LinkedIn Companies Scraper

LinkedIn Companies Scraper: public company page data from the URLs you have

Paste a list of linkedin.com/company/... URLs and get back what each page publishes: the company name, the About text, follower count, employee count, the headquarters address broken into parts, the website it links to and the logo. No account, no cookie, nothing to paste in.

It reads the public version of the page, the one LinkedIn writes for search engines. So the fields are the ones a logged-out visitor can see, and no more: there is no industry tag, no specialties list and no employee directory in here.

🏢 What LinkedIn Companies Scraper does

You bring the company URLs. This fills in the page behind each one.

A public company page carries a structured block that LinkedIn puts there for search engines: the name, the About text, the employee count, the postal address, the outbound links and the logo. The follower count is read off the page's own description line, the one that reads "N followers on LinkedIn". Each URL becomes one row.

The slug in the structured data has to match the slug you asked for before a row is written. That check is there because company URLs get renamed, and without it you would get the new company's numbers filed under the old name.

📥 What you give it

{
  "companyUrls": [
    "https://www.linkedin.com/company/microsoft/",
    "https://www.linkedin.com/company/apify/"
  ],
  "maxCompanies": 10
}

Set maxCompanies to match your list. It starts at 10. Paste 50 URLs and leave it and you get 10 rows and nothing for the other 40, not even a row saying they were skipped. The same goes for a duplicate URL: it is fetched once and you get one row, so the row count will not always match the line count you pasted.

A long list needs a longer run. Pages are fetched one at a time and the run ends at the timeout on the actor's Settings tab, 180 seconds unless you raise it. A hundred URLs at a 60-second page timeout can outlast that.

📤 What you get back

A real row from a recent run. The About text and the logo link are long, so they are cut short here with ...:

{
  "ok": true,
  "_sample": false,
  "sourceType": "public_company_profile",
  "companyUrl": "https://www.linkedin.com/company/microsoft",
  "companySlug": "microsoft",
  "name": "Microsoft",
  "description": "Every company has a mission. What's ours? ...",
  "followerCount": 29087688,
  "employeeCount": 232078,
  "headquarters": "1 Microsoft Way, Redmond, Washington, 98052, US",
  "address": {
    "type": "PostalAddress",
    "streetAddress": "1 Microsoft Way",
    "addressLocality": "Redmond",
    "addressRegion": "Washington",
    "postalCode": "98052",
    "addressCountry": "US"
  },
  "website": "https://news.microsoft.com/",
  "logoUrl": "https://media.licdn.com/dms/image/v2/D560BAQH32RJQCl3dDQ/company-logo_200_200/...",
  "transport": "direct_http",
  "retrievedAt": "2026-09-14T05:45:25.226Z"
}

A field the page does not publish comes back null. Nothing is filled in from elsewhere.

🧾 Reading the output

Three kinds of row, and only real company rows cost anything.

There is no charged field on these rows. sourceType is what separates a real company row from a sample or a diagnostic, so filter on it.

A diagnostic row carries an errorCode:

Neither a sample nor a diagnostic row fails the run.

▶️ How to run it

1. Open LinkedIn Companies Scraper and click Try for free. 2. Paste your URLs into Public LinkedIn company URLs, one per line. 3. Set Maximum companies to at least the number of URLs you pasted. 4. Click Start. 5. Download the dataset as JSON, CSV or Excel, or read it from the Apify API.

💰 How much does it cost?

$0.90 per 1,000 companies. Flat on every Apify plan, no volume tiers.

One charge per company row. Sample rows and diagnostic rows are never charged, and a duplicate URL is billed once because it is only fetched once. Be aware that a page which publishes no follower or employee count still counts as a company row and is still charged: you paid for the lookup, and the answer is that the page does not say.

💡 What people use it for

• Putting size and location against a list of target accounts before a sales push.
• Checking whether the companies in an old list still have a live page, and under which slug.
• Pulling logos and About text for a partner directory or a pitch deck.
• Watching follower and employee counts move over time by re-running the same list on a schedule.

🚧 What it does not do

• No employee list. Company pages do not publish one. The employees actor below does that job.
• No posts, jobs, funding, revenue, industry tag or specialties. None of that is in the public

block.

• No lookup by name or domain. You bring the URLs.
• Slug URLs only. A numeric /company/12345/ URL, a showcase page or a school page is refused

as BAD_INPUT.

• A renamed company reads as NO_RESULTS, because the page that answers is about a different

slug than the one you asked for. Look the company up again and use the new URL.

• URLs past maxCompanies get no row at all. They are dropped, silently.
• No retries. A URL that fails, fails for that run.

🧭 Which LinkedIn scraper do you need?

❓ Questions people ask

Do I need a LinkedIn account? No, and there is no cookie field. Nothing to paste, nothing to renew.

Is employeeCount the real headcount? It is LinkedIn's count of people who list that company on their profile. Close to headcount for most firms, wrong for some, and never an audited figure.

Why is website not the company's main domain? Because the page lists it that way. It is the first link the page publishes, which is sometimes a newsroom or a careers site.

What happens if I run it with nothing in the input? You get one sample row, clearly labelled, and you are not charged for it.

Is scraping LinkedIn legal? This reads only pages LinkedIn publishes to the open web for search engines. Apify's write-up on scraping and the law is a reasonable starting point, and we are not lawyers.

🆘 If something breaks

Open the Issues tab on the actor page. Send the run ID and one of the URLs that went wrong. The errorCode on the diagnostic row usually explains it on its own.