Wikipedia Article Scraper: 300+ Languages Covered (2026)

Each record carries 11 output fields, including the article summary, categories, and image filenames, delivered without the need for proxies or cookies. A thousand results cost $1.00 on the free-plan price, allowing for cost-effective collection of structured encyclopedic data across more than 300 languages. This tool is designed for data scientists and researchers who require lead section extracts and metadata rather than full-page HTML. It is specifically for those needing structured summaries and should not be used by anyone requiring the full body text of the article.

Try it: open Wikipedia Article Scraper on Apify, sign in on the free plan and run the prefilled example.

Can you try Wikipedia Article Scraper before paying?

Yes. Apify's free plan includes $5.00 of prepaid usage every month and asks for no credit card. At $0.001 per result, that covers up to 5,000 results of Wikipedia Article Scraper a month, before run-start charges and platform usage.

The example request further down caps maxArticlesPerQuery at 5 for each of its 1 articleUrls, so a first run returns at most 5 results and costs at most $0.005 in result charges. That is enough to see the real shape of the data before deciding anything.

Wikipedia Article Scraper was last updated on 2026-04-09. It is one of 1,725 Actors CrawlerBros publishes on Apify, which together have 674,790 lifetime public runs and an average rating of 4.63 out of 5 across 416 reviews.

What does it cost to run Wikipedia Article Scraper?

Each result costs $0.001 on Apify's free plan, which is $1.00 per 1,000 results. Starting a run is charged separately at $0.005 per GB of Actor memory. Apify also bills the platform usage each run consumes, at the rates of your Apify plan, on top of these charges.

The number of results written to the dataset is the primary cost driver, which is determined by the size of the articleUrls array or the maxArticlesPerQuery setting. To verify the data structure without significant spend, a first run using the example input costs at most $0.005 in result charges.

How do you run Wikipedia Article Scraper from the API?

None of its 4 controls is strictly required, so the defaults below produce a valid run on their own. Every value in the payload below comes from the published schema's own prefills, which means you can paste it, swap the token, and get a real result.

Call the synchronous endpoint to start a run and receive dataset items in one request:

curl -X POST "https://api.apify.com/v2/acts/crawlerbros~wikipedia-scraper/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"articleUrls":["https://en.wikipedia.org/wiki/Python_(programming_language)"],"maxArticlesPerQuery":5,"language":"en"}'

The same run from Python, using the official client:

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")

run_input = {
  "articleUrls": [
    "https://en.wikipedia.org/wiki/Python_(programming_language)"
  ],
  "maxArticlesPerQuery": 5,
  "language": "en"
}

run = client.actor("crawlerbros~wikipedia-scraper").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

And from Node.js:

import { ApifyClient } from 'apify-client'

const client = new ApifyClient({ token: '<YOUR_APIFY_TOKEN>' })

const input = {
  "articleUrls": [
    "https://en.wikipedia.org/wiki/Python_(programming_language)"
  ],
  "maxArticlesPerQuery": 5,
  "language": "en"
}

const run = await client.actor('crawlerbros~wikipedia-scraper').call(input)
const { items } = await client.dataset(run.defaultDatasetId).listItems()
console.log(items)

That endpoint blocks until the run completes. Fine while you are testing a handful of records, risky once a run takes minutes: a dropped connection loses the response even though the run itself finished. Switch to an asynchronous start with polling or a webhook before you schedule anything.

Which Wikipedia Article Scraper inputs matter, and which can you skip?

The searchQueries and articleUrls controls determine which pages are processed, and both can be used in the same run to combine discovery with specific targets. Use the language parameter to switch between Wikipedia editions; the default is English.

• articleUrls (array): Wikipedia article URLs to scrape.
• searchQueries (array): Search terms to find Wikipedia articles.
• maxArticlesPerQuery (integer): Maximum articles per search query. Default: 5.
• language (string): Wikipedia language code (e.g., 'en', 'fr', 'de', 'es', 'ja'). Default: "en".

What does Wikipedia Article Scraper return?

The output is ideal for building knowledge bases or enriching catalogs with summaries and metadata, though it does not include the full article body. Each item includes the pageId, lastModified timestamp, and a list of categories for classification.

• title: String - Article title
• url: String - Full Wikipedia URL
• summary: String - Lead section extract (first few paragraphs)
• description: String - Wikidata short description
• categories: Array - Article categories
• thumbnail: Object - Thumbnail image with source, width, height
• images: Array - Image filenames from the article
• lastModified: String - Last edit timestamp
• language: String - Language code
• pageId: Integer - Wikipedia page ID
• scrapedAt: String - ISO timestamp when scraped

These are the documented fields. Optional ones can be empty on a given record, so measure how often each field your deliverable depends on is populated across a real sample before automating the handoff.

How do you build the workflow end to end?

Open Wikipedia Article Scraper and work through these in order. Each step ends with something to check, so a bad configuration surfaces on a small run rather than a scheduled one.

1. Provide specific Wikipedia article URLs in the articleUrls array for direct extraction of known topics.
2. Enter keywords into searchQueries to discover articles when specific URLs are not available.
3. Adjust maxArticlesPerQuery to a value between 1 and 50 to control the breadth of search results for each keyword.
4. Define the language code, such as 'en' or 'es', to target one of the 300+ supported Wikipedia editions.
5. Check that the summary field in the first few records contains the lead section extract as expected.
6. Verify the images array and categories list to ensure the metadata fits your downstream requirements.
7. Scale the input by adding more items to searchQueries while monitoring the result count to manage costs.

How do you apply it? Three worked playbooks

These are Wikipedia Article Scraper's own documented use cases, each worked through as an operating pattern rather than a description.

Use case 1: Research

Outcome: Collect structured article data for academic or business research

Configure: Set articleUrls to ["https://en.wikipedia.org/wiki/Quantum_computing", "https://en.wikipedia.org/wiki/Superconductivity"].

Working method: Input a curated list of relevant URLs and run the Actor to capture structured summaries and categories. Review the output fields for consistency across different technical topics. Expand the list once the structure is verified.

Deliverable: A JSON file containing structured summaries, metadata, and category classifications for specified research topics.

Stop condition: The summary field consistently returns truncated or missing text for articles known to have lead sections.

Use case 2: Content enrichment

Outcome: Augment your database with Wikipedia descriptions and metadata

Configure: Set searchQueries to a specific entity name and set maxArticlesPerQuery to 1.

Working method: Use unique identifiers from your internal database as search terms to find matching Wikipedia entries. Capture the description and thumbnail fields to provide visual and textual context for your application. Compare the returned title with your internal label to ensure a match.

Deliverable: A dataset of short descriptions and image URLs linked to specific search terms for database integration.

Stop condition: The search results return articles in a language different from the one specified in the language control.

Use case 3: Knowledge graphs

Outcome: Build knowledge bases from Wikipedia's categorized data

Configure: Set searchQueries to ["Programming languages", "Software frameworks"] and maxArticlesPerQuery to 20.

Working method: Run broad searches to gather a large set of related articles. Use the categories and images fields to map relationships between different entities. Aggregate the categories across multiple runs to identify common themes and hierarchy nodes.

Deliverable: A collection of records including category arrays and page IDs suitable for mapping entity relationships.

Stop condition: The categories array is empty for more than 20% of the returned results in a single run.

What breaks, and how do you design around it?

When a search query needs more than 50 results, you should use more specific terms across separate entries in the searchQueries array. If metadata like the description is missing, it typically indicates the field is not populated on the source page via Wikidata.

When should you not use Wikipedia Article Scraper?

Do not use this Actor if your project requires the full, multi-section text of a Wikipedia article, as it only extracts the lead summary section. If you need to track traffic trends rather than content, Wikimedia Pageviews Scraper is the correct choice. For users focusing on academic journals and peer-reviewed papers, DOAJ Open Access Journal & Article Scraper provides specialized access that this scraper cannot. If your interest lies specifically in travel guides and destination itineraries, WikiVoyage Travel Guides Scraper will offer more relevant structured sections than a general Wikipedia extraction.

What should you check before trusting the output?

• Verify that the summary field is not empty, as this contains the primary text extract for the article.
• Check the categories array for at least one entry to ensure metadata extraction is functioning correctly.
• Monitor the language field to confirm results match the requested language code.
• Look for a valid pageId to ensure the record corresponds to a unique Wikipedia entry.
• Confirm the lastModified timestamp is present if your use case requires tracking article freshness.

None of this proves a record is correct. It gives a scheduled Wikipedia Article Scraper run defined points where it should stop instead of quietly passing bad data downstream.

Frequently asked questions

What is the cost of running this scraper?

The free-plan price is $1.00 per 1,000 results, which works out to $0.001 per result. Each run also incurs a start fee based on platform usage. Apify's free plan includes $5.00 of monthly usage, which can cover up to 5,000 results from this Actor before you need to consider a paid plan.

How can I scrape Wikipedia in different languages?

You can target any of the 300+ Wikipedia language editions by using the language input control. Enter the standard code for your target edition, such as 'fr' for French or 'de' for German. The Actor will then interface with the corresponding language-specific API to retrieve content.

Does this Actor extract the full text of an article?

No, this Actor is designed to extract structured metadata and the lead section summary. The summary field contains the opening paragraphs of the article, which provide a clean text extract. If your use case requires every section and paragraph of a page, you would need a different tool.

Can I use this for keyword discovery?

Yes, by using the searchQueries input, you can find articles related to specific terms. The maxArticlesPerQuery setting allows you to control how many matching articles are returned for each keyword, up to a limit of 50 articles per query, making it useful for topical research.

Is it necessary to use a proxy for Wikipedia?

No proxy is required for this Actor. It uses the official Wikipedia and MediaWiki APIs, which are designed for public access. The scraper includes built-in rate limiting, with delays between 0.3 and 0.5 seconds, to ensure it remains compliant with Wikipedia's access guidelines.

Where to go next

When you are ready to run it, open Wikipedia Article Scraper on Apify; the free plan covers up to 5,000 results a month.

Start with the Wikipedia Article Scraper Actor page for the current input schema, pricing tier, and run history.

Other Actors we maintain for related data:

• Wikimedia Pageviews Scraper: Scrape Wikipedia & Wikimedia pageview metrics from the official REST API.
• DOAJ Open Access Journal & Article Scraper: Scrape the Directory of Open Access Journals (DOAJ) - 20K+ peer-reviewed open access journals and 10M+ articles.
• Article Furniture Scraper: Scrape Article.com's furniture catalog - browse any room/category or search by keyword, get prices (incl.
• WikiVoyage Travel Guides Scraper: Scrape Wikivoyage, the free travel guide with 30,000+ destination articles.
• Medium Article Scraper: Scrape Medium articles by tag/topic, user, publication, or search query.
• Apartment Therapy Article Scraper: Scrape articles from Apartment Therapy - home decor, interior design, DIY, and small-space content from apartmenttherapy.com via their official RSS feed.
• Google News Scraper: Scrape Google News in real-time.

Related guides:

• Google News Scraper: 12 Data Fields, Up to 2,500 Free Results/Month

Resources

• Actor documentation, input schema, and pricing: verified against the published Actor on 2026-09-25.

• Actor last updated by its maintainers on 2026-04-09.

• Run outcome figures cover the 30 day public window ending 2026-09-25.

• Wikipedia Article Scraper on Apify