Wikipedia Category Scraper — Article Lists, Summaries & Data API (No API Key)

Extract structured article lists from any Wikipedia category across 10 language editions — English, German, French, Spanish, Italian, Japanese, Chinese, Russian, Portuguese and Arabic. Give the Actor a category name or URL (e.g. Category:Machine_learning) and it returns every listed article with title, full Wikipedia URL, page ID, source category and an optional first-paragraph summary in clean structured JSON. Automatic multi-page navigation and one-level subcategory discovery included. Fast, no browser, no API key, no login.

🏆 Why this Wikipedia scraper?

Every article in a category, in minutes · thousands of article references per run · 10 language editions · optional AI-ready summaries · direct HTTP (no browser) · export to JSON / CSV / Excel. The unofficial Wikipedia / MediaWiki API alternative for knowledge graphs, SEO topic research and multilingual NLP datasets.

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✨ What this Actor does / Key features

• 📚 Full category extraction — every article listed on a category page (Category:...), typically 50–200 per page, with automatic pagination through multi-page categories.
• 🌍 10 language editions — scrape the same topic in English, German, French, Spanish, Italian, Japanese, Chinese, Russian, Portuguese or Arabic to build parallel corpora.
• 🌳 Subcategory discovery — automatically follows one level of subcategory links to walk deeper into Wikipedia's human-curated category tree.
• 📝 Optional article summaries — flip scrapeSummaries: true to fetch each article's first paragraph, perfect as ready-to-use text for embeddings, RAG and NLP training.
• 🔗 Clean references — every row carries the article title, full canonical URL, Wikipedia internal pageId, source category name and category URL.
• 🎛️ Two ways to target — pass full category URLs or just bare category names and the Actor builds the correct per-language URL for you.
• 📦 Volume control — cap the run with maxArticles (up to 2,000) so you pay only for what you need.
• ⚡ Direct HTTP extraction — parses Wikipedia's clean, consistent HTML; no browser, no DOM guesswork, no MediaWiki API token or rate limit.
• 🛡️ Proxy friendly — Wikipedia rarely blocks; Apify Proxy with datacenter groups works fine out of the box.

🚀 Quick start (3 steps)

1. Configure — paste one or more category names (e.g. Machine_learning) or full category URLs, and pick a language edition. Toggle scrapeSummaries if you want the first paragraph of each article.
2. Run — click Start. The Actor paginates each category, follows subcategories one level deep, and streams article records into your dataset.
3. Get your data — open the Output tab and export to JSON, CSV, Excel or XML, or pull it via the Apify API.

📥 Input

You only need one of categoryNames or categoryUrls. Everything else is optional.

Example — AI & machine-learning topic map (English)

{
  "categoryNames": ["Machine_learning", "Artificial_intelligence", "Large_language_models"],
  "language": "en",
  "scrapeSummaries": false,
  "maxArticles": 500,
  "proxyConfiguration": { "useApifyProxy": true }
}

Example — summaries for an NLP / RAG dataset

{
  "categoryUrls": [
    { "url": "https://en.wikipedia.org/wiki/Category:Deep_learning" },
    { "url": "https://en.wikipedia.org/wiki/Category:Neural_network_architectures" }
  ],
  "scrapeSummaries": true,
  "maxArticles": 300
}

Example — same topic in another language (multilingual corpus)

{
  "categoryNames": ["Apprentissage_automatique"],
  "language": "fr",
  "scrapeSummaries": true,
  "maxArticles": 400
}

Field	Type	Description
categoryUrls	array	Full Wikipedia category page URLs, each as { "url": "…/Category:Name" }. Use this or categoryNames.
categoryNames	array	Bare category names (no URL) — e.g. Machine_learning, Artificial_intelligence. The Actor builds the URL for the chosen language.
language	string	Wikipedia edition: en, de, fr, es, it, ja, zh, ru, pt or ar. Default en.
scrapeSummaries	boolean	Also fetch each article page and extract the first-paragraph summary. Default false (faster).
maxArticles	integer	Total article cap across all categories, 1–2000. Default 300.
proxyConfiguration	object	Apify Proxy settings. Datacenter proxies are fine — Wikipedia is proxy-friendly.

Finding a category: open any Wikipedia article, scroll to the Categories bar at the bottom, and click one — the URL will read …/wiki/Category:Some_Topic. Copy either the full URL into categoryUrls or just the Some_Topic part into categoryNames. Underscores replace spaces.

📤 Output

One row per article, exportable to JSON, CSV, Excel or XML. Here is a trimmed sample record:

{
  "type": "article",
  "articleTitle": "Convolutional neural network",
  "articleUrl": "https://en.wikipedia.org/wiki/Convolutional_neural_network",
  "categoryName": "Deep learning",
  "categoryUrl": "https://en.wikipedia.org/wiki/Category:Deep_learning",
  "summary": "A convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns features by itself via filter (or kernel) optimization…",
  "pageId": "40409788",
  "language": "en",
  "scrapedAt": "2026-07-06T12:00:00Z"
}

💡 Use cases

• Knowledge-graph construction — turn Wikipedia's human-curated category system into structured taxonomies and entity-relationship graphs.
• SEO topic & content research — map topic clusters and content silos; every category is a ready-made list of subtopics to build topical authority around.
• NLP / RAG datasets — with scrapeSummaries: true, collect clean first-paragraph text by topic for embeddings, classification and retrieval-augmented generation.
• Multilingual corpora — scrape the same subject across language editions to build parallel or cross-lingual datasets.
• Directory & catalog building — assemble structured lists of companies, technologies, historical figures or scientific concepts represented in Wikipedia.
• Link building & outreach — surface in-niche Wikipedia articles worth referencing or auditing for broken links.

👥 Who uses it

Data engineers & ML teams building training corpora · SEO and content-marketing agencies mapping topic clusters · academic and NLP researchers · knowledge-management and taxonomy teams · data journalists · anyone who needs a clean, structured slice of Wikipedia by subject.

💰 Pricing

This Actor runs on a simple pay-per-result model — you pay for the article records you extract, with no separate Apify platform fees to calculate. Try it on the free tier first, then scale up. See the Pricing tab on this page for the current rate.

❓ Frequently Asked Questions

Do I need a Wikipedia or MediaWiki API key? No. This Actor reads Wikipedia's public HTML pages directly, so there's no API key, no OAuth and no MediaWiki rate limit to manage — only an Apify account.

Does Wikipedia have a public API? Wikipedia offers the MediaWiki API, but it's paginated, rate-limited and awkward for bulk category traversal. This Actor works as an unofficial Wikipedia API alternative: point it at a category and it returns a flat, structured list of every article with summaries and page IDs.

How do I get a list of all articles in a Wikipedia category? Enter the category name or URL and the Actor returns every listed article with its title, full URL and internal page ID, automatically paginating multi-page categories and following one level of subcategory links.

Can it fetch article text, not just the list? Yes — set scrapeSummaries: true and each article's first paragraph is added to the summary field, which is ideal ready-to-use text for embeddings and NLP.

How deep does category crawling go? The Actor follows one level of subcategory links from each category page. To go deeper, feed the discovered subcategory URLs back into a second run.

Which Wikipedia language editions are supported? Ten: English (en), German (de), French (fr), Spanish (es), Italian (it), Japanese (ja), Chinese (zh), Russian (ru), Portuguese (pt) and Arabic (ar).

Is it legal to scrape Wikipedia? Wikipedia content is published under a free license (CC BY-SA) and this Actor only collects publicly available page data. You remain responsible for attribution and for using the data in line with Wikipedia's terms and applicable law.

How do I export Wikipedia category data to CSV or JSON?

Run the Actor, then export the resulting dataset as CSV, JSON, Excel or XML from the Apify console, or pull it via the Apify API.

Can I build a multilingual dataset from Wikipedia?

Yes. Run the same category name across different language editions (e.g. en, de, fr) to assemble a parallel, cross-lingual corpus of article references and summaries.

🔗 More data scrapers by logiover

Building datasets from public knowledge and media sources? Pair the Wikipedia scraper with the rest of the logiover research & social suite:

Source	Actor
📰 News	Google News Scraper · Hacker News Search
🎬 Film & TV	IMDb Scraper · TVmaze Scraper · Letterboxd Reviews
💬 Forums	Reddit Search Scraper · Stack Exchange Questions
📺 YouTube	YouTube Search Scraper · YouTube Video Details
✍️ Writing	Substack Newsletter Scraper · Dev.to Articles Scraper
🎓 Research	arXiv Paper Scraper · Semantic Scholar Scraper

👉 Browse all logiover scrapers on Apify Store — 180+ actors across real estate, jobs, crypto, social media & B2B data.

⏰ Scheduling & integration

Schedule this Actor on Apify to refresh a knowledge dataset daily or weekly as Wikipedia categories grow. Export results to JSON, CSV or Excel, sync to Google Sheets, or push to your vector database, BI tools and webhooks through the Apify API. Connect it to Make, n8n or Zapier to build automated knowledge-ingestion pipelines.

⭐ Support & feedback

Found a bug or need an extra field? Open an issue on the Issues tab — response is usually fast. If this Actor saves you time, a ★★★★★ review on the Store page genuinely helps and is hugely appreciated. 🙏

⚖️ Legal

This Actor extracts only publicly available Wikipedia data (published under CC BY-SA) and is intended for legitimate research, analytics and dataset-building use. You are responsible for proper attribution and for complying with Wikipedia's terms of use and any applicable local laws.

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📝 Changelog

2026-07-06

• ✨ README overhaul: richer output sample with page IDs, ready-to-run example scenarios (topic maps, RAG summaries, multilingual corpora), full field reference, and a research & media cross-promo grid.

2026-07-01

• Maintenance pass: re-verified end-to-end on live data and confirmed successful runs within the 5-minute quality window on the default input.
• Sharpened Store metadata (SEO title & description) and expanded the FAQ with high-intent, long-tail questions for easier discovery in Google and Apify Store search.
• Added ready-to-run example tasks that cover common real-world use cases.