High memory usage when processing many images

Issue

When using generateText or streamText with many images (e.g., in a loop or batch processing), you may notice:

  • Memory usage grows continuously and doesn't decrease
  • Application eventually runs out of memory
  • Memory is not reclaimed even after garbage collection

This is especially noticeable when using experimental_download to process images from URLs, or when sending base64-encoded images in prompts.

Background

By default, the AI SDK includes the full request and response bodies in the step results. When processing images, the request body contains the base64-encoded image data, which can be very large (a single image can be 1MB+ when base64 encoded). If you process many images and keep references to the results, this data accumulates in memory.

For example, processing 100 images of 500KB each would include ~50MB+ of request body data in memory.

Solution

Use the experimental_include option to disable inclusion of request and/or response bodies:

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


const result = await generateText({
  model: openai('gpt-4o'),
  messages: [
    {
      role: 'user',
      content: [
        { type: 'text', text: 'Describe this image' },
        { type: 'image', image: imageUrl },
      ],
    },
  ],
  // Disable inclusion of request body to reduce memory usage
  experimental_include: {
    requestBody: false,
    responseBody: false,
  },
});

Options

The experimental_include option accepts:

  • requestBody: Set to false to exclude the request body from step results. This is where base64-encoded images are stored. Default: true. Available in both generateText and streamText.
  • responseBody: Set to false to exclude the response body from step results. Default: true. Only available in generateText.

When to use

  • Batch processing images: When processing many images in a loop
  • Long-running agents: When an agent may process many images over its lifetime
  • Memory-constrained environments: When running in environments with limited memory

Trade-offs

When you disable body inclusion:

  • You won't have access to result.request.body or result.response.body
  • Debugging may be harder since you can't inspect the raw request/response
  • If you need the bodies for logging or debugging, consider extracting the data you need before the next iteration

Example: Processing multiple images 

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


const imageUrls = [
  /* array of image URLs */
];
const results = [];


for (const imageUrl of imageUrls) {
  const result = await generateText({
    model: openai('gpt-4o'),
    messages: [
      {
        role: 'user',
        content: [
          { type: 'text', text: 'Describe this image' },
          { type: 'image', image: imageUrl },
        ],
      },
    ],
    experimental_include: {
      requestBody: false,
    },
  });


  // Only store the text result, not the full result object
  results.push(result.text);
}

Learn more