I’m working on parsing forwarded email messages and need to extract email addresses from different fields. Specifically, I want to grab all the emails from the TO field, FROM field, and CC field as separate groups.
I’m pretty new to regular expressions and finding it hard to wrap my head around the syntax. The forwarded emails have a standard format but I can’t figure out how to write a pattern that will catch all the addresses correctly.
Has anyone done something like this before? I’d really appreciate some help with creating the regex pattern that can handle this task. Is it even possible to do this reliably with regex or should I be looking at other approaches?
hey, totally get it, regex can be a pain. you might wanna use TO:\s*(.+?)\n for the TO line. try breaking it down into smaller regex parts, it’s way easier than one big string. let me know if ya need more help!
Regex works fine for simple cases, but you’ll hit problems fast with multiline headers and encoded content. Had this exact issue processing customer emails at my last job. The real challenge isn’t the regex pattern - it’s handling inconsistent formatting between email clients and servers. Gmail forwards look different from Outlook, Apple Mail has its own quirks, and mobile clients sometimes break line breaks. I found a hybrid approach works best: first identify forwarded message boundaries with a simple pattern, then use specific regex to extract headers. For basic Gmail forwards, try ^(To|From|Cc):\s*(.*)$ with multiline flags, but you’ll need to handle continuation lines and addresses that span multiple lines. Here’s the real gotcha - some forwarded messages don’t preserve original headers cleanly, so you might be parsing reconstructed header text instead of actual email headers.
Regex can work but it’s pretty limited for email parsing. I’d go with Python’s email library instead - way more reliable than pure regex. If you’re stuck with regex though, try (?:TO|FROM|CC):\s*([^\n]+) to grab content after each field. Forwarded emails are a nightmare because every client formats them differently. I wasted weeks debugging regex patterns just to hit edge cases where addresses wrapped across lines or had weird characters. The email library actually follows RFC standards and handles headers properly. If you’re dead set on regex, test it hard with different forwarded formats - what works in Gmail will probably break with Outlook forwards.
Regex gets messy fast with email parsing. Start simple with (TO|FROM|CC):\s*([^\n\r]+) and test it on your Gmail forwards first. Try a few examples before building anything complex.
Been there with email parsing headaches. Regex and Python libraries work, but they’re a mess when you scale across different formats and process tons of messages.
I solved this exact problem last year - we needed contact info from thousands of forwarded support emails. Instead of wrestling with regex or custom parsing code, I built an automation for the whole pipeline.
It pulls Gmail messages, identifies forwarded content automatically, extracts all TO/FROM/CC addresses with smart parsing (not basic regex), and outputs clean structured data. Takes 10 minutes to set up and handles those weird edge cases josephk mentioned.
Runs automatically, so no manual processing or worrying about different forwarding formats breaking your code. Way cleaner than maintaining regex patterns that break every time someone uses a different email client.
Gmail forwarding isn’t too bad since they keep a consistent format with clear header blocks. I had good luck with (?<=^(To|From|Cc): ).* when parsing our company’s forwarded sales emails. You’ll run into trouble when people manually edit forwards or when the original email has messy address formatting - display names with commas, parentheses, that kind of stuff. Gmail cleans up most of it, but you still need to handle multiple addresses separated by semicolons or commas. Skip trying to build the perfect regex upfront. Just grab a bunch of real forwards from your Gmail and test against those. The edge cases pop up fast once you’re working with actual data.