# Wikipedia Scraper - Article Content Extractor (`lulzasaur/wikipedia-scraper`) Actor Scrape Wikipedia articles. Search by topic and extract full structured content: summaries, sections, infobox data, categories, references, images, and edit history for any article. - **URL**: https://apify.com/lulzasaur/wikipedia-scraper.md - **Developed by:** [lulz bot](https://apify.com/lulzasaur) (community) - **Categories:** E-commerce - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $10.00 / 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.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 ## Wikipedia Scraper Extract structured content from Wikipedia articles. Search by topic or provide direct article URLs to get summaries, full text, infobox data, categories, references, images, and metadata. ### Features - **Search by topic** — find the most relevant Wikipedia articles for any query - **Direct URL scraping** — provide specific article URLs for targeted extraction - **Structured content** — articles are parsed into sections with headers - **Infobox extraction** — key-value data from article infoboxes (e.g., programming language details, country stats) - **Multi-language** — supports all Wikipedia language editions (en, es, fr, de, ja, etc.) - **References** — extracted reference list from each article - **Categories & images** — article classification and image file names ### Input | Field | Type | Description | |-------|------|-------------| | `searchQueries` | string[] | Topics to search (e.g., "artificial intelligence", "JavaScript") | | `articleUrls` | string[] | Direct Wikipedia article URLs | | `maxArticles` | number | Max articles per search query (default: 5) | | `maxResults` | number | Max total results (default: 25) | | `language` | string | Wikipedia language code (default: "en") | | `extractSections` | boolean | Extract full section content (default: true) | ### Output Each article includes: ```json { "title": "JavaScript", "pageId": 9845, "summary": "JavaScript is a programming language...", "content": "Full article text...", "sections": [ { "title": "Introduction", "content": "..." }, { "title": "History", "content": "..." } ], "infobox": { "Paradigm": "Multi-paradigm", "Designed by": "Brendan Eich", "First appeared": "December 4, 1995" }, "categories": ["Programming languages", "Web development"], "images": ["File:JavaScript_code.png"], "thumbnail": "https://upload.wikimedia.org/...", "references": ["..."], "lastEdited": "2026-04-20T12:00:00Z", "url": "https://en.wikipedia.org/wiki/JavaScript", "language": "en" } ```` ### Use Cases - **Research** — gather structured data on any topic - **Knowledge bases** — build datasets from Wikipedia's encyclopedia - **NLP training data** — extract clean text with metadata - **Fact-checking** — cross-reference claims with Wikipedia sources - **Content enrichment** — add Wikipedia context to your applications ### Pricing This actor uses pay-per-event pricing at **$0.005 per article scraped**. ### Notes - Uses the official Wikipedia API (free, no auth required) - HTML is fetched for infobox and reference extraction - Rate-limited to be respectful of Wikipedia servers - Article content is capped at 50,000 characters per article # Actor input Schema ## `searchQueries` (type: `array`): Topics to search on Wikipedia (e.g. "artificial intelligence", "JavaScript", "climate change"). Returns the most relevant articles for each query. ## `articleUrls` (type: `array`): Direct Wikipedia article URLs to scrape (e.g. "https://en.wikipedia.org/wiki/JavaScript"). Takes priority over search queries. ## `maxArticles` (type: `integer`): Maximum number of articles to return per search query (1-50). ## `maxResults` (type: `integer`): Maximum total articles to scrape across all queries (1-500). ## `language` (type: `string`): Wikipedia language code (e.g. 'en', 'es', 'fr', 'de', 'ja'). ## `extractSections` (type: `boolean`): Extract full section-by-section content (not just summary). Provides richer data but uses more memory. ## `proxyConfiguration` (type: `object`): Optional proxy configuration for requests. ## Actor input object example ```json { "searchQueries": [ "artificial intelligence" ], "articleUrls": [], "maxArticles": 5, "maxResults": 25, "language": "en", "extractSections": 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 = { "searchQueries": [ "artificial intelligence" ] }; // Run the Actor and wait for it to finish const run = await client.actor("lulzasaur/wikipedia-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 = { "searchQueries": ["artificial intelligence"] } # Run the Actor and wait for it to finish run = client.actor("lulzasaur/wikipedia-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 '{ "searchQueries": [ "artificial intelligence" ] }' | apify call lulzasaur/wikipedia-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=lulzasaur/wikipedia-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wikipedia Scraper - Article Content Extractor", "description": "Scrape Wikipedia articles. Search by topic and extract full structured content: summaries, sections, infobox data, categories, references, images, and edit history for any article.", "version": "1.0", "x-build-id": "hFOKJDMQQSJGmocXy" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/lulzasaur~wikipedia-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-lulzasaur-wikipedia-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/lulzasaur~wikipedia-scraper/runs": { "post": { "operationId": "runs-sync-lulzasaur-wikipedia-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/lulzasaur~wikipedia-scraper/run-sync": { "post": { "operationId": "run-sync-lulzasaur-wikipedia-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "searchQueries": { "title": "Search Queries", "type": "array", "description": "Topics to search on Wikipedia (e.g. \"artificial intelligence\", \"JavaScript\", \"climate change\"). Returns the most relevant articles for each query.", "default": [], "items": { "type": "string" } }, "articleUrls": { "title": "Article URLs", "type": "array", "description": "Direct Wikipedia article URLs to scrape (e.g. \"https://en.wikipedia.org/wiki/JavaScript\"). Takes priority over search queries.", "default": [], "items": { "type": "string" } }, "maxArticles": { "title": "Max Articles per Query", "minimum": 1, "maximum": 50, "type": "integer", "description": "Maximum number of articles to return per search query (1-50).", "default": 5 }, "maxResults": { "title": "Max Total Results", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum total articles to scrape across all queries (1-500).", "default": 25 }, "language": { "title": "Language", "type": "string", "description": "Wikipedia language code (e.g. 'en', 'es', 'fr', 'de', 'ja').", "default": "en" }, "extractSections": { "title": "Extract Full Sections", "type": "boolean", "description": "Extract full section-by-section content (not just summary). Provides richer data but uses more memory.", "default": true }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Optional proxy configuration for requests." } } }, "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 } } } } } } } } } } ```