# Smart Article Extractor (`parseforge/article-extractor`) Actor Extract clean article content from any news, blog, or publisher site! Pull full body text, author, publish date, word count, language, reading time, images, and metadata at scale. Ideal for content research, media monitoring, SEO audits, and AI training. Start extracting articles in minutes! - **URL**: https://apify.com/parseforge/article-extractor.md - **Developed by:** [ParseForge](https://apify.com/parseforge) (community) - **Categories:** News, AI, Automation - **Stats:** 6 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $40.00 / 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. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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 ![ParseForge Banner](https://github.com/ParseForge/apify-assets/blob/ad35ccc13ddd068b9d6cba33f323962e39aed5b2/banner.jpg?raw=true) ## 📰 Smart Article Extractor > 🚀 **Parse any news article or blog post into clean structured text in seconds.** Get **23 metadata fields** per article including authors, tags, publish date, lead image, paywall flag, and reading time. No API key, no registration, no manual parser maintenance.
Pull structured records from Smart Article Extractor — clean fields ready as CSV, JSON, JSONL, Excel, or XML for downstream pipelines.
##### Copy to your AI assistant Copy this block into ChatGPT, Claude, Cursor, or any LLM to start using this actor. ``` parseforge/article-extractor on Apify. Call: ApifyClient("TOKEN").actor("parseforge/article-extractor").call(run_input={...}), then client.dataset(run["defaultDatasetId"]).list_items().items for results. Key inputs: startUrls (array, default [{"url": "https://www.bbc.com/news/articles/c86w8elez74o"}]), maxItems (integer, default 10). Full actor spec: fetch build via GET https://api.apify.com/v2/acts/parseforge~article-extractor (Bearer TOKEN). Get token: https://console.apify.com/account/integrations ``` The **Smart Article Extractor** takes any article URL and returns the main body as clean Markdown alongside 22 metadata fields. It scores DOM nodes by paragraph count, word count, and link density to identify the main content block, then strips navigation, sidebars, and ads. Author, tags, section, publishedAt, modifiedAt, and canonical URL are pulled from meta tags, JSON-LD, and itemprop attributes. Extras include a paywall-detection heuristic, inline image collection, lead image (Open Graph), language detection, word count, and reading time. Concurrent fetching keeps 10 articles flying in parallel, so a list of 100 news URLs finishes in about 15 seconds. Works out of the box on most major news sites, blogs, and publishing platforms. | 🎯 Target Audience | 💡 Primary Use Cases | |---|---| | News aggregators, media monitoring teams, AI app developers, content researchers, data journalists, archivists | News datasets, summarization pipelines, media monitoring, sentiment analysis, archive assembly | ### 📋 What the Smart Article Extractor does Five extraction workflows in a single run: - 📝 **Main body extraction.** DOM scoring isolates the article content and strips navigation, ads, and sidebars. - 👥 **Author detection.** Pulls authors from meta tags, JSON-LD, and itemprop attributes. - 📅 **Date stamps.** Captures both `article:published_time` and `article:modified_time`. - 🏷️ **Tags and section.** Extracts `article:tag` and `article:section` metadata. - 💰 **Paywall flag.** Heuristic detects common paywall markers so you can filter downstream. Every record also includes the canonical URL, lead image, inline images, word count, reading time, language, site name, HTTP status, and timestamp. > 💡 **Why it matters:** news sites each have their own HTML structure. Writing per-site parsers is brittle and breaks every time a publisher redesigns their pages. This Actor uses readability-style scoring that works across any article-shaped page. ### 📊 Data fields Each record includes: `author`, `authors`, `canonicalUrl`, `description`, `hasPaywall`, `httpStatus`, `images`, `language`, `leadImage`, `markdown`, `modifiedAt`, `publishedAt`, `readingTimeMinutes`, `results`, `scrapedAt`, `section`, `siteName`, `subtitle`, `tags`, `title`, `url`, `wordCount`. These field names come straight from the actor's dataset schema, so what you see here is what lands in your dataset. > ⚠️ **Good to Know:** works best on article-shaped pages (one headline, one author, one body). Homepages, category pages, and list views return thin extractions because there is no single article to score. ### 🚀 How to use 1. 📝 **Sign up.** [Create a free account with $5 credit](https://console.apify.com/sign-up?fpr=vmoqkp) (takes 2 minutes). 2. 🌐 **Open the Actor.** Go to the Smart Article Extractor page on the Apify Store. 3. 🎯 **Paste URLs.** Add article URLs to the startUrls field and set maxItems. 4. 🚀 **Run it.** Click **Start** and let the Actor extract the content. 5. 📥 **Download.** Grab your results in the **Dataset** tab as CSV, Excel, JSON, or XML. > ⏱️ Total time from signup to downloaded dataset: **3-5 minutes.** No coding required. ### 🔗 Recommended Actors - [**🤖 RAG Web Browser**](https://apify.com/parseforge/rag-web-browser) - Search or fetch URLs with LLM-ready output - [**🕸️ Website Content Crawler**](https://apify.com/parseforge/website-content-crawler) - Deep-crawl a domain with depth control - [**🔍 Google Search Scraper**](https://apify.com/parseforge/google-search-scraper) - SERP results with rank and description - [**📈 Google Trends Scraper**](https://apify.com/parseforge/google-trends-scraper) - Interest over time and related queries - [**📧 Contact Info Scraper**](https://apify.com/parseforge/contact-info-scraper) - Emails, phones, and socials from URLs > 💡 **Pro Tip:** browse the complete [ParseForge collection](https://apify.com/parseforge) for more content-extraction tools. > **⚠️ Disclaimer:** this Actor is an independent tool and is not affiliated with any publisher, news outlet, or readability library. Only publicly accessible article URLs are processed. Respect the copyright and terms of service of every publisher you extract from. ### 🆘 Need Help? If you hit a bug, have questions about setup, or need a scraper we haven't built yet, open our [contact form](https://tally.so/r/BzdKgA) or write to parseforge@protonmail.com. We also take on paid custom data projects. For faster answers, join our [Discord](https://parseforge.co/discord). It's the best place to get support and suggest new actors. # Actor input Schema ## `startUrls` (type: `array`): URLs to process ## `maxItems` (type: `integer`): How many articles to collect per run. ## Actor input object example ```json { "startUrls": [ { "url": "https://www.bbc.com/news/articles/c86w8elez74o" } ], "maxItems": 10 } ``` # Actor output Schema ## `results` (type: `string`): Complete dataset # 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 = { "startUrls": [ { "url": "https://www.bbc.com/news/articles/c86w8elez74o" } ], "maxItems": 10 }; // Run the Actor and wait for it to finish const run = await client.actor("parseforge/article-extractor").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 = { "startUrls": [{ "url": "https://www.bbc.com/news/articles/c86w8elez74o" }], "maxItems": 10, } # Run the Actor and wait for it to finish run = client.actor("parseforge/article-extractor").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 '{ "startUrls": [ { "url": "https://www.bbc.com/news/articles/c86w8elez74o" } ], "maxItems": 10 }' | apify call parseforge/article-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=parseforge/article-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification Download the OpenAPI definition: https://api.apify.com/v2/acts/gI3pEhdCyRW1avH0e/builds/M1t3DQq28e8qpHbmw/openapi.json