> For the complete documentation index, see [llms.txt](https://docs.agenticflow.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.agenticflow.ai/workflows/workflow-inputs/upload-image.md).

# Upload Image

The **Upload Image** control is an input component that allows a user to upload an image file (e.g., JPEG, PNG) when they run a workflow. The uploaded image is then accessible via a temporary URL that can be used by downstream nodes.

## When to Use

Use the Upload Image control for any workflow that needs to process or analyze a user-provided image. Common use cases include:

* Analyzing the content of an image with a vision-capable LLM.
* Extracting text from an image (OCR).
* Resizing or modifying an image.
* Using an image as a source or reference for generating other content.

## How to Add

1. From your workflow's **Build** page, click on **+ Add Input**.
2. Select **Upload Image** from the list of available controls.

## Configuration

* **Title:** The main label for the file upload field.
* **Description:** Optional helper text to provide more context.
* **Variable Name:** The name used to access the URL of the uploaded image.
* **Optional/Required:** A toggle to specify if the user must upload an image.

## Accessing the Value

After the user uploads an image, this control outputs a temporary, secure URL pointing to that image. This URL is accessible via the variable name you assigned.

For example, if the variable name is `source_image`, the value of `{{source_image}}` will be a URL (e.g., `https://cdn.agenticflow.ai/.../image.png`).

**Note:** For security, this URL expires after a few days. It is intended for immediate processing within the workflow run, not for permanent storage.

### Example

**Goal:** Allow a user to upload a picture of a meal and get its nutritional information.

1. **Add an Upload Image Control:**
   * Title: "Upload a Photo of Your Meal"
   * Variable Name: `meal_photo_url`
2. **Add an OpenAI MCP Node (GPT-4 Vision):**
   * **Action:** `Analyze Image Content`
   * **Image URL:** `{{meal_photo_url}}`
   * **Prompt:** `Analyze the attached image of a meal. Identify the food items and provide an estimated nutritional breakdown (calories, protein, carbs, fat).`

When a user uploads an image and runs this workflow, the image URL is sent to the vision model, which then analyzes the image and provides the requested information based on the prompt.


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