# Wikipedia — Article Summaries & Search (`omao/wikipedia`) Actor

Get clean Wikipedia article summaries by title or search: title, short description, extract, thumbnail, coordinates and URL, in any language. Powered by the official Wikipedia API. No API key, no anti-bot.

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

## Pricing

$1.00 / 1,000 articles

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 — Article Summaries & Search to JSON

**Get clean Wikipedia article summaries by title or search — as JSON, in any language.** Pass article titles or search queries and get back a tidy row per article: title, short description, extract, thumbnail, coordinates and URL. Powered by the official Wikipedia API — no API key, no anti-bot.

The easy way to pull encyclopedic knowledge into apps, datasets and AI pipelines.

### Who uses this

- 🤖 **AI / LLM & RAG pipelines** — feed clean article extracts to your models.
- 🔎 **Researchers & analysts** — build knowledge datasets on any topic.
- ✍️ **Content & education tools** — enrich pages with summaries and thumbnails.
- 🌍 **Localization** — pull the same topic in multiple languages.
- 🧑‍💻 **Developers** — a clean Wikipedia endpoint by title or search.

### What you get — one clean row per article

| Field | Description |
|---|---|
| `title` | Article title |
| `description` | Short description |
| `extract` | Clean summary text |
| `thumbnail` + `image` | Images |
| `lang` | Language |
| `pageId` | Wikipedia page ID |
| `latitude` + `longitude` | Coordinates (for places) |
| `wikibaseItem` | Linked Wikidata ID |
| `url`, `timestamp`, `scrapedAt` | Link & provenance |

### Example

**Input**

```json
{ "titles": ["Alan Turing", "Python (programming language)"], "searchQueries": ["machine learning"], "language": "en", "resultsPerQuery": 10 }
```

**Output (one item)**

```json
{
  "title": "Alan Turing",
  "description": "English mathematician and computer scientist (1912–1954)",
  "extract": "Alan Mathison Turing was an English mathematician, computer scientist, logician...",
  "lang": "en", "pageId": 1208,
  "wikibaseItem": "Q7251",
  "url": "https://en.wikipedia.org/wiki/Alan_Turing"
}
```

### Why this actor

- ✅ **Official source** — the Wikipedia API, always current.
- 🌐 **Any language** — set the language code (en, fr, de, es…).
- 🔎 **Title *or* search** — exact titles or keyword queries.
- 🧹 **Clean extracts** — plain-text summaries, ready for LLM/RAG.
- ⚡ **Fast & affordable** — pay only per article returned. No key, no anti-bot.

### Input options

- `titles` — exact article titles.
- `searchQueries` — keyword searches.
- `language` — Wikipedia language code (default `en`).
- `resultsPerQuery` — cap results per search query.

### FAQ

**Title or search?** Both — pass `titles` for exact articles and `searchQueries` for keyword search.

**Which languages?** Any Wikipedia language — set `language` (e.g. `en`, `fr`, `de`, `es`, `ja`).

**Is the summary clean text?** Yes — `extract` is plain text, ideal for LLM/RAG ingestion.

**How fresh is the data?** Live from the Wikipedia API on every run.

**Can I export to CSV/Excel/Google Sheets?** Yes — the dataset exports to JSON, CSV, Excel and HTML, or pull it via the API.

**What does it cost?** Pay-per-article — you're only charged for articles actually returned.

***

*Tip: pair this with the Wikidata actor to enrich articles with structured facts via the `wikibaseItem` ID.*

# Actor input Schema

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

Exact article titles to fetch summaries for.

## `searchQueries` (type: `array`):

Search Wikipedia; each hit's summary is returned.

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

Wikipedia language edition (e.g. en, fr, es, de).

## `resultsPerQuery` (type: `integer`):

Number of articles to return per search query (1-50).

## `healthCheckMode` (type: `boolean`):

Internal monitoring: fetch a canary article and FAIL if broken (no billing).

## Actor input object example

```json
{
  "titles": [
    "Alan Turing",
    "Python (programming language)"
  ],
  "searchQueries": [
    "quantum computing"
  ],
  "language": "en",
  "resultsPerQuery": 10,
  "healthCheckMode": false
}
```

# Actor output Schema

## `articles` (type: `string`):

All article summaries (title, description, extract, thumbnail, URL).

# 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 = {
    "searchQueries": [
        "machine learning"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("omao/wikipedia").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 = { "searchQueries": ["machine learning"] }

# Run the Actor and wait for it to finish
run = client.actor("omao/wikipedia").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 '{
  "searchQueries": [
    "machine learning"
  ]
}' |
apify call omao/wikipedia --silent --output-dataset

```

## MCP server setup

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

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/mzL5GXDKjgpR4eabb/builds/crdPtCS6VhoMXubxg/openapi.json