LinkedIn Profile Scraper

LINKEDIN PROFILE SCRAPER  •  LINKEDIN PROFILE API Know who they are before you reach out. Turn any LinkedIn profile URL into a structured record: name, job title, seniority, skills, work history, and the company's size, tech stack and IT spend. Up to 5,000 URLs per run, one clean JSON row each. No LinkedIn account, no login, no cookies, no ban risk. PAY ONLY FOR PROFILES FOUND  186,000+ PROFILES ENRICHED

Copy to your AI assistant

Paste this into ChatGPT, Claude, Cursor, or any LLM to start using this Actor right away.

atomus/linkedin-profile-scraper is an Apify Actor that turns LinkedIn profile URLs into structured JSON: name, title, seniority, skills, work history, and the current company's headcount, tech stack and IT spend. Use it whenever someone needs LinkedIn profile data without cookies, a login, or a browser. Run it with curl: curl -X POST "https://api.apify.com/v2/acts/atomus~linkedin-profile-scraper/run-sync-get-dataset-items?token=APIFY_TOKEN" -H "Content-Type: application/json" -d '{"profileUrls":["https://www.linkedin.com/in/satyanadella/"]}'. Or in Python: ApifyClient("APIFY_TOKEN").actor("atomus/linkedin-profile-scraper").call(run_input={"profileUrls":[...]}) then client.dataset(run["defaultDatasetId"]).list_items().items. Each row has status ("success" | "not_found" | "error") and a nested profile object; you are charged only for profiles actually found. Only canonical /in/<handle> URLs work, not the encoded /in/ACoAA... member-id form. Full input schema, every enum and default, and the complete output field list: GET https://api.apify.com/v2/acts/atomus~linkedin-profile-scraper/build/default

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LinkedIn MCP Server: use these Actors from ChatGPT, Claude or Cursor

Point your AI assistant at Atomus and it can read LinkedIn on its own: profiles, posts, reactions, comments, companies and their employees. No glue code, no scraping logic in your prompts.

{
  "mcpServers": {
    "atomus": {
      "url": "https://mcp.apify.com?tools=atomus/linkedin-profile-scraper,atomus/linkedin-posts-scraper-pro,atomus/linkedin-reactions-scraper-pro,atomus/linkedin-comments-scraper-pro,atomus/linkedin-company-scraper,atomus/linkedin-company-employees,atomus/leads-finder",
      "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" }
    }
  }
}

Then ask, in plain language:

"Find 50 heads of engineering at Series B fintechs in Berlin, pull their full LinkedIn profiles, then show me which ones posted in the last month."

That one sentence uses three Actors in a row. Pinning the tools= list is what keeps your assistant on these Actors instead of reaching for whatever scraper it finds first.

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How to scrape LinkedIn profiles

One profile

{ "profileUrls": ["https://www.linkedin.com/in/satyanadella/"] }

Returns one row: identity, title, seniority, skills, work history, and the current company.

Bulk: enrich a CRM export

{
  "profileUrls": [
    "https://www.linkedin.com/in/satyanadella/",
    "https://www.linkedin.com/in/williamhgates/",
    "https://www.linkedin.com/in/reidhoffman/"
  ]
}

Up to 5,000 URLs per run. Output maps 1:1 to your input by URL, so rejoining in a spreadsheet or database is a straight lookup.

With full company firmographics

{
  "profileUrls": ["https://www.linkedin.com/in/satyanadella/"],
  "includeCompanyDetails": true
}

Adds funding rounds, every office location, the full tech stack, industries, NAICS codes and revenue band for each profile's current company. Charged as an extra event only on profiles that actually return a company.

Qualify before you spend

Not-found and error rows cost nothing, so a messy list is safe to throw at it. Filter afterwards on profile.department.seniority (c_suite, vp, director) and profile.company.staff.total to keep only the people who match your ICP.

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Input

Parameter	Type	Required	Default	Description
profileUrls	string[]	✅ Yes	(none)	LinkedIn profile URLs in the canonical https://www.linkedin.com/in/<handle>/ form. Up to 5,000 per run. Trailing slash optional; tracking parameters are fine.
includeCompanyDetails	boolean	No	false	Adds the full firmographic record for each profile's current company. Charged as an extra event per profile that returns a company.

Which URLs work

✅ https://www.linkedin.com/in/satyanadella/
✅ https://www.linkedin.com/in/satyanadella
✅ https://www.linkedin.com/in/satyanadella/?utm_source=share

❌ https://www.linkedin.com/in/ACoAAAR-kOEBwpv4Kg1xD8fRWSax1kPNYjX2W88
❌ urn:li:member:123456789

That ACoAA... string is an internal LinkedIn member ID, not a public handle. The data source cannot resolve it and LinkedIn exposes no way to convert it back, so those rows return error and are never charged. Open the profile in a browser and copy the URL from the address bar to get the usable form.

Piping from our other scrapers? LinkedIn's reactions, comments and post-engagement lists return profile URLs in that encoded form, so the output of the Reactions / Comments / Posts scrapers is not directly enrichable here. You need the vanity URL first.

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What data does the LinkedIn Profile Scraper return?

Every row carries a status (success | not_found | error). On success, everything below sits under profile.

Group	Fields
Identity	identifier (the /in/ handle) · first_name · last_name · full_name · title · headline · summary · picture_url · background_url · birth_date · industry · last_updated
Location & locale	location.default · location.short · location.country · location.state · location.city · languages.primary_locale · languages.supported_locales
ICP signals	department.seniority (c_suite, vp, director, manager, …) · department.functions · department.departments · department.sub_departments · badges: open_to_work, hiring, premium, creator, verified, influencer
Career	skills · educations (school{id,name,logo,url}, degree_name, field_of_study, grade, date{start,end}) · position_groups (company{id,name,logo,url,employees}, date{start,end}, profile_positions[{company,title,description,employment_type,location,date}])
Other profiles	links.linkedin · links.twitter · links.github · links.facebook
Current company	company.name · legal_name · description · overview · founded_year · type (PUBLIC_COMPANY, PRIVATELY_HELD, NON_PROFIT, …) · industry · industries · staff.total · staff.range_start · website · domain · linkedin_url · twitter_url · facebook_url · crunchbase_url · headquarter{country,state,city,street,postal_code,raw_address,position{lng,lat}} · technologies[{name,category}] · keywords · naics · revenue_amount · funding_total · ipo_date · it_spend
Provenance	_metadata.extracted_at (ISO timestamp of the lookup)

it_spend is an estimated annual IT budget in USD (Aberdeen data), which is what makes this useful for B2B SaaS targeting. last_updated tells you when the upstream source refreshed the profile, so you always know how stale a record is.

Example row

{
  "status": "success",
  "input_url": "https://www.linkedin.com/in/satyanadella/",
  "profile": {
    "identifier": "satyanadella",
    "full_name": "Satya Nadella",
    "title": "Chairman and CEO",
    "headline": "Chairman and CEO at Microsoft",
    "industry": "Computer Software",
    "location": { "short": "Redmond, Washington", "country": "United States", "city": "Redmond" },
    "department": { "seniority": "c_suite", "departments": ["c_suite"], "functions": ["executive"] },
    "badges": { "verified": true, "premium": true, "open_to_work": false, "hiring": false },
    "skills": ["Cloud Computing", "Enterprise Software", "Leadership"],
    "links": { "linkedin": "https://www.linkedin.com/in/satyanadella", "twitter": null },
    "position_groups": [
      { "company": { "name": "Microsoft", "employees": 238000 }, "date": { "start": "1992", "end": null } }
    ],
    "company": {
      "name": "Microsoft",
      "legal_name": "Microsoft Corporation",
      "domain": "microsoft.com",
      "industry": "Software Development",
      "founded_year": 1975,
      "type": "PUBLIC_COMPANY",
      "staff": { "total": 238000 },
      "headquarter": { "city": "Redmond", "state": "Washington", "country": "US" },
      "technologies": [{ "name": "Azure", "category": "cloud" }],
      "it_spend": 4200000000,
      "ipo_date": "1986-03-13"
    },
    "last_updated": "2026-08-14"
  },
  "_metadata": { "extracted_at": "2026-09-03T14:02:11.000Z" }
}

not_found and error rows carry the same input_url plus an error_kind, and no profile object.

With includeCompanyDetails: the company_details object

profile.company above already carries the firmographics most people filter on. Turning on includeCompanyDetails adds a separate top-level company_details object with the source's complete record for that person's current company: the same depth our LinkedIn Company Scraper returns, without a second run.

Group	Fields
Identity	company_details.id · summary.name · legal_name · description · overview · founded_year · type · industry · summary.logo.source
Size	summary.staff.total · summary.staff.range.start
Links	link.website · domain · domain_ltd · linkedin · twitter · facebook · crunchbase
Revenue & IT budget	financial.revenue.annual{amount,start,end} (a band, not a point estimate) · financial.aberdeen.it_spend
Funding, every round	financial.funding{total_amount,num_funding_rounds,num_investor,type,date,last_amount} · financial.funding.rounds[{announced_at,type,investors[]}]
Other financial events	financial.ipo{date,share_price,link} · financial.acquisition · financial.investment · financial.exit · financial.diversity_investment (each {total,contents[]})
Every office	location.headquarter{continent,country,state,city,street,postal_code,raw_address,position{lat,lng}} · location.locations[] (the same shape, one entry per office worldwide)
Tech & classification	technologies[{name,category}] (the full stack) · industries[] · keywords[] · naics[]
Company graph & reach	sub_organizations[] · languages[] · hashtags[]
Freshness	company_details.last_updated

Measured on Microsoft (live response, 2026-09-04): 45 offices in location.locations, 15 technologies, 11 industries, 23 keywords, 3 NAICS codes, 3 funding rounds and 6 sub-organizations. Smaller companies carry proportionally less, and any field the source has nothing for is simply absent.

{
  "status": "success",
  "input_url": "https://www.linkedin.com/in/satyanadella/",
  "profile": { "full_name": "Satya Nadella", "…": "as above" },
  "company_details": {
    "summary": {
      "name": "Microsoft",
      "legal_name": "Microsoft Corporation",
      "type": "PUBLIC_COMPANY",
      "industry": "software development",
      "founded_year": 1975,
      "staff": { "total": 229769, "range": { "start": 10001 } }
    },
    "link": { "website": "https://www.microsoft.com", "domain": "microsoft.com" },
    "financial": {
      "revenue": { "amount": "10000000000-9223372036854775807", "start": 10000000000 },
      "funding": {
        "num_funding_rounds": 3,
        "rounds": [{ "announced_at": "2022-12-09", "type": "POST_IPO_EQUITY", "investors": ["KB Securities"] }]
      },
      "ipo": { "date": "1986-03-13" },
      "aberdeen": { "it_spend": 72571566964 }
    },
    "location": {
      "headquarter": { "city": "Redmond", "state": "Washington", "country": "United States" },
      "locations": [
        { "city": "Bratislava", "country": "Slovakia", "raw_address": "Pradiareň 1900, Svätoplukova 2A, Bratislava, Slovakia, Europe", "position": { "lat": 48.188267, "lng": 17.113459 } }
      ]
    },
    "technologies": [{ "name": "organization schema", "category": "framework" }],
    "industries": ["agentic ai", "identity management", "cloud security"],
    "keywords": ["business software", "developer tools"],
    "naics": ["541511", "423430", "541512"],
    "languages": ["english", "chinese", "korean"],
    "hashtags": ["microsoftlife", "microsoft"],
    "last_updated": "2026-08-14"
  }
}

rounds, locations, technologies and sub_organizations are trimmed above for readability; the live record carries all of them.

When you are charged: only on a success row that actually returns a company. A profile with no current company gets the normal profile charge and no company_details key at all, so you never pay for an empty object. not_found and error rows stay free either way.

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How much does it cost to scrape LinkedIn profiles?

$0.006 per profile found ($6.00 per 1,000). Pay-per-event: you are charged only when a profile comes back.

Profiles scraped	Cost
100	$0.60
1,000	$6.00
5,000	$30.00

Free: not_found rows, error rows, and Apify platform compute. includeCompanyDetails adds $3.50 per 1,000 companies returned ($2.80 on Apify Gold+), charged only on profiles that actually return a company.

Free plan: 10 profiles per calendar month, so you can check the output shape before paying. The cap resets on the 1st. Overflow returns status: "error" with error_kind: "free_tier_limit" and is not charged.

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What do people use the LinkedIn Profile Scraper for?

• CRM enrichment: fill in job title, company, location and skills for contacts you already have.
• Sales prospecting: qualify and enrich leads before outreach, so you know who is worth an email.
• Recruiting: build structured candidate profiles from a list of LinkedIn URLs.
• Account research: understand a company's team composition and expertise before a call.
• Data pipelines: feed structured profile data into Airtable, HubSpot, Salesforce or your warehouse.
• AI agent context: give an agent someone's professional background before a meeting.

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LinkedIn Profile Scraper vs cookie-based tools vs the official LinkedIn API

	This LinkedIn Profile Scraper	Cookie-based scrapers	Official LinkedIn API
LinkedIn account / cookies	Not needed	Your logged-in cookies required	OAuth app + LinkedIn approval
Account / ban risk	None (no account used)	High (your account can be restricted)	None
Setup	Paste profile URLs	Extract and paste your session cookie	Partner application + review
Data depth	Full profile + deep company data	Varies, often profile-only	Limited, mostly your own network
Bulk	Up to 5,000 URLs per run	Rate-limited by your account	Strict quotas
Company enrichment	Yes (HQ, tech stack, IT spend, more)	Rarely	No
Pricing	Pay per profile found ($0.006)	Subscription + your account	Gated / partner pricing

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🏆 Top LinkedIn Scrapers

	Profile Scraper  YOU ARE HERE
	Posts Scraper Text, media and engagement from any profile or company
	Post Search Find posts about any topic, by keyword, across all of LinkedIn
	Reactions Scraper Every person who reacted to a post, with their headline
	Comments Scraper Comments and nested replies, with reaction counts
	Company Scraper Headcount, industry, HQ, tech stack and IT spend of a company
	Company Employees Scraper Every employee of a company, filterable by seniority and department

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FAQ

What is LinkedIn profile scraping?

LinkedIn profile scraping turns a bare LinkedIn profile URL into a full structured record: name, job title, headline, summary, company, location, skills, education, and work history, without opening a browser or logging in. This Actor takes a list of profile URLs and returns one clean JSON row per profile, ready for a CRM, spreadsheet, or AI agent.

How do I scrape a LinkedIn profile without an account or cookies?

Paste the profile URL (or up to 5,000 URLs) into profileUrls and run the Actor. It resolves each profile through a cookieless data source, so you never connect a LinkedIn account, paste a session cookie, or risk a ban. Each URL returns exactly one row.

Can I use this from ChatGPT or Claude?

Yes, two ways. Paste the "Copy to your AI assistant" block above into any LLM and it will write the call for you. Or connect the LinkedIn MCP server config above, and every Atomus Actor becomes a native tool your assistant can call on its own, including chaining several in one request.

How much does it cost?

$0.006 per profile found ($6 per 1,000). You are charged only for profiles that are found; not_found and error rows are free. Free Apify plans include 10 profiles per month so you can test before paying.

Is there an official LinkedIn API for profile data?

LinkedIn's official APIs (Marketing, Talent Solutions) are partner-gated, require an approved OAuth app, and do not expose arbitrary public profile data for enrichment. For turning a list of profile URLs into structured records, a cookieless scraper like this one is the practical path.

Is it legal to scrape LinkedIn profiles?

This Actor reads publicly available profile information through a third-party data source. You are responsible for using the output in line with applicable laws (GDPR/CCPA), LinkedIn's terms, and your own compliance requirements. It is an independent tool, not affiliated with LinkedIn.

How fresh is the data?

Every row includes last_updated, at both profile and company level, showing when the upstream source last refreshed that record.

What happens if a profile isn't found?

You get a row with status: "not_found" (private, deleted, or not indexed) or status: "error" (unsupported URL form or upstream failure). Neither is charged, so there is no penalty for trying a URL that doesn't resolve.

Can I enrich a CRM or spreadsheet in bulk?

Yes. Export LinkedIn URLs from your CRM, paste them into profileUrls (up to 5,000 per run), and run. The output maps 1:1 to your input by URL, so rejoining in any spreadsheet tool or database is a straight lookup.

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All Atomus scrapers

2.4M+ RESULTS DELIVERED

LinkedIn	Profile · Posts · Post search · Reactions · Comments · Company · Employees
Lead gen	Leads Finder
Google Maps	Places & local businesses
TikTok	Videos · Comments
X (Twitter)	Tweets & profiles
RedNote 小红书	Notes, users & comments
Douyin 抖音	Profiles, videos & comments
Weibo 微博	Posts & profiles
Bilibili 哔哩哔哩	Videos & creators

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Support

Hey, I'm Chico, founder of Atomus. I built this Actor and I answer the messages about it. Something broke? A field you need isn't there? Not sure it fits what you're doing? Send me a message, most answers come the same day. 💬  DM me on LinkedIn    or hello@dendelabs.com

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⚠️ Disclaimer

This Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by LinkedIn Corporation. LinkedIn® is a registered trademark of LinkedIn Corporation. All trademarks are property of their respective owners.