and suppresses errors to prevent server crashes. Use the onError callback to log errors.

You can use streamText on its own or in combination with AI SDK UI and AI SDK RSC. The result object contains several helper functions to make the integration into AI SDK UI easier:

result.toUIMessageStreamResponse(): Creates a UI Message stream HTTP response (with tool calls etc.) that can be used in a Next.js App Router API route.
result.pipeUIMessageStreamToResponse(): Writes UI Message 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.

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

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

result.content: The content that was generated in the last step.
result.text: The generated text.
result.reasoning: The full reasoning that the model has generated.
result.reasoningText: The reasoning text of the model (only available for some models).
result.files: Files that have been generated by the model in the last step.
result.sources: Sources that have been used as references in the last step (only available for some models).
result.toolCalls: The tool calls that have been executed in the last step.
result.toolResults: The tool results that have been generated in the last step.
result.finishReason: The reason the model finished generating text.
result.usage: The usage of the model during the final step of text generation.
result.totalUsage: The total usage across all steps (for multi-step generations).
result.warnings: Warnings from the model provider (e.g. unsupported settings).
result.steps: Details for all steps, useful for getting information about intermediate steps.
result.request: Additional request information from the last step.
result.response: Additional response information from the last step.
result.providerMetadata: Additional provider-specific metadata from the last step.

`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.

import { streamText } from 'ai';

const result = streamText({
  model: "anthropic/claude-sonnet-5.5",
  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
reasoning
source
tool-call
tool-input-start
tool-input-delta
tool-result
raw

import { streamText } from 'ai';

const result = streamText({
  model: "anthropic/claude-sonnet-5.5",
  prompt: 'Invent a new holiday and describe its traditions.',
  onChunk({ chunk }) {
    // implement your own logic here, e.g.:
    if (chunk.type === 'text') {
      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 ). It contains the text, usage information, finish reason, messages, steps, total usage, and more:

import { streamText } from 'ai';

const result = streamText({
  model: "anthropic/claude-sonnet-5.5",
  prompt: 'Invent a new holiday and describe its traditions.',
  onFinish({ text, finishReason, usage, response, steps, totalUsage }) {
    // 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:

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

const result = streamText({
  model: "anthropic/claude-sonnet-5.5",
  tools: {
    cityAttractions: {
      inputSchema: 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 'start': {
      // handle start of stream
      break;
    }
    case 'start-step': {
      // handle start of step
      break;
    }
    case 'text-start': {
      // handle text start
      break;
    }
    case 'text-delta': {
      // handle text delta here
      break;
    }
    case 'text-end': {
      // handle text end
      break;
    }
    case 'reasoning-start': {
      // handle reasoning start
      break;
    }
    case 'reasoning-delta': {
      // handle reasoning delta here
      break;
    }
    case 'reasoning-end': {
      // handle reasoning end
      break;
    }
    case 'source': {
      // handle source here
      break;
    }
    case 'file': {
      // handle file here
      break;
    }
    case 'tool-call': {
      switch (part.toolName) {
        case 'cityAttractions': {
          // handle tool call here
          break;
        }
      }
      break;
    }
    case 'tool-input-start': {
      // handle tool input start
      break;
    }
    case 'tool-input-delta': {
      // handle tool input delta
      break;
    }
    case 'tool-input-end': {
      // handle tool input end
      break;
    }
    case 'tool-result': {
      switch (part.toolName) {
        case 'cityAttractions': {
          // handle tool result here
          break;
        }
      }
      break;
    }
    case 'tool-error': {
      // handle tool error
      break;
    }
    case 'finish-step': {
      // handle finish step
      break;
    }
    case 'finish': {
      // handle finish here
      break;
    }
    case 'error': {
      // handle error here
      break;
    }
    case 'raw': {
      // handle raw value
      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 that can be used to smooth out text streaming.

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:

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

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') {
          controller.enqueue(chunk);
          return;
        }

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

          // simulate the finish-step event
          controller.enqueue({
            type: 'finish-step',
            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.

const result = streamText({
  model,
  prompt,
  experimental_transform: [firstTransform, secondTransform],
});

Sources

Some providers such as Perplexity and Google Generative AI 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:

const result = await generateText({
  model: 'google/gemini-2.5-flash',
  tools: {
    google_search: google.tools.googleSearch({}),
  },
  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:

const result = streamText({
  model: 'google/gemini-2.5-flash',
  tools: {
    google_search: google.tools.googleSearch({}),
  },
  prompt: 'List the top 5 San Francisco news from the past week.',
});

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

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