# Wikimedia Commons Scraper — Free Images & Media (No Key) (`ninhothedev/wikimedia-commons-scraper`) Actor $0.5/1K 🔥 Fast Wikimedia Commons scraper! Free images & media by search — url, author, license & description. No key. JSON, CSV, Excel or API in seconds. Pull thousands of free-to-use media for datasets & design ⚡ - **URL**: https://apify.com/ninhothedev/wikimedia-commons-scraper.md - **Developed by:** [ninhothedev](https://apify.com/ninhothedev) (community) - **Categories:** Developer tools, Videos - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.50 / 1,000 results 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 ## Wikimedia Commons Scraper — Free Images & Media (No API Key) Scrape **free, reusable images and media from [Wikimedia Commons](https://commons.wikimedia.org/)** at scale. Give the Actor a list of search terms and it returns clean, structured records for every matching file — direct file URL, author/artist, license, usage terms, description, categories, dimensions and more. No API key, no login, datacenter-proxy friendly. Wikimedia Commons hosts **100M+ freely licensed media files** (public domain and Creative Commons). This Actor turns that library into a ready-to-use dataset for image sourcing, machine-learning pipelines, content and design workflows, and research. ### Features - **No API key required** — uses the public MediaWiki API. - **Datacenter-proxy friendly** — clean JSON endpoint, no browser needed. - **Rich metadata** — direct URL, artist, license, usage terms, description, credit, categories, dimensions, MIME type, uploader. - **HTML cleaned** — author/description/credit fields are stripped of HTML for direct use. - **Multi-query** — pass many search terms in one run. - **Cheap & fast** — lightweight HTTP requests, pay only for what you collect. ### Input | Field | Type | Description | |-------|------|-------------| | `mode` | select | Scraping mode. `search` runs your text queries against the Commons file namespace. | | `queries` | array | One or more search terms (e.g. `["sunset", "golden gate bridge"]`). | | `maxItems` | integer | Max total media files to collect (default 100, max 1000). | #### Example input ```json { "mode": "search", "queries": ["sunset", "golden gate bridge"], "maxItems": 100 } ```` ### Output Each item is one media file: ```json { "page_id": 312884, "title": "File:Sunset 2007-1.jpg", "url": "https://upload.wikimedia.org/wikipedia/commons/5/58/Sunset_2007-1.jpg", "description_url": "https://commons.wikimedia.org/wiki/File:Sunset_2007-1.jpg", "width": 2789, "height": 1980, "size": 1327087, "mime": "image/jpeg", "uploader": "Alvesgaspar", "artist": "Alvesgaspar", "license": "CC BY-SA 3.0", "usage_terms": "Creative Commons Attribution-Share Alike 3.0", "description": "Sunset at Porto Covo, Portugal.", "credit": "Own work", "date": "2007", "categories": ["Sunsets", "Porto Covo"], "source": "wikimedia_commons", "scraped_at": "2026-07-20T00:00:00+00:00" } ``` ### Pricing Priced to be cheap: roughly **~$0.5 per 1,000 media files**. You only pay for the items you collect. Costs scale linearly with `maxItems`. ### Use cases - **Free image sourcing** — find openly licensed images for blogs, apps and products. - **ML / AI datasets** — build labelled image datasets with license provenance baked in. - **Content & design** — pull illustrations, photos and diagrams with proper attribution. - **Research** — study licensing, categories and media metadata across topics. ### Licensing note Files on Wikimedia Commons are freely licensed, but many require **attribution** or share-alike terms. Always respect each file's `license` / `usage_terms` and credit the `artist` when required. ### Related Actors - [Openverse Media Scraper](https://apify.com/ninhothedev/openverse-media-scraper) - [Met Museum Scraper](https://apify.com/ninhothedev/met-museum-scraper) - [Art Institute of Chicago Scraper](https://apify.com/ninhothedev/art-institute-chicago-scraper) - [Wikipedia Scraper](https://apify.com/ninhothedev/wikipedia-scraper) ### Keywords wikimedia commons scraper, free images api, creative commons images, public domain images, image dataset, media scraper, stock photos free, openverse alternative, ml training images, no api key image scraper *** Questions or a custom media source? Reach out on the Actor's Apify page. # Actor input Schema ## `mode` (type: `string`): Scraping mode. Currently 'search' runs your text queries against the Wikimedia Commons file namespace and returns matching media files. ## `queries` (type: `array`): One or more search terms. Each query is run against Wikimedia Commons and returns matching free images/media files. ## `maxItems` (type: `integer`): Maximum total number of media files to collect across all queries. ## Actor input object example ```json { "mode": "search", "queries": [ "sunset", "golden gate bridge" ], "maxItems": 100 } ``` # 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 = { "queries": [ "sunset", "golden gate bridge" ] }; // Run the Actor and wait for it to finish const run = await client.actor("ninhothedev/wikimedia-commons-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 = { "queries": [ "sunset", "golden gate bridge", ] } # Run the Actor and wait for it to finish run = client.actor("ninhothedev/wikimedia-commons-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 '{ "queries": [ "sunset", "golden gate bridge" ] }' | apify call ninhothedev/wikimedia-commons-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=ninhothedev/wikimedia-commons-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wikimedia Commons Scraper — Free Images & Media (No Key)", "description": "$0.5/1K 🔥 Fast Wikimedia Commons scraper! Free images & media by search — url, author, license & description. No key. JSON, CSV, Excel or API in seconds. Pull thousands of free-to-use media for datasets & design ⚡", "version": "0.1", "x-build-id": "aDWdSCc8acZxkYCta" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ninhothedev~wikimedia-commons-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ninhothedev-wikimedia-commons-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/ninhothedev~wikimedia-commons-scraper/runs": { "post": { "operationId": "runs-sync-ninhothedev-wikimedia-commons-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/ninhothedev~wikimedia-commons-scraper/run-sync": { "post": { "operationId": "run-sync-ninhothedev-wikimedia-commons-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": [ "queries" ], "properties": { "mode": { "title": "Mode", "enum": [ "search" ], "type": "string", "description": "Scraping mode. Currently 'search' runs your text queries against the Wikimedia Commons file namespace and returns matching media files.", "default": "search" }, "queries": { "title": "Search queries", "type": "array", "description": "One or more search terms. Each query is run against Wikimedia Commons and returns matching free images/media files.", "default": [ "sunset", "golden gate bridge" ], "items": { "type": "string" } }, "maxItems": { "title": "Max items", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum total number of media files to collect across all queries.", "default": 100 } } }, "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 } } } } } } } } } } ```