# 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 a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use 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 '' with your token const client = new ApifyClient({ 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 '' with your token. client = ApifyClient("") # 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 " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Wikipedia Scraper — Articles, Summaries & Views", "description": "Search Wikipedia in any language — article summaries, extracts, descriptions, thumbnails & monthly pageview trends. Free keyless Wikimedia APIs. For research, content & LLMs.", "version": "0.1", "x-build-id": "4JbeULdU8ddtZHy3Z" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/ponderable_hydrometer~wikipedia-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-ponderable_hydrometer-wikipedia-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/ponderable_hydrometer~wikipedia-scraper/runs": { "post": { "operationId": "runs-sync-ponderable_hydrometer-wikipedia-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/ponderable_hydrometer~wikipedia-scraper/run-sync": { "post": { "operationId": "run-sync-ponderable_hydrometer-wikipedia-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "query": { "title": "Search query", "type": "string", "description": "Full-text search across Wikipedia articles, e.g. \"machine learning\"." }, "titles": { "title": "Specific titles", "type": "array", "description": "Fetch specific article titles directly, e.g. [\"Albert Einstein\",\"Photosynthesis\"].", "items": { "type": "string" } }, "language": { "title": "Language", "type": "string", "description": "Wikipedia language edition code, e.g. \"en\", \"de\", \"fr\", \"es\", \"ro\".", "default": "en" }, "includeSummary": { "title": "Include summary", "type": "boolean", "description": "Fetch each article's description, extract (intro text) and thumbnail.", "default": true }, "includePageviews": { "title": "Include pageviews", "type": "boolean", "description": "Fetch pageview counts per article (popularity trend). Uses more requests.", "default": false }, "pageviewsGranularity": { "title": "Pageviews granularity", "enum": [ "monthly", "daily" ], "type": "string", "description": "monthly or daily.", "default": "monthly" }, "pageviewsStart": { "title": "Pageviews start (YYYYMMDD)", "type": "string", "description": "Start date for pageviews, e.g. \"20250101\". Default: ~12 months ago." }, "pageviewsEnd": { "title": "Pageviews end (YYYYMMDD)", "type": "string", "description": "End date for pageviews, e.g. \"20260101\". Default: today." }, "maxResults": { "title": "Max results", "minimum": 1, "type": "integer", "description": "Cap on number of articles from search.", "default": 100 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```