# Google Lens Scraper — Reverse Image Search (`scrape.badger/google-lens-scraper`) Actor
Reverse image search at scale via Google Lens: visual matches, exact matches, and shoppable product matches with price chips. Feed any public image URL, get structured JSON — title, source, source\_favicon, thumbnail, price tag, in-stock flag, plus related search chips.
- **URL**: https://apify.com/scrape.badger/google-lens-scraper.md
- **Developed by:** [Scrape Badger](https://apify.com/scrape.badger) (community)
- **Categories:** AI, E-commerce, Developer tools
- **Stats:** 3 total users, 1 monthly users, 0.0% runs succeeded, NaN bookmarks
- **User rating**: No ratings yet
## Pricing
from $6.00 / 1,000 lens images
This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".
## How to integrate an Actor?
If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md).
For usage examples, see the [API](#api) section below.
For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).
# README
### What does Google Lens Scraper do?
Reverse image search with [Google Lens](https://lens.google.com). Submit an image URL — get back visual matches, exact matches, shopping matches, and related searches.
### Why use Google Lens Scraper?
- **Visual + exact + shopping matches.** Every match type Lens surfaces, tagged `visual_match` / `exact_match` / `shopping_match`.
- **Related search chips.** Google's suggested follow-up queries, emitted as `type: related_search` records.
- **No Apify image upload needed.** Pass a URL — we fetch it.
- **Shopping integration.** Shopping matches include price, merchant, `gpcid` (feed into `google-products-scraper`).
- **No CAPTCHAs reach you.** Lens's JS challenge is handled by ScrapeBadger's browser farm.
### What data can Google Lens Scraper extract?
| Field | Type | Description |
|---|---|---|
| type | string | `visual_match` / `exact_match` / `shopping_match` / `related_search` |
| title | string | Match description |
| link | string | Source page URL |
| source | string | Host website |
| thumbnail | string | Match image URL |
| price | object | Shopping match: `{value, currency, extracted}` |
### How to scrape Google Lens
1. Click **Try for free**.
2. Paste the **image URL** (must be publicly reachable).
3. Optional: add a text `query` to constrain results, set `country` / `language`.
4. Toggle `visual_matches` / `exact_matches` (default: both on).
5. Click **Start** — matches stream into the dataset.
### How much will it cost?
**$0.006 per image (≈ $6 per 1,000 images).** One call per image. Shopping + visual + related search chips are all included — no extra credits.
#### Competitor benchmark
| Actor | Author | Price | Notes |
|---|---|---|---|
| zen-studio/google-lens-scraper | zen-studio | ~$7.99 / 1k images | Pay-per-event |
| newyear/google-lens-scraper | newyear | $15 /mo subscription | Deprecated |
| **scrape-badger/google-lens-scraper** | **ScrapeBadger** | **$6 / 1k images** | **25% under zen-studio, no subscription** |
### Input
Configure the run in the **Input** tab above, or pass a JSON object matching the fields below when calling the Actor via the Apify API.
| Field | Required | Description |
|---|---|---|
| image_url | ✅ | Publicly reachable image URL. |
| query | — | Optional text constraint. |
| product | — | Pre-narrow to shopping matches. |
| visual_matches | — | Default `true`. |
| exact_matches | — | Default `false` — slower. |
| country / language | — | Defaults `us` / `en`. |
### Output
Every successful run streams records into the run's dataset. Download as JSON, CSV, XML, Excel, or HTML from the **Dataset** tab; consume programmatically via the Apify API or webhooks.
Example record:
```json
[
{
"type": "visual_match",
"title": "Red running shoes \u2014 Nike Pegasus 41",
"link": "https://example.com/shoes",
"source": "example.com",
"thumbnail": "https://encrypted-tbn0.gstatic.com/\u2026"
},
{
"type": "shopping_match",
"title": "Nike Pegasus 41 Men's",
"link": "https://nike.com/\u2026",
"source": "Nike.com",
"thumbnail": "https://\u2026",
"price": {
"value": "$140.00",
"currency": "USD",
"extracted": 140.0
}
},
{
"type": "related_search",
"title": "Running shoes for marathons"
}
]
````
### Tips / Advanced options
- **Cache your source images.** Google's CDN returns 404 if the source URL disappears mid-run. Host on S3 / Cloudflare for reliability.
- **Shopping matches ship with `gpcid`.** Pipe them through `google-products-scraper` for full specs, offers, variants.
- **`exact_matches` is slower.** Only enable when you need to track visually identical images across the web (copyright / brand monitoring).
- **Related searches are cheap ideation fuel.** For SEO content research, related chips often beat autocomplete for breadth.
### FAQ, Disclaimers, Support
#### Does it work with private / auth'd images?
No — Google Lens needs to fetch the image; it must be public.
#### Can I upload a local file?
Not directly from the Apify input. Host your image on S3 / Cloudflare / imgur and pass the URL.
#### What's a `visual match` vs. `exact match`?
Visual = visually similar (same scene, different angle). Exact = the same image file (copyright / reuse tracking).
#### How big can the image be?
Google Lens accepts up to ~10 MB per image. Larger images are downscaled server-side.
#### Disclaimer
This Actor scrapes public Google data only. You're responsible for compliance with Google's Terms of Service and any applicable data-protection laws (GDPR, CCPA, etc.) in your jurisdiction. ScrapeBadger does not store the scraped results — they are delivered directly to your Apify dataset.
#### Support
Something not working? Open a ticket in the **Issues** tab above — we triage within one business day. Full API reference: [docs.scrapebadger.com](https://docs.scrapebadger.com).
#### Related Actors
- [`google-images-scraper`](https://apify.com/scrape-badger/google-images-scraper) — Text-based image search
- [`google-products-scraper`](https://apify.com/scrape-badger/google-products-scraper) — Deep product detail from `gpcid`
#### Powered by
[ScrapeBadger](https://scrapebadger.com) — Google-optimised residential proxy pool + browser-farm fallback, 99.7% uptime, unmetered bandwidth. No CAPTCHAs reach you.
# Actor input Schema
## `image_url` (type: `string`):
Public URL of the image to reverse-search. Example: https://example.com/photo.jpg. Google must be able to fetch it — no auth-gated / private-bucket URLs.
## `query` (type: `string`):
Optional text query to bias Lens towards a specific category (e.g. pizza, sneakers, vintage dress). Optional.
## `product` (type: `boolean`):
When true, Lens biases results toward shoppable product matches — each tile carries a tag (price chip) and in\_stock flag. Perfect for product-identification / reverse-image-to-SKU workflows.
## `visual_matches` (type: `boolean`):
Include the standard Visual matches carousel (pages / sources using the same or a similar image). Default on — matches Google's default Lens bucket.
## `exact_matches` (type: `boolean`):
Restrict results to Google's exact-match bucket (same image, not just similar). Useful for image-copyright / leak detection.
## `country` (type: `string`):
ISO 3166-1 alpha-2 country code (lowercase). Controls which country's Lens results are returned. Example: us, gb, de.
## `language` (type: `string`):
ISO 639-1 language code. Example: en, es, de.
## Actor input object example
```json
{
"image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/PNG_transparency_demonstration_1.png/280px-PNG_transparency_demonstration_1.png",
"product": false,
"visual_matches": true,
"exact_matches": false,
"country": "us",
"language": "en"
}
```
# Actor output Schema
## `dataset` (type: `string`):
No description
# API
You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.
## JavaScript example
```javascript
import { ApifyClient } from 'apify-client';
// Initialize the ApifyClient with your Apify API token
// Replace the '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/PNG_transparency_demonstration_1.png/280px-PNG_transparency_demonstration_1.png"
};
// Run the Actor and wait for it to finish
const run = await client.actor("scrape.badger/google-lens-scraper").call(input);
// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
console.dir(item);
});
// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs
```
## Python example
```python
from apify_client import ApifyClient
# Initialize the ApifyClient with your Apify API token
# Replace '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = { "image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/PNG_transparency_demonstration_1.png/280px-PNG_transparency_demonstration_1.png" }
# Run the Actor and wait for it to finish
run = client.actor("scrape.badger/google-lens-scraper").call(run_input=run_input)
# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
print(item)
# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start
```
## CLI example
```bash
echo '{
"image_url": "https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/PNG_transparency_demonstration_1.png/280px-PNG_transparency_demonstration_1.png"
}' |
apify call scrape.badger/google-lens-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=scrape.badger/google-lens-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Google Lens Scraper — Reverse Image Search",
"description": "Reverse image search at scale via Google Lens: visual matches, exact matches, and shoppable product matches with price chips. Feed any public image URL, get structured JSON — title, source, source_favicon, thumbnail, price tag, in-stock flag, plus related search chips.",
"version": "1.0",
"x-build-id": "ZtoFk3Er0wOJanGAo"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/scrape.badger~google-lens-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-scrape.badger-google-lens-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/scrape.badger~google-lens-scraper/runs": {
"post": {
"operationId": "runs-sync-scrape.badger-google-lens-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/scrape.badger~google-lens-scraper/run-sync": {
"post": {
"operationId": "run-sync-scrape.badger-google-lens-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"required": [
"image_url"
],
"properties": {
"image_url": {
"title": "Image URL",
"type": "string",
"description": "Public URL of the image to reverse-search. Example: https://example.com/photo.jpg. Google must be able to fetch it — no auth-gated / private-bucket URLs."
},
"query": {
"title": "Text refinement",
"type": "string",
"description": "Optional text query to bias Lens towards a specific category (e.g. pizza, sneakers, vintage dress). Optional."
},
"product": {
"title": "Shopping-biased matches",
"type": "boolean",
"description": "When true, Lens biases results toward shoppable product matches — each tile carries a tag (price chip) and in_stock flag. Perfect for product-identification / reverse-image-to-SKU workflows.",
"default": false
},
"visual_matches": {
"title": "Visual matches",
"type": "boolean",
"description": "Include the standard Visual matches carousel (pages / sources using the same or a similar image). Default on — matches Google's default Lens bucket.",
"default": true
},
"exact_matches": {
"title": "Exact matches only",
"type": "boolean",
"description": "Restrict results to Google's exact-match bucket (same image, not just similar). Useful for image-copyright / leak detection.",
"default": false
},
"country": {
"title": "Country",
"type": "string",
"description": "ISO 3166-1 alpha-2 country code (lowercase). Controls which country's Lens results are returned. Example: us, gb, de.",
"default": "us"
},
"language": {
"title": "Language",
"type": "string",
"description": "ISO 639-1 language code. Example: en, es, de.",
"default": "en"
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```