TLDR: Choose Apify for raw post data and API control. Choose Signado when you want a post's commenters or reactors enriched with person and company data, qualified against your ICP, and sent to Warm Leads without connecting your LinkedIn account. Choose PhantomBuster for profile-led activity extraction.
Choose Apify when you want raw LinkedIn post data, exports, or an API. Choose Signado when you want the people behind a post's engagement enriched and qualified.
With Signado, paste the URL and choose Commenters, Reactors, or Both. Each person goes to Warm Leads with their profile, company data, original activity, and ICP qualification. Your LinkedIn account is not connected.
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 |
| Signado | You want a post's commenters, reactors, or both, enriched with person and company data and qualified against your ICP | Runs once per post and returns enriched, qualified people rather than a raw scraping API or complete post archive |
| 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 someone trying to get a short 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 |
Choose Apify for post or search data. Choose Signado for enriched and qualified people. Choose PhantomBuster for profile activity. Skip Chrome extensions unless this is a one-off export you can afford to repeat.
How Signado extracts leads from a LinkedIn post
The setup is short:
- Add a LinkedIn post source.
- Paste a public LinkedIn post or feed-update URL.
- Choose Commenters, Reactors, or Both.
- Set the lead limit and run the source once.
- Review the results in Warm Leads.
You do not provide a LinkedIn login, session cookie, or browser extension. One new warm lead costs 5 credits. That includes collection, person and company enrichment, qualification against your ICP, scoring, and the original comment or reaction.
The result can include the person's role and profile, company summary, offer, size, industry, target market, score and reason, and buyer stage when available. A post source runs once and uses the lead limit you choose. Use a raw scraper when you need a complete archive, JSON output, or your own API workflow.
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 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 system." If you need commenters from one relevant 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.
Account risk matters because the LinkedIn profile belongs to a real person and business. A broken export is annoying. A restricted account is expensive.
What happens after a raw LinkedIn post export
Raw scraper rows still need deduplication, person and company enrichment, fit scoring, and the source activity kept beside each person. Signado includes those steps for 5 credits per new warm lead. Finding an email is optional and separate.
Generating a message inside Signado costs another 7 credits. You can skip that step and write it yourself. You can also connect the free read-only Signado MCP tools to Claude Code or Codex, ask your AI to read the lead and draft the message, and spend no Signado message credits.
For the wider market, read the LinkedIn scraping tools guide. If you want the enriched people instead of raw rows, Start Free.
FAQ
What is the best LinkedIn post scraper?
Choose Apify when you need raw exports and API access. Choose Signado when you want commenters and reactors enriched with person and company data and qualified against your ICP without connecting your LinkedIn account. Choose PhantomBuster for profile-led activity extraction and Bright Data for large data operations.
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?
If you used a raw scraper, deduplicate the export, enrich people and companies, score fit, find a contact route, and write from the actual post or comment. Signado handles the collection, enrichment, ICP check, scoring, and source context before the person reaches Warm Leads.
Skip the scraper setup
Signado watches LinkedIn keywords, competitors, and creators, then delivers the commenters worth contacting, scored and enriched, with what they actually said. No LinkedIn login. Nothing to get your account restricted.