Reference · Tools
Gemini Vision
Analyze images using Google Gemini\
Gemini Vision sends an image — from binary data in your workflow or a direct URL — alongside a text prompt to a Google Gemini model and returns the model's written analysis. Use it to build workflows that read text out of screenshots, describe uploaded photos, interpret charts, or classify images at scale. The result lands in a configurable output field along with the model name and token usage.
- Node type
- Action (binary)
- Parameters
- 9
- Outputs
- Output, Error
- Credentials
- Google AI
Gemini Vision
Analyze images using Google Gemini’s vision capabilities.
Overview
Gemini Vision sends an image together with a text prompt to a Google Gemini model and returns the model’s written analysis. The image can come from binary data attached to the incoming item — a download, a file read, an upload — or from a URL you supply. Use it to describe scenes, read text out of screenshots and scans, interpret charts, or classify pictures at scale. The analysis text is written to a configurable field alongside the model name and token usage.
Category: AI
Tool Name: gemini_vision
Version: 1
Appearance: Icon: gemini | Color: #ffffff
Node Type
Action (Binary) — handles file/binary data operations
Input / Output
| Direction | Port(s) |
|---|---|
| Input | Input |
| Output | Output, Error |
Credentials
This tool requires Google AI credentials. See the Credentials Guide for setup instructions.
Parameters
| Parameter | Type | Required | Default | Description |
|---|---|---|---|---|
| Model | options | No | — | The Gemini model to use for vision analysis. Pick a specific version — labels are pinned, not aliased to “latest”. The dropdown lists the models currently available for this node; leave it unset to use the default. |
| System Prompt | string | No | — | Optional system instruction to set the model’s behavior and context for image analysis. Supports expressions. |
| User Message | string | No | Describe this image in detail | The text prompt to send alongside the image. Describes what analysis to perform. Falls back to item.message or item.prompt when cleared. Supports expressions. |
| Image Source | options | No | binary | Where to get the image — from binary data in the input item or from a URL. |
Options: binary (read the image from binary data attached to the input item), url (give Gemini an image URL to analyze) | ||||
| Binary Property Name | string | No | data | The name of the binary property containing the image data. Names are case-sensitive — see the upstream node’s Binary Data panel for the exact names to use. (shown when Image Source is binary) |
| Image URL | string | No | — | URL of the image to analyze. Falls back to item.imageUrl if empty. (shown when Image Source is url) |
| Options | collection | No | {} | Optional generation settings. |
| — Temperature | number | No | — | Sampling temperature (0-2). Lower values are more deterministic. |
| — Max Output Tokens | number | No | — | Maximum number of tokens to generate in the response. |
| — Response Field Name | string | No | response | The output field name where the vision analysis text will be stored. |
| Include Input | boolean | No | false | Whether to include the original input item fields in the output alongside the response. |
| Max Concurrency | number | No | 5 | Maximum number of items to process concurrently. Lower values recommended for vision due to larger payloads. |
Output Data
One output item per input item. Binary data arriving from upstream is preserved, so the analyzed image stays on the item for later steps.
The output item’s JSON contains only the fields below. The input item’s JSON is merged in only when Include Input is on:
| Field | Description |
|---|---|
response | The model’s written analysis of the image. Renamed by Response Field Name. |
model | The model that produced the analysis. |
usage | Token usage metadata reported by the model. |
With Image Source set to binary, an item that carries no binary data under the configured property name is an item error — it never reaches the model.
Reference the result downstream by expression, e.g. {{ $json.response }}.
Usage Examples
- Describe what is in an uploaded image using Gemini
- Extract text from a document image with Gemini vision
- Classify product photos by category
- Analyze medical or scientific images
- Identify landmarks in photos from URLs
Example Configuration
Describe the image attached to each item:
{
"type": "gemini_vision",
"parameters": {
"imageSource": "binary",
"binaryPropertyName": "data",
"userMessage": "Describe this image in detail"
}
}
Read text out of a scanned page, deterministically:
{
"type": "gemini_vision",
"parameters": {
"imageSource": "binary",
"binaryPropertyName": "data",
"systemPrompt": "You are an OCR assistant. Extract all visible text exactly as it appears.",
"userMessage": "Extract all text from this image. Preserve formatting where possible.",
"maxConcurrency": 5,
"options": {
"temperature": 0,
"responseFieldName": "extractedText"
}
}
}
Analyze a remotely hosted image and keep the source fields:
{
"type": "gemini_vision",
"parameters": {
"imageSource": "url",
"imageUrl": "{{ $json.photoUrl }}",
"systemPrompt": "You are a product cataloging assistant. Extract structured attributes from product images.",
"userMessage": "List the product name, color, and any visible text or labels.",
"includeInput": true,
"maxConcurrency": 3,
"options": {
"temperature": 0.2,
"maxOutputTokens": 1024,
"responseFieldName": "productAttributes"
}
}
}
Error Handling
| Mode | Behavior |
|---|---|
| stop | Halts workflow on first error |
| continue | Skips failed items, passes successful ones through |
| errorPort | Routes failed items to Error output port |
Tips
Gemini Vision analyzes images by sending binary data or URL references alongside a text prompt to the Google Gemini generateContent API using Gemini 2.0 Flash or Pro models. Use this tool when a workflow requires visual understanding of image content, such as extracting text, describing scenes, classifying objects, or answering questions about uploaded images. It returns a response text field containing the model analysis, along with the model name and usage metadata on the main output.
Frequently asked questions
What credential do I need to use this node?
You need a Google AI credential (credential type: googleAi) configured in BusyBot. This is a Google AI Studio API key, not a Google Cloud service account. Create one at ai.google.dev and add it to BusyBot's credential store before connecting the node.
Can I pass an image URL instead of uploading binary data?
Yes. The node accepts either binary data attached to the incoming workflow item — from a file download, file read, or form upload — or a URL you supply directly in the node's parameters. If you already have a publicly accessible image URL, you do not need to download the image first.
Which Gemini models are available, and does the choice matter for vision tasks?
The node supports Gemini 2.0 Flash and Gemini Pro models. Both are multimodal and handle image input, but Pro is generally more capable for complex reasoning tasks like detailed chart interpretation or nuanced scene description, while Flash is faster and cheaper for high-volume jobs like bulk OCR or simple classification.
What exactly does the node return on success?
On the Output branch, the node writes the model's analysis text to a configurable field on the item, along with the model name that was used and token usage metadata. The Error output fires instead if the API call fails, letting you handle failures separately without stopping the whole workflow.
What kinds of tasks is this node actually suited for versus tasks it cannot do?
Gemini Vision is built for visual understanding: extracting text from screenshots or scans (OCR), describing scenes or objects in photos, reading values from charts and graphs, and classifying images based on a prompt. It returns written analysis only — it does not edit images, generate images, or return structured JSON unless you write a prompt that instructs the model to format its response that way.
Build with the Gemini Vision node
Drop it into a workflow, wire it to an agent, or call it on a schedule. You'll need Google AI credentials first.
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