Test and debug regex patterns in real time — see all matches highlighted instantly, with group captures and match position details.
Also use our Find and Replace tool or clean text with the Text Formatter.
🔒 Your regex and test string are processed locally — nothing is stored or uploaded.
A regex tester is an online tool that lets you write and test regular expressions against a sample text string and see which parts of the text match your pattern. Regex flags modify how the pattern matches: g (global) finds all matches instead of stopping at the first, i (insensitive) ignores case, and m (multiline) makes ^ and $ match the start and end of each line, not just the whole string.
This regular expression tester works as a regex debugger, pattern validator, and regex match highlighter for developers, data analysts, and anyone who works with text processing, form validation, or string manipulation.
Developers use regex testers constantly when writing input validation logic. A single pattern like \b\w+@\w+\.\w+\b can validate email addresses, but small mistakes in the pattern — a missing backslash, a misplaced quantifier — can cause it to match incorrectly or fail entirely. Testing the pattern here before embedding it into code catches those issues instantly without needing to redeploy or reload an application.
Data analysts use regex patterns to extract structured fields from unstructured text — pulling phone numbers from a customer database, isolating dates from a log file, or splitting comma-separated values that contain irregular spacing. The real-time highlighting in this tool makes it immediately obvious which parts of the text the pattern is capturing, and the match details panel shows the exact index position and length of each match.
The group capture display is especially useful when working with patterns that use parentheses to extract sub-matches. For example, a pattern like (\d{4})-(\d{2})-(\d{2}) on a date string shows each year, month, and day captured in a separate group — making it easy to verify that the extraction logic is working correctly before using it in production code or a data pipeline.