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Dartantic provides a unified generateMedia() API that works across multiple providers. Each provider uses its own underlying mechanism (native image generation, code execution, etc.) but the API surface is consistent.

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Provider Support

ProviderImagesPDFsCSV/FilesMechanism
OpenAI ResponsesNative + CodeCodeCodeDALL-E + Code Interpreter
GoogleNative + CodeCodeCodeNano Banana Pro (gemini-3-pro-image-preview) + Code Execution
AnthropicCodeCodeCodeCode Interpreter (matplotlib, reportlab)
When you request an image, Dartantic routes to the provider’s native image model (DALL-E for OpenAI, Nano Banana Pro for Google). For other file types, it uses each provider’s code execution environment.

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Basic Usage

final agent = Agent('google'); // or 'openai-responses', 'anthropic'

final result = await agent.generateMedia(
  'Create a minimalist robot mascot for a developer conference.',
  mimeTypes: const ['image/png'],
);

// Access generated assets
for (final asset in result.assets) {
  if (asset is DataPart) {
    print('Generated: ${asset.name} (${asset.mimeType})');
    File('output/${asset.name}').writeAsBytesSync(asset.bytes);
  }
}

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Image Editing with Attachments

All providers support image editing by providing an input image as an attachment. Google uses native Imagen for high-quality editing, while OpenAI and Anthropic use code execution (PIL/Pillow): 
import 'package:cross_file/cross_file.dart';

final agent = Agent('google'); // or 'openai-responses', 'anthropic'

// Load the input image
const imagePath = 'input/robot_bw.png';
final imageFile = XFile.fromData(
  await File(imagePath).readAsBytes(),
  path: imagePath,
  mimeType: 'image/png',
);
final imagePart = await DataPart.fromFile(imageFile);

// Edit the image
final result = await agent.generateMedia(
  'Colorize this black and white robot drawing. '
  'Make the robot body blue, the eyes bright green, and add '
  'orange/yellow accents.',
  mimeTypes: const ['image/png'],
  attachments: [imagePart],
);

// Save the edited image
for (final asset in result.assets) {
  if (asset is DataPart) {
    File('output/${asset.name}').writeAsBytesSync(asset.bytes);
  }
}
Common image editing use cases:
  • Colorization: Add color to black and white images
  • Style Transfer: Apply artistic styles to existing images
  • Object Removal: Remove unwanted objects (inpainting)
  • Background Replacement: Change backgrounds while preserving subjects
  • Enhancement: Improve quality, lighting, or resolution

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Example MIME Types

The mimeTypes parameter tells the provider what kind of output you want, e.g.
  • image/png, image/jpeg - Images
  • application/pdf - PDF documents
  • text/csv - CSV files
  • Other file types depend on provider code execution capabilities

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Streaming Generation

Use generateMediaStream() to receive incremental updates: 
final agent = Agent('openai-responses');

await for (final chunk in agent.generateMediaStream(
  'Create a colorful abstract art piece.',
  mimeTypes: const ['image/png'],
)) {
  // Check for partial/preview images in metadata
  final previews = chunk.metadata['image_generation'] as List?;
  if (previews != null) {
    for (final preview in previews) {
      final b64 = preview['partial_image_b64'] as String?;
      if (b64 != null) {
        print('Preview available at index ${preview['partial_image_index']}');
      }
    }
  }

  // Check for completed assets
  if (chunk.isComplete) {
    for (final asset in chunk.assets) {
      if (asset is DataPart) {
        File('final.png').writeAsBytesSync(asset.bytes);
      }
    }
  }
}

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Specifying Media Models

Use the model string to specify which media model to use: 
// Use Nano Banana Pro explicitly for Google image generation
final agent = Agent('google?media=gemini-3-pro-image-preview');

// Or combine with a chat model
final agent = Agent('google?chat=gemini-3-pro-preview&media=gemini-3-pro-image-preview');
The model string is passed through to the provider’s API, so you can use new models as soon as they’re available without waiting for a Dartantic update.

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Provider-Specific Options

Each provider accepts options for customization:

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OpenAI Responses

final agent = Agent(
  'openai-responses',
  mediaModelOptions: OpenAIResponsesMediaGenerationModelOptions(
    imageModel: 'gpt-image-1',  // Specific image model
  ),
);

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Google

final agent = Agent(
  'google?media=gemini-3-pro-image-preview',  // Use Nano Banana Pro
  mediaModelOptions: GoogleMediaGenerationModelOptions(
    aspectRatio: '16:9',  // Image aspect ratio
  ),
);

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Anthropic

final agent = Agent(
  'anthropic',
  mediaModelOptions: AnthropicMediaGenerationModelOptions(
    // Uses code interpreter for all generation
  ),
);

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Result Structure

MediaGenerationResult contains:
  • assets - List of Part objects (typically DataPart with bytes)
  • links - List of LinkPart for hosted URLs (if applicable)
  • messages - Conversation messages generated during the run
  • metadata - Provider-specific metadata (previews, progress, etc.)
  • isComplete - Whether generation is finished (for streaming)

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Examples