TLDR: A LinkedIn post scraper pulls post text, authors, dates, comments, reactions, and CSV or JSON exports. Start with Apify if you want a dataset/API workflow. Use PhantomBuster if you already have profile URLs. Bright Data and browser extensions are more situational.
A LinkedIn post scraper is useful when you know what you want to collect and where the data will go next. If you only know "we need leads from LinkedIn," scraping is too early in the workflow. Treat it as data extraction first.
The first decision is the input. A post URL, a LinkedIn search URL, a profile URL, and a browser feed all produce different data. A scraper that works well for one can be clumsy for another.
Quick comparison: LinkedIn post scraper tools
People typing this query want a working method. Here is the short version.
| Tool | Use it when | Watch out for |
|---|---|---|
| Apify LinkedIn Post Scraper | You want search URLs, post URLs, datasets, CSV/JSON exports, API access, or n8n/Make/Zapier handoff | Actor quality varies by publisher; comments and reactions may need companion actors |
| PhantomBuster LinkedIn Activity Extractor | You already have profile URLs and want recent posts, comments, reactions, and activity exports without code | It uses a connected LinkedIn account or session cookie, and daily limits make large watchlists slow |
| Bright Data LinkedIn Post Scraper | You have a data team, many URLs, and need scraper API or managed delivery | Better for data operations than a sales rep trying to get a list today |
| Chrome Web Store extension | You need a quick CSV from what your browser can see | Fragile, browser-session-based, and not a serious recurring workflow |
Default recommendation: start with Apify for post/search scraping, PhantomBuster for profile activity, and skip Chrome extensions unless this is a one-off export you can afford to lose.
How to scrape LinkedIn posts with Apify
Apify is the cleanest default when you want data you can route somewhere else. It gives you a run, a dataset, export formats, and an API path. That matters if the next step is n8n, a warehouse, a spreadsheet, a CRM, or an AI agent.
The practical flow:
- Open the LinkedIn Post Scraper actor.
- Choose the source: LinkedIn post search URL, profile URL, company URL, or post URL.
- For search, build the search inside LinkedIn first, apply filters, then paste the full search URL into Apify.
- Set the record limit and cookie/proxy fields the actor requires.
- Run the actor.
- Export the dataset as CSV, JSON, or Excel, or call it through the API.
For a post-search workflow, Apify is usually better than PhantomBuster because the source is the search result itself. For example: search LinkedIn posts for "looking for SOC 2 consultant," filter by recency, paste the resulting URL, and export matching posts. That gives you post text, author fields, timestamps, post URLs, and engagement counts.
The field check is simple. You want url, text, postedAtISO, authorProfileUrl, authorHeadline, numLikes, numComments, and ideally commenter or reaction records if your workflow needs engagers. If the actor only returns comment counts, you still need a separate LinkedIn comment scraper before you have people to contact.
That is the main Apify trap. It can look like you have a lead source because the dataset shows engagement. But "14 comments" is not a lead list. Fourteen commenter profile URLs with comment text might be.
Before using an Apify actor in a recurring workflow, check the sample output, last modified date, issues, and whether the publisher has separate actors for comments, reactions, profiles, or Sales Navigator. Apify is a strong building block. It is still a building block. For social listening or content research, that may be enough. For lead generation, scraped data still needs a person-level next step.
How to scrape LinkedIn activity with PhantomBuster
PhantomBuster is a better fit when your starting point is a list of profiles. The LinkedIn Activity Extractor is built around that shape: give it profile URLs, connect a LinkedIn account, choose which activity types to collect, and export the results.
The flow is straightforward:
- Choose the LinkedIn Activity Extractor Phantom.
- Add profile URLs through a paste, Google Sheet, CSV, saved Leads list, or previous Phantom result.
- Connect your LinkedIn account through the PhantomBuster extension, or use a session cookie.
- Pick activity types: posts, articles, comments, reactions, documents, newsletters, or events.
- Set how many profiles and activities to process, then choose manual or scheduled launches.
This works when you already know who matters: founders, creators, executives, customers, competitors, or target accounts. It is weaker for broad discovery because you need the profile list first.
The daily caps are the real constraint. PhantomBuster's guide puts standard LinkedIn accounts up to 80 profiles per day and Premium or Sales Navigator accounts up to 150, with lower limits when extracting 100+ activities per profile. That is fine for a few dozen high-value profiles. It is impractical for monitoring hundreds of creators or competitors at depth unless someone owns scheduling, batching, and cleanup.
There is also a field gap. The Activity Extractor returns activity beside the profile URL, but PhantomBuster's troubleshooting notes say it does not include profile details like name, company, or headline in the same output. If you need those fields, a LinkedIn profile scraper is the natural second step.
So use PhantomBuster when you want controllable profile activity extraction. Do not expect it to magically become a qualified prospecting feed. You still have to enrich, dedupe, score, and decide who deserves outreach.
Other options: Bright Data, extensions, and GitHub scripts
Bright Data is the enterprise/data-team option. Its LinkedIn Post Scraper is framed around API or no-code scraping, bulk URL handling up to 5K URLs, and delivery formats for bigger workflows. Use it when the sentence starts with "we have thousands of URLs" or "we need this in our data pipeline." If the sentence starts with "our SDR needs commenters from this post," it is probably too heavy.
Browser extensions are the opposite. The Chrome Web Store LinkedIn Post Scraper says it auto-scrolls the feed and exports posts to CSV. That is fine for a quick experiment, but browser extensions touch the session where your LinkedIn account lives and tend to break when the page changes. Treat them like disposable utilities, not infrastructure.
GitHub and Python scrapers can be useful for learning or internal experiments. A general web scraper can also work for narrow public-page tests. They are also where maintenance becomes yours. If LinkedIn changes markup, login behavior, or visible fields, your script is the product now.
LinkedIn terms and account risk
LinkedIn is direct about this. Its User Agreement prohibits using software, scripts, robots, crawlers, browser plug-ins, add-ons, or other processes to scrape or copy LinkedIn services and data. Its Help Center page on prohibited software and extensions also names crawlers, bots, browser plug-ins, browser extensions, scraping, and automated activity.
The practical risk test is short: does the tool need a cookie, a logged-in account, a browser extension, high volume, or automated engagement? If yes, slow down. You may still decide to use it, but do not treat "public data" copy from a vendor as the whole answer.
For sales teams, account risk matters because the LinkedIn account is part of the rep's selling identity. A broken export is annoying. A restricted account is expensive.
If you want sales leads, scraping is only step one
Scrapers give you rows. Sales needs judgment: which commenter fits, which company is worth touching, whether the context is fresh, and what a rep should say. You can build that yourself with Apify or PhantomBuster plus enrichment, email finding, CRM rules, and outreach tooling. If competitor posts are the source, read the playbook on how to find leads on LinkedIn from competitor posts. If you do not want to maintain that chain, Signado handles keyword and competitor monitoring through scoring and outreach context. For the broader scraper stack, read the LinkedIn scraping tools guide or the PhantomBuster alternative comparison.
FAQ
What is the best LinkedIn post scraper?
Apify is the best starting point for search URLs, post URLs, exports, and API access. PhantomBuster is better when you already have profile URLs and want no-code activity extraction. Bright Data fits large data operations. Chrome extensions fit one-off CSV exports.
Can I scrape LinkedIn post comments?
Yes, but the tool must explicitly support comments or activity extraction. PhantomBuster can collect comments from profile activity. Bright Data lists comments in its post-scraper fields. Some Apify workflows need a separate LinkedIn Comment Scraper or reaction actor after you collect post URLs.
Is using a LinkedIn post scraper allowed?
LinkedIn's official terms prohibit unauthorized scraping and automated tools, including crawlers, browser plug-ins, browser extensions, bots, scripts, and processes used to copy LinkedIn data. Risk rises when the workflow uses a logged-in account, cookie, browser extension, profile visits, automated engagement, or high-volume extraction.
What fields should I check before choosing a tool?
Check for post URL, post text, author profile URL, author headline, company fields, publish date, comments, commenter profile URLs, reaction details, media, timestamps, and export format. If you want leads, commenter profile URLs matter more than engagement counts.
What should I do after scraping LinkedIn post data?
Deduplicate the export, enrich people and companies, score fit, find a contact route, and write outreach from the actual post or comment.
Start sending outreach that references real events
Your next warm lead is already commenting on LinkedIn.