LinkedIn Profile Scraper

A LinkedIn profile scraper that turns any LinkedIn profile URL into a full structured record: name, job title, headline, summary, company, location, skills, education, and work history, plus deeply enriched company data. Paste a list of profile URLs and the Actor returns one clean JSON record per profile. No LinkedIn account, login, or cookies required.

Submit up to 5,000 URLs in a single run, pay only for profiles found (not-found and errors are always free), and get a predictable 1:1 mapping back to your input list.

💡 More LinkedIn tools by Atomus: https://apify.com/atomus

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What does this LinkedIn Profile Scraper do?

Give it one or more LinkedIn profile URLs and it returns a structured JSON record for each one, ready to load into a CRM, spreadsheet, or AI agent.

Feature	Description
📋 Rich profile data	First name, last name, full name, title, headline, summary, picture URL, location, skills, education, and work history.
🎯 Sales / ICP signals	open_to_work, hiring, verified, premium badges; seniority (c_suite, vp, director, etc.); department + functions.
🏢 Deeply enriched company	Legal name, founded year, type, industry, staff count, website, domain, social URLs, full HQ address, tech stack, IPO date, IT spend, and more.
🔗 Cross-platform links	LinkedIn, Twitter / X, GitHub, and Facebook URLs surfaced when discoverable.
💰 Pay only for results	You are charged only when a profile is found. not_found and error responses are always free.
📦 Batch processing	Submit up to 5,000 URLs in a single run.
⚡ 1:1 enrichment	One input URL produces exactly one output record, predictable and easy to join back to your data.
🔐 No cookies needed	No LinkedIn account, cookies, or risk of bans.

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How do I scrape a LinkedIn profile?

1. Open the Actor and paste one or more LinkedIn profile URLs into the profileUrls field (format: https://www.linkedin.com/in/<handle>/).
2. Click Start. The Actor processes each URL and pushes one record per profile to the dataset.
3. Read the results. Each record has a status (success | not_found | error); successful records carry the full profile object.
4. Export or pipe the data. Pull the dataset via the Apify API or Console, or feed it straight into an AI agent over MCP.

No login, no browser session, no cookies, just URLs in and structured data out.

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Input

Basic Usage

Just paste one or more LinkedIn profile URLs:

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

All Input Parameters

Parameter	Type	Required	Default	Description
profileUrls	string[]	✅ Yes	(none)	LinkedIn profile URLs. Each must match https://www.linkedin.com/in/<handle>/. Up to 5,000 per run.

Supported URL Formats

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

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Output

Each input URL produces exactly one record in the dataset. The status field tells you what happened:

Status	Charged?	Description
success	✅ Yes ($0.006)	Profile found, full data in the profile field.
not_found	❌ No	Profile not found in the data source (private, deleted, or not indexed).
error	❌ No	Invalid URL format or upstream network failure.

Success Record

{
  "url": "https://www.linkedin.com/in/satyanadella/",
  "status": "success",
  "profile": {
    "identifier": "satyanadella",
    "first_name": "Satya",
    "last_name": "Nadella",
    "full_name": "Satya Nadella",
    "title": "Chairman and CEO",
    "headline": "Chairman and CEO at Microsoft",
    "summary": "As chairman and CEO of Microsoft, I define my mission...",
    "picture_url": "https://images.ai-ark.com/...",
    "background_url": "https://media.licdn.com/dms/image/...",
    "industry": "Computer Software",
    "location": {
      "default": "Redmond, Washington, United States, North America",
      "short": "Redmond, Washington",
      "country": "United States",
      "state": "Washington",
      "city": "Redmond"
    },
    "languages": {
      "primary_locale": { "country": "US", "language": "en" },
      "supported_locales": [{ "country": "US", "language": "en" }]
    },
    "badges": {
      "premium": true,
      "creator": true,
      "open_to_work": false,
      "hiring": false,
      "verified": true,
      "influencer": true
    },
    "department": {
      "departments": ["c_suite"],
      "sub_departments": ["founder", "executive"],
      "functions": ["entrepreneurship"],
      "seniority": "c_suite"
    },
    "links": {
      "linkedin": "https://www.linkedin.com/in/satyanadella",
      "twitter": null,
      "github": null,
      "facebook": null
    },
    "skills": [],
    "educations": [
      {
        "school": {
          "name": "The University of Chicago Booth School of Business",
          "logo": "https://images.ai-ark.com/...",
          "url": "https://www.linkedin.com/school/universityofchicagoboothschoolofbusiness/"
        },
        "date": { "start": "1994-01-01", "end": "1996-12-31" }
      }
    ],
    "position_groups": [
      {
        "company": {
          "name": "Microsoft",
          "logo": "https://images.ai-ark.com/...",
          "url": "https://www.linkedin.com/company/microsoft",
          "employees": { "start": 10001 }
        },
        "date": { "start": "2014-02-01", "end": null },
        "profile_positions": [
          {
            "company": "Microsoft",
            "title": "Chairman and CEO",
            "date": { "start": "2014-02-01", "end": null }
          }
        ]
      }
    ],
    "company": {
      "name": "Microsoft",
      "legal_name": "Microsoft Corporation",
      "founded_year": 1975,
      "type": "PUBLIC_COMPANY",
      "industry": "software development",
      "staff": { "total": 228018, "range_start": 10001, "range_end": null },
      "website": "https://news.microsoft.com",
      "domain": "microsoft.com",
      "linkedin_url": "https://www.linkedin.com/company/microsoft",
      "twitter_url": "https://x.com/Microsoft",
      "crunchbase_url": "https://www.crunchbase.com/organization/microsoft",
      "headquarter": {
        "country": "United States",
        "state": "Washington",
        "city": "Redmond",
        "street": "1 Microsoft Way",
        "postal_code": "98052",
        "raw_address": "1 Microsoft Way, Redmond, Washington, United States, North America",
        "position": { "lng": -122.33279, "lat": 47.591396 }
      },
      "technologies": [{ "name": "organization schema", "category": "framework" }],
      "industries": ["Software Development"],
      "keywords": ["AI", "Cloud", "Productivity"],
      "naics": ["511210"],
      "revenue_amount": "10000000000-...",
      "funding_total": 1000000,
      "ipo_date": "1986-03-13",
      "it_spend": 66692471976,
      "last_updated": "2026-04-12"
    },
    "last_updated": "2026-04-12"
  },
  "_metadata": {
    "extracted_at": "2026-04-28T12:00:00.000Z"
  }
}

Not Found Record

{
  "url": "https://www.linkedin.com/in/someunknownperson/",
  "status": "not_found",
  "_metadata": {
    "extracted_at": "2026-04-28T12:00:00.000Z"
  }
}

Error Record

{
  "url": "https://example.com/not-a-linkedin-url",
  "status": "error",
  "error_kind": "user_error",
  "reason": "Invalid LinkedIn profile URL. Expected: https://www.linkedin.com/in/<handle>/",
  "_metadata": {
    "extracted_at": "2026-04-28T12:00:00.000Z"
  }
}

Output Fields Reference

All fields below are nested under profile (present when status = success).

Identity:

Field	Type	Description
profile.identifier	string | null	LinkedIn handle (the slug after /in/, e.g. satyanadella).
profile.first_name	string | null	First name.
profile.last_name	string | null	Last name.
profile.full_name	string | null	Full display name.
profile.title	string | null	Current job title.
profile.headline	string | null	LinkedIn headline (short tagline under the name).
profile.summary	string | null	About / summary section.
profile.picture_url	string | null	Profile photo URL.
profile.background_url	string | null	Banner / background image URL.
profile.birth_date	string | null	Birth date if exposed by the source.
profile.industry	string | null	Top-level industry classification (e.g. Computer Software).
profile.last_updated	string | null	ISO date the upstream source last refreshed this profile.

Location & locale:

Field	Type	Description
profile.location.default	string	Compact one-liner (e.g. Redmond, Washington, United States, North America).
profile.location.short	string	Short one-liner (e.g. Redmond, Washington).
profile.location.country	string	Country name.
profile.location.state	string	State or region.
profile.location.city	string	City.
profile.languages.primary_locale	object	{ country, language } of the profile's primary locale.
profile.languages.supported_locales	object[]	Other locales the profile is translated into.

Sales / ICP signals:

Field	Type	Description
profile.badges.open_to_work	boolean	True when the profile is flagged Open to Work.
profile.badges.hiring	boolean	True when the profile is hiring.
profile.badges.premium	boolean	LinkedIn Premium subscriber.
profile.badges.creator	boolean	LinkedIn Creator.
profile.badges.verified	boolean	LinkedIn-verified profile.
profile.badges.influencer	boolean	LinkedIn Influencer.
profile.department.seniority	string | null	Seniority level (e.g. c_suite, vp, director, manager).
profile.department.functions	string[]	Functional areas (e.g. engineering, sales).
profile.department.departments	string[]	Departments (e.g. engineering, c_suite).
profile.department.sub_departments	string[]	Sub-departments.

Social links:

Field	Type	Description
profile.links.linkedin	string | null	Canonical LinkedIn URL from the data source.
profile.links.twitter	string | null	Twitter / X profile URL if discoverable.
profile.links.github	string | null	GitHub profile URL if discoverable.
profile.links.facebook	string | null	Facebook profile URL if discoverable.

Career history:

Field	Type	Description
profile.skills	string[]	List of skills.
profile.educations	object[]	Education history. Each entry: { school: { id, name, logo, url }, degree_name, field_of_study, grade, date: { start, end } }.
profile.position_groups	object[]	Work experience grouped by company. Each entry: { company: { id, name, logo, url, employees }, date: { start, end }, profile_positions: [{ company, title, description, employment_type, location, date }] }.

Current company (deeply enriched):

Field	Type	Description
profile.company.name	string | null	Current company name.
profile.company.legal_name	string | null	Legal company name (e.g. Microsoft Corporation).
profile.company.description	string | null	Long-form company description.
profile.company.overview	string | null	Short company overview.
profile.company.founded_year	integer | null	Year founded.
profile.company.type	string | null	PUBLIC_COMPANY, PRIVATELY_HELD, NON_PROFIT, etc.
profile.company.industry	string | null	Company-level industry.
profile.company.staff.total	integer | null	Total employees.
profile.company.staff.range_start	integer | null	Lower bound of employee range.
profile.company.website	string | null	Company website URL.
profile.company.domain	string | null	Root domain (e.g. microsoft.com).
profile.company.linkedin_url	string | null	Company LinkedIn page.
profile.company.twitter_url	string | null	Company Twitter / X.
profile.company.facebook_url	string | null	Company Facebook.
profile.company.crunchbase_url	string | null	Company Crunchbase.
profile.company.headquarter	object | null	{ country, state, city, street, postal_code, raw_address, position: { lng, lat } }.
profile.company.technologies	object[]	Tech stack: [{ name, category }].
profile.company.industries	string[]	Industry tags.
profile.company.keywords	string[]	SEO / topic keywords.
profile.company.naics	string[]	NAICS classification codes.
profile.company.revenue_amount	string | null	Revenue range (e.g. 10000000000-...).
profile.company.funding_total	integer | null	Total funding raised in USD.
profile.company.ipo_date	string | null	IPO date if public.
profile.company.it_spend	integer | null	Estimated annual IT spend in USD (Aberdeen data). Useful for B2B SaaS targeting.

Provenance:

Field	Type	Description
_metadata.extracted_at	string	ISO timestamp of the lookup.

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How much does it cost to scrape LinkedIn profiles?

This Actor uses pay-per-event pricing. You are charged $0.006 per successfully scraped profile (= $6.00 per 1,000 profiles).

Cost examples:

Profiles scraped	Cost
100	$0.60
500	$3.00
1,000	$6.00
5,000	$30.00

What you are NOT charged for:

• Profiles that returned not_found (not in the data source)
• Profiles that returned error (invalid URL or network failure)
• Apify platform compute time (memory/CPU)

Free plan limit

Apify Free plan users can scrape up to 20 profiles per calendar month so you can validate the output and field shape before paying. The cap resets on the 1st of each month. To run unlimited profiles, upgrade to any paid Apify plan. Free-tier overflow returns a status: "error" record with error_kind: "free_tier_limit" and is not charged.

💡 Profiles that aren't found cost nothing, there's no penalty for trying.

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Use it with AI agents and MCP

This Actor is built for AI agents and LLMs via the Apify MCP server. Each record has a status field (success | not_found | error) and a flat, predictable profile object when status is success, ideal for agent post-processing.

Key fields for agents:

• profile.full_name: Who this person is
• profile.title + profile.headline: Their current role and self-description
• profile.industry: Industry classification
• profile.department.seniority: Seniority level (c_suite, vp, director, etc.) for ICP matching
• profile.badges.open_to_work / profile.badges.hiring: Intent signals
• profile.company.name + profile.company.industry + profile.company.staff.total: Where they work, what the company does, headcount
• profile.company.technologies: Tech stack used by the company
• profile.company.it_spend: Annual IT spend (B2B SaaS targeting)
• profile.skills: Areas of expertise
• profile.position_groups: Career history
• profile.links.{twitter, github, facebook}: Other social profiles for outreach

Example agent prompt:

"Scrape these 10 LinkedIn profiles and summarize each person's role, company, and top 3 skills in a table."

The agent passes the URL list as profileUrls and processes the structured profile output directly, no scraping logic, no HTML parsing.

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Use Cases

• 📊 CRM enrichment: Automatically fill in job title, company, location, and skills for contacts in your CRM.
• 🔎 Sales prospecting: Validate and enrich leads before outreach.
• 🧩 Recruiting: Quickly build structured candidate profiles from a list of LinkedIn URLs.
• 🏢 Account research: Understand a company's team composition and expertise.
• 📋 Data pipelines: Feed structured profile data into downstream tools (Airtable, HubSpot, Salesforce, etc.).
• 🤖 AI agent context: Give AI agents structured professional background on a person before a meeting or call.

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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 (legal name, HQ, tech stack, IT spend, more)	Rarely	No
Pricing	Pay per profile found ($0.006)	Subscription + your account	Gated / partner pricing

For most teams that just have a list of profile URLs to enrich, the cookieless approach returns full structured data with zero account risk, no session to maintain, and no partner approval to wait on.

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Other LinkedIn scrapers by Atomus

Actor	What it does
LinkedIn Post Reactions Scraper Pro	Extract every reactor on a LinkedIn post (name, headline, profile URL, reaction type).
LinkedIn Posts Scraper	Scrape posts, text, media, and engagement counts from any LinkedIn profile or company.
LinkedIn Post Comments & Replies Scraper Pro	Extract every comment and reply on a LinkedIn post, with reaction counts and reply tracking.

See all tools at apify.com/atomus.

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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 record 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 record.

What data does the LinkedIn Profile Scraper return?

For each found profile: identity (name, title, headline, summary), location, ICP signals (seniority, department, open-to-work / hiring / verified / premium badges), skills, education, full work history, cross-platform links, and a deeply enriched company object (legal name, HQ address, industry, staff count, tech stack, IT spend, IPO date, and more).

How much does it cost?

$0.006 per successfully scraped profile ($6 per 1,000). You are charged only for profiles that are found, not_found and error results are free. Free Apify plans include 20 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?

Each record includes a last_updated field (both at the profile and company level) showing when the upstream source last refreshed that profile, so you always know how recent the data is.

What happens if a profile isn't found?

You get a record with status: "not_found" (private, deleted, or not indexed) or status: "error" (invalid URL 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 or spreadsheet, paste them into profileUrls (up to 5,000 per run), and run. The output maps 1:1 to your input by URL, so it's easy to rejoin in any spreadsheet tool or database.

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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.