Collector
LinkedIn Post Comments scraper
The people who publicly commented on any LinkedIn post — name, what they wrote, when, and how many likes it drew, one row per comment. LinkedIn shows a signed-out reader the first eight to ten and walls the rest, and it never shows who merely reacted.
On this page
What it collects
The people who publicly commented on any LinkedIn post — name, what they wrote, when, and how many likes it drew, one row per comment. LinkedIn shows a signed-out reader the first eight to ten and walls the rest, and it never shows who merely reacted. It is a scraper, which means the whole contract is: fill in the form, and one table comes back. No browser window opens, it costs none of your plan's automation slots, and the requests go out over plain HTTP carrying a real browser's TLS fingerprint.
It runs fine from your own address; a proxy is available on the run and this target does not need one. Whichever way it went, every row records it — see the provenance columns below.
What you give it — 6
The form the app draws, read out of the card itself. Each hint below is the one shown beside that field in the run dialog, so nothing here is a description of the product written separately from it.
The one answer it needs
- posts
- Posts · list · required
One post per line — the linkedin.com/posts/… URL, the lnkd.in/… short link the Share button copies, a /feed/update/urn:li:activity:… address, or the bare activity id. The same input LinkedIn Posts takes, so run both over one list and join them on the post.
Everything else is optional
- maxCommentsPerPost
- Most recent, per post · number
- minLikes
- Minimum likes · number
- keywords
- Comment contains · list
- commenters
- Commenter is · list
- commentedWithinDays
- Commented in the last (days) · number
Keeps the first few from each post rather than all of them. It saves no requests — the comments all arrive in one reply.
Reactions on the comment itself. A comment with none carries no counter at all and fails this rather than counting as zero.
Keeps a comment only when its text contains one of these — the quickest way to turn a thread into a short list worth reading.
Matches part of the name, so a first name is enough. LinkedIn publishes no profile URL beside a comment, so the name is all there is to match on.
Counted from the moment the run starts.
What comes back — 10 columns
One dataset per run — a typed table your workspace owns, which you can then sort, filter, edit in the grid, export whole, or read back over the local API. These are the columns it is created with.
One column is marked may be empty. That is a fact about the record rather than about the collector — a post with nowhere tagged, an account with no business category — and it is said out loud so a blank cell does not read as a broken scraper. A column the reply never carries at all is deleted upstream rather than shipped empty, so nothing here is decoration.
6 from LinkedIn Post Comments
| Key | Column | Type |
|---|---|---|
| commenter | Commenter | text |
| text | Comment | longText |
| postedAt | Commented | datetime |
| likes | Likesmay be empty | number |
| commentId | Comment id | text |
| searchTerm | On post | text |
4 that every scraper writes
The same four on every card, so a table can still answer months later how its rows got there: which service they came from, when, which profile's identity the requests carried, and whether that identity was signed in.
| Key | Column | Type |
|---|---|---|
| platform | Platform | text |
| collected_at | Collected at | datetime |
| profile | Collected by | profile |
| logged_in | Signed in | select |
Four ways to run it
The Scrapers tab. Pick the card, fill in the form, press Start. Every run started here makes a new table, named after what you searched for and when. Check first if you like — it collects one page, writes nothing, and reports which columns came back filled.
Ask the assistant. It has the whole catalogue, so this card is a sentence rather than a form. It fills in the parameters above from what you said and shows you them before it starts.
Over MCP or the local API. The same card, from a coding agent — argus_run_scraper over the MCP server, or POST http://127.0.0.1:39219/v1/scrapers/run on the local API. There is a sample call beside it that writes nothing.
{
"kind": "linkedin_post_comments",
"inputs": {
"posts": [
"…"
]
}
}As a step inside a workflow. The Run scraper step puts this collector in the middle of an automation — collect, then filter, then mail — and the tree still opens no window doing it. It is also the one caller that can overrule the table-per-run rule: pointed at a table you name, it creates that table with the columns above if it is missing and files every run into it after, appending or updating the row in place on a column you match on. The step hands the next one the table's name, its id and the row count.
Where it stops
About 8 rows per target is the whole answer. That is every comment LinkedIn embeds for a signed-out reader. The Comments column of a LinkedIn Posts table is the real total, and the rest of the thread is behind a login wall.
It will not drive a page. Anything that needs a real browser — a site with no list endpoint behind it, or anything operating an account of your own — is an automation instead, and that is a different tool rather than a worse version of this one.
It signs in to nothing. Reading a public page signed out is the settled position — Bright Data scraped Meta signed out and won; hiQ scraped LinkedIn signed in and lost — so this card asks for no account and holds none.
The rest of the LinkedIn cards
One card per job rather than per service — profiles, posts and the people around them are three collections with three schemas.