{ "data": { "id": "1YYbI4LHcmUllQYzW", "actId": "HcEUCKHXJu3SvAVnC", "userId": "f0zhjvuZBYFAxHnCG", "startedAt": "2026-10-15T20:13:06.704Z", "finishedAt": "2026-10-15T20:14:01.856Z", "status": "SUCCEEDED", "meta": { "origin": "API" }, "stats": { "durationMillis": 55057, "runTimeSecs": 55.057, "computeUnits": 0.06117444444444444, "imageSizeBytes": 602648064 }, "options": { "useCache": false, "betaPackages": false, "tag": "latest", "memoryMbytes": 4096, "diskMbytes": 8192 }, "exitCode": 0, "inputSchema": "{\n \"title\": \"LinkedIn Profile Scraper\",\n \"description\": \"Provide a list of LinkedIn profile URLs to enrich. URLs are enriched in fast bulk batches. You are only charged for profiles that return data. Not-found and errors are free.\",\n \"type\": \"object\",\n \"schemaVersion\": 1,\n \"properties\": {\n \"profileUrls\": {\n \"title\": \"๐Ÿ”— LinkedIn profile URLs\",\n \"description\": \"List of LinkedIn profile URLs to enrich. Each must match the format https://www.linkedin.com/in//.\",\n \"type\": \"array\",\n \"editor\": \"stringList\",\n \"items\": {\n \"type\": \"string\"\n },\n \"minItems\": 1,\n \"maxItems\": 5000,\n \"prefill\": [\n \"https://www.linkedin.com/in/satyanadella/\"\n ]\n },\n \"findEmail\": {\n \"sectionCaption\": \"๐Ÿ“ง Add-on: Verified work email ($6/1k emails found, paid Apify plans only)\",\n \"sectionDescription\": \"Finds the verified work email of each profile returned. Only emails actually found are charged; profiles with no email cost nothing. Available on paid Apify plans only: on a free plan, turning this on stops the whole run before anything is scraped or charged, so leave it off to run without emails.\",\n \"title\": \"๐Ÿ“ง Find the verified work email\",\n \"description\": \"โš ๏ธ THIS ADDS COST. When enabled, each returned profile also includes a real-time, real-time verified work email, billed as a SEPARATE paid event for every email found (on top of the profile charge). You are charged ONLY when an email is actually found: not-found profiles and profiles without a verifiable email are free. Available on PAID Apify plans only (a free-tier run with this on is rejected without charge). Leave OFF unless you specifically want emails.\",\n \"type\": \"boolean\",\n \"default\": false,\n \"groupCaption\": \"Enrichment options\"\n },\n \"includeCompanyDetails\": {\n \"sectionCaption\": \"๐Ÿฆ Add-on: Full company firmographics ($3.50/1k โ†’ $2.80/1k by Apify plan)\",\n \"sectionDescription\": \"Adds the full firmographic record of each profile's current company. Charged only for profiles that actually return a company: profiles with no company, not-found and errors add nothing.\",\n \"title\": \"๐Ÿฆ Include full company details\",\n \"description\": \"When enabled, each successfully enriched profile also returns the FULL firmographic record for the person's current company (complete funding-round breakdown, every office location, full tech stack, industries, keywords, NAICS codes, revenue band, sub-organizations) in a top-level company_details field. Charged as an extra event per successful profile that has a company; not-found, errors, and profiles with no company are free. Leave off for profile-only enrichment.\",\n \"type\": \"boolean\",\n \"default\": false,\n \"groupCaption\": \"Enrichment options\"\n }\n },\n \"required\": [\n \"profileUrls\"\n ]\n}", "readme": "# LinkedIn Profile Scraper\n\n\n\n
\nLINKEDIN PROFILE SCRAPER  •  LINKEDIN PROFILE API

\nKnow who they are
before you reach out.

\nTurn 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.

\nPAY ONLY FOR PROFILES FOUND\n 186,000+ PROFILES ENRICHED \n
\n\n### Copy to your AI assistant\n\nPaste this into ChatGPT, Claude, Cursor, or any LLM to start using this Actor right away.\n\n```\natomus/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/ URLs work, not the internal member-id form (/in/ACoAA..., /in/ACwAA... and the rest of that family). 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\n```\n\n---\n\n## LinkedIn MCP Server: use these Actors from ChatGPT, Claude or Cursor\n\nPoint 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.\n\n```json\n{\n \"mcpServers\": {\n \"atomus\": {\n \"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\",\n \"headers\": { \"Authorization\": \"Bearer YOUR_APIFY_TOKEN\" }\n }\n }\n}\n```\n\nThen ask, in plain language:\n\n> \"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.\"\n\nThat 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.\n\n---\n\n## How to scrape LinkedIn profiles\n\n### One profile\n\n```json\n{ \"profileUrls\": [\"https://www.linkedin.com/in/satyanadella/\"] }\n```\n\nReturns one row: identity, title, seniority, skills, work history, and the current company.\n\n### Bulk: enrich a CRM export\n\n```json\n{\n \"profileUrls\": [\n \"https://www.linkedin.com/in/satyanadella/\",\n \"https://www.linkedin.com/in/williamhgates/\",\n \"https://www.linkedin.com/in/reidhoffman/\"\n ]\n}\n```\n\nUp 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.\n\n### With full company firmographics\n\n```json\n{\n \"profileUrls\": [\"https://www.linkedin.com/in/satyanadella/\"],\n \"includeCompanyDetails\": true\n}\n```\n\nAdds 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.\n\n### Qualify before you spend\n\nNot-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.\n\n---\n\n## Input\n\n| Parameter | Type | Required | Default | Description |\n|-----------|------|----------|---------|-------------|\n| `profileUrls` | string[] | โœ… Yes | (none) | LinkedIn profile URLs in the canonical `https://www.linkedin.com/in//` form. Up to 5,000 per run. Trailing slash optional; tracking parameters are fine. |\n| `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. |\n\n### Which URLs work\n\n```\nโœ… https://www.linkedin.com/in/satyanadella/\nโœ… https://www.linkedin.com/in/satyanadella\nโœ… https://www.linkedin.com/in/satyanadella/?utm_source=share\n\nโŒ https://www.linkedin.com/in/ACoAAAR-kOEBwpv4Kg1xD8fRWSax1kPNYjX2W88\nโŒ urn:li:member:123456789\n```\n\nThat `ACoAA...` string is an internal LinkedIn member ID, not a public handle (they also start `ACwAA`, `ACgAA` and so on). The data source cannot resolve it and this Actor cannot turn it back into a handle, 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.\n\n> **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.\n\n---\n\n## What data does the LinkedIn Profile Scraper return?\n\nEvery row carries a `status` (`success` | `not_found` | `error`). On `success`, everything below sits under `profile`.\n\n| Group | Fields |\n|---|---|\n| **Identity** | `identifier` (the `/in/` handle) ยท `first_name` ยท `last_name` ยท `full_name` ยท `title` ยท `headline` ยท `summary` ยท `picture_url` ยท `background_url` ยท `birth_date` ยท `industry` ยท `last_updated` |\n| **Location & locale** | `location.default` ยท `location.short` ยท `location.country` ยท `location.state` ยท `location.city` ยท `languages.primary_locale` ยท `languages.supported_locales` |\n| **ICP signals** | `department.seniority` (`c_suite`, `vp`, `director`, `manager`, โ€ฆ) ยท `department.functions` ยท `department.departments` ยท `department.sub_departments` ยท badges: `open_to_work`, `hiring`, `premium`, `creator`, `verified`, `influencer` |\n| **Career** | `skills` ยท `educations` (`school{id,name,logo,url}`, `degree_name`, `field_of_study`, `grade`, `date{start,end}`) ยท `position_groups` (`company{id,name,logo,url,employees}`, `date{start,end}`, `profile_positions[{company,title,description,employment_type,location,date}]`) |\n| **Certifications** | `certifications[]`: `name` ยท `authority` (the issuer) ยท `url` ยท `license_number` ยท `display_source` ยท `company{id,name,logo,url}` ยท `date{start,end}` (issued, expires) |\n| **Volunteering & organizations** | `volunteer_experiences[]`: `role` ยท `company` ยท `cause` ยท `description` ยท `date{start,end}` ยท `organizations[]`: `name` ยท `position` ยท `date{start,end}` |\n| **Network & tenure** | `statistics.network.followers_count` ยท `statistics.network.connections_count` ยท `statistics.network.influencer` ยท `statistics.experience.tenure.start` (career start) ยท `tenure.company` (joined the current company) ยท `tenure.title` (in the current role since) ยท `statistics.experience.prev_job_titles` |\n| **Other profiles** | `links.linkedin` ยท `links.twitter` ยท `links.github` ยท `links.facebook` |\n| **Current company** | `company.name` ยท `legal_name` ยท `description` ยท `overview` ยท `founded_year` ยท `type` (`PUBLIC_COMPANY`, `PRIVATELY_HELD`, `NON_PROFIT`, โ€ฆ) ยท `industry` ยท `industries` ยท `staff.total` ยท `staff.range_start` ยท `website` ยท `domain` ยท `linkedin_url` ยท `twitter_url` ยท `facebook_url` ยท `crunchbase_url` ยท `headquarter{country,state,city,street,postal_code,raw_address,position{lng,lat}}` ยท `technologies[{name,category}]` ยท `keywords` ยท `naics` ยท `revenue_amount` ยท `funding_total` ยท `ipo_date` ยท `it_spend` |\n| **Provenance** | `_metadata.extracted_at` (ISO timestamp of the lookup) |\n\n`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.\n\nA profile that lists no certifications, volunteering or organizations comes back with an empty list for that block, and `statistics` is `null` when the source has no network figures for the person. When they are filled, the blocks look like this:\n\n```json\n{\n \"certifications\": [\n {\n \"name\": \"English proficiency: Intermediate\",\n \"authority\": \"Duolingo\",\n \"url\": \"https://testcenter.duolingo.com\",\n \"company\": { \"name\": \"Duolingo\", \"logo\": \"https://img.atomusapi.dev/i/\", \"url\": \"https://www.linkedin.com/company/duolingo\" },\n \"date\": { \"start\": \"2016-06-01\", \"end\": null }\n }\n ],\n \"volunteer_experiences\": [\n { \"role\": \"Youth leader\", \"cause\": \"SOCIAL_SERVICES\", \"description\": null, \"date\": { \"start\": \"1985-01-01\", \"end\": \"2000-12-31\" } }\n ],\n \"organizations\": [\n { \"name\": \"BNI\", \"position\": \"Event Coordinator\", \"date\": { \"start\": \"2015-01-01\", \"end\": null } }\n ],\n \"statistics\": {\n \"network\": { \"followers_count\": 5430, \"connections_count\": 1200, \"influencer\": false },\n \"experience\": { \"tenure\": { \"start\": \"1990-11-01\", \"company\": \"1991-08-01\", \"title\": \"1991-08-01\" }, \"prev_job_titles\": null }\n }\n}\n```\n\n### Example row\n\n```json\n{\n \"status\": \"success\",\n \"input_url\": \"https://www.linkedin.com/in/satyanadella/\",\n \"profile\": {\n \"identifier\": \"satyanadella\",\n \"full_name\": \"Satya Nadella\",\n \"title\": \"Chairman and CEO\",\n \"headline\": \"Chairman and CEO at Microsoft\",\n \"industry\": \"Computer Software\",\n \"location\": { \"short\": \"Redmond, Washington\", \"country\": \"United States\", \"city\": \"Redmond\" },\n \"department\": { \"seniority\": \"c_suite\", \"departments\": [\"c_suite\"], \"functions\": [\"executive\"] },\n \"badges\": { \"verified\": true, \"premium\": true, \"open_to_work\": false, \"hiring\": false },\n \"skills\": [\"Cloud Computing\", \"Enterprise Software\", \"Leadership\"],\n \"links\": { \"linkedin\": \"https://www.linkedin.com/in/satyanadella\", \"twitter\": null },\n \"position_groups\": [\n { \"company\": { \"name\": \"Microsoft\", \"employees\": 238000 }, \"date\": { \"start\": \"1992\", \"end\": null } }\n ],\n \"company\": {\n \"name\": \"Microsoft\",\n \"legal_name\": \"Microsoft Corporation\",\n \"domain\": \"microsoft.com\",\n \"industry\": \"Software Development\",\n \"founded_year\": 1975,\n \"type\": \"PUBLIC_COMPANY\",\n \"staff\": { \"total\": 238000 },\n \"headquarter\": { \"city\": \"Redmond\", \"state\": \"Washington\", \"country\": \"US\" },\n \"technologies\": [{ \"name\": \"Azure\", \"category\": \"cloud\" }],\n \"it_spend\": 4200000000,\n \"ipo_date\": \"1986-03-13\"\n },\n \"last_updated\": \"2026-08-14\"\n },\n \"_metadata\": { \"extracted_at\": \"2026-09-03T14:02:11.000Z\" }\n}\n```\n\n`not_found` and `error` rows carry the same `url` you sent plus a `reason` saying what happened, and no `profile` object. `error` rows additionally carry an `error_kind` (`user_error` when the input cannot work, `actor_error` when the lookup itself failed). An `error_kind` of `provider_unavailable` means the data provider was temporarily unavailable: that row was not charged, so run that URL again later.\n\n### With `includeCompanyDetails`: the `company_details` object\n\n`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](https://apify.com/atomus/linkedin-company-scraper) returns, without a second run.\n\n| Group | Fields |\n|---|---|\n| **Identity** | `company_details.id` ยท `summary.name` ยท `legal_name` ยท `description` ยท `overview` ยท `founded_year` ยท `type` ยท `industry` ยท `summary.logo.source` |\n| **Size** | `summary.staff.total` ยท `summary.staff.range.start` |\n| **Links** | `link.website` ยท `domain` ยท `domain_ltd` ยท `linkedin` ยท `twitter` ยท `facebook` ยท `crunchbase` |\n| **Revenue & IT budget** | `financial.revenue.annual{amount,start,end}` (a band, not a point estimate) ยท `financial.aberdeen.it_spend` |\n| **Funding, every round** | `financial.funding{total_amount,num_funding_rounds,num_investor,type,date,last_amount}` ยท `financial.funding.rounds[{announced_at,type,investors[]}]` |\n| **Other financial events** | `financial.ipo{date,share_price,link}` ยท `financial.acquisition` ยท `financial.investment` ยท `financial.exit` ยท `financial.diversity_investment` (each `{total,contents[]}`) |\n| **Every office** | `location.headquarter{continent,country,state,city,street,postal_code,raw_address,position{lat,lng}}` ยท `location.locations[]` (the same shape, one entry per office worldwide) |\n| **Tech & classification** | `technologies[{name,category}]` (the full stack) ยท `industries[]` ยท `keywords[]` ยท `naics[]` |\n| **Company graph & reach** | `sub_organizations[]` ยท `languages[]` ยท `hashtags[]` |\n| **Freshness** | `company_details.last_updated` |\n\nMeasured 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.\n\n```json\n{\n \"status\": \"success\",\n \"input_url\": \"https://www.linkedin.com/in/satyanadella/\",\n \"profile\": { \"full_name\": \"Satya Nadella\", \"โ€ฆ\": \"as above\" },\n \"company_details\": {\n \"summary\": {\n \"name\": \"Microsoft\",\n \"legal_name\": \"Microsoft Corporation\",\n \"type\": \"PUBLIC_COMPANY\",\n \"industry\": \"software development\",\n \"founded_year\": 1975,\n \"staff\": { \"total\": 229769, \"range\": { \"start\": 10001 } }\n },\n \"link\": { \"website\": \"https://www.microsoft.com\", \"domain\": \"microsoft.com\" },\n \"financial\": {\n \"revenue\": { \"amount\": \"10000000000-9223372036854775807\", \"start\": 10000000000 },\n \"funding\": {\n \"num_funding_rounds\": 3,\n \"rounds\": [{ \"announced_at\": \"2022-12-09\", \"type\": \"POST_IPO_EQUITY\", \"investors\": [\"KB Securities\"] }]\n },\n \"ipo\": { \"date\": \"1986-03-13\" },\n \"aberdeen\": { \"it_spend\": 72571566964 }\n },\n \"location\": {\n \"headquarter\": { \"city\": \"Redmond\", \"state\": \"Washington\", \"country\": \"United States\" },\n \"locations\": [\n { \"city\": \"Bratislava\", \"country\": \"Slovakia\", \"raw_address\": \"Pradiareลˆ 1900, Svรคtoplukova 2A, Bratislava, Slovakia, Europe\", \"position\": { \"lat\": 48.188267, \"lng\": 17.113459 } }\n ]\n },\n \"technologies\": [{ \"name\": \"organization schema\", \"category\": \"framework\" }],\n \"industries\": [\"agentic ai\", \"identity management\", \"cloud security\"],\n \"keywords\": [\"business software\", \"developer tools\"],\n \"naics\": [\"541511\", \"423430\", \"541512\"],\n \"languages\": [\"english\", \"chinese\", \"korean\"],\n \"hashtags\": [\"microsoftlife\", \"microsoft\"],\n \"last_updated\": \"2026-08-14\"\n }\n}\n```\n\n`rounds`, `locations`, `technologies` and `sub_organizations` are trimmed above for readability; the live record carries all of them.\n\n**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.\n\n---\n\n## How much does it cost to scrape LinkedIn profiles?\n\n**$0.006 per profile found** ($6.00 per 1,000). Pay-per-event: you are charged only when a profile comes back.\n\n| Profiles scraped | Cost |\n|-------------------|------|\n| 100 | $0.60 |\n| 1,000 | $6.00 |\n| 5,000 | $30.00 |\n\n**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.\n\n**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.\n\n---\n\n## What do people use the LinkedIn Profile Scraper for?\n\n- **CRM enrichment**: fill in job title, company, location and skills for contacts you already have.\n- **Sales prospecting**: qualify and enrich leads before outreach, so you know who is worth an email.\n- **Recruiting**: build structured candidate profiles from a list of LinkedIn URLs.\n- **Account research**: understand a company's team composition and expertise before a call.\n- **Data pipelines**: feed structured profile data into Airtable, HubSpot, Salesforce or your warehouse.\n- **AI agent context**: give an agent someone's professional background before a meeting.\n\n---\n\n## LinkedIn Profile Scraper vs cookie-based tools vs the official LinkedIn API\n\n| | This LinkedIn Profile Scraper | Cookie-based scrapers | Official LinkedIn API |\n|---|---|---|---|\n| **LinkedIn account / cookies** | Not needed | Your logged-in cookies required | OAuth app + LinkedIn approval |\n| **Account / ban risk** | None (no account used) | High (your account can be restricted) | None |\n| **Setup** | Paste profile URLs | Extract and paste your session cookie | Partner application + review |\n| **Data depth** | Full profile + deep company data | Varies, often profile-only | Limited, mostly your own network |\n| **Bulk** | Up to 5,000 URLs per run | Rate-limited by your account | Strict quotas |\n| **Company enrichment** | Yes (HQ, tech stack, IT spend, more) | Rarely | No |\n| **Pricing** | Pay per profile found ($0.006) | Subscription + your account | Gated / partner pricing |\n\n---\n\n\n## ๐Ÿ† Top LinkedIn Scrapers\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n Profile Scraper\n YOU ARE HERE \n
\n Posts Scraper
\nText, media and engagement from any profile or company\n
\n Company Posts Scraper
\nEvery post from a company page, with media and engagement\n
\n Post Search
\nFind posts about any topic, by keyword, across all of LinkedIn\n
\n Reactions Scraper
\nEvery person who reacted to a post, with their headline\n
\n Comments Scraper
\nComments and nested replies, with reaction counts\n
\n Company Scraper
\nHeadcount, industry, HQ, tech stack and IT spend of a company\n
\n Company Employees Scraper
\nEvery employee of a company, filterable by seniority and department\n
\n\n---\n\n## FAQ\n\n### What is LinkedIn profile scraping?\n\nLinkedIn 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.\n\n### How do I scrape a LinkedIn profile without an account or cookies?\n\nPaste 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.\n\n### Can I use this from ChatGPT or Claude?\n\nYes, 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.\n\n### How much does it cost?\n\n$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.\n\n### Is there an official LinkedIn API for profile data?\n\nLinkedIn'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.\n\n### Is it legal to scrape LinkedIn profiles?\n\nThis 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.\n\n### How fresh is the data?\n\nEvery row includes `last_updated`, at both profile and company level, showing when the upstream source last refreshed that record.\n\n### What happens if a profile isn't found?\n\nYou 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.\n\n### Can I enrich a CRM or spreadsheet in bulk?\n\nYes. 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.\n\n---\n\n\n## All Atomus scrapers\n\n2.4M+ RESULTS DELIVERED\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
LinkedInProfile ยท Posts ยท Company posts ยท Post search ยท Reactions ยท Comments ยท Company ยท Employees
Lead genLeads Finder ยท Profiles by role & company
Google MapsPlaces & local businesses
TikTokVideos ยท Comments
X (Twitter)Tweets & profiles
RedNote ๅฐ็บขไนฆNotes, users & comments
Douyin ๆŠ–้ŸณProfiles, videos & comments
Weibo ๅพฎๅšPosts & profiles
Bilibili ๅ“”ๅ“ฉๅ“”ๅ“ฉVideos & creators
YouTubeThumbnails
\n\n---\n\n\n## Support\n\n\n\n\n\n
\n\n\nHey, 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.

\n๐Ÿ’ฌ  DM me on LinkedIn\n   or hello@dendelabs.com\n
\n\n---\n\n## โš ๏ธ Disclaimer\n\nThis Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by LinkedIn Corporation. LinkedInยฎ is a registered trademark of LinkedIn Corporation. All trademarks are property of their respective owners.\n", "actorDefinition": { "actorSpecification": 1, "name": "linkedin-profile-scraper", "title": "LinkedIn Profile Scraper โœ… NO COOKIES", "description": "Scrape LinkedIn profiles into structured records: name, title, headline, summary, company, location, skills, education, and work history. Submit up to 5,000 URLs per run, each returns one record. Pay only for profiles found; not-found and errors are free. No account or cookies needed.", "version": "1.0", "buildTag": "latest", "input": { "title": "LinkedIn Profile Scraper", "description": "Provide a list of LinkedIn profile URLs to enrich. URLs are enriched in fast bulk batches. You are only charged for profiles that return data. Not-found and errors are free.", "type": "object", "schemaVersion": 1, "properties": { "profileUrls": { "title": "๐Ÿ”— LinkedIn profile URLs", "description": "List of LinkedIn profile URLs to enrich. Each must match the format https://www.linkedin.com/in//.", "type": "array", "editor": "stringList", "items": { "type": "string" }, "minItems": 1, "maxItems": 5000, "prefill": [ "https://www.linkedin.com/in/satyanadella/" ] }, "findEmail": { "sectionCaption": "๐Ÿ“ง Add-on: Verified work email ($6/1k emails found, paid Apify plans only)", "sectionDescription": "Finds the verified work email of each profile returned. Only emails actually found are charged; profiles with no email cost nothing. Available on paid Apify plans only: on a free plan, turning this on stops the whole run before anything is scraped or charged, so leave it off to run without emails.", "title": "๐Ÿ“ง Find the verified work email", "description": "โš ๏ธ THIS ADDS COST. When enabled, each returned profile also includes a real-time, real-time verified work email, billed as a SEPARATE paid event for every email found (on top of the profile charge). You are charged ONLY when an email is actually found: not-found profiles and profiles without a verifiable email are free. Available on PAID Apify plans only (a free-tier run with this on is rejected without charge). Leave OFF unless you specifically want emails.", "type": "boolean", "default": false, "groupCaption": "Enrichment options" }, "includeCompanyDetails": { "sectionCaption": "๐Ÿฆ Add-on: Full company firmographics ($3.50/1k โ†’ $2.80/1k by Apify plan)", "sectionDescription": "Adds the full firmographic record of each profile's current company. Charged only for profiles that actually return a company: profiles with no company, not-found and errors add nothing.", "title": "๐Ÿฆ Include full company details", "description": "When enabled, each successfully enriched profile also returns the FULL firmographic record for the person's current company (complete funding-round breakdown, every office location, full tech stack, industries, keywords, NAICS codes, revenue band, sub-organizations) in a top-level company_details field. Charged as an extra event per successful profile that has a company; not-found, errors, and profiles with no company are free. Leave off for profile-only enrichment.", "type": "boolean", "default": false, "groupCaption": "Enrichment options" } }, "required": [ "profileUrls" ] }, "output": { "actorOutputSchemaVersion": 1, "title": "LinkedIn Profile Enricher Output", "description": "Each result corresponds to one input URL. status=success includes a full profile object. status=not_found means the profile was not in the data source. status=error means a validation or network issue occurred. Charges apply only to success results.", "properties": { "profiles": { "type": "string", "title": "Enriched Profiles", "template": "{{links.apiDefaultDatasetUrl}}/items" } } }, "dockerfile": "../Dockerfile", "environmentVariables": { "AIARK_API_KEYS": "@aiark_api_keys", "AIARK_MIN_CREDITS": "1000", "AIARK_SEMAPHORE_URL": "@aiark_semaphore_url", "AIARK_SEM_SECRET": "@aiark_sem_secret", "MONITOR_WEBHOOK_URL": "@monitor_webhook_url", "OBS_COLLECTOR_URL": "@obs_collector_url", "OBS_TELEMETRY_SECRET": "@obs_telemetry_secret", "OBS_WEBHOOK_URL": "@obs_webhook_url" }, "pricingModel": "PAY_PER_EVENT", "defaultRunOptions": { "build": "latest", "timeoutSecs": 3600, "memoryMbytes": 256 }, "minMemoryMbytes": 256, "maxMemoryMbytes": 512, "defaultMemoryMbytes": "max(256, min(256 + get(input, 'profileUrls.length', 1) * 0.05, 512))", "storages": { "dataset": { "actorSpecification": 1, "fields": { "url": { "type": "string", "title": "LinkedIn URL", "description": "The input LinkedIn profile URL.", "example": "https://www.linkedin.com/in/satyanadella/" }, "status": { "type": "string", "title": "Status", "description": "Enrichment result: success (data found and charged), not_found (no data, free), error (validation or network issue, free).", "example": "success" }, "profile": { "type": "object", "title": "Profile", "description": "Enriched profile data. Present only when status = success.", "example": { "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...", "picture_url": "https://img.atomusapi.dev/i/", "industry": "Computer Software", "location": { "default": "Redmond, Washington, United States, North America", "country": "United States", "state": "Washington", "city": "Redmond" }, "badges": { "premium": true, "creator": true, "open_to_work": false, "hiring": false, "verified": true, "influencer": true }, "department": { "seniority": "c_suite", "departments": [ "Engineering" ], "functions": [ "Engineering" ] }, "links": { "linkedin": "https://www.linkedin.com/in/satyanadella", "twitter": null }, "company": { "name": "Microsoft", "industry": "software development", "founded_year": 1975, "staff": { "total": 228018 } }, "skills": [], "educations": [], "position_groups": [] } }, "profile.identifier": { "type": "string", "title": "LinkedIn Handle", "description": "LinkedIn handle (the slug after /in/, e.g. 'satyanadella').", "example": "satyanadella" }, "profile.first_name": { "type": "string", "title": "First Name", "description": "First name (dotted path for table views).", "example": "Satya" }, "profile.last_name": { "type": "string", "title": "Last Name", "description": "Last name (dotted path for table views).", "example": "Nadella" }, "profile.full_name": { "type": "string", "title": "Full Name", "description": "Full display name (dotted path for table views).", "example": "Satya Nadella" }, "profile.title": { "type": "string", "title": "Job Title", "description": "Current job title (dotted path for table views).", "example": "Chairman and CEO" }, "profile.headline": { "type": "string", "title": "Headline", "description": "Short tagline shown below the name on LinkedIn (dotted path for table views).", "example": "Chairman and CEO at Microsoft" }, "profile.summary": { "type": "string", "title": "Summary", "description": "About / summary section (dotted path for table views).", "example": "As chairman and CEO of Microsoft..." }, "profile.picture_url": { "type": "string", "title": "Picture URL", "description": "Profile photo URL (dotted path for table views).", "example": "https://img.atomusapi.dev/i/" }, "profile.industry": { "type": "string", "title": "Industry", "description": "Industry classification of the profile (dotted path for table views).", "example": "Computer Software" }, "profile.location.default": { "type": "string", "title": "Location", "description": "Compact one-liner location string (dotted path for table views).", "example": "Redmond, Washington, United States, North America" }, "profile.location.country": { "type": "string", "title": "Country", "description": "Country name of the profile location (dotted path for table views).", "example": "United States" }, "profile.location.state": { "type": "string", "title": "State", "description": "State or region of the profile location (dotted path for table views).", "example": "Washington" }, "profile.location.city": { "type": "string", "title": "City", "description": "City of the profile location (dotted path for table views).", "example": "Redmond" }, "profile.badges.open_to_work": { "type": "boolean", "title": "Open to Work", "description": "True when the profile is flagged Open to Work (dotted path for table views).", "example": false }, "profile.badges.hiring": { "type": "boolean", "title": "Hiring", "description": "True when the profile is hiring (dotted path for table views).", "example": false }, "profile.badges.premium": { "type": "boolean", "title": "LinkedIn Premium", "description": "True when the profile has LinkedIn Premium (dotted path for table views).", "example": true }, "profile.badges.creator": { "type": "boolean", "title": "Creator", "description": "True when the profile is a LinkedIn Creator (dotted path for table views).", "example": true }, "profile.badges.verified": { "type": "boolean", "title": "Verified", "description": "True when the profile is verified (dotted path for table views).", "example": true }, "profile.badges.influencer": { "type": "boolean", "title": "Influencer", "description": "True when the profile is a LinkedIn Influencer (dotted path for table views).", "example": true }, "profile.department.seniority": { "type": "string", "title": "Seniority", "description": "Seniority level (e.g. c_suite, vp, director, manager, senior, mid, junior). Dotted path for table views.", "example": "c_suite" }, "profile.links.linkedin": { "type": "string", "title": "LinkedIn URL", "description": "Canonical LinkedIn URL from the data source (dotted path for table views).", "example": "https://www.linkedin.com/in/satyanadella" }, "profile.links.twitter": { "type": "string", "title": "Twitter / X URL", "description": "Twitter / X profile URL if discoverable (dotted path for table views).", "example": "https://twitter.com/satyanadella" }, "profile.links.github": { "type": "string", "title": "GitHub URL", "description": "GitHub profile URL if discoverable (dotted path for table views).", "example": null }, "profile.links.facebook": { "type": "string", "title": "Facebook URL", "description": "Facebook profile URL if discoverable (dotted path for table views).", "example": null }, "profile.skills": { "type": "array", "title": "Skills", "description": "List of skills declared on the profile.", "example": [ "Cloud Computing", "Artificial Intelligence", "Leadership" ] }, "profile.educations": { "type": "array", "title": "Educations", "description": "Education history. Each entry: { school: { id, name, logo, url }, degree_name, field_of_study, grade, date: { start, end } }.", "example": [] }, "profile.position_groups": { "type": "array", "title": "Work History", "description": "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 }] }.", "example": [] }, "profile.certifications": { "type": "array", "title": "Certifications", "description": "Licenses and certifications on the profile. Each entry: { name, authority, url, license_number, display_source, company: { id, name, logo, url }, date: { start, end } }. Empty list when the profile has none.", "example": [ { "name": "English proficiency: Intermediate", "authority": "Duolingo", "date": { "start": "2016-06-01", "end": null } } ] }, "profile.volunteer_experiences": { "type": "array", "title": "Volunteering", "description": "Volunteer experience. Each entry: { role, company, cause, description, date: { start, end } }. Empty list when the profile has none.", "example": [ { "role": "Youth leader", "cause": "SOCIAL_SERVICES", "date": { "start": "1985-01-01", "end": "2000-12-31" } } ] }, "profile.organizations": { "type": "array", "title": "Organizations", "description": "Organizations the person belongs to. Each entry: { name, position, date: { start, end } }. Empty list when the profile has none.", "example": [ { "name": "BNI", "position": "Event Coordinator", "date": { "start": "2015-01-01", "end": null } } ] }, "profile.statistics.network.followers_count": { "type": "number", "title": "Followers", "description": "LinkedIn follower count.", "example": 5430 }, "profile.statistics.network.connections_count": { "type": "number", "title": "Connections", "description": "LinkedIn connection count.", "example": 1200 }, "profile.statistics.experience.tenure": { "type": "object", "title": "Tenure", "description": "Career dates: { start (career start), company (joined the current company), title (in the current role since) }.", "example": { "start": "1990-11-01", "company": "1991-08-01", "title": "1991-08-01" } }, "profile.company": { "type": "object", "title": "Current Company", "description": "Current company data: name, legal_name, description, founded_year, type, industry, staff (total + range), website, domain, social URLs, headquarter address, technologies, industries, keywords, naics, revenue_amount, funding_total, ipo_date, it_spend.", "example": { "id": "...", "name": "Microsoft", "legal_name": "Microsoft Corporation", "founded_year": 1975, "type": "PUBLIC_COMPANY", "industry": "software development", "staff": { "total": 228018 }, "website": "https://news.microsoft.com", "domain": "microsoft.com", "linkedin_url": "https://www.linkedin.com/company/microsoft", "twitter_url": "https://x.com/Microsoft", "headquarter": { "country": "United States", "state": "Washington", "city": "Redmond", "raw_address": "1 Microsoft Way, Redmond, Washington, United States, North America" }, "ipo_date": "1986-03-13" } }, "profile.company.name": { "type": "string", "title": "Company", "description": "Current company name (dotted path for table views).", "example": "Microsoft" }, "profile.company.industry": { "type": "string", "title": "Company Industry", "description": "Current company's industry (dotted path for table views).", "example": "software development" }, "profile.company.founded_year": { "type": "integer", "title": "Company Founded", "description": "Year the current company was founded (dotted path for table views).", "example": 1975 }, "profile.company.type": { "type": "string", "title": "Company Type", "description": "Current company type, e.g. PUBLIC_COMPANY, PRIVATELY_HELD, NON_PROFIT (dotted path for table views).", "example": "PUBLIC_COMPANY" }, "profile.company.staff.total": { "type": "integer", "title": "Company Headcount", "description": "Total employees at the current company (dotted path for table views).", "example": 228018 }, "profile.company.website": { "type": "string", "title": "Company Website", "description": "Current company website URL (dotted path for table views).", "example": "https://news.microsoft.com" }, "profile.company.domain": { "type": "string", "title": "Company Domain", "description": "Current company root domain (dotted path for table views).", "example": "microsoft.com" }, "profile.company.linkedin_url": { "type": "string", "title": "Company LinkedIn", "description": "Current company LinkedIn page URL (dotted path for table views).", "example": "https://www.linkedin.com/company/microsoft" }, "profile.company.headquarter.country": { "type": "string", "title": "Company HQ Country", "description": "Current company headquarter country (dotted path for table views).", "example": "United States" }, "profile.company.headquarter.city": { "type": "string", "title": "Company HQ City", "description": "Current company headquarter city (dotted path for table views).", "example": "Redmond" }, "profile.company.ipo_date": { "type": "string", "title": "Company IPO Date", "description": "IPO date if the current company is public (dotted path for table views).", "example": "1986-03-13" }, "profile.company.it_spend": { "type": "integer", "title": "Company IT Spend", "description": "Estimated annual IT spend in USD (Aberdeen data). Useful for B2B SaaS targeting (dotted path for table views).", "example": 66692471976 }, "profile.last_updated": { "type": "string", "title": "Source Last Updated", "description": "Date the upstream data source last refreshed this profile (YYYY-MM-DD).", "example": "2026-04-12" }, "error_kind": { "type": "string", "title": "Error Kind", "description": "Present only when status = error. user_error = bad input URL; actor_error = network or upstream failure.", "example": "user_error" }, "reason": { "type": "string", "title": "Reason", "description": "Human-readable explanation. Present only when status = error.", "example": "Invalid LinkedIn profile URL." }, "company_details": { "type": "object", "title": "Full Company Details", "description": "Full firmographic record for the person's current company. Present only when the 'Include full company details' option is enabled and the profile has a company (paid upsell). Includes the complete funding-round breakdown, every office location, full tech stack, industries, keywords, NAICS codes, revenue band and sub-organizations.", "example": { "id": "...", "summary": { "name": "Microsoft" }, "financial": { "funding": { "rounds": [], "num_funding_rounds": 2 } }, "technologies": [], "naics": [] } }, "_metadata": { "type": "object", "title": "Metadata", "description": "Provenance: extracted_at (ISO timestamp of the lookup).", "example": { "extracted_at": "2026-04-28T12:00:00.000Z" } } }, "views": { "profiles": { "title": "Profiles", "transformation": { "fields": [ "profile.full_name", "profile.title", "profile.company.name", "profile.location.default", "profile.industry", "profile.department.seniority", "profile.badges.open_to_work", "profile.statistics.network.followers_count", "profile.statistics.network.connections_count", "profile.headline", "status", "reason", "error_kind", "url", "profile", "company_details", "_metadata" ], "flatten": [ "profile", "_metadata" ] }, "display": { "component": "table", "properties": { "url": { "label": "Input URL", "format": "link" }, "status": { "label": "Status", "format": "text" }, "reason": { "label": "Reason", "format": "text" }, "error_kind": { "label": "Error Kind", "format": "text" }, "profile.full_name": { "label": "Name", "format": "text" }, "profile.title": { "label": "Title", "format": "text" }, "profile.company.name": { "label": "Company", "format": "text" }, "profile.location.default": { "label": "Location", "format": "text" }, "profile.industry": { "label": "Industry", "format": "text" }, "profile.department.seniority": { "label": "Seniority", "format": "text" }, "profile.badges.open_to_work": { "label": "Open to Work", "format": "text" }, "profile.statistics.network.followers_count": { "label": "Followers", "format": "number" }, "profile.statistics.network.connections_count": { "label": "Connections", "format": "number" }, "profile.headline": { "label": "Headline", "format": "text" }, "profile": { "label": "Full Profile", "format": "object" }, "company_details": { "label": "Company Details", "format": "object" }, "_metadata": { "label": "Metadata", "format": "object" } } } } } } }, "readme": "# LinkedIn Profile Scraper\n\n\n\n
\nLINKEDIN PROFILE SCRAPER  •  LINKEDIN PROFILE API

\nKnow who they are
before you reach out.

\nTurn 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.

\nPAY ONLY FOR PROFILES FOUND\n 186,000+ PROFILES ENRICHED \n
\n\n### Copy to your AI assistant\n\nPaste this into ChatGPT, Claude, Cursor, or any LLM to start using this Actor right away.\n\n```\natomus/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/ URLs work, not the internal member-id form (/in/ACoAA..., /in/ACwAA... and the rest of that family). 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\n```\n\n---\n\n## LinkedIn MCP Server: use these Actors from ChatGPT, Claude or Cursor\n\nPoint 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.\n\n```json\n{\n \"mcpServers\": {\n \"atomus\": {\n \"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\",\n \"headers\": { \"Authorization\": \"Bearer YOUR_APIFY_TOKEN\" }\n }\n }\n}\n```\n\nThen ask, in plain language:\n\n> \"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.\"\n\nThat 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.\n\n---\n\n## How to scrape LinkedIn profiles\n\n### One profile\n\n```json\n{ \"profileUrls\": [\"https://www.linkedin.com/in/satyanadella/\"] }\n```\n\nReturns one row: identity, title, seniority, skills, work history, and the current company.\n\n### Bulk: enrich a CRM export\n\n```json\n{\n \"profileUrls\": [\n \"https://www.linkedin.com/in/satyanadella/\",\n \"https://www.linkedin.com/in/williamhgates/\",\n \"https://www.linkedin.com/in/reidhoffman/\"\n ]\n}\n```\n\nUp 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.\n\n### With full company firmographics\n\n```json\n{\n \"profileUrls\": [\"https://www.linkedin.com/in/satyanadella/\"],\n \"includeCompanyDetails\": true\n}\n```\n\nAdds 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.\n\n### Qualify before you spend\n\nNot-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.\n\n---\n\n## Input\n\n| Parameter | Type | Required | Default | Description |\n|-----------|------|----------|---------|-------------|\n| `profileUrls` | string[] | โœ… Yes | (none) | LinkedIn profile URLs in the canonical `https://www.linkedin.com/in//` form. Up to 5,000 per run. Trailing slash optional; tracking parameters are fine. |\n| `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. |\n\n### Which URLs work\n\n```\nโœ… https://www.linkedin.com/in/satyanadella/\nโœ… https://www.linkedin.com/in/satyanadella\nโœ… https://www.linkedin.com/in/satyanadella/?utm_source=share\n\nโŒ https://www.linkedin.com/in/ACoAAAR-kOEBwpv4Kg1xD8fRWSax1kPNYjX2W88\nโŒ urn:li:member:123456789\n```\n\nThat `ACoAA...` string is an internal LinkedIn member ID, not a public handle (they also start `ACwAA`, `ACgAA` and so on). The data source cannot resolve it and this Actor cannot turn it back into a handle, 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.\n\n> **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.\n\n---\n\n## What data does the LinkedIn Profile Scraper return?\n\nEvery row carries a `status` (`success` | `not_found` | `error`). On `success`, everything below sits under `profile`.\n\n| Group | Fields |\n|---|---|\n| **Identity** | `identifier` (the `/in/` handle) ยท `first_name` ยท `last_name` ยท `full_name` ยท `title` ยท `headline` ยท `summary` ยท `picture_url` ยท `background_url` ยท `birth_date` ยท `industry` ยท `last_updated` |\n| **Location & locale** | `location.default` ยท `location.short` ยท `location.country` ยท `location.state` ยท `location.city` ยท `languages.primary_locale` ยท `languages.supported_locales` |\n| **ICP signals** | `department.seniority` (`c_suite`, `vp`, `director`, `manager`, โ€ฆ) ยท `department.functions` ยท `department.departments` ยท `department.sub_departments` ยท badges: `open_to_work`, `hiring`, `premium`, `creator`, `verified`, `influencer` |\n| **Career** | `skills` ยท `educations` (`school{id,name,logo,url}`, `degree_name`, `field_of_study`, `grade`, `date{start,end}`) ยท `position_groups` (`company{id,name,logo,url,employees}`, `date{start,end}`, `profile_positions[{company,title,description,employment_type,location,date}]`) |\n| **Certifications** | `certifications[]`: `name` ยท `authority` (the issuer) ยท `url` ยท `license_number` ยท `display_source` ยท `company{id,name,logo,url}` ยท `date{start,end}` (issued, expires) |\n| **Volunteering & organizations** | `volunteer_experiences[]`: `role` ยท `company` ยท `cause` ยท `description` ยท `date{start,end}` ยท `organizations[]`: `name` ยท `position` ยท `date{start,end}` |\n| **Network & tenure** | `statistics.network.followers_count` ยท `statistics.network.connections_count` ยท `statistics.network.influencer` ยท `statistics.experience.tenure.start` (career start) ยท `tenure.company` (joined the current company) ยท `tenure.title` (in the current role since) ยท `statistics.experience.prev_job_titles` |\n| **Other profiles** | `links.linkedin` ยท `links.twitter` ยท `links.github` ยท `links.facebook` |\n| **Current company** | `company.name` ยท `legal_name` ยท `description` ยท `overview` ยท `founded_year` ยท `type` (`PUBLIC_COMPANY`, `PRIVATELY_HELD`, `NON_PROFIT`, โ€ฆ) ยท `industry` ยท `industries` ยท `staff.total` ยท `staff.range_start` ยท `website` ยท `domain` ยท `linkedin_url` ยท `twitter_url` ยท `facebook_url` ยท `crunchbase_url` ยท `headquarter{country,state,city,street,postal_code,raw_address,position{lng,lat}}` ยท `technologies[{name,category}]` ยท `keywords` ยท `naics` ยท `revenue_amount` ยท `funding_total` ยท `ipo_date` ยท `it_spend` |\n| **Provenance** | `_metadata.extracted_at` (ISO timestamp of the lookup) |\n\n`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.\n\nA profile that lists no certifications, volunteering or organizations comes back with an empty list for that block, and `statistics` is `null` when the source has no network figures for the person. When they are filled, the blocks look like this:\n\n```json\n{\n \"certifications\": [\n {\n \"name\": \"English proficiency: Intermediate\",\n \"authority\": \"Duolingo\",\n \"url\": \"https://testcenter.duolingo.com\",\n \"company\": { \"name\": \"Duolingo\", \"logo\": \"https://img.atomusapi.dev/i/\", \"url\": \"https://www.linkedin.com/company/duolingo\" },\n \"date\": { \"start\": \"2016-06-01\", \"end\": null }\n }\n ],\n \"volunteer_experiences\": [\n { \"role\": \"Youth leader\", \"cause\": \"SOCIAL_SERVICES\", \"description\": null, \"date\": { \"start\": \"1985-01-01\", \"end\": \"2000-12-31\" } }\n ],\n \"organizations\": [\n { \"name\": \"BNI\", \"position\": \"Event Coordinator\", \"date\": { \"start\": \"2015-01-01\", \"end\": null } }\n ],\n \"statistics\": {\n \"network\": { \"followers_count\": 5430, \"connections_count\": 1200, \"influencer\": false },\n \"experience\": { \"tenure\": { \"start\": \"1990-11-01\", \"company\": \"1991-08-01\", \"title\": \"1991-08-01\" }, \"prev_job_titles\": null }\n }\n}\n```\n\n### Example row\n\n```json\n{\n \"status\": \"success\",\n \"input_url\": \"https://www.linkedin.com/in/satyanadella/\",\n \"profile\": {\n \"identifier\": \"satyanadella\",\n \"full_name\": \"Satya Nadella\",\n \"title\": \"Chairman and CEO\",\n \"headline\": \"Chairman and CEO at Microsoft\",\n \"industry\": \"Computer Software\",\n \"location\": { \"short\": \"Redmond, Washington\", \"country\": \"United States\", \"city\": \"Redmond\" },\n \"department\": { \"seniority\": \"c_suite\", \"departments\": [\"c_suite\"], \"functions\": [\"executive\"] },\n \"badges\": { \"verified\": true, \"premium\": true, \"open_to_work\": false, \"hiring\": false },\n \"skills\": [\"Cloud Computing\", \"Enterprise Software\", \"Leadership\"],\n \"links\": { \"linkedin\": \"https://www.linkedin.com/in/satyanadella\", \"twitter\": null },\n \"position_groups\": [\n { \"company\": { \"name\": \"Microsoft\", \"employees\": 238000 }, \"date\": { \"start\": \"1992\", \"end\": null } }\n ],\n \"company\": {\n \"name\": \"Microsoft\",\n \"legal_name\": \"Microsoft Corporation\",\n \"domain\": \"microsoft.com\",\n \"industry\": \"Software Development\",\n \"founded_year\": 1975,\n \"type\": \"PUBLIC_COMPANY\",\n \"staff\": { \"total\": 238000 },\n \"headquarter\": { \"city\": \"Redmond\", \"state\": \"Washington\", \"country\": \"US\" },\n \"technologies\": [{ \"name\": \"Azure\", \"category\": \"cloud\" }],\n \"it_spend\": 4200000000,\n \"ipo_date\": \"1986-03-13\"\n },\n \"last_updated\": \"2026-08-14\"\n },\n \"_metadata\": { \"extracted_at\": \"2026-09-03T14:02:11.000Z\" }\n}\n```\n\n`not_found` and `error` rows carry the same `url` you sent plus a `reason` saying what happened, and no `profile` object. `error` rows additionally carry an `error_kind` (`user_error` when the input cannot work, `actor_error` when the lookup itself failed). An `error_kind` of `provider_unavailable` means the data provider was temporarily unavailable: that row was not charged, so run that URL again later.\n\n### With `includeCompanyDetails`: the `company_details` object\n\n`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](https://apify.com/atomus/linkedin-company-scraper) returns, without a second run.\n\n| Group | Fields |\n|---|---|\n| **Identity** | `company_details.id` ยท `summary.name` ยท `legal_name` ยท `description` ยท `overview` ยท `founded_year` ยท `type` ยท `industry` ยท `summary.logo.source` |\n| **Size** | `summary.staff.total` ยท `summary.staff.range.start` |\n| **Links** | `link.website` ยท `domain` ยท `domain_ltd` ยท `linkedin` ยท `twitter` ยท `facebook` ยท `crunchbase` |\n| **Revenue & IT budget** | `financial.revenue.annual{amount,start,end}` (a band, not a point estimate) ยท `financial.aberdeen.it_spend` |\n| **Funding, every round** | `financial.funding{total_amount,num_funding_rounds,num_investor,type,date,last_amount}` ยท `financial.funding.rounds[{announced_at,type,investors[]}]` |\n| **Other financial events** | `financial.ipo{date,share_price,link}` ยท `financial.acquisition` ยท `financial.investment` ยท `financial.exit` ยท `financial.diversity_investment` (each `{total,contents[]}`) |\n| **Every office** | `location.headquarter{continent,country,state,city,street,postal_code,raw_address,position{lat,lng}}` ยท `location.locations[]` (the same shape, one entry per office worldwide) |\n| **Tech & classification** | `technologies[{name,category}]` (the full stack) ยท `industries[]` ยท `keywords[]` ยท `naics[]` |\n| **Company graph & reach** | `sub_organizations[]` ยท `languages[]` ยท `hashtags[]` |\n| **Freshness** | `company_details.last_updated` |\n\nMeasured 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.\n\n```json\n{\n \"status\": \"success\",\n \"input_url\": \"https://www.linkedin.com/in/satyanadella/\",\n \"profile\": { \"full_name\": \"Satya Nadella\", \"โ€ฆ\": \"as above\" },\n \"company_details\": {\n \"summary\": {\n \"name\": \"Microsoft\",\n \"legal_name\": \"Microsoft Corporation\",\n \"type\": \"PUBLIC_COMPANY\",\n \"industry\": \"software development\",\n \"founded_year\": 1975,\n \"staff\": { \"total\": 229769, \"range\": { \"start\": 10001 } }\n },\n \"link\": { \"website\": \"https://www.microsoft.com\", \"domain\": \"microsoft.com\" },\n \"financial\": {\n \"revenue\": { \"amount\": \"10000000000-9223372036854775807\", \"start\": 10000000000 },\n \"funding\": {\n \"num_funding_rounds\": 3,\n \"rounds\": [{ \"announced_at\": \"2022-12-09\", \"type\": \"POST_IPO_EQUITY\", \"investors\": [\"KB Securities\"] }]\n },\n \"ipo\": { \"date\": \"1986-03-13\" },\n \"aberdeen\": { \"it_spend\": 72571566964 }\n },\n \"location\": {\n \"headquarter\": { \"city\": \"Redmond\", \"state\": \"Washington\", \"country\": \"United States\" },\n \"locations\": [\n { \"city\": \"Bratislava\", \"country\": \"Slovakia\", \"raw_address\": \"Pradiareลˆ 1900, Svรคtoplukova 2A, Bratislava, Slovakia, Europe\", \"position\": { \"lat\": 48.188267, \"lng\": 17.113459 } }\n ]\n },\n \"technologies\": [{ \"name\": \"organization schema\", \"category\": \"framework\" }],\n \"industries\": [\"agentic ai\", \"identity management\", \"cloud security\"],\n \"keywords\": [\"business software\", \"developer tools\"],\n \"naics\": [\"541511\", \"423430\", \"541512\"],\n \"languages\": [\"english\", \"chinese\", \"korean\"],\n \"hashtags\": [\"microsoftlife\", \"microsoft\"],\n \"last_updated\": \"2026-08-14\"\n }\n}\n```\n\n`rounds`, `locations`, `technologies` and `sub_organizations` are trimmed above for readability; the live record carries all of them.\n\n**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.\n\n---\n\n## How much does it cost to scrape LinkedIn profiles?\n\n**$0.006 per profile found** ($6.00 per 1,000). Pay-per-event: you are charged only when a profile comes back.\n\n| Profiles scraped | Cost |\n|-------------------|------|\n| 100 | $0.60 |\n| 1,000 | $6.00 |\n| 5,000 | $30.00 |\n\n**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.\n\n**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.\n\n---\n\n## What do people use the LinkedIn Profile Scraper for?\n\n- **CRM enrichment**: fill in job title, company, location and skills for contacts you already have.\n- **Sales prospecting**: qualify and enrich leads before outreach, so you know who is worth an email.\n- **Recruiting**: build structured candidate profiles from a list of LinkedIn URLs.\n- **Account research**: understand a company's team composition and expertise before a call.\n- **Data pipelines**: feed structured profile data into Airtable, HubSpot, Salesforce or your warehouse.\n- **AI agent context**: give an agent someone's professional background before a meeting.\n\n---\n\n## LinkedIn Profile Scraper vs cookie-based tools vs the official LinkedIn API\n\n| | This LinkedIn Profile Scraper | Cookie-based scrapers | Official LinkedIn API |\n|---|---|---|---|\n| **LinkedIn account / cookies** | Not needed | Your logged-in cookies required | OAuth app + LinkedIn approval |\n| **Account / ban risk** | None (no account used) | High (your account can be restricted) | None |\n| **Setup** | Paste profile URLs | Extract and paste your session cookie | Partner application + review |\n| **Data depth** | Full profile + deep company data | Varies, often profile-only | Limited, mostly your own network |\n| **Bulk** | Up to 5,000 URLs per run | Rate-limited by your account | Strict quotas |\n| **Company enrichment** | Yes (HQ, tech stack, IT spend, more) | Rarely | No |\n| **Pricing** | Pay per profile found ($0.006) | Subscription + your account | Gated / partner pricing |\n\n---\n\n\n## ๐Ÿ† Top LinkedIn Scrapers\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n Profile Scraper\n YOU ARE HERE \n
\n Posts Scraper
\nText, media and engagement from any profile or company\n
\n Company Posts Scraper
\nEvery post from a company page, with media and engagement\n
\n Post Search
\nFind posts about any topic, by keyword, across all of LinkedIn\n
\n Reactions Scraper
\nEvery person who reacted to a post, with their headline\n
\n Comments Scraper
\nComments and nested replies, with reaction counts\n
\n Company Scraper
\nHeadcount, industry, HQ, tech stack and IT spend of a company\n
\n Company Employees Scraper
\nEvery employee of a company, filterable by seniority and department\n
\n\n---\n\n## FAQ\n\n### What is LinkedIn profile scraping?\n\nLinkedIn 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.\n\n### How do I scrape a LinkedIn profile without an account or cookies?\n\nPaste 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.\n\n### Can I use this from ChatGPT or Claude?\n\nYes, 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.\n\n### How much does it cost?\n\n$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.\n\n### Is there an official LinkedIn API for profile data?\n\nLinkedIn'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.\n\n### Is it legal to scrape LinkedIn profiles?\n\nThis 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.\n\n### How fresh is the data?\n\nEvery row includes `last_updated`, at both profile and company level, showing when the upstream source last refreshed that record.\n\n### What happens if a profile isn't found?\n\nYou 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.\n\n### Can I enrich a CRM or spreadsheet in bulk?\n\nYes. 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.\n\n---\n\n\n## All Atomus scrapers\n\n2.4M+ RESULTS DELIVERED\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
LinkedInProfile ยท Posts ยท Company posts ยท Post search ยท Reactions ยท Comments ยท Company ยท Employees
Lead genLeads Finder ยท Profiles by role & company
Google MapsPlaces & local businesses
TikTokVideos ยท Comments
X (Twitter)Tweets & profiles
RedNote ๅฐ็บขไนฆNotes, users & comments
Douyin ๆŠ–้ŸณProfiles, videos & comments
Weibo ๅพฎๅšPosts & profiles
Bilibili ๅ“”ๅ“ฉๅ“”ๅ“ฉVideos & creators
YouTubeThumbnails
\n\n---\n\n\n## Support\n\n\n\n\n\n
\n\n\nHey, 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.

\n๐Ÿ’ฌ  DM me on LinkedIn\n   or hello@dendelabs.com\n
\n\n---\n\n## โš ๏ธ Disclaimer\n\nThis Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by LinkedIn Corporation. LinkedInยฎ is a registered trademark of LinkedIn Corporation. All trademarks are property of their respective owners.\n" }, "buildNumber": "1.0.121", "imageDigest": "4316b1be454f1cfbea00b8d70669f61511009c80f7016e223e4e0cb36bf223cb", "actVersion": { "sourceType": "SOURCE_FILES", "buildTag": "latest", "versionNumber": "1.0" }, "usage": { "ACTOR_COMPUTE_UNITS": 0.06117444444444444 } } }