On July 4, 2025, Proxycurl shut down for good. It was one of the largest LinkedIn data companies in the world, close to 10 million dollars a year in revenue, and it lasted only months after LinkedIn sued it in January 2025 for running hundreds of thousands of fake accounts to harvest member data.
You are not running a fake-account army. You just want the list of people who liked a post. But the thing that ended Proxycurl is the same thing that quietly decides which of the six ways to get that list is safe: not the data, which is public, but the access to an account that the method demands, and who holds that access when you are done.
That question rarely appears in the roundups, so here is the comparison built around it.
The six methods, ranked by what they cost your account
Two rows deserve expanding, because they are the ones people get wrong.
The cloud tool row is the highest risk and the most popular. You paste your session token into a dashboard, and from then on that tool replays your session from its own infrastructure. Your account is now being used from an IP that is not yours, on a schedule you did not set. That mismatch between where your account normally signs in and where it is suddenly active is one of the more legible automation signals there is. The tool works. The exposure is real, and it persists until you rotate the session.
The official API row is a genuine dead end, and it is worth saying plainly rather than implying otherwise. LinkedIn's partner APIs do not expose the reaction list of an arbitrary post to third-party developers. If a page tells you to "just use the official API" for this, it has not tried.
The manual method, and where it stops
Open the post, click the reaction count, scroll the panel. This is free, it breaks no rules, and for a post with thirty reactions it is genuinely the right answer.
It stops being the right answer quickly. The panel loads in small batches, there is no export, and there is no way to filter. By a few hundred reactions you are scrolling a modal and retyping names into a spreadsheet, and every name a hand mistypes is a prospect the search bar can never find again.
Doing it without connecting anything
Reaction lists on public posts are public. That means a server can read them the same way a person can, without acting as you and without touching your account.
- Copy the post URL. Use the full permalink, not the shortened share link. Open the post on its own page and copy the address bar.
- Paste it into the finder. The free LinkedIn likes finder takes the URL and returns the people. Nothing is connected, so nothing is at risk.
- Filter before you export, not after. This is the step everyone skips. Cutting the list down while you still have it on screen is faster than cleaning a CSV later.
Time: a couple of minutes. Account risk: none, because nothing is connected.
The same applies to commenters, which are a stronger signal and usually a shorter list. The comments finder works the same way.
Qualifying the list is the half that actually matters
A list of 400 names is not 400 leads, and the export is the easy half. What you do next is what separates a useful hour from a wasted one.
Reactions are a weak signal on their own. A like costs nothing and often means "I read the first line". Comments cost effort, so they carry more intent. Neither tells you the person has a problem you solve. The qualification step is what converts an engagement list into a prospect list, and skipping it is why most engagement exports go stale in a spreadsheet.
Two filters do most of the work:
- Fit. Does this person match who you actually sell to? Title alone is a poor proxy. Company shape usually matters more.
- Relevance of the post. Someone who engaged with a post about the problem you solve is worth far more than someone who engaged with a viral post about hiring. The topic of the post is part of the signal, not just the act of engaging.
The reason this matters shows up in reply rates. Expandi's 2026 outreach benchmarks, built on more than 13 million connection requests and 6.7 million messages sent between May 2025 and April 2026, put the reply rate on outbound messages at 10.4%, more than three times the 3.0% reply rate on a cold connection request. That gap is real, but it is not magic, and it collapses the moment "warm" only means "this person clicked something once".
For the longer version of how to read engagement as intent, see signal-based selling on LinkedIn.
What to do with the list once it is qualified
Referencing the specific post is the whole advantage, and it is easy to waste. "I saw you liked a post about X" is not personalisation, it is surveillance with extra steps. The better version references the idea, not the click: you are writing to someone because they are engaged with a topic, and you have something useful to say about that topic.
If you are sending from your own account, pace it. The list arriving all at once does not mean the outreach should.
Every viral post is 100+ warm conversations waiting.
Tell your agent who you want to reach. It finds leads, qualifies them, sends personalized outreach, and follows up.
Where the compliance line actually sits
Reading a public page is not the same as automating your account, and the two get conflated constantly. The short version: public data is public, and the enforcement of the last few years has landed on bulk resale of profile data and on tools that operate accounts at machine speed, not on reading a page. LinkedIn's case against Proxycurl turned on the automated account army behind the harvesting, not on the fact that profiles were public. hiQ Labs, which fought LinkedIn for six years over exactly this line, lost on the contract question and settled in December 2022 with a permanent injunction and a 500,000 dollar judgment against it.
The longer version of what both cases actually established is in the LinkedIn data compliance guide.
Can you see who liked a LinkedIn post without an account?
Yes, for public posts. The reaction list on a public post is publicly readable, so a tool can retrieve it without you connecting or signing into anything. Posts shared to a restricted audience are not publicly readable and will not appear.
Does exporting post likers get your account restricted?
Reading a public post from a server does not touch your account at all, so there is nothing to restrict. The risk comes from methods that act as you: browser extensions and cloud tools that replay your session. Those operate your account, and that is what detection looks for.
Is there an official LinkedIn API for post reactions?
No. LinkedIn's partner APIs do not expose the reaction list of arbitrary posts to third-party developers. Any guide telling you to use the official API for this is describing something that is not available.
Are likers or commenters the better lead source?
Commenters, in almost every case. Commenting costs visible effort and usually says something about the person's view, which gives you both a stronger intent signal and a better opening. Liker lists are longer and weaker.
How many people can you export from one post?
The practical limit is the post itself rather than the method. Most posts have far fewer engagers than people expect, and the useful subset after qualification is smaller again. Optimising for list size is usually the wrong goal.
