# Wikipedia Article Data: Summary, Facts & Images (`scrapemint/wikipedia-article-data`) Actor

Pull clean data from Wikipedia articles in bulk. For each article: the summary text, short description, main image, map location, categories, number of language versions, page link and last edit date. Look up by title or by keyword search. Official Wikipedia API. No API key needed.

- **URL**: https://apify.com/scrapemint/wikipedia-article-data.md
- **Developed by:** [Ken M](https://apify.com/scrapemint) (community)
- **Categories:** Business, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: 5.00 out of 5 stars

## Pricing

$2.00 / 1,000 articles

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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
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.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — 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 Article Data: Summary, Facts & Images

Pull clean, structured data from Wikipedia articles in bulk. Look up a list of article titles, or search by keyword, and get one tidy row per article instead of messy page HTML.

Built for **researchers, content and SEO teams, app builders and anyone building a knowledge base or AI dataset**. Wikipedia is one of the most visited sites in the world, but its pages are built for reading, not for data. This turns them into rows you can drop into a spreadsheet or database.

> Looking for view counts and trending topics instead? See our separate **Wikipedia Trends Scraper**. This actor is about article content and facts.

### What you get for each article

- **Summary**: the opening of the article as clean plain text
- **Short description**: the one line under the title
- **Main image**: a thumbnail URL
- **Map location**: latitude and longitude for places
- **Categories**: what the article is filed under
- **Language versions**: how many languages the article exists in
- **Wikidata ID**: to join with structured data
- **Page facts**: page link, page size and the last edit date
- **Found flag**: missing or misspelled titles are clearly marked

### Two ways to use it

1. **By title**: paste exact article names (Berlin, Marie Curie, Quantum computing).
2. **By keyword search**: type something like "electric cars" and get the top matching articles, fully expanded.

Pick any language edition (en, es, de, fr, ja and more).

### Example output

```json
{
  "title": "Berlin",
  "found": true,
  "pageId": 3354,
  "description": "Capital and largest city of Germany",
  "summary": "Berlin is the capital of Germany as well as its largest city...",
  "thumbnail": "https://upload.wikimedia.org/.../Berlin_Skyline.jpg",
  "latitude": 52.52,
  "longitude": 13.405,
  "categories": ["Capitals in Europe", "Port cities in Germany"],
  "languageCount": 268,
  "wikidataId": "Q64",
  "pageLengthBytes": 229903,
  "lastEdited": "2026-07-13T08:28:35Z",
  "url": "https://en.wikipedia.org/wiki/Berlin",
  "language": "en"
}
```

### Pricing

**$0.002 per article**, and titles that do not exist are **free**. The first 2 rows of every run are free. Pulling 1,000 articles costs about $2.

### How to run it via API

```bash
curl -X POST "https://api.apify.com/v2/acts/scrapemint~wikipedia-article-data/run-sync-get-dataset-items?token=YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"titles": ["Berlin", "Marie Curie"], "language": "en"}'
```

Or by keyword:

```bash
  -d '{"search": "electric cars", "searchLimit": 50}'
```

### Frequently asked questions

**Where does the data come from?** The official MediaWiki API that Wikipedia itself runs. No scraping of page HTML, so the data is clean and stable.

**Can I get the full article text?** This returns the summary (the intro), which is what most uses need. The page link is included if you want the full article.

**Which languages work?** Any Wikipedia language edition. Set the language code and the titles or search run against that edition.

### More tools from Scrapemint

- [Wikipedia Trends Scraper](https://apify.com/scrapemint/wikipedia-trends-scraper): top and trending articles by country, with view counts.
- [Google News Scraper](https://apify.com/scrapemint/google-news-scraper): news headlines by topic.
- [arXiv Papers Scraper](https://apify.com/scrapemint/arxiv-papers-scraper): academic papers by topic and author.

# Actor input Schema

## `titles` (type: `array`):

Article names to look up, one per line, e.g. Berlin, Quantum computing. Leave empty to use keyword search below instead.

## `search` (type: `string`):

Find articles by keyword instead of exact titles, e.g. electric cars. Ignored when titles are given.

## `searchLimit` (type: `integer`):

How many articles to pull when using keyword search.

## `language` (type: `string`):

Wikipedia language edition, e.g. en (English), es, de, fr, ja.

## `maxRows` (type: `integer`):

Cap on articles returned. Controls total cost. First 2 rows per run are free; missing articles are always free.

## Actor input object example

```json
{
  "titles": [
    "Berlin",
    "Quantum computing",
    "Marie Curie"
  ],
  "searchLimit": 20,
  "language": "en",
  "maxRows": 500
}
```

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "titles": [
        "Berlin",
        "Quantum computing",
        "Marie Curie"
    ],
    "language": "en"
};

// Run the Actor and wait for it to finish
const run = await client.actor("scrapemint/wikipedia-article-data").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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "titles": [
        "Berlin",
        "Quantum computing",
        "Marie Curie",
    ],
    "language": "en",
}

# Run the Actor and wait for it to finish
run = client.actor("scrapemint/wikipedia-article-data").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 '{
  "titles": [
    "Berlin",
    "Quantum computing",
    "Marie Curie"
  ],
  "language": "en"
}' |
apify call scrapemint/wikipedia-article-data --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=scrapemint/wikipedia-article-data",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/ldvcHqhJQhoJIO5Bg/builds/LiE0DxfYin5GOvxL4/openapi.json