harvestapi
LinkedIn Profile Posts Scraper (No Cookies)Extract posts from LinkedIn profiles, including content, media, engagement, reactions, comments, and more. No cookies or account required.
Publisher description, verbatimon LinkedIn, by users in the last 30 days among actors classified to this platform.
best #1 for “linkedin post”
Good for basic scraping because it provides a date filter and low base cost, but it struggles with strict/truncated date limits, high costs for reactions/comments, incorrect post/author counts, and requires manual multi-field checks.
| Theme | Sentiment | Said | What they mean |
|---|---|---|---|
| Caps and ceilings | 1 praised · 3 complained | 4 | built-in date filter availablestrict exact date-range cutoff“This is a great actor! The only issues are the output and the limit. If the limit was stated, then it would be much better. Regarding the output, it would be nice to have summary columns (number of posts, number of reposts and number of quoted posts) for easy analysis.” — 4/5 |
| What it costs | 1 praised · 2 complained | 3 | affordable for basic scrapingexpensive for reactions and comments“Exceptionally expensive for reactions and comments. Use "supreme_coder/linkedin-post" if you want a more reasonable price - charged by the post, reactions and comments are free.” — 1/5 |
| Does it return everything | 0 praised · 2 complained | 2 | returns more posts than requested“Got excited when i saw this. But sadly the data is not organized in a reliable way. For example, put in 2 posts to scrape as a test. It pulled 10 posts. Sometimes the target person is under one column, while other times they're in another. ihave to check 2-3 fields to see which target person reposted a certain post. I wish there was simply a constant "author name" field that didn't change if it was a repost. Because the goal is to ensure that the scraped post is tied to the original scraped person, not suddenly saying that the author is now a new person. that new person can be nested within. Maybe a repost author category? Otherwise, the data would have been good.” — 3/5 |
| Is the data right | 0 praised · 1 complained | 1 | author field incorrect for reposts |
| Setup and docs | 0 praised · 1 complained | 1 | requires checking multiple fields to verify |
The actors either side of #3 of 1,383, ranked by users in the last 30 days. Same platform, adjacent adoption — the comparable set.
| # | Actor | Grade | Price / 1k | Users /30d | Success | Rating | Age | Fresh | |
|---|---|---|---|---|---|---|---|---|---|
| 1 | A | RES$2.00 /1k | 17,672161.9K total | 98.6% | 4.6 | 2.1y | fresh | ||
| 2 | A | EVT$4.00 /1k profilehigh price | 10,31973.1K total | 100.0% | 4.6 | 17m | fresh | ||
| 3 | A | EVT$2.00 /1k postgood price | 9,12636.8K total | 100.0% | 4.4 | 17m | fresh | This | |
| 4 | A | EVT$2.00 /1k postgood price | 6,64029.3K total | 99.6% | 5.0 | 17m | fresh | ||
| 5 | A | EVT$100 /1k search-pagesteep price | 6,34644.9K total | 99.6% | 4.4 | 17m | fresh | ||
| 6 | A | RES$4.00 /1k | 6,08122.1K total | 100.0% | 4.5 | 16m | fresh | ||
| 7 | B | EVT$3.00 /1k short-profilefair price | 5,73329.9K total | 99.4% | 3.9 | 15m | fresh | ||
| 8 | B | RES$0.40 /1k | 4,28324.6K total | 98.7% | 4.7 | 17m | aging |
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LinkedIn
curious_coder
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