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

Search Wikipedia in any language — article summaries, extracts, descriptions, thumbnails & monthly pageview trends. Free keyless Wikimedia APIs. For research, content & LLMs.

- **URL**: https://apify.com/ponderable\_hydrometer/wikipedia-scraper.md
- **Developed by:** [Ponderable Hydrometer](https://apify.com/ponderable_hydrometer) (community)
- **Categories:** Developer tools, Automation, SEO tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.50 / 1,000 results

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 Scraper — Articles, Summaries & Views

**Search Wikipedia in any language edition and get article summaries, intro extracts, short
descriptions, thumbnails — and monthly or daily pageview trends — in one actor.** Look up specific
titles or run full-text searches. Free, keyless Wikimedia APIs.

Perfect for research, content generation, LLM training data, popularity/trend analysis and
knowledge-base building.

### What you get

Per article (enrichment toggled by options):

- **Base (search)** — `title`, `pageId`, `wordCount`, `snippet` (plain text), `lastEdit`, `url`
- **Summary** (`includeSummary`) — `description`, `extract` (intro text), `thumbnail`, `coordinates`, `lang`
- **Pageviews** (`includePageviews`) — `pageviews[]` of `{ date, views }` plus `totalViews` over the window

### Input

| Field | Type | Default | Description |
|-------|------|---------|-------------|
| `query` | string | — | Full-text search across Wikipedia articles |
| `titles` | array | — | Fetch specific article titles directly |
| `language` | string | `en` | Wikipedia language edition, e.g. `de`, `fr`, `ro` |
| `includeSummary` | boolean | `true` | Fetch description, extract and thumbnail |
| `includePageviews` | boolean | `false` | Fetch pageview counts per article |
| `pageviewsGranularity` | enum | `monthly` | `monthly` or `daily` |
| `pageviewsStart` / `pageviewsEnd` | string | ~12mo window | `YYYYMMDD` range for pageviews |
| `maxResults` | integer | `100` | Cap on articles from search |

Provide a `query` and/or `titles`.

#### Example input

```json
{"query":"large language model","includeSummary":true,"maxResults":100}
```

Popularity of specific topics:

```json
{"titles":["ChatGPT","Bitcoin","Taylor Swift"],"includePageviews":true,"pageviewsGranularity":"monthly"}
```

Non-English edition:

```json
{"query":"inteligență artificială","language":"ro","includeSummary":true,"maxResults":50}
```

### Output (one article, summary + pageviews on)

Fields below are the real normalizer output:

```json
{
  "title": "ChatGPT",
  "pageId": 68675117,
  "wordCount": 14230,
  "snippet": "ChatGPT is a generative artificial intelligence chatbot...",
  "lastEdit": "2026-07-10T14:22:11Z",
  "url": "https://en.wikipedia.org/wiki/ChatGPT",
  "description": "Chatbot developed by OpenAI",
  "extract": "ChatGPT is a generative artificial intelligence chatbot developed by OpenAI...",
  "thumbnail": "https://upload.wikimedia.org/wikipedia/commons/thumb/0/04/ChatGPT_logo.svg/330px-ChatGPT_logo.svg.png",
  "coordinates": null,
  "lang": "en",
  "pageviews": [
    { "date": "2026060100", "views": 18234510 },
    { "date": "2026070100", "views": 17540221 }
  ],
  "totalViews": 35774731
}
```

`description`/`extract`/`thumbnail` appear only with `includeSummary`; `pageviews`/`totalViews` only
with `includePageviews`.

### Why this actor

- **Three signals in one** — full-text search, article summaries, and pageview time-series.
- **Popularity trends** — the pageviews angle most Wikipedia actors skip.
- **Any language, keyless** — every language edition via the public Wikimedia APIs.

### Pricing

Pay per result — **$1.50 per 1,000 results** (one result = one article with its nested summary and
pageviews). No subscription or platform fees.

### Related actors

- **CrossRef Scraper** / **arXiv Scraper** — scholarly works and preprints.
- **Open Library Scraper** — books and ISBNs.

### Notes & limits

- Pageviews default to roughly the last 12 months; set `pageviewsStart`/`pageviewsEnd` (`YYYYMMDD`) to widen or narrow.
- Public data via the Wikimedia APIs; text is available under Wikipedia's licensing (attribution
  applies). This is an independent tool, not affiliated with the Wikimedia Foundation. You are
  responsible for compliant use of the output.

# Actor input Schema

## `query` (type: `string`):

Full-text search across Wikipedia articles, e.g. "machine learning".

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

Fetch specific article titles directly, e.g. \["Albert Einstein","Photosynthesis"].

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

Wikipedia language edition code, e.g. "en", "de", "fr", "es", "ro".

## `includeSummary` (type: `boolean`):

Fetch each article's description, extract (intro text) and thumbnail.

## `includePageviews` (type: `boolean`):

Fetch pageview counts per article (popularity trend). Uses more requests.

## `pageviewsGranularity` (type: `string`):

monthly or daily.

## `pageviewsStart` (type: `string`):

Start date for pageviews, e.g. "20250101". Default: ~12 months ago.

## `pageviewsEnd` (type: `string`):

End date for pageviews, e.g. "20260101". Default: today.

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

Cap on number of articles from search.

## Actor input object example

```json
{
  "query": "machine learning",
  "language": "en",
  "includeSummary": true,
  "includePageviews": false,
  "pageviewsGranularity": "monthly",
  "maxResults": 100
}
```

# 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 = {
    "query": "machine learning",
    "language": "en"
};

// Run the Actor and wait for it to finish
const run = await client.actor("ponderable_hydrometer/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 = {
    "query": "machine learning",
    "language": "en",
}

# Run the Actor and wait for it to finish
run = client.actor("ponderable_hydrometer/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 '{
  "query": "machine learning",
  "language": "en"
}' |
apify call ponderable_hydrometer/wikipedia-scraper --silent --output-dataset

```

## MCP server setup

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

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/cSOKGJQhDSp9AskfW/builds/4JbeULdU8ddtZHy3Z/openapi.json