# Wikipedia Category Scraper — Articles for RAG & AI (`fast_api/wikipedia-category-scraper`) Actor

Extract Wikipedia articles from any category with titles, URLs, extracts, page IDs, categories, and metadata. A fast MediaWiki API actor for building RAG datasets, knowledge bases, research corpora, and AI training data.

- **URL**: https://apify.com/fast\_api/wikipedia-category-scraper.md
- **Developed by:** [Fast API](https://apify.com/fast_api) (community)
- **Categories:** AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $3.00 / 1,000 wikipedia 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 Category Scraper — Articles for RAG & AI

Extract clean, structured JSON from **MediaWiki API**. This Actor is built for RAG datasets, knowledge bases, research corpora, AI training data.

### What you can do with it

- Rag datasets
- Knowledge bases
- Research corpora
- Ai training data

### Features

- Extract all pages in a Wikipedia category
- Titles, URLs, page IDs, extracts, categories, and metadata
- Fast MediaWiki API extraction
- Great for knowledge-base and RAG pipelines

### Example input

```json
{
  "category": "Machine learning",
  "maxItems": 100,
  "includeExtracts": true
}
```

### Example output

```json
{
  "pageId": 12345,
  "title": "Machine learning",
  "url": "https://en.wikipedia.org/wiki/Machine_learning",
  "extract": "Machine learning is a field of study...",
  "namespace": 0
}
```

### Output

Results are saved to the default Apify dataset as structured JSON. The actor includes an output schema so results are easy to preview, export, and consume from API clients.

### Pricing

This Actor is configured for pay-per-result monetization using Apify's pay-per-event model. Users pay for dataset items/results rather than a large fixed upfront fee.

### Common use cases

- AI/RAG dataset creation
- Market research and competitive intelligence
- Trend monitoring
- Data enrichment pipelines
- Scheduled data extraction

### Keywords

wikipedia category scraper, MediaWiki API, RAG dataset, knowledge base data, AI training data

### Notes

This Actor focuses on practical structured data extraction with clean defaults and low overhead. For large runs, start with a small `maxItems` value, verify the output, then scale up.

# Actor input Schema

## `category` (type: `string`):

Wikipedia category name (without 'Category:' prefix)

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

Wikipedia language code (en, de, fr, da, etc.)

## `maxArticles` (type: `integer`):

The maxArticles parameter.

## `includeContent` (type: `boolean`):

The includeContent parameter.

## `includeLinks` (type: `boolean`):

The includeLinks parameter.

## Actor input object example

```json
{
  "category": "Machine_learning",
  "language": "en",
  "maxArticles": 100,
  "includeContent": true,
  "includeLinks": 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 = {};

// Run the Actor and wait for it to finish
const run = await client.actor("fast_api/wikipedia-category-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 = {}

# Run the Actor and wait for it to finish
run = client.actor("fast_api/wikipedia-category-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 '{}' |
apify call fast_api/wikipedia-category-scraper --silent --output-dataset

```

## MCP server setup

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

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/58tR4kEHOTYGtqfhc/builds/bgF9dGtvzLemgKt99/openapi.json