LinkedIn Profile Search + Email

LINKEDIN PROFILE SEARCH  •  LINKEDIN PEOPLE SEARCH API Search by who they are. Get the whole profile. No input list of URLs and no profile links to collect first. Describe the person (job title, seniority, skills, location, and their company's industry, size, tech stack, revenue and funding stage) and get one structured record per matching person: current role, full work history, education, skills, languages, certifications and LinkedIn badges. Then, optionally, their verified business email. Every filter is free; you pay per profile returned. No LinkedIn account, no login, no cookies. PAY ONLY FOR PROFILES RETURNED  FULL WORK HISTORY, SKILLS & EDUCATION

Copy to your AI assistant

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

atomus/linkedin-profile-search is an Apify Actor that searches LinkedIn profiles from a description of the person instead of from a list of profile URLs: it searches a database of 500M+ people by job title, seniority, function, skills, location and the employer's industry, size, technology, revenue and funding stage, and returns one structured JSON row per person with 28 plain fields - full_name, headline, summary, title, seniority, location, linkedin_url, photo_url, industry, company_name, company_id, company_logo, at_company_since, in_role_since, career_started, months_at_company, months_in_role, years_experience, open_to_work, hiring, premium, verified, followers, connections, twitter_url, facebook_url, github_url, last_updated - plus a raw object carrying the complete work history, educations, skills, languages, certifications, LinkedIn badges and statistics, and optionally a verified business email. Use it whenever someone needs to FIND people who match a description and then know who they actually are - prospecting, recruiting sourcing, market and org mapping, or feeding a sales agent full profiles rather than name-and-title stubs - without a LinkedIn account, cookies, or a per-seat subscription. Run it with curl: curl -X POST "https://api.apify.com/v2/acts/atomus~linkedin-profile-search/run-sync-get-dataset-items?token=APIFY_TOKEN" -H "Content-Type: application/json" -d '{"title":["Head of Growth"],"seniority":["director","vp"],"personCountry":["United States"],"maxResults":100}'. Or in Python: ApifyClient("APIFY_TOKEN").actor("atomus/linkedin-profile-search").call(run_input={"jobFunction":["revenue_operations"],"personCountry":["United States"],"maxResults":50}) then client.dataset(run["defaultDatasetId"]).list_items().items. Every input is optional: title (string[]), titleMatchMode ("SMART"|"WORD"|"STRICT"), seniority (string[] of c_suite, vp, director, manager, senior, mid-level, entry, intern, owner, founder, head, partner), department (string[], 39 broad departments), jobFunction (string[], 557 narrow functions), the three location levels personCountry (202 countries) / personState (513 states and regions) / personCity (free text, combined with the two above so "Austin" + "Texas" + "United States" searches Austin, Texas (spell places out, "Austin, TX" matches nobody)), keyword, skill, profileBadge (openToWork, hiring, jobSeeker, premium, creator, openLink), the company filters companyDomain, companyIndustry (dropdown, 919 catalog industries) with companyIndustryOther as free text for anything outside it, companyCountry / companyState / companyCity, companyTechnology (dropdown of the 1,668 most-used technologies) with companyTechnologyOther as free text for anything outside it, companyType, employeeSize and companyRevenue (each a tick-list of bands, e.g. ["1-10","201-500"], and the ticks need NOT be adjacent), companyFundingType, exclude twins for keyword, title, seniority and company industry (excludeKeyword drops the recruiters and consultants a broad keyword drags in, before they are returned and billed), maxResults (int 1-10000, default 100, and this is your spend cap), skipFirst (int, default 0: skip the first N matches of the SAME search and start there, so a second run continues where the first stopped instead of paying for those people again; every row carries total_matches so you know how many are left), findEmail (bool, adds a verified business email per person, paid Apify plans only, and much slower) and includeCompanyDetails (bool, adds a company_details firmographic block). Fields no longer in the form still work when sent by API: minEmployees, maxEmployees, minRevenue, maxRevenue, fullName, previousTitle, certification, language, degree, fieldOfStudy, minCurrentJobYears, minTotalExperienceYears, companyLinkedin, companyNaics, companySic and the remaining exclude twins. Type free-text values in English: the data is indexed in English, so "Alemanha" matches 0 where "Germany" matches 296,021. Use department OR jobFunction, never both: they combine with OR upstream, so filling both either changes nothing or widens the search and the bill. Every row carries a status ("success" | "not_found" | "error"); you are charged 0.006 USD per profile returned, plus 0.006 per email actually found and 0.0035 per company_details returned, while not_found and error rows are free. Full input schema, every enum and default, and the complete output field list: GET https://api.apify.com/v2/acts/atomus~linkedin-profile-search/build/default

---

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

Point your AI assistant at Atomus and it can map a market on its own: find the people, read their full profiles, and enrich the companies behind them. No glue code, no scraping logic in your prompts.

{
  "mcpServers": {
    "atomus": {
      "url": "https://mcp.apify.com?tools=atomus/linkedin-profile-search,atomus/linkedin-profile-scraper,atomus/linkedin-company-employees,atomus/linkedin-company-scraper",
      "headers": { "Authorization": "Bearer YOUR_APIFY_TOKEN" }
    }
  }
}

Then ask, in plain language:

"Find 100 heads of revenue operations at Series B software companies in the US, then pull the full firmographics for every company they work at."

That one sentence uses two 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.

---

How to search LinkedIn profiles

Find a role in one query

{
  "title": ["Head of Marketing"],
  "seniority": ["director", "vp"],
  "companyIndustry": ["software"],
  "employeeSize": ["51-100", "101-200", "201-500", "501-1000"],
  "personCountry": ["United States"],
  "maxResults": 100
}

Person filters and company filters combine with AND, so each one you add narrows the list. Every filter here is free: only the profiles that come back are billed.

Search by skill instead of by title

{ "skill": ["Kubernetes", "Terraform"], "seniority": ["senior", "manager"], "personCountry": ["Germany"], "maxResults": 100 }

Titles lie and vary by company; skills are what the person listed about themselves. This is the search a recruiter wants and a title-only tool cannot run.

Target a niche function, not a whole department

{ "jobFunction": ["revenue_operations"], "personCountry": ["United States"], "maxResults": 100 }

Measured live: the sales department matches 28,449,940 people, while the revenue_operations function matches 86,195. Use jobFunction when you want the niche and leave department empty.

People at companies running a specific technology

{
  "seniority": ["c_suite", "vp"],
  "companyTechnology": ["salesforce"],
  "companyIndustry": ["financial services"],
  "maxResults": 200
}

companyTechnology and companyIndustry only accept values from the supported catalogs (salesforce, react, software, financial services), so a free-text guess quietly matches nothing.

Catch companies at the moment they start hiring and spending

{
  "title": ["Head of Sales", "VP Sales"],
  "companyFundingType": ["series_a", "series_b"],
  "employeeSize": ["11-20", "21-50", "51-100", "101-200"],
  "maxResults": 150
}

Funding stage plus headcount is the timing filter: a company that just raised and is still small is the one about to build a team.

Filter on hiring and open-to-work signals

{ "profileBadge": ["openToWork"], "skill": ["React"], "personCountry": ["Portugal"], "maxResults": 100 }

openToWork and hiring are LinkedIn's own badges, so they are current-state signals: who is open to moving, and who is actively recruiting.

Add the verified business email

{
  "title": ["Account Executive"],
  "companyIndustry": ["software"],
  "personCountry": ["United Kingdom"],
  "maxResults": 100,
  "findEmail": true
}

Emails are a paid add-on on paid Apify plans, charged only when an email is actually found. The run gets much slower and the first rows take minutes: see the warning under Input before you turn it on.

Everyone at a named account list

{
  "companyLinkedin": [
    "https://www.linkedin.com/company/stripe",
    "https://www.linkedin.com/company/figma"
  ],
  "seniority": ["director", "vp", "c_suite"],
  "maxResults": 200
}

companyLinkedin pins exact companies. companyDomain is broader and also drags in subsidiaries.

---

Input

Every parameter is optional. Leave them all empty and the search is unconstrained, capped only by maxResults.

Parameter	Type	Default	Description
maxResults	integer	100	1 to 10,000 profiles. This is your hard spend cap: you pay per profile returned, so a run can never cost more than this.
title	string[]	(none)	Current job titles to match, e.g. ["Account Executive"].
titleMatchMode	string	SMART	SMART (fuzzy, catches "Sr. Software Engineer"), WORD (whole words), STRICT (exact title). Also applies to excludeTitle.
seniority	string[]	(none)	c_suite, vp, director, manager, senior, mid-level, entry, intern, owner, founder, head, partner.
department	string[]	(none)	39 broad departments (35 current plus 4 legacy aliases), e.g. sales, marketing, engineering_technical.
jobFunction	string[]	(none)	557 narrow functions, each prefixed by its department: Sales › Revenue Operations, Medical Health › Clinical Trials.
findEmail	boolean	false	Add-on: a verified business email per person. Paid Apify plans only. Billed only when an email is found.
includeCompanyDetails	boolean	false	Add-on: the full firmographic record of each person's current company, in company_details. Billed per profile that returns a company.

Free person filters: personCountry, personState, personCity, keyword (headline / summary text), skill, profileBadge (openToWork, hiring, jobSeeker, premium, creator, openLink).

Free company filters: companyCountry, companyState, companyCity, companyDomain, companyIndustry, companyTechnology, companyType (public_company, privately_held, self_owned, self_employed, partnership, non_profit, educational, government_agency), employeeSize, companyRevenue, companyFundingType (19 stages from pre_seed and angel through series_a–series_f, private_equity, ipo and post_ipo).

Free exclusions: excludeKeyword, excludeTitle, excludeSeniority, excludeCompanyIndustry. Exclusion happens upstream, so an excluded person is never returned and never billed.

excludeKeyword is what keeps a broad keyword affordable. Searching keyword: ["payments"] also brings in every recruiter and consultant who wrote payments in their headline, and you pay for those rows because billing is per profile returned. Excluding drops them before they are returned. Measured live on the c-suite baseline of 8,628,955: payments alone matches 6,912, and payments minus recruiter matches 6,911. It searches the same place keyword does (headline and summary), so type it in English too.

Company size and revenue are one tick-list each, and the ticks do not have to be next to each other. employeeSize takes any of 1-10, 11-20, 21-50, 51-100, 101-200, 201-500, 501-1000, 1001-2000, 2001-5000, 5001-10000, 10000+; companyRevenue takes 0-100000 through 10000000000+. Ticking 1-10 and 201-500 returns exactly those two groups and none of the companies in between, in a single search. Measured live on the c-suite baseline: 1-10 alone matches 1,285,255 and 201-500 alone 349,539, and the two together come back as 1,634,794, the exact sum, not the 3,191,599 that searching 1 to 500 returns.

Filters that still work but are no longer in the form. The form was cut from 58 fields to 29 so that the ones that matter are findable. These keep working exactly as before in a saved task or an API call, they are simply not rendered any more: minEmployees, maxEmployees, minRevenue, maxRevenue (superseded by the two band tick-lists above, and ignored as a pair as soon as a band is ticked), fullName, previousTitle, certification, language, degree, fieldOfStudy, minCurrentJobYears, minTotalExperienceYears, companyLinkedin, companyNaics, companySic, location, companyLocation, and the exclude twins excludeDepartment, excludeJobFunction, excludePersonCountry, excludePersonState, excludePersonCity, excludeCompanyCountry, excludeCompanyState, excludeCompanyCity, excludeCompanyTechnology, excludeCompanyType, excludeLocation, excludeCompanyLocation.

Type free-text values in English. Anything you type by hand (title, keyword, skill, personCity, companyCity) goes to the search as written, and the data is indexed in English. Measured live: Germany matches 296,021 c-suite people and Alemanha matches 0; United States matches 2,905,694 and Estados Unidos matches 0. Watch out for the quiet version of this: Spain matches 109,723 and Espanha matches 2, which looks like a working run rather than an empty one. Casing and accents are safe: São Paulo, Sao Paulo and sao paulo all match the same 69,452.

Department and Job function are the same filter at two levels of detail. Use one or the other, never both. They combine with OR upstream, so filling both either changes nothing (measured live: sales alone matches 28,449,940 people, and sales + revenue_operations matches 28,449,940 too, because the function already sits inside the department) or widens your results and your bill (a function from a different department gets ADDED). Want a whole department? Use department. Want a niche? Use jobFunction and leave department empty. This does not apply to the exclude twins, where combining them legitimately drops both groups.

A continent works too. personCountry and companyCountry list the six continents above the 202 countries, because the provider matches them: Europe returns 2,135,173 people and North America 3,354,584. Named blocs do not: European Union, Latin America, Middle East and Nordics all match nobody, so they are not offered.

Location is three fields, not one: country, state and city. They combine into a single place, so country United States + state Texas + city Austin searches Austin, Texas. Country and state are dropdowns; city is free text because no usable list of every city exists. Spell places out. Measured live: Austin, TX matches 0 people and Austin, Texas matches 23,273; London, UK matches 0 and London matches 114,442. The same applies to the company side (companyCountry, companyState, companyCity).

companyIndustry and companyTechnology are dropdowns of the data source's own catalog: all 919 industries, and the 1,668 technologies used by 500+ companies out of the 16,041 it knows. The option values are the source's own lowercase spelling (software, salesforce), which is what these fields already accepted, so an existing API call keeps working. The LinkedIn-style label is the wrong one: Computer Software matches 0, software matches 161,323. For a value outside either dropdown, use companyIndustryOther or companyTechnologyOther: both are free text and are merged into the same filter. They matter because the option lists are a snapshot, and Apify refuses a run whose value is not in a field's list, so an industry the data source adds later would stop a saved run rather than merely not filter it.

companyNaics and companySic need the full code. 541511 and 7372 work; the sector prefixes 54 and 73 match nothing at all. Both are API-only now, listed above.

⏳ The email add-on makes a run take minutes instead of seconds. Email lookup is an asynchronous batch job on the provider's side. Up to 100 profiles it is submitted in one piece and every result arrives together at the finish, so the dataset stays empty until then. Above 100 the emails are bought 100 at a time and each page is delivered as soon as it is verified, so rows appear as the run goes. Either way, watch the run status message: it reports how many emails have been found so far. Do not abort. If you do stop the run, hit Resurrect in the Apify console: it resumes from its checkpoint, nothing is re-submitted, and nothing is charged twice.

---

What data does the LinkedIn Profile Search return?

One row per person, each carrying a status (success | not_found | error). The dataset opens with 37 columns: 28 plain fields describing the person, including how long they have been in the role, plus the add-on fields, the run status and raw, which carries the complete profile underneath. Every promoted field below is pulled out of that same person record, so none of them costs an extra credit.

Group	Fields
Identity	full_name · headline · summary · title (current role) · seniority · location · linkedin_url · photo_url · industry
Company	company_name · company_id · company_logo
Tenure	at_company_since · in_role_since · career_started, and the same three as durations: months_at_company · months_in_role · years_experience
Signals	open_to_work · hiring · premium · verified · followers · connections
Other profiles	twitter_url · facebook_url · github_url
Email (add-on)	email · email_status (e.g. VALID), present only when findEmail is on and a deliverable email was found
Company details (add-on)	company_details, the full firmographic block: funding rounds, every office, complete tech stack, industries, revenue band. Present only when includeCompanyDetails is on and the person has a company
Full profile	raw: profile (name, headline, summary, picture, industry), position_groups (complete work history with company, dates and titles), educations, skills, languages, certifications, member_badges, statistics (follower and connection counts), volunteer_experiences, link (LinkedIn and other profile URLs)
Freshness	last_updated, when the upstream source last refreshed this person (YYYY-MM-DD)
Provenance	_metadata.extracted_at, ISO timestamp of the lookup

Firmographics live only in company_details, the paid add-on. They are stripped out of raw, so a run without the add-on never receives them.

Example row

{
  "status": "success",
  "full_name": "Leonardo Specht",
  "headline": "SWE @ Stripe | Ex Amazon, Meta",
  "title": "Software Engineer",
  "company_name": "Stripe",
  "location": "Dublin, County Dublin, Ireland, Europe",
  "seniority": "senior",
  "linkedin_url": "https://www.linkedin.com/in/leospecht",
  "photo_url": "https://images.apifyusercontent.com/...",
  "months_at_company": 41,
  "months_in_role": 41,
  "years_experience": 11,
  "open_to_work": false,
  "hiring": false,
  "followers": 4182,
  "last_updated": "2026-07-20",
  "email": "lspecht@stripe.com",
  "email_status": "VALID",
  "raw": {
    "profile": { "full_name": "Leonardo Specht", "summary": "...", "industry": "Computer Software" },
    "position_groups": [
      { "company": { "name": "Stripe" }, "date": { "start": "2022", "end": null },
        "profile_positions": [{ "title": "Software Engineer", "location": "Dublin" }] }
    ],
    "educations": [{ "school": { "name": "UFRGS" }, "degree_name": "BSc", "field_of_study": "Computer Science" }],
    "skills": ["Go", "Kubernetes", "Distributed Systems"],
    "languages": ["English", "Portuguese"],
    "member_badges": { "premium": true, "open_to_work": false }
  },
  "_metadata": { "extracted_at": "2026-09-04T12:44:21.000Z" }
}

status: "not_found" means the filters matched nobody, and it is free. status: "error" carries a plain-language reason and an error_kind, and is also free.

What one real search returned

Measured on a live run, 2026-09-22, searching Head of Growth in the United States for 25 people: 25 of 25 came back with a real name, a company, a title and a seniority, and 20 of 25 carried a business email, every one of them with email_status: "VALID". The last_updated on the first row was 2026-09-21, the day before the run.

One search is one search, not a guarantee, and fill rates move with how niche your filters are. It is worth publishing anyway, because the quiet failure mode of this category is a row that costs the same as a real one and identifies nobody: an anonymous "LinkedIn Member" placeholder, or a name with nothing attached. Every row here carries status, and you are billed only for the ones that come back.

Where the data comes from

The Actor queries a continuously refreshed database of enriched professional profiles rather than crawling LinkedIn itself, which is why there is no cookie, no session and no ban risk. It filters on each person's current title and company, so you get people in the role today, and last_updated on every row tells you how recent that record is.

---

How much does it cost to search LinkedIn profiles?

$0.006 per profile returned ($6.00 per 1,000). Pay-per-event: not_found and error rows are free, and maxResults is a hard cap on both the profiles and the bill. There is no per-search fee, so the search itself never costs anything.

Profiles	Cost
100	$0.60
1,000	$6.00
2,500	$15.00
10,000	$60.00

Add-ons, only when you turn them on:

Add-on	Price	Charged when
Verified business email	$0.006 per email ($6.00 per 1,000)	Only when a deliverable email is found. A person with no verifiable email is free. Paid Apify plans only.
Full company details	$0.0035 per company ($3.50 per 1,000)	Only on profiles that actually return a company.

There is no search fee and no floor: filters cost nothing however aggressive they are, and a run that matches nobody charges nothing. If you set Apify's maximum-charge cap below the cost of a single profile, the run stops before doing any paid work and tells you so, rather than half-billing.

Free plan: 10 profiles per calendar month so you can inspect every field before paying. The counter resets on the 1st. Turning the email add-on on from a free plan does not fail the run: the add-on is skipped and never charged, and your profiles are still delivered in full.

---

What do people use LinkedIn Profile Search for?

• Prospecting from a description: name the buyer by title, seniority, industry and company size, and get the people instead of a list of URLs to enrich later.
• Recruiting sourcing: search on skills, languages, certifications, degrees, previous titles, years of experience and the openToWork badge, then sort the export by months_in_role to find the people who have been in the same seat longest.
• Org and market mapping: pull every director and above in a function across an industry, and see where they worked before.
• Technographic targeting: find decision-makers at companies already running a specific technology, so the pitch writes itself.
• Funding-triggered outreach: catch Series A and B companies while the budget is new.
• Competitor and talent-flow research: position_groups carries the complete work history, so you can see who moved from where.
• CRM and warehouse enrichment: pull people by company domain or LinkedIn URL and load structured rows into HubSpot, Salesforce or your database.
• Territory and market sizing: tick Count only and the match count tells you how many people fit before you spend anything.
• AI agent context: hand an agent full profiles rather than name-and-title stubs, so it can qualify without a second lookup.

---

LinkedIn Profile Search vs cookie-based scrapers vs Apollo-style databases

	This LinkedIn Profile Search	Apollo / ZoomInfo / Lusha	Cookie-based LinkedIn scrapers
What you start from	A description of the person	A description of the person	A search page you are logged into
Pricing	Pay per profile returned ($0.006), hard spend cap per run	Per-seat subscriptions, usually annual	Subscription plus your own account
Commitment	None: run once and stop	Contract and seat count	Ongoing tool plus account risk
Account / ban risk	None (no account, no cookies)	None	High: your LinkedIn account can be restricted
Depth per person	28 plain fields (tenure, buying signals, photo, follower counts) plus full work history, educations, skills, languages and certifications in raw	Contact record, shallow history	Whatever the search page shows you
Targeting depth	557 job functions, 39 departments, 12 seniorities, plus company tech, revenue and funding	Comparable, behind the platform UI	Whatever the search page shows you
Emails	Add-on, billed only when found	Bundled into plan credits	Rarely, and unverified
Freshness	last_updated on every row, so you can see it	Not exposed per record	Live, at your account's risk
Automation	API, scheduler and MCP out of the box	API on higher tiers	Manual, or a fragile browser session
Best for	Pay-as-you-go search, pipelines, AI agents	Teams wanting one all-in-one sales platform	One-off manual list pulls

---

FAQ

What is a LinkedIn profile search?

A LinkedIn profile search finds people by what is true about them rather than by their URL: job title, seniority, function, skills, location, and the industry, size, technology, revenue and funding stage of the company they work at. You describe the person and get back the matching profiles, instead of collecting profile links first and enriching them one by one.

How do I search LinkedIn profiles without an account or cookies?

Fill in the filters and run the Actor. It queries an enriched profile database rather than crawling LinkedIn, so there is no login, no session cookie, no browser and no ban risk. Filters in, structured profile rows out.

Do I get real names, or anonymous "LinkedIn Member" rows?

On a live run of 25 people (Head of Growth, United States, 2026-09-22) all 25 came back named, with company, title and seniority. That is one search rather than a promise, but it is the thing worth checking in any profile-search tool, because an anonymous row costs the same as a real one and identifies nobody.

What is the difference between this and the LinkedIn Profile Scraper?

This one finds people from a description and returns their profiles. The LinkedIn Profile Scraper starts from profile URLs you already have and enriches them. Use this Actor when you do not have the list yet, and that one when you do.

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 returned ($6 per 1,000). Emails are $0.006 each and only when found; full company details are $0.0035 each. not_found and error rows are free, and the search itself is free. Free Apify plans include 10 profiles per month so you can check the output before paying.

How do I control what I spend?

Set maxResults. You are charged per profile returned and the run stops at your cap, so the maximum cost of a run is known before you start. Filters themselves are always free, so narrowing the audience only ever lowers the bill. To check a filter set for nothing at all, tick Count only: it reports how many people match and pulls none.

Should I use Department or Job function?

One or the other, never both. They are two levels of the same taxonomy and combine with OR, so filling both either changes nothing or widens your search. Use department for a whole department and jobFunction for a niche: that is the difference between all 28,449,940 people in Sales and the 86,195 in Revenue Operations.

Do I get email addresses?

Yes, as an opt-in add-on on paid Apify plans. Turn on findEmail and each person comes back with a verified business email and an email_status. You are charged only for emails actually found. Expect the run to take much longer, and up to 100 profiles to produce nothing until the end.

Why is my run with emails on taking so long and showing no results?

That is normal up to 100 profiles: at that size the lookup is one batch job, so the results all arrive together at the finish. Above 100 the emails are bought 100 at a time and each page is delivered as soon as it is verified, so rows appear as the run goes. Either way the run status message reports progress, and if you stop the run use Resurrect, which resumes from the checkpoint without re-charging.

How many profiles can one run return?

Up to 10,000. The underlying source surfaces at most a few thousand people per query, so a very broad search returns what exists rather than the number you asked for, and you are never charged for profiles that do not exist. For a bigger pull, split the query by location, seniority or company size, or use skipFirst to continue the same search where the last run stopped.

How fresh is the data?

Every row carries last_updated, the date the upstream source last refreshed that person, so you always know how stale a record is before you use it. On the 2026-09-22 test run the first row had been refreshed the previous day.

Is it legal to use?

This Actor returns professional and business data from licensed and publicly available sources. You are responsible for using the output in line with applicable laws (GDPR/CCPA), platform terms, and your own compliance requirements. It is an independent tool, not affiliated with LinkedIn, Apollo, ZoomInfo or Lusha.

Can I run this on a schedule?

Yes. Apify Schedules run the Actor on a cron interval and webhooks push each finished run into your systems, which is how a funding-stage filter becomes a weekly feed of newly funded accounts.

---

All Atomus scrapers

2.4M+ RESULTS DELIVERED

LinkedIn	Profile · Posts · Company posts · Post search · Reactions · Comments · Company · Employees
Lead gen	Leads Finder · Profiles by role & company
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
YouTube	Thumbnails

---

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

---

⚠️ Disclaimer

This Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by LinkedIn Corporation, Apollo.io, ZoomInfo or Lusha. All trademarks are property of their respective owners.

Use the data returned by this Actor in compliance with applicable data protection laws (GDPR, CCPA) and the terms of the platforms involved. Do not use it for spam, harassment, or unlawful purposes.