Wikimedia Commons License Templates Scraper
by parseforge
Scrapes license template metadata from Wikimedia Commons categories using the MediaWiki API. Pricing: $0.004/event. Apify records 2 users with a 5/5 rating.
Actor pages summarize saved Apify platform data. Listings are sorted by recorded user counts; inclusion is not a test result. Affiliate links may earn us a commission. Read our methodology or send a correction to hello@tryapify.com.
TL;DR: Wikimedia Commons License Templates Scraper by parseforge is built for ai & llm workflows. It has 2 recorded actor users. Pricing: $0.004/event. 5/5 stars.
What It Does
Scrapes license template metadata from Wikimedia Commons categories using the MediaWiki API. Returns each template as a flat row with pageid, namespace, and title. Supports any category and up to one million items per run.
Key Stats
- Users
- 2
- Total Runs
- 59
- Pricing
- $0.004/event
- Rating
- View on Apify →
Quick Answers
Teams that need a ready-made ai & llm actor and want to compare pricing, reviews, and actor activity before running it.
Test a small run first, export the sample output, then decide whether the pricing model works for your volume.
parseforge/wikimedia-commons-license-scraper
$0.004/event
Related Docs
What is Apify? Plain-English review of actors, pricing, and pros
Apify is a cloud platform with pre-built web scrapers (actors). Free tier included. Here is what it does, how pricing works, and who it suits.
Read guide →Apify API: get your token and run actors from Python or JavaScript
How to use the Apify API: get your token, install the Python or JS client, start actor runs, and pull datasets into your app. Code examples included.
Read guide →Scraping Dynamic Websites
How to scrape JavaScript-rendered websites using Playwright and Puppeteer. Learn browser automation, wait strategies, and handling single-page apps for dynamic scraping.
Read guide →Common Questions
What is Wikimedia Commons License Templates Scraper?
Scrapes license template metadata from Wikimedia Commons categories using the MediaWiki API. Returns each template as a flat row with pageid, namespace, and title. Supports any category and up to one million items per run.
How much does Wikimedia Commons License Templates Scraper cost?
Pricing: $0.004/event. Saved event rates may exclude other events and platform charges. See the actor pricing on Apify before running it.
Is Wikimedia Commons License Templates Scraper free?
Apify platform usage or actor charges may apply. Pricing: $0.004/event. Check the current actor terms and your plan before running it.
How to use Wikimedia Commons License Templates Scraper?
Open Wikimedia Commons License Templates Scraper on Apify, configure the input for the data you want to scrape, run the actor, and export the results when the run completes.
Saved event rates may exclude other events and platform charges. See current pricing on Apify.
We earn commission from qualifying purchases. This does not affect our ratings.
Related Tools
RAG Web Browser
Web search and fetch tool for AI agents and RAG pipelines. It queries Google Search, scrapes the top N pages using a full web browser, and returns their content as clean Markdown for further processing by an LLM. Can also fetch individual URLs.
Website Content Crawler
Crawl websites and extract text content to feed AI models, LLM applications, vector databases, or RAG pipelines. The Actor supports rich formatting using Markdown, cleans the HTML, downloads files, and integrates well with 🦜🔗 LangChain, LlamaIndex, and the wider LLM ecosystem.
Manga Colorizer
AI-powered manga page colorizer. Takes black-and-white manga image URLs and returns fully colored pages with the original line art preserved. Supports batch processing and multiple original languages for accurate text region handling.
Website Content Crawler - Markdown, 45 Fields
Crawls websites from start URLs with depth, domain and URL-pattern limits. 45 fields per page: Markdown and plain text, word count, title and meta tags, headings, Open Graph and JSON-LD, author and dates, emails, phones and social links, detected technologies, links and HTTP status. Ready for LLMs.