Article Extractor - Clean Text, Author, Date & Image

Give it a list of article, blog or news URLs and get the clean article back for each - stripped of nav, ads, cookie banners and related-links clutter with Mozilla's Readability engine (the same algorithm behind Firefox Reader View). For every URL you get the title, author/byline, publish date, excerpt, lead image, tags, site name, language, word count, reading time and the full article text.

Pure HTTP + parsing, no browser - fast and cents per article. Perfect for feeding clean content to an LLM (RAG), archiving, content analysis or building a reading list.

What you get per URL

title · byline (author) · publishedTime · modifiedTime · excerpt · leadImageUrl · siteName · lang · section · tags[] · canonical · textContent (clean full text) · wordCount · readingTimeMinutes · charCount · isArticle · optional contentHtml.

A URL that is unreachable or has no extractable article body is reported (status: "failed" or isArticle: false) and is not charged.

Input

{
  "urls": [
    "https://en.wikipedia.org/wiki/Web_scraping",
    "https://blog.example.com/some-post"
  ],
  "includeHtml": false
}

• URLs - one per line; a bare domain gets https://. Leave empty for a small free sample.
• Import URLs from a file - paste a list, or link a public .txt/.csv, a Google Sheet/Drive link, or an Apify key-value-store record.
• Include cleaned content HTML - add the article body as HTML on top of the plain text.
• Output fields - tick only the columns you need for a lean export.

Notes

• Reads the server-rendered HTML. Pages that render their article entirely client-side (rare for articles, which want to be crawlable) may return little text - use a proxy or a browser-based scraper for those.
• readingTimeMinutes assumes ~220 words per minute.

Use with AI assistants (MCP)

Available through the Apify MCP server - an agent can pull clean article text for retrieval-augmented generation, summarisation, or a research pipeline without the surrounding page noise.

Agent-ready: autonomous payments (x402 & Skyfire)

This actor is agent-ready - AI agents can discover it, run it, and pay for it autonomously, with no Apify account and no human in the loop. It uses pay-per-event pricing and limited permissions, so it qualifies for Apify's agentic-payment standards:

• x402 - an open, HTTP-native payment protocol. Agents pay per run in USDC on the Base network directly through the Apify MCP server - no account, no API key.
• Skyfire - agent-to-service payments for fully autonomous AI-agent workflows.

Building an AI agent, MCP tool, or autonomous data pipeline? This scraper is ready to plug in and pay as it goes.