Wikipedia On This Day Scraper

Wikipedia On This Day Scraper

Scrape historical events, births, deaths, and holidays from Wikipedia's On This Day feeds for any date and language. Every entry returns with its year, description, and links to the full articles. No API key required. Export to CSV, JSON, Excel, or XML.

Wikipedia's On This Day pages are a rich source of historical facts, but manually copying them for multiple dates and languages is slow and error-prone. This scraper reads the public feeds directly, pulling curated or full-category slices for any calendar date across ten language editions. You get a clean, structured dataset instead of raw HTML.

Who uses it	What they scrape Wikipedia for
Content creators	Find historical hooks for daily social media posts or newsletters.
Educators	Build a classroom calendar of daily historical facts for students.
Trivia app developers	Populate a quiz database with verified historical events and birthdays.
Researchers	Analyze which historical events are highlighted across different language editions.

What it does

This Actor collects historical events, births, deaths, and holidays from Wikipedia's On This Day feeds by date and language, and returns each entry as a flat row.

• 📅 Multiple dates: supply a list of MM-DD dates or leave empty to scrape today's events automatically.
• 🌐 Ten languages: pull feeds from English, Spanish, French, German, Italian, Portuguese, Russian, Chinese, Japanese, and Korean editions.
• 📚 Category slices: choose from All, Selected, Births, Deaths, Events, or Holidays for each date.
• ⚙️ Flexible volume: set a maximum number of items to collect, from a single preview row up to a million entries per run.

Results export to CSV, JSON, Excel, or XML, or straight from the API.

What you can do with Wikipedia data

📰 Automate daily history content.

A social media manager runs the scraper each morning for today's date to get a list of notable events and birthdays for posts.

🌍 Compare cross-cultural history.

A researcher pulls the same date from English, Chinese, and Russian Wikipedia to study which events each edition considers significant.

🎂 Build a birthday calendar.

A developer scrapes the Births feed for every day of the year to create a searchable database of famous people's birthdays.

📚 Create a classroom resource.

A teacher collects the Selected events for each school day to start lessons with a relevant historical fact.

Why choose this scraper

	What you get
Structured historical data	Year, description, and Wikipedia article links in a consistent schema across all languages.
Multi-language support	Compare how different cultures record the same date with feeds from ten major Wikipedia editions.
No coding required	Configure dates, language, and category in the input UI and download results immediately.

How it compares

Company Deep Research includes Wikipedia as one of seven sources in a broader company intelligence report, while this Actor focuses exclusively on Wikipedia's On This Day historical feeds with dedicated date and category controls.

Feature	ParseForge	Company Deep Research
Dedicated On This Day date input	Yes, MM-DD format with multiple dates	Not listed
Category filtering (Births, Deaths, Events, Holidays)	Yes, six feed type options	Not listed
Multi-language Wikipedia editions	Yes, 10 languages supported	Not listed
Structured historical event rows	Yes, year, description, and article links	Not listed
No API key required	Yes	Not listed

Configure the run

Drive the Actor with calendar dates in MM-DD format and pick a feed slice and language. Filters apply as each entry is read so only matching historical records reach your dataset. The Input tab lists every parameter.

A first run with the defaults:

{
  "maxItems": 10,
  "dates": [
    "05-24"
  ],
  "type": "selected",
  "language": "en"
}

A larger pull:

{
  "maxItems": 200,
  "dates": [
    "05-24"
  ],
  "type": "selected",
  "language": "en"
}

Pricing

Pay-per-result: $0.012 per result collected. You pay only for the results written to your dataset.

Results collected	Approximate cost
100 results	$1.20
1,000 results	$12.00
10,000 results	$120.00

New Apify accounts start with $5 in free credit.

Free users

Free-plan runs return up to 10 results as a preview. Upgrade your Apify plan to collect up to 1,000,000 results per run.

Run it

1. Create a free Apify account with $5 in credit.
2. Open the Wikipedia On This Day Scraper.
3. Set your inputs and any filters, then click Start.
4. Export the results as CSV, Excel, JSON, or XML from the Dataset tab.

Run it programmatically through the Apify API (run-sync-get-dataset-items) or the ApifyClient for JavaScript and Python.

Use with AI agents (MCP)

Give an AI agent live access to Wikipedia through the Model Context Protocol. Add the Actor to Claude, Cursor, or any MCP client:

claude mcp add --transport http apify "https://mcp.apify.com?tools=parseforge/wikipedia-on-this-day-scraper"

Then prompt it in plain language to run the scraper and read back the results.

Troubleshooting

Why am I getting no results for a specific date?

Check that the date is in MM-DD format with leading zeros, like 01-05 instead of 1-5. Also verify that the selected language edition has content for that date, as coverage varies by language.

Why do I only get 10 items even though I set a higher number?

Free Apify accounts are limited to 10 items as a preview. Upgrade to a paid plan to increase the maximum items limit up to 1,000,000 per run.

The scraper returns events in the wrong language.

Check the Language setting in your input. It defaults to English. Change it to your desired Wikipedia edition, such as fr for French or zh for Chinese.

Some dates return fewer events than others.

This is normal. Wikipedia's On This Day feeds vary in size by date and language edition. Some dates have more recorded historical events than others.

I see duplicate entries in my results.

If you selected All as the feed type, an event may appear in multiple categories. Use a specific type like Events or Births to avoid overlap, or deduplicate by the Wikipedia page link in post-processing.

FAQ

Question	Answer
What dates can I scrape?	Any calendar date in MM-DD format, like 05-24 or 12-25. You can supply a single date, a list of dates, or leave the field empty to scrape today's date automatically.
What is the difference between Selected and All feed types?	Selected returns only the events that Wikipedia editors have chosen as the most notable for that date. All returns every category combined: births, deaths, events, and holidays.
Which languages are supported?	The Actor supports ten Wikipedia language editions: English, Spanish, French, German, Italian, Portuguese, Russian, Chinese, Japanese, and Korean.
Does this scraper require a Wikipedia API key?	No. It reads the public On This Day feeds directly, so no API key, login, or app registration is needed.
What data does each row contain?	Each row includes the event year, a text description, the category type like birth or event, and links to the full Wikipedia articles for further reading.
Can I scrape multiple dates in one run?	Yes. You can provide a list of dates like 01-01, 07-04, 12-25, and the Actor will collect entries for all of them in a single run.
Is there a limit on how many items I can scrape?	Free users are limited to 10 items as a preview. Paid users can set a custom maximum up to 1,000,000 items per run.
What export formats are available?	You can export your dataset in CSV, JSON, Excel, or XML formats from the Apify platform.
Can I schedule this scraper to run daily?	Yes. You can set up a scheduled run in Apify to scrape today's events automatically every day without manual input.

Related actors

Browse the full ParseForge collection for more scrapers.

🆘 Need help? Email parseforge@protonmail.com with your run ID, your input, and what you expected.

⚠️ Disclaimer. This Actor is unofficial and is not affiliated with, endorsed by, or sponsored by Wikimedia Foundation, Inc. It collects only publicly available data. You are responsible for using the collected data in compliance with the source's terms of service and applicable data-protection laws, including GDPR, CCPA, and PIPL. Do not use it to collect personal data unlawfully.