# Wikipedia Article Scraper - Search & Extract Content (`klondikeking/wikipedia-article-scraper`) Actor Search and extract Wikipedia article metadata, summaries, and content via the official MediaWiki API. No scraping overhead — pure API integration with high reliability. - **URL**: https://apify.com/klondikeking/wikipedia-article-scraper.md - **Developed by:** [Pierrick McD0nald](https://apify.com/klondikeking) (community) - **Categories:** Developer tools, Business - **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 ## Wikipedia Article Scraper — Search & Extract Content Extract Wikipedia article metadata, summaries, and content via the official MediaWiki API. This Actor searches Wikipedia by keyword and returns structured data for every matching article — no browser overhead, no scraping complexity, just clean API integration. ### Use Cases - **Content Research** — Gather article summaries and metadata for academic research, content marketing, or knowledge base building. - **SEO & Topic Analysis** — Extract word counts, article sizes, and publication dates to analyze content depth and freshness across topics. - **Data Enrichment** — Augment datasets with Wikipedia summaries, thumbnail images, and canonical URLs for entity linking and NLP pipelines. - **Multilingual Content** — Search across 300+ Wikipedia language editions to build localized content collections. ### Input | Field | Type | Required | Description | |-------|------|----------|-------------| | `searchQuery` | String | Yes | Search term to find Wikipedia articles (e.g., "machine learning", "quantum computing") | | `maxResults` | Number | No | Maximum articles to extract, 1–500 (default: 25) | | `includeExtract` | Boolean | No | Fetch article introduction/summary text (default: true) | | `includeImages` | Boolean | No | Fetch thumbnail image URLs (default: false) | | `language` | String | No | Wikipedia language code: en, es, fr, de, ja, etc. (default: "en") | | `proxyConfiguration` | Object | No | Proxy settings (optional — Wikipedia API does not require proxy) | ### Output The Actor outputs a dataset with the following fields: ```json { "pageId": 233488, "title": "Machine learning", "url": "https://en.wikipedia.org/wiki/Machine_learning", "snippet": "Machine learning (ML) is a field of study in artificial intelligence...", "extract": "Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms...", "wordCount": 15287, "size": 141291, "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/thumb/...", "timestamp": "2026-05-15T10:30:00Z", "language": "en" } ```` ### Pricing Pay per event: **$0.001 per article extracted**. No minimums, no subscriptions. You only pay for the results you receive. The Wikipedia MediaWiki API is free and public, so compute costs are minimal and margins stay high. ### Limitations - Maximum 500 results per run (Wikipedia API limit) - Article extracts are limited to the introduction/summary section - Thumbnail images are only available when `includeImages` is enabled and the article has an image - Rate limits apply per Wikipedia language edition (handled automatically with retries) ### FAQ **Q: Do I need a Wikipedia API key?** A: No. This Actor uses the public MediaWiki API with no authentication required. **Q: Can I search in languages other than English?** A: Yes. Set the `language` field to any valid Wikipedia language code (e.g., "es" for Spanish, "ja" for Japanese). **Q: What happens if my search returns thousands of results?** A: The Actor respects the `maxResults` limit and paginates through the API automatically. You only pay for the number of articles actually extracted. ### Changelog - **v1.0.0** — Initial release # Actor input Schema ## `searchQuery` (type: `string`): Search term to find Wikipedia articles (e.g., 'machine learning', 'quantum computing') ## `maxResults` (type: `integer`): Maximum number of articles to extract (1-500) ## `includeExtract` (type: `boolean`): Fetch article introduction/summary text for each result (slower but more valuable) ## `includeImages` (type: `boolean`): Fetch thumbnail image URLs for each article ## `language` (type: `string`): Language code for Wikipedia (e.g., en, es, fr, de, ja) ## `proxyConfiguration` (type: `object`): Proxy settings for requests (optional — Wikipedia API does not require proxy) ## Actor input object example ```json { "searchQuery": "machine learning", "maxResults": 10, "includeExtract": true, "includeImages": false, "language": "en", "proxyConfiguration": { "useApifyProxy": true } } ``` # Actor output Schema ## `articles` (type: `string`): No description ## `stats` (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 = { "searchQuery": "machine learning", "maxResults": 10, "includeExtract": true, "includeImages": false, "language": "en", "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("klondikeking/wikipedia-article-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 = { "searchQuery": "machine learning", "maxResults": 10, "includeExtract": True, "includeImages": False, "language": "en", "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("klondikeking/wikipedia-article-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 '{ "searchQuery": "machine learning", "maxResults": 10, "includeExtract": true, "includeImages": false, "language": "en", "proxyConfiguration": { "useApifyProxy": true } }' | apify call klondikeking/wikipedia-article-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=klondikeking/wikipedia-article-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wikipedia Article Scraper - Search & Extract Content", "description": "Search and extract Wikipedia article metadata, summaries, and content via the official MediaWiki API. No scraping overhead — pure API integration with high reliability.", "version": "1.0", "x-build-id": "tcfgyYdX2zw4c0ArG" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/klondikeking~wikipedia-article-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-klondikeking-wikipedia-article-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/klondikeking~wikipedia-article-scraper/runs": { "post": { "operationId": "runs-sync-klondikeking-wikipedia-article-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/klondikeking~wikipedia-article-scraper/run-sync": { "post": { "operationId": "run-sync-klondikeking-wikipedia-article-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "searchQuery" ], "properties": { "searchQuery": { "title": "Search Query", "type": "string", "description": "Search term to find Wikipedia articles (e.g., 'machine learning', 'quantum computing')" }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of articles to extract (1-500)", "default": 25 }, "includeExtract": { "title": "Include Article Summary", "type": "boolean", "description": "Fetch article introduction/summary text for each result (slower but more valuable)", "default": true }, "includeImages": { "title": "Include Thumbnail Images", "type": "boolean", "description": "Fetch thumbnail image URLs for each article", "default": false }, "language": { "title": "Wikipedia Language", "type": "string", "description": "Language code for Wikipedia (e.g., en, es, fr, de, ja)", "default": "en" }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Proxy settings for requests (optional — Wikipedia API does not require proxy)" } } }, "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 } } } } } } } } } } ```