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Email ✉️ & Phone ☎️ extractor


Extract emails, phone numbers, and many other contact information from a list of website you provide. The difference with "Contact Details Scraper" is that it adds the ability to specify url patterns that your robot is going to follow. Useful for big website with useless section.

No credit card required

Author's avatarguillim
  • Modified
  • Users72
  • Runs185
Actor picture

Email ✉️ & Phone ☎️ extractor

What websites:

Anything. Just write an URL from which the crawler will begin to extract info, and click on links ("a" anchors).

What info:

  • Email
  • Phone numbers (from phone links or extracted from text)
  • LinkedIn
  • Twitter
  • Instagram
  • Facebook


lead generation for your marketing and sales teams for instance


The Apify team did a step-by-step guide to using Contact Details Scraper. Thanks to them.

Input Configuration

  • Start URLs - list of URLs where the crawler should start. You can enter multiple URLs, upload a text file with URLs, or even use a Google Sheets document.
  • Maximum link depth - how deep the actor will scrape links from the web pages specified in the Start URLs. If zero, the actor ignores the links and only crawls the Start URLs.
  • Stay within domain - If enabled, the actor will only follow links on the same startUrl domain. For example, while crawling "", if the crawler finds a link to, it will not follow this link.
  • Pseudo URLs - list of Regex the crawler will respect. Very useful when crawling a big website, to reduce the number of pages the crawler will follow.


The actor stores its results into the default dataset associated with the actor run. You can then download the results in formats such as JSON, HTML, CSV, XML, or Excel. For each page crawled, the following contact information is extracted (examples shown):

  • Emails
  • Phone numbers - These are extracted from phone links in HTML (e.g. <a href='tel://123456789'>phone</a>).
  • Uncertain phone numbers - These are extracted from the plain text of the web page using a number of regular expressions. Note that this approach can generate false positives.
  • LinkedIn profiles
  • Twitter profiles
  • Instagram profiles
  • Facebook profiles or pages

Personal data

You should be aware that your results might contain personal data. Personal data is protected by GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers. You can also read Apify blog post on the legality of web scraping.


Thanks to Apify who created the original crawler