# Google Images Scraper (`thescrappa/google-images-scraper`) Actor Search Google Images with Scrappa. Extract image thumbnails, original image URLs, source pages, dimensions, titles, product flags, filters, and localization metadata. - **URL**: https://apify.com/thescrappa/google-images-scraper.md - **Developed by:** [Scrappa](https://apify.com/thescrappa) (community) - **Categories:** Lead generation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## Google Images Scraper Search Google Images and export dataset-ready image metadata for product research, visual SEO, brand monitoring, content sourcing, and machine learning dataset collection. The actor wraps Scrappa's `/api/images` endpoint. ### What you get - Image result title, source website, source page URL, thumbnail URL, and original image URL - Original image width and height when Google exposes dimensions - Filters for size, type, color, aspect ratio, safe search, language, country, and time-based `tbs` - Full Scrappa response saved to key-value store record `OUTPUT` ### Input ```json { "q": "coffee product photography", "page": 1, "hl": "en", "gl": "us", "imgsz": "large", "imgtype": "photo", "imgcolor": "white", "imgar": "wide", "safe": "active" } ```` ### Output Each dataset item is one Google Images result: ```json { "position": 1, "thumbnail_url": "https://encrypted-tbn0.gstatic.com/images?q=tbn:...", "image_url": "https://example.com/images/coffee.jpg", "title": "Coffee product photo", "source": "Example Store", "source_url": "https://example.com/coffee", "width": 1920, "height": 1280, "is_product": false, "request_q": "coffee product photography", "request_gl": "us", "request_hl": "en", "request_imgsz": "large" } ``` ### Notes For higher-volume Google Images collection or direct API access, use Scrappa's Google Images API at `https://scrappa.co/api/images`. # Actor input Schema ## `q` (type: `string`): Keyword or phrase to search in Google Images. ## `page` (type: `integer`): Page number for pagination. Page 1 starts at the first image result. ## `hl` (type: `string`): Two-letter interface language code, such as en, de, es, or fr. ## `gl` (type: `string`): Two-letter country code for localized Google Images results, such as us, gb, de, or jp. ## `imgsz` (type: `string`): Filter by image size. ## `imgtype` (type: `string`): Filter by image type. ## `imgcolor` (type: `string`): Filter by dominant image color. ## `imgar` (type: `string`): Filter by image aspect ratio. ## `tbs` (type: `string`): Google tbs filter syntax, such as qdr:d for past day, qdr:w for past week, qdr:m for past month, or qdr:y for past year. ## `safe` (type: `string`): Safe search filtering. ## Actor input object example ```json { "q": "coffee product photography", "page": 1, "hl": "en", "gl": "us", "safe": "active" } ``` # 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 = { "q": "coffee product photography" }; // Run the Actor and wait for it to finish const run = await client.actor("thescrappa/google-images-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 = { "q": "coffee product photography" } # Run the Actor and wait for it to finish run = client.actor("thescrappa/google-images-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 '{ "q": "coffee product photography" }' | apify call thescrappa/google-images-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=thescrappa/google-images-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Google Images Scraper", "description": "Search Google Images with Scrappa. Extract image thumbnails, original image URLs, source pages, dimensions, titles, product flags, filters, and localization metadata.", "version": "1.0", "x-build-id": "psT5nsFjo6WkUpw3q" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/thescrappa~google-images-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-thescrappa-google-images-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/thescrappa~google-images-scraper/runs": { "post": { "operationId": "runs-sync-thescrappa-google-images-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/thescrappa~google-images-scraper/run-sync": { "post": { "operationId": "run-sync-thescrappa-google-images-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "q" ], "properties": { "q": { "title": "Search Query", "type": "string", "description": "Keyword or phrase to search in Google Images." }, "page": { "title": "Page", "minimum": 1, "type": "integer", "description": "Page number for pagination. Page 1 starts at the first image result.", "default": 1 }, "hl": { "title": "Language", "pattern": "^[A-Za-z]{2}$", "minLength": 2, "maxLength": 2, "type": "string", "description": "Two-letter interface language code, such as en, de, es, or fr.", "default": "en" }, "gl": { "title": "Country", "pattern": "^[A-Za-z]{2}$", "minLength": 2, "maxLength": 2, "type": "string", "description": "Two-letter country code for localized Google Images results, such as us, gb, de, or jp.", "default": "us" }, "imgsz": { "title": "Image Size", "enum": [ "large", "medium", "icon" ], "type": "string", "description": "Filter by image size." }, "imgtype": { "title": "Image Type", "enum": [ "photo", "clipart", "lineart", "gif", "face" ], "type": "string", "description": "Filter by image type." }, "imgcolor": { "title": "Image Color", "enum": [ "color", "gray", "trans", "red", "orange", "yellow", "green", "teal", "blue", "purple", "pink", "white", "black", "brown" ], "type": "string", "description": "Filter by dominant image color." }, "imgar": { "title": "Aspect Ratio", "enum": [ "tall", "square", "wide" ], "type": "string", "description": "Filter by image aspect ratio." }, "tbs": { "title": "Advanced Filters", "maxLength": 500, "type": "string", "description": "Google tbs filter syntax, such as qdr:d for past day, qdr:w for past week, qdr:m for past month, or qdr:y for past year." }, "safe": { "title": "Safe Search", "enum": [ "active", "off" ], "type": "string", "description": "Safe search filtering.", "default": "active" } } }, "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 } } } } } } } } } } ```