Email Extractor

Paste any text and pull out every valid email address. Deduplicate, filter by domain, and copy or download the cleaned list. Runs in your browser. A research helper for cleaning lists you already own, not a scraper.

Email Extractor Link Extractor soon Extract Numbers From Text soon Alphabetical Order Sorter Compare Texts Online
Input Load sample

How to extract emails from text

1. Paste your source text

Drop your text into the Input box. This can be an exported email thread, a copy-pasted contact list, an HTML page source, a forum thread, or any text where email addresses are mixed in with prose. The tool scans every line and finds every address that matches the email format.

2. Set filters and sorting

Deduplicate keeps each unique address once. Lowercase domain prevents GMAIL.com and gmail.com from being treated as different addresses. Sort A-Z gives alphabetical order. Domain filter limits the result to specific domains, useful when you want to pull every @cliqpod.io address out of a forwarded chain.

3. Copy or export

Use Copy to paste into a spreadsheet or CRM. Use Download .csv to export a two-column file with email and count (how many times each address appeared in the source).

When you need an email extractor

Cleaning a list you already own is the main job. Five examples.

Email thread cleanup
A long forwarded thread contains 30+ addresses across To, CC, BCC, and the body. Paste the raw thread, get a clean deduplicated list ready for the new email.
CRM import prep
A spreadsheet export has email addresses sprinkled across notes, comments, and a header row. Extract them into a single column, dedupe, and import.
Form-submission consolidation
Daily form notifications pile up in an inbox. Bulk-select all messages, paste the bodies, and pull out every reply-to address in one pass.
Survey response audit
A free-text survey response field accidentally captured email addresses inline. Extract them before re-running an outreach campaign so nobody gets contacted twice.
Forum or community moderation
A pasted thread shows every user who emailed support during a beta. Pull the addresses to follow up with the active subset.

What this tool does and does not do

The job of this tool is to find email addresses inside a block of text and produce a clean, deduplicated list. It runs entirely in your browser tab. It does not connect to any external URL, does not perform DNS or MX lookups, does not verify that the address actually receives mail, and does not perform any kind of web scraping. The tool is a list cleaner for content you already have, not a discovery tool.

The regex used to match email addresses is conservative. It catches the vast majority of well-formed addresses (jose@cliqpod.io, jane.doe+tag@gmail.com, support@vendor-name.io) and skips badly-formed ones (name@@host, @domain.com, missing TLD). Edge cases like internationalised domain names (üser@münchen.de) and the very long quoted-local-part syntax allowed by RFC 5321 are not handled. For most cleanup work this is the right tradeoff: fewer false positives, slightly fewer false negatives.

Email extraction patterns

Four common input shapes and the settings that work best for each.

Input shapeSettingsResult
Forwarded email thread with To/CC headersDeduplicate on, Lowercase domain onClean recipient list with no duplicates
HTML page source with mailto: linksDeduplicate on, Sort A-Z onAlphabetical list of every contact address on the page
Spreadsheet with mixed email and notes columnsDomain filter set, Deduplicate onOnly addresses matching the target domain
Forum thread with quoted reply chainsSort by frequency (default off), Deduplicate offEach address listed with its appearance count

Once you have the list, pipe it through Sort Text for custom ordering, Remove Duplicates for stricter dedup, or Column to CSV to flatten it into a comma-separated string.

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Frequently asked questions

How do I extract email addresses from a block of text?

Paste the text into the Input box on the left. Click Extract or just type and the right panel updates live. Every valid email address found is listed in the Output box, one per line, with duplicates removed.

What counts as a valid email address?

The tool uses a conservative regex that matches the pattern local@domain.tld where local is one or more letters, digits, dots, plus signs, hyphens, or underscores, and the domain is one or more dot-separated labels ending in a 2+ character TLD. It catches the vast majority of real-world email formats while skipping junk like name@@host or @domain.com.

Can I filter by domain?

Yes. The Domain filter accepts a list of domains separated by commas or spaces. Only addresses whose domain matches one of the entries are kept in the Output box. Leave it blank to include all domains. Wildcards (*.example.com) are supported.

Does it sort the results?

Sort by alphabetical lists every unique email in A-Z order. Sort by frequency lists the most-seen address first, useful for quickly spotting the dominant sender or recipient in a long thread. Sort by domain groups every gmail.com, then every outlook.com, etc.

Is this a scraper?

No. The tool processes text you paste into the browser tab. It does not connect to any URL, does not request any external page, and does not perform any kind of web scraping. Think of it as a list cleaner for content you already have.

Can I export as CSV?

Yes. The Download button writes a .csv file with two columns: email and count (how many times that address appeared in the source text). Use this for re-importing into a CRM or spreadsheet.

Does it find emails inside HTML?

Yes. The regex matches addresses inside attribute values like href="mailto:..." and inside body text alike. Tags themselves are ignored. If you paste raw HTML, the tool extracts the addresses without you needing to strip tags first.

Is my pasted text uploaded anywhere?

No. The extraction runs in client-side JavaScript. The text you paste and the email list output both stay in the browser tab. We have no upload endpoint and we do not log content. See also: Link Extractor. See also: extract numbers. See also: text-cleaning tools.

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