# Bing Images Scraper (`searchapi/bing-images-scraper`) Actor Scrapes image results from Bing Images search including titles, image URLs, thumbnails, source URLs, dimensions, file format, and rich metadata. - **URL**: https://apify.com/searchapi/bing-images-scraper.md - **Developed by:** [Search API](https://apify.com/searchapi) (community) - **Categories:** Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.99 / 1,000 bing images scraper results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 ## Bing Images Scraper ### What is this tool? Bing Images Scraper helps you collect images data from Bing (Microsoft's search engine). Just type in what you're looking for, and the tool will gather images with their full-size URLs, thumbnails, dimensions, file formats, and the websites where they were published. ### What can you use it for? - Find images on any topic - Visual research and design inspiration - Build image collections for content, reports, or presentations - Track visual trends ### What data do you get? Each result includes up to **38 fields**, organized into the following categories: **Identity** (7 fields): Type, Result Type, Position, Page, Thumbnail ID (e.g. OIP.*), Image ID (Bing mid), Content ID (Bing ccid) **URLs** (8 fields): Search URL, Image URL (full resolution), Thumbnail URL, Host Page URL, Source URL (alias of hostPageUrl), Host Page Domain, Creator URL, Favicon URL **Text** (5 fields): Title, Alt Text, Description, Source Name, Is Adult Content **Media** (1 fields): Creator / Photographer **Image Details** (7 fields): Width (px), Height (px), Aspect Ratio (width/height), File Format (e.g. jpg, webp, png), File Size (bytes), Is Animated (GIF/WebP animated), Dominant Color **Location** (1 fields): Country **Search Context** (4 fields): Search Query, Query (legacy alias of searchQuery), Language, Search Metadata **Other** (5 fields): Scraped At (ISO 8601), Cache Key (Bing ck), MD5 Hash, License, Additional Info ### How to use it #### Step 1: Enter your search Type in your search term, like "nature photography". #### Step 2: Set your preferences Adjust the options below to customize your search. #### Step 3: Run the tool Click the run button and wait for your results. The tool will gather all the data and save it in a clean, organized format. ### Input options | Option | What it means | Required | Default | |--------|---------------|----------|---------| | **Search Query** | The search query to use for Bing Images. | Yes | nature photography | | **Maximum Items** | Max image results to scrape. | No | 100 | | **Image Size Filter** | Filter images by size. Options: any (Any Size), small (Small), medium (Medium), large (Large), wallpaper (Wallpaper). | No | any | ### Output Your results come in a clean, organized table with **two views**: #### Overview view Shows the most important fields at a glance: - **Image URL (full resolution)** - **Title** - **Thumbnail URL** - **Host Page URL** - **Source Name** - **Width (px)** - **Height (px)** - **File Format (e.g. jpg, webp, png)** - **Position** - **Page** - **Search Query** - **Scraped At (ISO 8601)** #### Full details view Shows all 38 fields in detail. See "What data do you get?" above for the complete list. ### Tips for best results ✅ **Be specific** with your search terms for more relevant results ✅ **Try different variations** of your search term to discover more results ✅ **Browse multiple pages** of results to get a wider variety ### Frequently asked questions **Why use Bing?** Bing is Microsoft's search engine and the second-largest search engine in the world. It often returns different results than Google, giving you a wider variety of websites and perspectives. **How do I get the results?** Your results are saved in your Apify dashboard. You can download them in various formats (JSON, CSV, Excel) or access them via the API. **Can I run it on a schedule?** Yes! Apify supports scheduled runs. You can set up the tool to run automatically at any interval (hourly, daily, weekly, etc.) from your dashboard. **What if I get fewer results than I asked for?** This can happen if the search engine doesn't have enough matching results for your search, or if some results are blocked. Try different keywords or relax your filters. **Need more help?** If you have questions or run into issues, our support team is ready to help. Just reach out through the Apify platform and we'll get back to you quickly. # Actor input Schema ## `query` (type: `string`): The search query to use for Bing Images. ## `maxItems` (type: `integer`): Maximum number of image results to scrape. ## `imageSize` (type: `string`): Filter images by size. ## `proxyConfiguration` (type: `object`): Select proxies to be used by the scraper. ## Actor input object example ```json { "query": "nature photography", "maxItems": 100, "imageSize": "any", "proxyConfiguration": { "useApifyProxy": true } } ```` # 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 = { "query": "nature photography", "maxItems": 100, "imageSize": "any", "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("searchapi/bing-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 = { "query": "nature photography", "maxItems": 100, "imageSize": "any", "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("searchapi/bing-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 '{ "query": "nature photography", "maxItems": 100, "imageSize": "any", "proxyConfiguration": { "useApifyProxy": true } }' | apify call searchapi/bing-images-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=searchapi/bing-images-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Bing Images Scraper", "description": "Scrapes image results from Bing Images search including titles, image URLs, thumbnails, source URLs, dimensions, file format, and rich metadata.", "version": "2.0", "x-build-id": "YhkPzRRAIoKpESFlE" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/searchapi~bing-images-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-searchapi-bing-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/searchapi~bing-images-scraper/runs": { "post": { "operationId": "runs-sync-searchapi-bing-images-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/searchapi~bing-images-scraper/run-sync": { "post": { "operationId": "run-sync-searchapi-bing-images-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "query" ], "properties": { "query": { "title": "Search Query", "type": "string", "description": "The search query to use for Bing Images." }, "maxItems": { "title": "Maximum Items", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of image results to scrape.", "default": 100 }, "imageSize": { "title": "Image Size Filter", "enum": [ "any", "small", "medium", "large", "wallpaper" ], "type": "string", "description": "Filter images by size.", "default": "any" }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Select proxies to be used by the scraper." } } }, "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 } } } } } } } } } } ```