# Wikipedia & Wikidata Knowledge Scraper (`chrisp1211/wikipedia-scraper-max`) Actor

Scrape Wikipedia and Wikidata: search articles, get summaries and extracts, resolve entities and pull pageview trends. Returns title, description, extract and URL. No API key. Pay per record; empty runs are free.

- **URL**: https://apify.com/chrisp1211/wikipedia-scraper-max.md
- **Developed by:** [Christian Pichichero](https://apify.com/chrisp1211) (community)
- **Categories:** Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$0.60 / 1,000 records

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 & Wikidata Knowledge Scraper

**Wikipedia & Wikidata Knowledge Scraper** — a fast, reliable wikipedia scraper that needs **no API key**. You pay only for the results you get: failed or empty runs are always free.

This wikipedia scraper runs on the [Apify platform](https://apify.com), so you can call it from the API, run it on a schedule, or export results to JSON, CSV, Excel, or Google Sheets.

### What this scraper does

- Extracts structured **wikipedia** data with no browser or API key required
- Returns clean JSON, one record per result — ready for sheets, databases, or apps
- Pay-per-result pricing: you are never charged for a run that returns nothing
- Runs on demand or on a schedule, and integrates with 5,000+ apps via the Apify API and webhooks

### What data you get

Each result record includes fields such as:

- **Query** (`query`) — e.g. `"neural network"`
- **Id** (`id`) — e.g. `76121942`
- **Key** (`key`) — e.g. `"Neural_network"`
- **Title** (`title`) — e.g. `"Neural network"`
- **Description** (`description`) — e.g. `"Structure in biology and artificial intelligence"`
- **Excerpt** (`excerpt`) — e.g. `"neural network is a group of interconnected units called...`
- **Extract** (`extract`) — e.g. `"A neural network is a group of interconnected units call...`
- **Url** (`url`) — e.g. `"https://en.wikipedia.org/wiki/Neural_network"`
- **Thumbnail** (`thumbnail`) — e.g. `null`
- **Lang** (`lang`) — e.g. `"en"`
- **Wikidata Id** (`wikidataId`) — e.g. `"Q12811862"`
- **Coordinates** (`coordinates`) — e.g. `null`
- **Views30d** (`views30d`) — e.g. `63428`

### Input

| Field | Type | Description |
|---|---|---|
| `searchTerms` | array | Wikipedia search queries. One task runs per term; each returns up to maxResults matching articles. |
| `lang` | string | Wikipedia language edition code, e.g. 'en', 'de', 'es', 'fr'. Applies to search, summary and pageviews. |
| `maxResults` | integer | Maximum number of articles to return for each search term (1-100). |
| `includeSummary` | boolean | Fetch the article summary (plain-text extract, canonical URL, thumbnail, coordinates and exact Wikidata id)... |
| `includePageviews` | boolean | Fetch and sum the last 30 days of daily pageviews for each article into views30d. Adds one request per arti... |
| `apiKey` | string | Optional free Wikimedia personal API token (Bearer) to raise rate limits on the search endpoint. Leave blan... |

### Example output

```json
{
  "type": "article",
  "scrapedAt": "2026-07-11T00:00:00Z",
  "query": "neural network",
  "id": 76121942,
  "key": "Neural_network",
  "title": "Neural network",
  "description": "Structure in biology and artificial intelligence",
  "excerpt": "neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or mathematical",
  "extract": "A neural network is a group of interconnected units called neurons that send signals to one another. Neurons can be either biological cells or mathematical models. While individual neurons are simple, many of them together in a network can perform complex tasks.",
  "url": "https://en.wikipedia.org/wiki/Neural_network",
  "thumbnail": null,
  "lang": "en",
  "wikidataId": "Q12811862",
  "coordinates": null,
  "views30d": 63428
}
```

### Use cases

- Automate research and data collection
- Feed dashboards, sheets, and databases
- Enrich records in your CRM or app
- Monitor changes on a schedule

### Pricing

This actor uses **pay-per-result** pricing at **$0.0006 per record**. There is no monthly fee and no start fee — and **empty or failed runs cost $0**, so you only ever pay for data you actually receive.

### Frequently asked questions

**Do I need an API key or account for the source?** No. This wikipedia scraper works out of the box with no API key required.

**What happens if a run returns no results?** You are not charged. Billing is per result, so empty or failed runs are free.

**Can I run the wikipedia scraper on a schedule?** Yes. Use the Apify Scheduler to run it hourly, daily, or on any cron schedule, and get results by webhook or API.

**What export formats are supported?** Results can be exported as JSON, CSV, Excel, HTML, or pushed to Google Sheets, a database, or your own app via the Apify API.

**Is the data structured?** Yes. Every wikipedia result is a clean, flat JSON record you can use immediately.

# Actor input Schema

## `searchTerms` (type: `array`):

Wikipedia search queries. One task runs per term; each returns up to maxResults matching articles.

## `lang` (type: `string`):

Wikipedia language edition code, e.g. 'en', 'de', 'es', 'fr'. Applies to search, summary and pageviews.

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

Maximum number of articles to return for each search term (1-100).

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

Fetch the article summary (plain-text extract, canonical URL, thumbnail, coordinates and exact Wikidata id) for each hit.

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

Fetch and sum the last 30 days of daily pageviews for each article into views30d. Adds one request per article.

## `apiKey` (type: `string`):

Optional free Wikimedia personal API token (Bearer) to raise rate limits on the search endpoint. Leave blank to run fully keyless.

## `maxCostUsd` (type: `integer`):

HARD budget cap. The run stops cleanly before exceeding this.

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

This is an open API; no proxy needed. Default is fine.

## Actor input object example

```json
{
  "searchTerms": [
    "neural network",
    "photosynthesis"
  ],
  "lang": "en",
  "maxResults": 25,
  "includeSummary": true,
  "includePageviews": 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 = {
    "searchTerms": [
        "neural network",
        "photosynthesis"
    ],
    "proxyConfiguration": {
        "useApifyProxy": false
    }
};

// Run the Actor and wait for it to finish
const run = await client.actor("chrisp1211/wikipedia-scraper-max").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 = {
    "searchTerms": [
        "neural network",
        "photosynthesis",
    ],
    "proxyConfiguration": { "useApifyProxy": False },
}

# Run the Actor and wait for it to finish
run = client.actor("chrisp1211/wikipedia-scraper-max").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 '{
  "searchTerms": [
    "neural network",
    "photosynthesis"
  ],
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}' |
apify call chrisp1211/wikipedia-scraper-max --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=chrisp1211/wikipedia-scraper-max",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/qIX91wFAZGpgag3jt/builds/s9Rbw9lJr7GIRrojO/openapi.json