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Productivity5 min read

How to Extract Emails and URLs from Any Text Online (Free Tool)

Got a wall of text with email addresses or links buried inside? Here is how to pull them all out in seconds using a free browser tool — no code, no regex knowledge needed.

The Problem: Emails and URLs Buried in Text

You've got a wall of text — a pasted email thread, a scraped webpage, a document, or exported contact data — and somewhere inside it are the email addresses or URLs you actually need. Manually scanning through to find and copy each one is error-prone and slow.

There's a better way: an automated extractor that finds every email address or URL in a block of text and returns a clean list in seconds.

Tool 1: Extract Emails from Text

The Extract Emails from Text tool finds every email address in any block of text and returns them as a clean, deduplicated list.

How to use it:

  1. Open the Extract Emails from Text tool on TextToolbox.
  2. Paste your text — can be a paragraph, multiple pages, or raw HTML.
  3. The tool scans for anything matching the user@domain.tld pattern.
  4. All matching email addresses appear in the output, one per line.
  5. Click Copy to copy the list.

The extractor handles common email formats including:

  • Standard addresses: name@company.com
  • Subdomains: name@mail.company.org
  • Plus addressing: name+tag@gmail.com
  • Hyphenated domains: name@my-company.net

Tool 2: Extract URLs from Text

The Extract URLs from Text tool works the same way for links.

How to use it:

  1. Open the Extract URLs from Text tool.
  2. Paste your text.
  3. All URLs — whether https://, http://, or bare www. links — are extracted and listed.
  4. Copy the clean URL list.

Handles:

  • Full URLs with paths: https://texttoolbox.net/unicode-text-generator/
  • URLs with query strings: https://example.com/page?q=search&id=42
  • Bare domains when prefixed with www.
  • URLs inside HTML href attributes if you paste raw HTML

When You'd Use These Tools

Prospecting and outreach — you've exported a list of LinkedIn profiles or a contact directory as plain text and need to pull out all the email addresses. Paste the entire export, copy the email list, import into your CRM.

Web scraping cleanup — scraped text often includes emails and URLs mixed into paragraphs. The extractors separate them from the surrounding content without you needing to write regex.

Auditing a document for links — paste a long report or spec document to instantly see every URL it references. Useful for checking which external sites a document links to.

Cleaning up forum or chat exports — if you've exported a Discord server history or forum thread, email and URL extractors let you quickly find all shared links without reading every message.

Email list verification — if you have a messy text file with email addresses mixed in with names and other data, extract the emails first, then run them through a validation tool.

Finding broken links — extract all URLs from a page's HTML, then check each one for HTTP status. A faster starting point than manually inspecting anchor tags.

What These Tools Won't Do

  • They won't validate emails — the extractor finds strings that look like email addresses based on pattern. Whether those addresses are real and active is a separate problem (use an email verification service for that).
  • They won't extract obfuscated addresses — email addresses written as name [at] domain [dot] com to avoid scraping won't match the standard pattern. You'd need to handle those manually or with a custom regex.
  • They won't extract mailto links as separate entries — if your HTML has <a href="mailto:name@domain.com">, the extractor will pull name@domain.com from it correctly, but it won't identify that it was a mailto link vs. a plain text email.

For Developers: DIY Regex Approach

If you need to extract emails or URLs in code rather than a browser tool, here are the starting patterns:

Email regex (Python):

import re
emails = re.findall(r'[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}', text)

URL regex (Python):

urls = re.findall(r'https?://[^\s<>"{}|\\^`\[\]]+', text)

These are good starting points, though production-grade email validation requires more complex patterns or a dedicated library.

Other Text Extraction Tools

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