image-input.md

title	Using image input with the Copilot SDK
shortTitle	Image input
intro	Send images to {% data variables.copilot.copilot_sdk_short %} sessions as file or blob attachments.
product	{% data reusables.gated-features.copilot-sdk %}
versions	feature copilot
contentType	how-tos
category	Author and optimize with Copilot

{% data reusables.copilot.copilot-sdk.technical-preview-note %}

There are two ways to attach images to a {% data variables.copilot.copilot_sdk_short %} session:

• File attachment (type: "file")—provide an absolute path; the runtime reads the file from disk, converts it to base64, and sends it to the LLM.
• Blob attachment (type: "blob")—provide base64-encoded data directly; useful when the image is already in memory (for example, screenshots, generated images, or data from an API).

For a sequence diagram of the image input flow, see the github/copilot-sdk repository.

Concept	Description
File attachment	An attachment with type: "file" and an absolute path to an image on disk
Blob attachment	An attachment with type: "blob", base64-encoded data, and a mimeType—no disk I/O needed
Automatic encoding	For file attachments, the runtime reads the image and converts it to base64 automatically
Auto-resize	The runtime automatically resizes or quality-reduces images that exceed model-specific limits
Vision capability	The model must have capabilities.supports.vision = true to process images

Quick start: file attachment

Attach an image file to any message using the file attachment type. The path must be an absolute path to an image on disk.

import { CopilotClient } from "@github/copilot-sdk";

const client = new CopilotClient();
await client.start();

const session = await client.createSession({
    model: "gpt-4.1",
    onPermissionRequest: async () => ({ kind: "approved" }),
});

await session.send({
    prompt: "Describe what you see in this image",
    attachments: [
        {
            type: "file",
            path: "/absolute/path/to/screenshot.png",
        },
    ],
});

For examples in Python, Go, and .NET, see the github/copilot-sdk repository. {% data reusables.copilot.copilot-sdk.java-sdk-link %}

Quick start: blob attachment

When you already have image data in memory (for example, a screenshot captured by your app, or an image fetched from an API), use a blob attachment to send it directly without writing to disk.

import { CopilotClient } from "@github/copilot-sdk";

const client = new CopilotClient();
await client.start();

const session = await client.createSession({
    model: "gpt-4.1",
    onPermissionRequest: async () => ({ kind: "approved" }),
});

const base64ImageData = "..."; // your base64-encoded image
await session.send({
    prompt: "Describe what you see in this image",
    attachments: [
        {
            type: "blob",
            data: base64ImageData,
            mimeType: "image/png",
            displayName: "screenshot.png",
        },
    ],
});

For examples in Python, Go, and .NET, see the github/copilot-sdk repository. {% data reusables.copilot.copilot-sdk.java-sdk-link %}

Supported formats

Supported image formats include JPG, PNG, GIF, and other common image types. For file attachments, the runtime reads the image from disk and converts it as needed. For blob attachments, you provide the base64 data and MIME type directly. Use PNG or JPEG for best results, as these are the most widely supported formats.

The model's capabilities.limits.vision.supported_media_types field lists the exact MIME types it accepts.

Automatic processing

The runtime automatically processes images to fit within the model's constraints. No manual resizing is required.

• Images that exceed the model's dimension or size limits are automatically resized (preserving aspect ratio) or quality-reduced.
• If an image cannot be brought within limits after processing, it is skipped and not sent to the LLM.
• The model's capabilities.limits.vision.max_prompt_image_size field indicates the maximum image size in bytes.

Vision model capabilities

Not all models support vision. Check the model's capabilities before sending images.

Capability fields

Field	Type	Description
capabilities.supports.vision	boolean	Whether the model can process image inputs
capabilities.limits.vision.supported_media_types	string[]	MIME types the model accepts (for example, ["image/png", "image/jpeg"])
capabilities.limits.vision.max_prompt_images	number	Maximum number of images per prompt
capabilities.limits.vision.max_prompt_image_size	number	Maximum image size in bytes

Vision limits type

vision?: {
    supported_media_types: string[];
    max_prompt_images: number;
    max_prompt_image_size: number; // bytes
};

Receiving image results

When tools return images (for example, screenshots or generated charts), the result contains "image" content blocks with base64-encoded data.

Field	Type	Description
type	"image"	Content block type discriminator
data	string	Base64-encoded image data
mimeType	string	MIME type (for example, "image/png")

These image blocks appear in tool.execution_complete event results. For more information, see AUTOTITLE.

Tips and limitations

Tip	Details
Use PNG or JPEG	Avoids conversion overhead—these are sent to the LLM as-is
Keep images reasonably sized	Large images may be quality-reduced, which can lose important details
Use absolute paths for file attachments	The runtime reads files from disk; relative paths may not resolve correctly
Use blob attachments for in-memory data	When you already have base64 data (for example, screenshots, API responses), blob avoids unnecessary disk I/O
Check vision support first	Sending images to a non-vision model wastes tokens without visual understanding
Multiple images are supported	Attach several attachments in one message, up to the model's max_prompt_images limit
SVG is not supported	SVG files are text-based and excluded from image processing

Further reading

• AUTOTITLE—event lifecycle including tool result content blocks
• AUTOTITLE—sending follow-up messages with attachments