📖 Wikipedia Scraper — Articles, Summaries & Metadata

Search Wikipedia by keyword — or look up exact article titles — and get clean, structured data for every result: the title, the short description, the intro extract as plain text, the full article URL, a thumbnail image, the categories, how many language editions the article exists in, the last-edit timestamp and the article size. Works in any Wikipedia language edition, from English to German, Spanish, French, Japanese and beyond.

The actor is powered by the public MediaWiki API, so it is fast and reliable with no browser, no login and no API key. Export to JSON/CSV/Excel, run on a schedule, call via API, or connect to Make, Zapier or n8n.

🔎 What is the Wikipedia Scraper?

Wikipedia is the world's largest encyclopedia and one of the most valuable sources of clean, general-knowledge text — which is exactly why it powers so many knowledge bases, search features and AI systems. But copying summaries by hand, or wrangling the raw MediaWiki API yourself, is slow and fiddly.

This actor makes it effortless. Give it topics or keywords (e.g. artificial intelligence, quantum computing) and it returns the top matching articles as structured rows — or pass a list of exact titles to pull specific pages. Every row includes a ready-to-use plain-text intro extract (no wiki markup, no HTML), a short one-line description, the article's categories and a thumbnail, so it drops straight into a RAG pipeline, a content database or an enrichment workflow. Choose any language edition to build multilingual datasets.

What data does it extract?

• Title of the article
• Page ID — the stable MediaWiki page identifier
• Description — the short one-line summary
• Extract — the intro section as clean plain text (no markup)
• URL — the full article link
• Thumbnail — a representative image URL
• Language — the Wikipedia edition the article is from
• Language-links count — how many language editions the article exists in
• Categories — the article's category labels
• Last edited — timestamp of the most recent edit
• Length (bytes) — the article's size, a rough proxy for depth
• Query — the search term (or lookup) that surfaced the row

⬇️ Input

Run it two ways — search by keyword, or look up exact titles. You can combine both in one run.

Field	Type	Description
searchTerms	array	Keywords/topics to search, e.g. artificial intelligence. One or many.
titles	array	Optional: exact article titles to pull, e.g. Alan Turing.
language	string	Wikipedia language edition code, e.g. en, de, es. Default en.
maxPerTerm	integer	Max articles to return per search term. Default 10, up to 50.

Example input

{
  "searchTerms": ["artificial intelligence", "quantum computing"],
  "language": "en",
  "maxPerTerm": 10
}

⬆️ Output

Each article is one clean row (view as a table, or export JSON / CSV / Excel):

{
  "title": "Artificial intelligence",
  "pageid": 1164,
  "description": "Intelligence of machines",
  "extract": "Artificial intelligence (AI) is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making...",
  "url": "https://en.wikipedia.org/wiki/Artificial_intelligence",
  "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/thumb/.../400px.jpg",
  "language": "en",
  "langlinks_count": 176,
  "categories": ["Artificial intelligence", "Cybernetics", "Computational neuroscience"],
  "last_edited": "2026-06-20T11:32:10Z",
  "length_bytes": 291845,
  "query": "artificial intelligence"
}

💡 Use cases

• 🧠 Knowledge bases — pull clean summaries, descriptions and categories for thousands of topics to populate an internal wiki or reference store.
• 🔎 RAG / LLM pipelines — feed plain-text intro extracts into retrieval and grounding systems for accurate, citeable answers.
• 📊 Entity enrichment — attach a description, thumbnail and category set to people, places, companies or products in your dataset.
• 🌐 Multilingual research — scrape the same topics across language editions to compare coverage and build cross-lingual corpora.

❓ FAQ

How do I scrape Wikipedia articles? Enter one or more searchTerms (or exact titles), pick a language, set maxPerTerm, and Run. You get structured rows with the summary, extract, URL, thumbnail and categories.

Do I need an API key or login? No. It uses the public MediaWiki API — just provide keywords or titles.

Which languages are supported? Any Wikipedia edition — set language to the edition code (e.g. en, de, es, fr, ja).

Can I look up exact pages? Yes — pass the exact article titles in titles and the actor pulls those specific pages.

Is the extract plain text or HTML? Clean plain text of the intro section — no wiki markup and no HTML, so it is ready for indexing, embeddings or display.

Can I get the full article body? This returns the intro extract plus rich metadata, which is ideal for summaries, enrichment and RAG. For most pipelines the intro plus categories is exactly what you want.

What does the language-links count tell me? How many language editions the article exists in — a quick signal of how globally significant or well-covered a topic is.

Can I run it on a schedule or via API? Yes — schedule recurring runs on Apify, call it via the API/SDK, or connect it to Make, Zapier or n8n.

How does pricing work? Pay per article returned — no subscription, no fixed monthly fee.

Is it legal? It uses the public MediaWiki API. Wikipedia text is licensed under Creative Commons (CC BY-SA) — attribute it accordingly and follow Wikimedia's terms.

🔗 You might also like

• Academic Papers Scraper — 150M+ scholarly works via Crossref.
• Books Scraper (Open Library) — books, authors & ISBNs.
• Wikidata Scraper — structured facts & entities.
• Google News Scraper — headlines by keyword and country.

---

Keywords: wikipedia scraper, mediawiki api, wikipedia summary, article extract, knowledge base, rag pipeline, wikipedia data, encyclopedia data, content enrichment, wikipedia api, plain text extract, multilingual wikipedia, topic summaries, wiki data, wikipedia articles, entity enrichment, wikipedia summaries, llm training data, wikipedia dataset, article metadata

Support and feedback

If this Actor saves time in your workflow, please leave a short Apify Store review. Reviews help other users evaluate the tool and help prioritize maintenance. If a source changes or a field stops populating, open an Actor issue with the run ID, public input, and expected field. Never include passwords, private cookies, or confidential data.