# Wikipedia Scraper — Articles, Summaries & Images (`hichemdev/wikipedia-scraper`) Actor

Scrape Wikipedia by search query or title: summary, description, full plain text, images, coordinates and canonical URL. Any language. No API key.

- **URL**: https://apify.com/hichemdev/wikipedia-scraper.md
- **Developed by:** [Hichem Ben Moussa](https://apify.com/hichemdev) (community)
- **Categories:** Developer tools, News
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 1,000 articles

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

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 Scraper — Articles, Summaries & Images

Turn Wikipedia into **clean structured data**. Search by keyword or pass exact article titles and get back **summary, short description, full plain text, images, coordinates and canonical URL** — in **any language**. No API key, no login.

Perfect for building datasets, powering RAG / LLM knowledge bases, enriching entities, or research.

### What you can do with it

- 🧠 Build a **knowledge base / RAG corpus** from clean article text
- 🏷️ **Enrich entities** (companies, people, places) with descriptions and images
- 🌍 Scrape the **same topic across languages** (`en`, `es`, `fr`, `ar`, `ja` …)
- 📍 Pull **coordinates** for places to plot on a map

### Input

| Field | Type | Description |
|-------|------|-------------|
| `searchQuery` | string | Free-text search, e.g. `machine learning`. Returns the top matching articles. |
| `pageTitles` | array | Exact titles, e.g. `Apple Inc.`, `Alan Turing`. Overrides the search query. |
| `language` | string | Wikipedia language code (`en`, `es`, `fr`, `de`, `ar`, `ja` …). |
| `maxResults` | integer | Maximum number of articles to fetch. |
| `includeFullText` | boolean | Attach the full plain text (not just the summary). |
| `proxyConfiguration` | object | Optional proxy for high-volume runs. |

#### Example — search

```json
{ "searchQuery": "machine learning", "maxResults": 5 }
```

#### Example — specific articles with full text

```json
{ "pageTitles": ["Alan Turing", "Apple Inc."], "includeFullText": true }
```

### Output

Each item is one article:

```json
{
  "title": "Apple Inc.",
  "description": "American multinational technology company",
  "extract": "Apple Inc. is an American multinational technology company headquartered in Cupertino, California…",
  "type": "standard",
  "lang": "en",
  "thumbnail": "https://upload.wikimedia.org/…/320px-Apple_logo.svg.png",
  "image": "https://upload.wikimedia.org/…/Apple_logo.svg.png",
  "coordinates": null,
  "pageId": 856,
  "url": "https://en.wikipedia.org/wiki/Apple_Inc.",
  "timestamp": "2026-07-20T10:00:00Z"
}
```

With `includeFullText: true` each item also has a `fullText` field with the complete plain-text article.

### Notes

- Data comes from the official Wikipedia REST & MediaWiki APIs.
- Set `language` to scrape any Wikipedia edition; titles and search are language-specific.

# Actor input Schema

## `searchQuery` (type: `string`):

Free-text search, e.g. "machine learning". Returns the top matching articles. Ignored if specific page titles are given.

## `pageTitles` (type: `array`):

Exact article titles, e.g. "Apple Inc.", "Alan Turing". Overrides the search query.

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

Wikipedia language code (e.g. en, es, fr, de, ar, ja).

## `maxResults` (type: `integer`):

Maximum number of articles to fetch.

## `includeFullText` (type: `boolean`):

Attach the full plain-text of the article (not just the summary).

## `proxyConfiguration` (type: `object`):

Optional. The Wikipedia API is public; a proxy can help at high volume.

## Actor input object example

```json
{
  "searchQuery": "machine learning",
  "pageTitles": [],
  "language": "en",
  "maxResults": 5,
  "includeFullText": false,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}
```

# 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 = {
    "searchQuery": "machine learning",
    "pageTitles": [],
    "language": "en",
    "maxResults": 5
};

// Run the Actor and wait for it to finish
const run = await client.actor("hichemdev/wikipedia-scraper").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 = {
    "searchQuery": "machine learning",
    "pageTitles": [],
    "language": "en",
    "maxResults": 5,
}

# Run the Actor and wait for it to finish
run = client.actor("hichemdev/wikipedia-scraper").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 '{
  "searchQuery": "machine learning",
  "pageTitles": [],
  "language": "en",
  "maxResults": 5
}' |
apify call hichemdev/wikipedia-scraper --silent --output-dataset

```

## MCP server setup

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

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/FJhh3RKcjYpgXzAW7/builds/0HdWGtiAMPcC4WkQk/openapi.json