# Wikipedia Scraper - Articles, Search & Recent Changes (`legend006/wikipedia-scraper`) Actor Scrape Wikipedia articles by title, run keyword searches, pull recent changes, or extract entire categories — across any of 300+ language editions. Returns clean text, summaries, references, links, and metadata. Built for AI/LLM training datasets, NLP research, and knowledge-graph building. - **URL**: https://apify.com/legend006/wikipedia-scraper.md - **Developed by:** [NIJ KANANI](https://apify.com/legend006) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.10 / 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 Scrape **Wikipedia articles**, search results, recent edits, and categories — across all 300+ language editions. Returns clean plain-text content, summaries, references, and rich metadata. > 🎯 Built for AI/LLM training datasets, NLP research, knowledge-graph construction, journalism, and education. ![Sample dataset output](https://raw.githubusercontent.com/Legend006/Apify-Assets/main/wikipedia-scraper/1-dataset.png) ![Input form](https://raw.githubusercontent.com/Legend006/Apify-Assets/main/wikipedia-scraper/2-input.png) ![Run log — clean success](https://raw.githubusercontent.com/Legend006/Apify-Assets/main/wikipedia-scraper/3-log.png) --- ### ✨ What you can do - 📄 **Fetch articles by title** — clean plain-text body, summary, sections, references - 🔎 **Search** — full-text search across an entire language edition - 📡 **Recent changes** — live feed of edits (title, user, comment, revid) - 📁 **Pull entire categories** — all members of `Category:Machine_learning`, etc. - 🌐 **Any language** — `en`, `es`, `fr`, `de`, `ja`, `zh`, `hi`, `ar`, etc. - 📦 Rich output: links (internal+external), categories, sections, last-modified --- ### 🚀 Quick start ```json { "mode": "articles", "language": "en", "titles": ["Artificial intelligence", "Large language model"], "includeContent": true, "includeReferences": false } ```` *** ### 📥 Input | Field | Used in mode | Description | |---|---|---| | `mode` | all | `articles` / `search` / `recentchanges` / `category` | | `language` | all | Wiki edition code (`en`, `de`, `ja`...) | | `titles` | articles | Article titles | | `searchQueries` | search | Keywords or phrases | | `category` | category | Category name without `Category:` prefix | | `maxItems` | all | Cap per query | | `includeContent` | articles, search, category | Full plain-text body | | `includeReferences` | articles, search, category | External + internal links + sections | *** ### 📤 Output (per item) ```json { "mode": "articles", "title": "Artificial intelligence", "language": "en", "pageId": 1164, "summary": "Artificial intelligence (AI) refers to...", "content": "Full article text...", "wordCount": 12873, "sections": ["Goals", "History", "Methods"], "externalLinks": ["https://..."], "internalLinks": ["Machine learning", "Neural network"], "categories": ["Artificial intelligence", "Cybernetics"], "url": "https://en.wikipedia.org/wiki/Artificial_intelligence", "lastModified": "2026-04-30T...", "scrapedAt": "2026-05-06T..." } ``` *** ### 🎯 Use cases | Who | Why | |---|---| | 🤖 LLM teams | Pretraining + fine-tuning datasets across languages | | 📚 NLP researchers | Multilingual corpora, named-entity benchmarks | | 📰 Journalists | Topic deep-dives + fact-checking pipelines | | 🎓 Educators | Auto-build study material from any topic | | 🧠 Knowledge graphs | Wikipedia as an entity backbone | *** ### ⚙️ Tech notes - Uses MediaWiki's official Action API + REST Summary API - No login, no key, no rate limits (within fair use) - Plain-text extraction via `explaintext=1` — already cleaned, no HTML/wikitext - Recent-changes uses `rctype=edit|new` to skip log noise *** ### ❓ FAQ **Are full Wikipedia dumps better?** For one-shot pre-training, yes (free at dumps.wikimedia.org). This Actor is for *targeted* scrapes — specific topics, ongoing freshness, multi-language slices, or recent-changes monitoring. **Schedule it?** Yes. Recent changes mode is perfect for hourly Apify Schedules. **Hits rate limits?** Almost never. MediaWiki's anonymous limit is generous and we add automatic retries with backoff. # Actor input Schema ## `mode` (type: `string`): What to scrape. 'articles' = fetch by title. 'search' = keyword search. 'recentchanges' = live edit feed. 'category' = all articles in a category. ## `language` (type: `string`): Wikipedia language edition (e.g. 'en', 'es', 'fr', 'de', 'ja', 'zh', 'hi'). Default English. ## `titles` (type: `array`): Article titles to fetch. Used when mode = articles. ## `searchQueries` (type: `array`): Keywords/phrases to search. Used when mode = search. ## `category` (type: `string`): Without 'Category:' prefix, e.g. 'Machine\_learning'. Used when mode = category. ## `maxItems` (type: `integer`): Cap per article-list / per search query / per category. ## `includeContent` (type: `boolean`): If true, fetches the full plain-text body (slower, much larger output). If false, only summary/extract. ## `includeReferences` (type: `boolean`): If true, includes external links, internal links, and section list per article. ## Actor input object example ```json { "mode": "articles", "language": "en", "titles": [ "Artificial intelligence" ], "searchQueries": [], "maxItems": 100, "includeContent": true, "includeReferences": false } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("legend006/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 = {} # Run the Actor and wait for it to finish run = client.actor("legend006/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 '{}' | apify call legend006/wikipedia-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=legend006/wikipedia-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wikipedia Scraper - Articles, Search & Recent Changes", "description": "Scrape Wikipedia articles by title, run keyword searches, pull recent changes, or extract entire categories — across any of 300+ language editions. Returns clean text, summaries, references, links, and metadata. Built for AI/LLM training datasets, NLP research, and knowledge-graph building.", "version": "0.1", "x-build-id": "sT8cMcSvihukBvEvV" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/legend006~wikipedia-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-legend006-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/legend006~wikipedia-scraper/runs": { "post": { "operationId": "runs-sync-legend006-wikipedia-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/legend006~wikipedia-scraper/run-sync": { "post": { "operationId": "run-sync-legend006-wikipedia-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "required": [ "mode", "language" ], "properties": { "mode": { "title": "Scrape mode", "enum": [ "articles", "search", "recentchanges", "category" ], "type": "string", "description": "What to scrape. 'articles' = fetch by title. 'search' = keyword search. 'recentchanges' = live edit feed. 'category' = all articles in a category.", "default": "articles" }, "language": { "title": "Language code", "type": "string", "description": "Wikipedia language edition (e.g. 'en', 'es', 'fr', 'de', 'ja', 'zh', 'hi'). Default English.", "default": "en" }, "titles": { "title": "Article titles", "type": "array", "description": "Article titles to fetch. Used when mode = articles.", "default": [ "Artificial intelligence" ], "items": { "type": "string" } }, "searchQueries": { "title": "Search queries", "type": "array", "description": "Keywords/phrases to search. Used when mode = search.", "default": [], "items": { "type": "string" } }, "category": { "title": "Category name", "type": "string", "description": "Without 'Category:' prefix, e.g. 'Machine_learning'. Used when mode = category." }, "maxItems": { "title": "Max items per query", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Cap per article-list / per search query / per category.", "default": 100 }, "includeContent": { "title": "Include full article text", "type": "boolean", "description": "If true, fetches the full plain-text body (slower, much larger output). If false, only summary/extract.", "default": true }, "includeReferences": { "title": "Include references and links", "type": "boolean", "description": "If true, includes external links, internal links, and section list per article.", "default": false } } }, "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 } } } } } } } } } } ```