# Wikipedia Page Summaries Scraper (`parseforge/wikipedia-rest-summaries-scraper`) Actor

Scrapes Wikipedia page summaries by direct title or keyword search. Returns each summary as a flat row with title, extract, description, and thumbnail URL. Supports any language code and bulk export.

- **URL**: https://apify.com/parseforge/wikipedia-rest-summaries-scraper.md
- **Developed by:** [ParseForge](https://apify.com/parseforge) (community)
- **Categories:** Education, News, Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $8.00 / 1,000 result items

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?

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

[![ParseForge](https://raw.githubusercontent.com/ParseForge/apify-assets/main/banner.jpg)](https://apify.com/parseforge?fpr=vmoqkp)

### Wikipedia Page Summaries Scraper

**Scrape Wikipedia page summaries from any title or search query, up to a million per run.** Each summary comes with the article's title, extract, description, and thumbnail URL. No login or API key. Export to CSV, JSON, Excel, or XML.

Wikipedia's official API needs a user agent, rate-limits you, and returns nested JSON you have to flatten yourself. This reads the public REST summaries endpoint directly, filtered by title or search term, and returns each match in one fixed schema.

| Who uses it | What they scrape Wikipedia for |
|---|---|
| Market researchers | Which topics a niche is discussing this week |
| Content marketers | Source material for articles and landing pages |
| Data scientists | Build a clean dataset of Wikipedia summaries for NLP |
| SEO analysts | Gather entity descriptions for knowledge graph enrichment |
| Students and academics | Collect summaries for literature reviews or study notes |

### What it does

This Actor collects Wikipedia page summaries by direct title or keyword search, and returns each one as a flat row.

- 🔍 **Direct title lookup:** paste up to a million titles, one per line, and get each summary back.
- 🔎 **Keyword search:** type a term like `quantum computing` and the Actor fetches matching summaries via Wikipedia's OpenSearch.
- 🌐 **Any language:** set the ISO 639-1 code (en, es, fr, de, it, ja, zh, ru, pt, ar, hi) to scrape that Wikipedia edition.
- 📄 **Flat rows:** every summary is returned as a simple record with title, extract, description, and thumbnail URL.
- ⚡ **Bulk ready:** set `maxItems` up to 1,000,000 per run and export to CSV, JSON, Excel, or XML.

Results export to CSV, JSON, Excel, or XML, or straight from the API.

### What you can do with Wikipedia data

**📈 Track topics and trends.**

A market researcher runs the Actor with a list of industry terms and uses the summaries to see which concepts are gaining coverage.

**📝 Build content briefs.**

A content marketer scrapes summaries for a set of target keywords and uses the extracts as a starting point for article outlines.

**🧠 Train NLP models.**

A data scientist collects thousands of summaries in one run and feeds the flat dataset into a text classification pipeline.

**🔗 Enrich knowledge graphs.**

An SEO analyst pulls summaries for entity names and uses the descriptions to populate a knowledge graph or schema markup.

**🎓 Study efficiently.**

A student scrapes summaries for a reading list and reviews the extracts before diving into full articles.

### Why choose this scraper

| | What you get |
|---|---|
| **No API key** | Uses the public REST endpoint, no registration or OAuth |
| **Fixed schema** | Every summary returns the same flat fields, ready for your pipeline |
| **Bulk scale** | Collect up to a million summaries in a single run |
| **Multi-language** | Scrape any Wikipedia edition by language code |

### How it compares

No other Store actor targets Wikipedia the same way, so the honest comparison is with the alternatives teams actually weigh.

| | Wikipedia Page Summaries Scraper | Build it in-house | By hand |
|---|---|---|---|
| Setup | Run it now, zero config | Days of engineering | None, but hours per pull |
| When Wikipedia changes | Maintained for you | You fix it | You re-learn the page |
| Proxies, retries, anti-bot | Built in | Your problem | Browser only |
| Output | Fixed JSON schema, CSV/Excel export | Whatever you build | Copy-paste |
| Cost | Pay per result | Engineering time | Analyst hours |

### Configure the run

Drive the Actor from article titles or a search query, and set a maximum number of summaries per run. The Input tab lists every parameter.

A first run with the defaults:

```json
{
 "maxItems": 10,
 "mode": "lookup",
 "titles": "Albert Einstein\nMarie Curie\nAda Lovelace\nAlan Turing\nNikola Tesla",
 "query": "quantum computing",
 "language": "en"
}
```

A larger pull:

```json
{
 "maxItems": 200,
 "mode": "lookup",
 "titles": "Albert Einstein\nMarie Curie\nAda Lovelace\nAlan Turing\nNikola Tesla",
 "query": "quantum computing",
 "language": "en"
}
```

### Pricing

Pay-per-result: **$0.01067 per result** collected. You pay only for the results written to your dataset.

| Results collected | Approximate cost |
|---|---|
| 100 results | $1.07 |
| 1,000 results | $10.67 |
| 10,000 results | $106.70 |

New Apify accounts start with $5 in free credit.

### Free users

Free-plan runs return up to 10 results as a preview. [Upgrade your Apify plan](https://console.apify.com/sign-up?fpr=vmoqkp) to collect up to 1,000,000 results per run.

### Run it

1. [Create a free Apify account with $5 in credit](https://console.apify.com/sign-up?fpr=vmoqkp).
2. Open the [Wikipedia Page Summaries Scraper](https://apify.com/parseforge/wikipedia-rest-summaries-scraper?fpr=vmoqkp).
3. Set your inputs and any filters, then click **Start**.
4. Export the results as CSV, Excel, JSON, or XML from the **Dataset** tab.

Run it programmatically through the [Apify API](https://docs.apify.com/api/v2) (`run-sync-get-dataset-items`) or the [ApifyClient](https://docs.apify.com/api/client/js) for JavaScript and Python.

### Use with AI agents (MCP)

Give an AI agent live access to Wikipedia through the Model Context Protocol. Add the Actor to Claude, Cursor, or any MCP client:

```bash
claude mcp add --transport http apify "https://mcp.apify.com/?tools=parseforge/wikipedia-rest-summaries-scraper"
```

Then prompt it in plain language to run the scraper and read back the results.

### Troubleshooting

**Why am I getting no results?**

Check that your titles are spelled correctly and that the language code matches the Wikipedia edition. In search mode, try a broader query.

**Why are some summaries missing an extract?**

Some Wikipedia pages do not have a short extract or description. The Actor returns whatever the REST endpoint provides, which may be empty for those fields.

**Why is the run slow?**

Wikipedia rate limits requests. If you are scraping many titles, the Actor will pace itself. You can increase concurrency in the run settings, but be mindful of Wikipedia's policies.

**Why did I get an error about the language code?**

Make sure you are using a valid ISO 639-1 code, such as `en`, `es`, or `fr`. Some languages use two-letter codes that are not obvious, like `zh` for Chinese.

**Why does search mode return fewer results than expected?**

Wikipedia's OpenSearch returns a limited number of suggestions. For broader coverage, use lookup mode with a list of titles you already know.

### FAQ

| Question | Answer |
|---|---|
| Do I need a Wikipedia API key? | No. This Actor uses the public REST summaries endpoint, which does not require authentication or an API key. |
| How many summaries can I get in one run? | You can set `maxItems` up to 1,000,000. The actual number depends on how many titles or search results match. |
| What is the difference between lookup and search mode? | Lookup mode takes exact article titles you provide, one per line. Search mode takes a free-text query and uses Wikipedia's OpenSearch to find matching titles, then fetches their summaries. |
| Can I scrape Wikipedia in other languages? | Yes. Set the `language` field to any ISO 639-1 code, such as `es` for Spanish, `de` for German, or `ja` for Japanese. |
| What fields does each summary include? | Each row includes the article title, a short extract, a description, and a thumbnail URL when available. The exact fields are shown in the sample output. |
| How do I format article titles in lookup mode? | You can use spaces or underscores. For example, `Albert Einstein` and `Albert_Einstein` both work. |
| Can I export the results? | Yes. The Actor supports CSV, JSON, Excel, and XML exports from the Apify dataset. |
| Is there a rate limit? | The Actor respects Wikipedia's rate limits and includes automatic retries. If you need very high volume, consider spreading requests across multiple runs. |
| What if a title does not exist? | The Actor skips missing titles and continues with the rest. You can check the run log for any skipped items. |
| Can I use this for commercial projects? | Yes, as long as you comply with Wikipedia's terms of use and the Creative Commons license for the content. |

### Related actors

Browse the full [ParseForge collection](https://apify.com/parseforge?fpr=vmoqkp) for more scrapers.

🆘 **Need help?** Email parseforge@protonmail.com with your run ID, your input, and what you expected.

⚠️ **Disclaimer.** This Actor is unofficial and is not affiliated with, endorsed by, or sponsored by Wikimedia Foundation, Inc. It collects only publicly available data. You are responsible for using the collected data in compliance with the source's terms of service and applicable data-protection laws, including GDPR, CCPA, and PIPL. Do not use it to collect personal data unlawfully.

# Actor input Schema

## `maxItems` (type: `integer`):

Maximum number of articles to collect per run.

## `mode` (type: `string`):

lookup = direct titles; search = OpenSearch suggestion + summary fetch.

## `titles` (type: `string`):

Wikipedia article titles, one per line (URL-style with underscores or spaces both OK). E.g. `Albert_Einstein` or `Albert Einstein`.

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

Free-text Wikipedia search.

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

ISO 639-1 (en, es, fr, de, it, ja, zh, ru, pt, ar, hi).

## Actor input object example

```json
{
  "maxItems": 10,
  "mode": "lookup",
  "titles": "Albert Einstein\nMarie Curie\nAda Lovelace\nAlan Turing\nNikola Tesla",
  "query": "quantum computing",
  "language": "en"
}
```

# Actor output Schema

## `overview` (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 = {
    "maxItems": 10,
    "mode": "lookup",
    "titles": `Albert Einstein
Marie Curie
Ada Lovelace
Alan Turing
Nikola Tesla`,
    "query": "quantum computing",
    "language": "en"
};

// Run the Actor and wait for it to finish
const run = await client.actor("parseforge/wikipedia-rest-summaries-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 = {
    "maxItems": 10,
    "mode": "lookup",
    "titles": """Albert Einstein
Marie Curie
Ada Lovelace
Alan Turing
Nikola Tesla""",
    "query": "quantum computing",
    "language": "en",
}

# Run the Actor and wait for it to finish
run = client.actor("parseforge/wikipedia-rest-summaries-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 '{
  "maxItems": 10,
  "mode": "lookup",
  "titles": "Albert Einstein\\nMarie Curie\\nAda Lovelace\\nAlan Turing\\nNikola Tesla",
  "query": "quantum computing",
  "language": "en"
}' |
apify call parseforge/wikipedia-rest-summaries-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,parseforge/wikipedia-rest-summaries-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/LScKkc1bvUFEvg9H4/builds/6yD2eizyZ6H3KKYgb/openapi.json