# Wikipedia Topic Summary & Knowledge Graph Extractor (`litezero/wikipedia-structured-data-scraper`) Actor

Extract clean Wikipedia article lead summaries, infobox structured key-value pairs, article metadata, and thumbnails in structured JSON.

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

## Pricing

from $0.56 / 1,000 item scrapeds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## 🌐 Wikipedia Topic Summary & Knowledge Graph Extractor

Extract clean structured Wikipedia article summaries, Wikidata descriptions, coordinates, high-resolution thumbnail images, and page revisions for AI RAG pipelines and educational applications.

***

### 🥊 Why This Actor Beats the Competition

| Feature | 🌐 Wikipedia Knowledge Extractor | HTML Web Scrapers | MediaWiki Action API |
| :--- | :--- | :--- | :--- |
| **Speed** | **Sub-second response** (< 500ms) | 5–15 seconds (bloated DOM) | Complex nested XML/JSON |
| **Pricing** | **Pay-Per-Result ($0.80 / 1k articles)** | Costly compute hours | Rate-limited infrastructure |
| **Clean Output** | Pure clean text summary + description | Cluttered wiki tables & banners | Requires multiple API hops |
| **Multi-Language** | Supports all major Wikimedia editions | Fails on RTL & encoding | Inconsistent schema formats |
| **AI / RAG Ready** | Formatted for direct vector embedding | Unclean tags & scripts | High token overhead |

***

### 🛠️ Step-by-Step: How to Use

#### Method 1: Apify Console (No-Code GUI)

1. Enter a list of topics or Wikipedia URLs (e.g. `Artificial intelligence`, `Quantum computing`, `Sam Altman`).
2. Select your desired language (default: `en`).
3. Click **Start** to run and export clean JSON or CSV.

#### Method 2: Python SDK

```python
from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")
run_input = {
    "topics": ["Artificial intelligence", "Albert Einstein"],
    "language": "en"
}

run = client.actor("litezero/wikipedia-structured-data-scraper").call(run_input=run_input)
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['title']} ({item['description']}):\n{item['extract'][:120]}...\n")
```

#### Method 3: Node.js SDK

```javascript
import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: 'YOUR_APIFY_TOKEN' });
const run = await client.actor('litezero/wikipedia-structured-data-scraper').call({
    topics: ['SpaceX', 'DeepMind'],
    language: 'en',
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items);
```

#### Method 4: cURL / REST API

```bash
curl --request POST \
  --url "https://api.apify.com/v2/acts/litezero~wikipedia-structured-data-scraper/runs?token=YOUR_APIFY_TOKEN" \
  --header "Content-Type: application/json" \
  --data '{"topics": ["Nvidia", "OpenAI"], "language": "en"}'
```

***

### 📥 Input Parameters

| Parameter | Type | Default | Description |
| :--- | :--- | :--- | :--- |
| `topics` | Array | `["Artificial intelligence", ...]` | List of article topic names or Wikipedia URLs. |
| `language` | Select | `"en"` | Language edition (`en`, `es`, `de`, `fr`, `ja`, `it`, `zh`). |
| `includeHtml` | Boolean | `false` | Include HTML snippet of the extract. |

***

### 📤 Sample Output JSON

```json
{
  "title": "Artificial intelligence",
  "displayTitle": "Artificial intelligence",
  "pageId": 1164,
  "description": "Intelligence of machines or software",
  "extract": "Artificial intelligence (AI) is the intelligence of machines or software, as opposed to the intelligence of living beings, primarily of humans...",
  "thumbnailUrl": "https://upload.wikimedia.org/wikipedia/commons/thumb/0/04/ChatGPT_logo.svg/320px-ChatGPT_logo.svg.png",
  "originalImageUrl": "https://upload.wikimedia.org/wikipedia/commons/0/04/ChatGPT_logo.svg",
  "coordinates": null,
  "desktopUrl": "https://en.wikipedia.org/wiki/Artificial_intelligence",
  "mobileUrl": "https://en.m.wikipedia.org/wiki/Artificial_intelligence",
  "language": "en",
  "timestamp": "2026-10-01T12:00:00Z",
  "revision": "1249821894",
  "scrapedAt": "2026-10-02T16:45:00.000Z"
}
```

***

### 💡 Top Commercial Use Cases

1. **Entity Resolution & Knowledge Graph Augmentation:** Enrich internal databases with authoritative Wikipedia entity summaries and images.
2. **AI RAG Context Ingestion:** Provide fast, zero-latency background knowledge to LLM conversational agents.
3. **Automated Content Curation:** Generate instant biographical and conceptual summaries for educational apps and media platforms.

# Actor input Schema

## `topics` (type: `array`):

List of topic search keywords (e.g. Artificial intelligence, Quantum computing, Nvidia) or Wikipedia URLs.

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

Language code for Wikipedia (en, es, de, fr, ja, it, zh).

## `includeHtml` (type: `boolean`):

Include HTML formatted snippet along with plain text extract.

## Actor input object example

```json
{
  "topics": [
    "Artificial intelligence",
    "Quantum computing",
    "Nvidia"
  ],
  "language": "en",
  "includeHtml": false
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# 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("litezero/wikipedia-structured-data-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("litezero/wikipedia-structured-data-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).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 litezero/wikipedia-structured-data-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,litezero/wikipedia-structured-data-scraper"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/5XTBrsxgEwvqpfQ5Z/builds/cUKmjh1RyXwk34ocY/openapi.json