Free Web Scraper Tool

How Web Scraping Works

Web scraping is the process of extracting structured data from web pages. building a price comparison dataset, gathering research material, or pulling contact information from a directory, scraping is the fastest way to collect information that doesn't come with an API. I've found that most people don't realize they can do basic scraping right in their browser without installing anything.

At its core, a scraper parses HTML source code and identifies the elements you want. HTML is a tree structure, so every piece of content on a page sits inside nested tags. A scraper navigates that tree to find matching nodes based on rules you define. Those rules might be CSS selectors, XPath expressions, or plain regex patterns.

This tool works differently from server-side scrapers like Scrapy or Puppeteer. It runs entirely in your browser, which means it can't fetch remote URLs due to CORS restrictions. But that's actually a feature for privacy: your data never leaves your machine. You paste the source, you extract what you need, and nothing gets transmitted anywhere. For most quick scraping tasks, that's all you'll ever need.

The workflow is straightforward. Open the page you scrape, press Ctrl+U (or Cmd+Option+U on Mac) to view the HTML source, copy it, and paste it into the textarea above. Then pick your extraction method. If you know the CSS class or ID of the elements you want, use the CSS Selector tab. If the structure is more involved, XPath gives you additional flexibility. For pattern matching across raw text, regex works well.

Most scraping tasks fall into a few categories: pulling all links from a page, extracting image URLs, converting HTML tables to spreadsheets, or grabbing specific elements by their CSS class. This tool has dedicated modes for each of those, so you won't write selectors for common operations.

CSS Selectors Explained

CSS selectors are the most way to target HTML elements. If you've written any CSS before, you already know the basics. The selector syntax lets you match elements by tag name, class, ID, attributes, and their position in the document tree. I'd say about 80% of scraping jobs can be handled with CSS selectors alone.

Here are the selectors I use most often when scraping:

• div.product-card selects all divs with the class "product-card"
• -content p selects all paragraphs inside the element with ID "main-content"
• a[href^="https"] selects links where the href starts with "https"
• table tr:nth-child(even) selects even-numbered table rows
• .price > span:first-child selects the first span directly inside elements with class "price"

Attribute selectors are especially useful for scraping. You can match elements where an attribute contains a specific value ([class*="price"]), starts with a value ([href^="/product"]), or ends with a value ([src$=".jpg"]). These patterns let you target elements even when class names are partially generated or include random suffixes.

You don't memorize every selector. Most scraping jobs only need tag names, classes, and occasionally attribute selectors. The CSS selector cheat sheet on StackOverflow is a good reference when you need something more advanced.

XPath Basics for Scraping

XPath is more than CSS selectors but also more verbose. It lets you navigate the HTML tree in any direction, including parent-to-child and child-to-parent, and it can filter by text content. Server-side scrapers often default to XPath because it handles edge cases that CSS selectors can't address.

The fundamental difference is that CSS selectors can only traverse down the tree (from parent to child), while XPath can go in any direction. If you select a parent element based on its child's content, or a sibling element that comes before (not after) the current one, XPath is the right tool.

Some XPath patterns that come up constantly in scraping work:

• //div[@class="item"] selects all divs with class "item" anywhere in the document
• //a[contains(@href, "product")] selects links whose href contains "product"
• //h2/following-sibling::p[1] selects the first paragraph after each h2
• //table//tr[position()>1] selects all table rows except the header
• //p[contains(text(), "price")] selects paragraphs containing the word "price"

There's a great XPath tutorial thread on StackOverflow if you go deeper. It won't take more than 20 minutes to learn the patterns that cover 90% of use cases. I'd recommend bookmarking it.

Using Regex for Data Extraction

Regular expressions work on raw text rather than the parsed DOM. They're extracting patterns like email addresses, phone numbers, or URLs that follow a predictable format. But I wouldn't recommend regex for general HTML parsing because HTML isn't a regular language, and regex can't reliably handle nested tags.

That said, regex shines for specific pattern extraction. If you need every email address on a page, a single regex can find them all regardless of what HTML tags surround them. Same for phone numbers, zip codes, prices, or any text that follows a consistent pattern.

Common regex patterns for scraping:

[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,} \(?\d{3}\)?[-.\s]?\d{3}[-.\s]?\d{4} https?://[^\s"'<>]+ Price: \$[\d,]+\.?\d{0,2} Attr: data-id="([^"]+)"

The famous StackOverflow answer about why you shouldn't parse HTML with regex is worth reading. It doesn't mean regex is useless for scraping. It means you should use the right tool for each specific job. CSS selectors for structured queries, regex for pattern matching.

Five Practical Scraping Examples

1. Extracting Product Prices from an E-Commerce Page

Most e-commerce sites wrap prices in a span or div with a predictable class. View the source, find the class name used for prices, and use a CSS selector like span.price or .product-price. This tool will return every matching element, and you can download them as a CSV for analysis. I've used this approach to compare prices across multiple retailers by saving the CSV from each page and combining them in a spreadsheet.

2. Pulling All External Links from a Blog Post

Switch to Links mode, paste the page source, and you'll get every anchor tag with its href. Filter the results to find only external links by looking for ones that start with "http" rather than relative paths. This is useful for backlink analysis or checking if a page links to specific resources. You can also use the CSS selector a[href^="http"] to get only absolute URLs.

3. Scraping a Data Table into a Spreadsheet

Tables mode automatically finds all HTML tables in the pasted source. Each table is parsed row by row, and you can download any table as a CSV. I've used this to pull statistics from Wikipedia, government data portals, and sports reference sites. It works for any well-structured HTML table, and the CSV output opens directly in Excel or Google Sheets.

4. Extracting Meta Tags for SEO Audits

Use the CSS selector meta[name], meta[property] to pull all meta tags from a page. You'll see the name/property and content of each tag, which makes it easy to audit titles, descriptions, Open Graph tags, and other SEO elements across multiple pages. For a quick audit of a competitor's on-page SEO, this takes about 30 seconds per page.

5. Finding All Image URLs on a Page

Images mode extracts every img tag's src attribute and alt text. This is handy for content migration, image audits, or checking if images have proper alt text for accessibility. If you download all images from a page, the URLs can be exported to a CSV and processed with a download manager.

Legal and Ethical Considerations

Web scraping exists in a legal gray area, but it's been getting clearer. The general consensus is that scraping publicly available data is legal, but there are important nuances you shouldn't ignore. The 2022 hiQ Labs v. LinkedIn ruling affirmed that scraping public data doesn't violate the Computer Fraud and Abuse Act., terms of service, copyright law, and data protection regulations like GDPR still apply.

Before scraping any site, check these things:

1. Read the site's robots.txt file (add /robots.txt to the domain). It won't stop a scraper, but it indicates the site owner's preferences and could be relevant in a legal dispute.
2. Review the Terms of Service. Some sites explicitly prohibit automated data collection, and violating ToS could expose you to a breach-of-contract claim.
3. Don't scrape personal data without a legitimate basis, especially in GDPR jurisdictions. Names, email addresses, and other PII have strict handling requirements.
4. Don't scrape at a rate that harms the server. This particular tool doesn't make requests at all, so this isn't a concern here, but it matters for automated scrapers.
5. Don't redistribute copyrighted content. Collecting data for personal analysis is generally fine; republishing articles or images usually isn't.

The US courts have been increasingly protective of scraping rights for public data. The Ninth Circuit's hiQ ruling was a significant win for the scraping community. But European courts and GDPR regulators take a stricter view when personal data is involved. If you're scraping at scale, it doesn't hurt to get legal advice specific to your jurisdiction and use case.

Popular npm Packages for Server-Side Scraping

If you go beyond browser-based scraping, these Node.js packages are the standard choices in the system:

• cheerio is a fast, flexible implementation of jQuery for the server. It doesn't execute JavaScript but parses HTML extremely quickly. Great for static pages.
• puppeteer provides headless Chrome automation. It handles JavaScript-rendered content and can interact with pages like a real user. Heavier than cheerio but more capable.
• axios is an HTTP client for fetching page content. Pair it with cheerio for a lightweight scraping stack that can handle most static sites.
• node-html-parser is an ultra-fast HTML parser that creates a simplified DOM tree. It's faster than cheerio for basic parsing tasks where you don't need the full jQuery API.

Browser Compatibility

Tested on Chrome 134.0.6998, Firefox 136, Safari 18.3, Edge 134. Last verified March 2026.

PageSpeed target: 95+ (inline CSS/JS, no external dependencies beyond Google Fonts Inter)

For more on web scraping techniques and best practices, these Hacker News discussions are worth reading:

• Open-source web scraping framework discusses the state of open-source scraping tools and their tradeoffs
• Legal precedent for web scraping in the US covers the hiQ v. LinkedIn decision and its implications

Frequently Asked Questions