# DuckDuckGo Images Scraper - Low-costπŸ’²πŸ”₯πŸ–ΌοΈπŸ¦† (`delectable_incubator/duckduckgo-images-scraper-low-cost`) Actor Scrape DuckDuckGo image search results πŸ–ΌοΈπŸ” with a powerful image scraper. Extract image URLs, thumbnails, titles, source pages, domains, dimensions, rankings, and more. Ideal for visual research, SEO analysis, brand monitoring, content discovery, AI datasets, and creative inspiration πŸ“ŠπŸš€ - **URL**: https://apify.com/delectable\_incubator/duckduckgo-images-scraper-low-cost.md - **Developed by:** [Prime Scrape](https://apify.com/delectable_incubator) (community) - **Categories:** Social media, SEO tools, Videos - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.00005 / actor start This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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

DuckDuckGo Images Scraper

--- ## DuckDuckGo Images Scraper πŸ¦†πŸ–ΌοΈπŸ” The DuckDuckGo Images Scraper is a powerful and scalable Apify Actor designed to extract structured image search results directly from DuckDuckGo Images. It enables image research, visual SEO analysis, brand monitoring, media discovery, content creation workflows, AI training dataset collection, image trend analysis, and structured visual search data extraction from one of the world's most privacy-focused search engines. --- ### 🎯 What This Scraper Does Simply provide one or more keywords and the scraper handles everything automatically. βœ… Extracts DuckDuckGo image search results βœ… Supports multiple keywords in a single run βœ… Supports regional image searches βœ… Supports image size filtering βœ… Supports date/time filtering βœ… Captures image metadata and dimensions βœ… Extracts source URLs and domains βœ… Collects thumbnails and full image URLs βœ… Generates clean and structured datasets βœ… Ready for SEO, analytics, research, and automation workflows βœ… Export-ready output format --- ### πŸ“Š Data Extracted #### πŸ–ΌοΈ Image Information | Field | Description | |---------|---------| | πŸ” keyword | Search keyword | | πŸ“ position | Position in image search results | | πŸ“ title | Image title | | πŸ–ΌοΈ imageUrl | Full image URL | | πŸ–ΌοΈ thumbnailUrl | Thumbnail image URL | | πŸ”— sourceUrl | Original source page URL | | 🌐 domain | Source website domain | | πŸ“ width | Image width | | πŸ“ height | Image height | | πŸ“ imageSize | Applied image size filter | | πŸ•’ searchTimestamp | Search execution timestamp | --- ### πŸ›  How to Use #### 1️⃣ Configure Input Provide one or multiple keywords: ```` { "keywords": \[ "Fashion", "Technology", "Nature" ], "TIME\_FILTER": "month", "IMAGE\_SIZE": "large", "MAX\_ITEMS\_PER\_KEYWORD": 20, "REGION": "us-en" } ``` #### 2️⃣ Run the Actor β€’ Opens DuckDuckGo Images search results β€’ Processes image listings automatically β€’ Extracts image metadata β€’ Collects image URLs and source domains β€’ Applies selected filters β€’ Applies maximum result limits β€’ Generates structured datasets #### 3️⃣ Export the Dataset Download results in multiple formats: βœ… JSON βœ… CSV βœ… Excel βœ… XML βœ… HTML --- ### βš™οΈ Input Configuration #### πŸ“₯ Input Example ``` { "keywords": \[ "Fashion", "Technology", "Nature" ], "TIME\_FILTER": "month", "IMAGE\_SIZE": "large", "MAX\_ITEMS\_PER\_KEYWORD": 20, "REGION": "us-en" } ``` #### Input Fields | Field | Type | Description | |---------|---------|---------| | keywords | array | List of search keywords | | TIME_FILTER | string | Time filter (any, day, week, month, year) | | IMAGE_SIZE | string | Image size filter | | MAX_ITEMS_PER_KEYWORD | integer | Maximum images collected per keyword | | REGION | string | DuckDuckGo region code | --- ### πŸ“€ Output Example ``` { "keyword": "Nature", "position": 1, "title": "Mountain Landscape at Sunrise", "imageUrl": "https://example.com/image.jpg", "thumbnailUrl": "https://example.com/thumb.jpg", "sourceUrl": "https://example.com/article", "domain": "example.com", "width": 1920, "height": 1080, "imageSize": "large", "searchTimestamp": "2026-02-08T15:10:45Z" } ```` --- ### πŸ“Š Output Explanation | Use Case | Description | |------------|------------| | πŸ–ΌοΈ Image Research | Collect visual content at scale | | 🎨 Content Creation | Discover images for creative workflows | | πŸ” Visual SEO | Analyze image search rankings | | 🏷️ Brand Monitoring | Track branded imagery across the web | | πŸ€– AI & ML Datasets | Build image datasets for machine learning | | πŸ“ˆ Trend Analysis | Monitor image trends and topics | | ⚑ Automation Pipelines | Feed image data into automated workflows | --- ### 🌍 Why Use This Scraper? πŸ–ΌοΈ Discover images at scale πŸ” Analyze DuckDuckGo image search results πŸ“ˆ Track image ranking positions 🏷️ Monitor brand-related imagery 🌐 Support multiple regions and filters ⚑ Fast and automated extraction πŸ€– Automation-ready structured output πŸ“¦ Bulk keyword support 🧠 Ideal for marketers, researchers, SEO specialists, journalists, agencies, and data analysts πŸš€ Scalable for both small and enterprise workloads --- ### ❓ FAQ #### How does this scraper work? The scraper performs image searches on DuckDuckGo Images and extracts structured information including image titles, URLs, thumbnails, source pages, domains, dimensions, and ranking positions. #### Can I scrape multiple keywords in one run? Yes. You can provide multiple keywords inside the `keywords` array. #### Does the scraper support image size filters? Yes. You can choose from available image size filters before running the Actor. #### Can I filter results by date? Yes. The Actor supports day, week, month, and year filtering options. #### Does the scraper collect image URLs? Yes. Both full image URLs and thumbnail URLs are extracted. #### Can I monitor image results over time? Yes. You can schedule recurring runs through Apify to monitor image ranking changes and new visual content. #### Is the data collected live? Yes. Data is extracted directly from DuckDuckGo Images during every run. #### What export formats are supported? JSON, CSV, Excel, XML, and HTML. #### Can I use the extracted data commercially? Yes. The extracted data can be used for analytics, research, automation, monitoring, content intelligence, and commercial applications. #### What happens if the scraper fails? The Actor includes retry mechanisms and automated error handling to improve reliability. #### How long does a run take? Most extractions complete within minutes depending on the number of keywords and requested results. --- ### πŸš€ How to Use 1️⃣ Sign up β€” Create a free Apify account 2️⃣ Find the tool β€” Search for "DuckDuckGo Images Scraper" in the Apify Store 3️⃣ Configure keywords β€” Add one or multiple search keywords 4️⃣ Run it β€” Start the Actor and wait for extraction 5️⃣ Export data β€” Download results in JSON, CSV, Excel, XML, or HTML --- ### ⚠️ Disclaimer This tool is an independent solution and is not affiliated with, endorsed by, or sponsored by DuckDuckGo. All trademarks, logos, and brand names belong to their respective owners. --- ### πŸ’Έ Pricing This scraper runs on a **pay per events subscription model**. You only pay for **successful runs**. πŸ’³ **Price:** $4.99 / 1000 results --- ### Related Actors If you're interested in other Search Engine, SEO, Lead Generation, Research, Jobs, Real Estate, Marketplace, Social Media, and Data Collection scraping solutions, explore more tools: *(Coming soon)* --- ### πŸ“¬ Support ⭐⭐⭐⭐⭐ Leave a 5-star rating if you like this tool. --- ### 🌍 PrimeScrape Built for scalable web data extraction & automation. Contact us for custom scraping solutions or enterprise requests via Apify or by email. # Actor input Schema ## `keywords` (type: `array`): List of keywords or subjects to search for images. Each keyword will be scraped separately. ## `TIME_FILTER` (type: `string`): Filter images by date ## `IMAGE_SIZE` (type: `string`): Filter images by size ## `MAX_ITEMS_PER_KEYWORD` (type: `integer`): Maximum number of images to extract per keyword ## `REGION` (type: `string`): Search region / country ## Actor input object example ```json { "keywords": [ "Fashion", "Technology", "Nature" ], "TIME_FILTER": "any", "IMAGE_SIZE": "any", "MAX_ITEMS_PER_KEYWORD": 50, "REGION": "wt-wt" } ```` # Actor output Schema ## `overview` (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 = { "keywords": [ "Fashion", "Technology", "Nature" ], "TIME_FILTER": "any", "IMAGE_SIZE": "any", "MAX_ITEMS_PER_KEYWORD": 50, "REGION": "wt-wt" }; // Run the Actor and wait for it to finish const run = await client.actor("delectable_incubator/duckduckgo-images-scraper-low-cost").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = { "keywords": [ "Fashion", "Technology", "Nature", ], "TIME_FILTER": "any", "IMAGE_SIZE": "any", "MAX_ITEMS_PER_KEYWORD": 50, "REGION": "wt-wt", } # Run the Actor and wait for it to finish run = client.actor("delectable_incubator/duckduckgo-images-scraper-low-cost").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "keywords": [ "Fashion", "Technology", "Nature" ], "TIME_FILTER": "any", "IMAGE_SIZE": "any", "MAX_ITEMS_PER_KEYWORD": 50, "REGION": "wt-wt" }' | apify call delectable_incubator/duckduckgo-images-scraper-low-cost --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=delectable_incubator/duckduckgo-images-scraper-low-cost", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "DuckDuckGo Images Scraper - Low-costπŸ’²πŸ”₯πŸ–ΌοΈπŸ¦†", "description": "Scrape DuckDuckGo image search results πŸ–ΌοΈπŸ” with a powerful image scraper. Extract image URLs, thumbnails, titles, source pages, domains, dimensions, rankings, and more. Ideal for visual research, SEO analysis, brand monitoring, content discovery, AI datasets, and creative inspiration πŸ“ŠπŸš€", "version": "0.0", "x-build-id": "puLRkMN48AFFyJSBS" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/delectable_incubator~duckduckgo-images-scraper-low-cost/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-delectable_incubator-duckduckgo-images-scraper-low-cost", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/delectable_incubator~duckduckgo-images-scraper-low-cost/runs": { "post": { "operationId": "runs-sync-delectable_incubator-duckduckgo-images-scraper-low-cost", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/delectable_incubator~duckduckgo-images-scraper-low-cost/run-sync": { "post": { "operationId": "run-sync-delectable_incubator-duckduckgo-images-scraper-low-cost", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keywords", "TIME_FILTER", "IMAGE_SIZE", "MAX_ITEMS_PER_KEYWORD" ], "properties": { "keywords": { "title": "Keywords or subjects you wish to search for πŸ”πŸ–ΌοΈ", "type": "array", "description": "List of keywords or subjects to search for images. Each keyword will be scraped separately.", "items": { "type": "string" }, "default": [ "Fashion", "Technology", "Nature" ] }, "TIME_FILTER": { "title": "Time Filter", "enum": [ "any", "day", "week", "month", "year" ], "type": "string", "description": "Filter images by date", "default": "any" }, "IMAGE_SIZE": { "title": "Image Size", "enum": [ "any", "small", "medium", "large", "wallpaper" ], "type": "string", "description": "Filter images by size", "default": "any" }, "MAX_ITEMS_PER_KEYWORD": { "title": "Max Items Per Keyword", "type": "integer", "description": "Maximum number of images to extract per keyword", "default": 50 }, "REGION": { "title": "Region 🌍", "enum": [ "wt-wt", "xa-ar", "xa-en", "ar-es", "au-en", "at-de", "be-fr", "be-nl", "br-pt", "bg-bg", "ca-en", "ca-fr", "ct-ca", "cl-es", "cn-zh", "co-es", "hr-hr", "cz-cs", "dk-da", "ee-et", "fi-fi", "fr-fr", "de-de", "gr-el", "hk-tzh", "hu-hu", "in-en", "id-en", "id-id", "ie-en", "it-it", "jp-jp", "kr-kr", "lv-lv", "lt-lt", "xl-es", "my-en", "my-ms", "mx-es", "nl-nl", "nz-en", "no-no", "pe-es", "ph-en", "ph-tl", "pl-pl", "pt-pt", "ro-ro", "ru-ru", "sg-en", "sk-sk", "sl-sl", "za-en", "es-ca", "es-es", "se-sv", "ch-de", "ch-fr", "tw-tzh", "th-en", "th-th", "tr-tr", "ua-uk", "uk-en", "us-en", "us-es", "ve-es", "vn-en", "vn-vi" ], "type": "string", "description": "Search region / country", "default": "wt-wt" } } }, "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 } } } } } } } } } } ```