LinkedIn Profile Scraper

Enrich LinkedIn profiles and companies in bulk — paste URLs or search by keyword/industry/company. Get structured data with name, headline, experience, education, follower count, company size, industry, and 70+ more fields.

🎯 What it does

You feed the actor LinkedIn URLs (profiles or companies) OR a search criteria (keyword + optional industry, or "decision-makers at this company") — it returns clean, structured rows ready for a CRM, sheet, or downstream agent.

• 44 fields per profile — full_name, headline, about, current job_title + company, full experiences[] / educations[], follower_count, verification flags, parsed location, current-employer enrichment (industry, employee count, year founded).
• 26 fields per company — company_name, description, tagline, industries[], specialties[], employee_count, employee_range, year_founded, website, logo_url, follower_count, full HQ address, locations[], affiliated_companies[].
• 4 operation modes — Lookup Profile by URL, Lookup Company by URL/Domain, Search Decision-Makers at given companies, Search Companies by keyword.
• 7 advanced filters — follower range, must-have email/phone, open-to-work, country, industry — applied across all modes.

📦 What you get — output sample

{
  "entity_type": "profile",
  "input": "https://www.linkedin.com/in/satyanadella/",
  "linkedin_url": "https://www.linkedin.com/in/satyanadella",
  "full_name": "Satya Nadella",
  "first_name": "Satya",
  "last_name": "Nadella",
  "headline": "Chairman and CEO at Microsoft",
  "job_title": "Chairman and CEO",
  "company": "Microsoft",
  "company_industry": "Software Development",
  "company_employee_count": 228000,
  "follower_count": 11489320,
  "city": "Redmond",
  "country": "United States",
  "is_premium": true,
  "is_verified": true,
  "experiences": [ { "title": "CEO", "company": "Microsoft", "starts_at": "2014-02-01" } ],
  "educations": [ { "school": "University of Chicago", "degree": "MBA" } ],
  "profile_image_url": "https://media.licdn.com/dms/image/.../satya-nadella.jpg"
}

{
  "entity_type": "company",
  "input": "microsoft.com",
  "linkedin_url": "https://www.linkedin.com/company/microsoft",
  "company_name": "Microsoft",
  "tagline": "Empowering every person and organization on the planet to achieve more.",
  "industries": ["Software Development"],
  "specialties": ["Software", "Cloud", "AI", "Productivity"],
  "employee_count": 228000,
  "employee_range": "10,001+",
  "year_founded": 1975,
  "follower_count": 25190000,
  "domain": "microsoft.com",
  "website": "https://news.microsoft.com",
  "logo_url": "https://media.licdn.com/dms/image/.../microsoft-logo.png",
  "hq_city": "Redmond",
  "hq_country": "US"
}

Missing optional fields come back as the string "N/A" — every always-emitted column appears in every row, so downstream tools (Sheets / Airtable / CSV) don't have to special-case missing values. See .actor/dataset_schema.json for the full per-column declaration.

⚡ Quick start

Minimal input that works on the free plan:

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

Run, open Storage → Dataset, see one enriched row. Add more URLs to scale up.

🧭 How to use it

1. Pick the mode that matches what you have: known LinkedIn URLs (Modes 1/2) vs criteria-based search (Modes 3/4).
2. Fill the mode's fields — URL list, or keyword / company URL / job title.
3. (Optional) tighten the output with Advanced filtering — minimum followers, must-have email, country/industry match — to drop irrelevant rows before they hit your CRM.

⭐ Choose your mode

🎯 Mode 1 — Lookup Profile by URL

You paste LinkedIn profile URLs, the actor returns one fully-enriched profile row per URL.

Input:

{
  "operationMode": "lookupByUrl",
  "profileUrls": [
    "https://www.linkedin.com/in/satyanadella/",
    "https://www.linkedin.com/in/jeffweiner08/"
  ],
  "maxItemsMode1": 100
}

Output: 1 row per URL, 44 fields per row.

Cost control (Mode 1): maxItemsMode1 caps how many profiles you'll pay for in this run. Free plan ceilings to 50 items regardless. Trial runs auto-cap at 10.

🏢 Mode 2 — Lookup Company by URL or Domain

Paste LinkedIn company URLs OR raw domains — each input becomes one company row. Domains are resolved against the company database; URLs are read directly.

Input:

{
  "operationMode": "companyLookup",
  "companyInputs": [
    "https://www.linkedin.com/company/microsoft/",
    "anthropic.com",
    "stripe.com"
  ],
  "maxItemsMode2": 50
}

Output: 1 row per input, 26 fields per row.

Cost control (Mode 2): maxItemsMode2. Same free-plan / trial caps as Mode 1.

🔍 Mode 3 — Search Leads (Decision-Makers at given companies)

Give the actor target company URLs + an optional job-title filter. It pulls decision-makers / employees matching the title.

Input:

{
  "operationMode": "searchLead",
  "searchLeadCompanyUrls": [
    "https://www.linkedin.com/company/microsoft/",
    "https://www.linkedin.com/company/anthropic/"
  ],
  "searchLeadJobTitle": "engineering manager",
  "searchLeadCount": 25,
  "maxItemsMode3": 50
}

Output: Slim profile rows for each match (full_name, headline, location, company, job_title, linkedin_url, profile_id). Run a Mode 1 follow-up pass on the returned URLs if you want full 44-field enrichment.

Cost control (Mode 3): maxItemsMode3 caps total leads returned across all source companies. searchLeadCount caps per-company.

🏭 Mode 4 — Search Companies (by keyword)

Free-text keyword search over the LinkedIn company graph, optionally narrowed to specific industries.

Input:

{
  "operationMode": "searchCompany",
  "searchCompanyKeywords": "saas analytics",
  "searchCompanyIndustries": ["software", "information technology"],
  "searchCompanyCount": 25,
  "maxItemsMode4": 50
}

Output: Slim company rows for each match (company_name, tagline, domain, industries, employee_count, linkedin_url). Use Mode 2 follow-up for full 26-field enrichment.

Cost control (Mode 4): maxItemsMode4 caps total companies returned.

🛠 Input

Section	Fields
Mode picker	operationMode (required)
Mode 1	profileUrls, maxItemsMode1
Mode 2	companyInputs, maxItemsMode2
Mode 3	searchLeadCompanyUrls, searchLeadJobTitle, searchLeadCount, maxItemsMode3
Mode 4	searchCompanyKeywords, searchCompanyIndustries, searchCompanyCount, maxItemsMode4
Advanced filtering	minFollowers, maxFollowers, mustHaveEmail, mustHavePhone, openToWorkOnly, countryFilter, industryFilter

See .actor/input_schema.json for the authoritative declaration.

💰 Pricing

Pay-per-event. You're charged once per fetched record, regardless of mode.

Event	Price	Trigger
Profile Analyzed	$0.030	Each profile or company successfully retrieved from upstream and processed by the actor

Examples:

• 100 profiles in Mode 1 → 100 × $0.030 = $3.00
• 5 companies in Mode 2 → 5 × $0.030 = $0.15
• Mode 3 with 4 target companies × 25 leads each = 100 leads → $3.00
• Mode 4 returning 25 company matches → 25 × $0.030 = $0.75

Filters and billing: Filters narrow your output, not your bill. The paid event fires when the upstream returns the row — even if your filter then drops it. Use cost-control caps (maxItemsModeN) to bound spend; use filters to narrow signal.

🆓 Free vs paid plan

	Free	Paid
Items per run	50 max	unlimited (capped only by your cost-control field)
Trial run cap	10 (auto, when input matches the example)	n/a
All 4 modes	✅	✅
Advanced filters	✅	✅
Live status page	✅	✅

🧪 Filter recipes

Drop these straight into the input editor.

Find SaaS founders in the US (Mode 4 → Mode 3 → Mode 1):

Step 1 — find SaaS companies:

{
  "operationMode": "searchCompany",
  "searchCompanyKeywords": "saas",
  "searchCompanyIndustries": ["software"],
  "countryFilter": "United States",
  "searchCompanyCount": 50
}

Step 2 — find founders at those companies (paste company URLs from Step 1):

{
  "operationMode": "searchLead",
  "searchLeadCompanyUrls": ["...", "...", "..."],
  "searchLeadJobTitle": "founder",
  "searchLeadCount": 25
}

Engineering hiring at top tech companies (Mode 3 with email-only filter):

{
  "operationMode": "searchLead",
  "searchLeadCompanyUrls": [
    "https://www.linkedin.com/company/microsoft/",
    "https://www.linkedin.com/company/google/",
    "https://www.linkedin.com/company/anthropic/"
  ],
  "searchLeadJobTitle": "engineering manager",
  "mustHaveEmail": true,
  "minFollowers": 500,
  "searchLeadCount": 50
}

Bulk enrich your CRM list (Mode 1 with relevance filter):

{
  "operationMode": "lookupByUrl",
  "profileUrls": ["...100 URLs..."],
  "minFollowers": 1000,
  "industryFilter": "software"
}

📡 Live status & storage records

Every run writes structured JSON records to its key-value store, AND serves a live status page over HTTP while running.

Key	Format	Content
RUN_SUMMARY	JSON	End-of-run aggregate: stop reason, mode, processedCount, paidEventsFired, itemsScraped/Filtered, demoCapped, wallClockSec
USER_MESSAGE	JSON	First-paid-run welcome / 3rd-run check-in (paid plan)
FREE_LIMITS_APPLIED	JSON	Which free-plan caps fired (e.g. free_plan_50_items)
SKIPPED_ITEMS	JSON	Inputs we couldn't process + reason
status.html	HTML	Live progress page (auto-refreshes every 3s)

While the run is active, the live page is reachable at the actor's container URL (Apify shows it under "Live view"). For programmatic monitoring:

# Poll JSON status
curl https://<run-container-url>/api/status

Returns { isRunning, progress: {total, analyzed, saved, skippedByFilters, percentage}, cost: {spent, currency}, timing: {elapsedSeconds, startedAt}, settings: {mode, plan} }.

🤖 Programmatic / API use

# Trigger a run via Apify API
curl -X POST "https://api.apify.com/v2/acts/afanasenko~linkedin-profile-api-scraper/runs?token=$APIFY_TOKEN" \
  -H 'Content-Type: application/json' \
  -d '{
    "operationMode": "lookupByUrl",
    "profileUrls": ["https://www.linkedin.com/in/satyanadella/"]
  }'

Fetch dataset items:

$curl "https://api.apify.com/v2/datasets/<datasetId>/items?token=$APIFY_TOKEN&clean=1&format=json"

Read RUN_SUMMARY:

$curl "https://api.apify.com/v2/key-value-stores/<storeId>/records/RUN_SUMMARY?token=$APIFY_TOKEN"

❓ FAQ

Are filters charged? Filters narrow output, not bill. The paid event fires when a row is fetched; filtering it out doesn't refund. Use maxItemsModeN to cap spend.

Can I scrape private profiles? No. Only public LinkedIn data is available. Profiles set to private return "Profile not found" with no charge.

What's the difference between Mode 3 (slim) and Mode 1 (full)? Mode 3 returns slim rows (10 fields per match) from the search index — cheap way to discover relevant profiles. Feed those URLs back into Mode 1 for full 44-field enrichment.

Is data live? Yes. Each lookup hits LinkedIn's public profile in real time (no stale cache).

Why do I sometimes see a row where most fields are "N/A"? For Mode 3/4 search results, the slim row holds only the search-index fields. Re-run those URLs through Mode 1 / Mode 2 to fill the full schema.

How do I pause / resume a long run? Apify's Resurrect feature works — the actor doesn't keep internal state across resurrections, so each resurrection starts fresh from the input. Better pattern for big lists: split inputs into batches of 500-1000 per run.

🔗 Other LinkedIn / lead-gen scrapers (same author)

You start with…	You want to find…	Use this actor
LinkedIn profile URLs	Bulk-enrich profiles + companies	This actor — Mode 1 / Mode 2
Search criteria (keyword / company / job title)	Decision-makers + companies via search index	This actor — Mode 3 / Mode 4
Instagram handles	Profile data + 5 discovery modes (followers, similar, keyword, location)	Instagram Profile Scraper
TikTok profile / hashtag	Profile data + posts	TikTok Profile Scraper
YouTube channel URL / handle	Channel + video metadata	YouTube Channel Scraper
US ZIP / address	Property listings + market data	Zillow Scraper

🛟 Support & feedback

Found a bug? Have a feature request? Open an issue on the actor's Apify Store page Issues tab — that's the canonical channel and I get a notification on every new issue.

💡 Tips & Best Practices

Getting max results

• For Mode 3 (Search Leads): job-title filter is fuzzy — engineer matches software engineer, staff engineer, principal engineer. Use specific titles (VP of engineering) when you want narrow.
• For Mode 4 (Search Companies): keywords are AND-style — saas analytics matches companies that mention both. Try single keywords first to gauge volume.
• For Mode 1: company-URL inputs auto-route through company lookup, so you can mix profile and company URLs in one run.

Cost optimization

• Use maxItemsModeN aggressively when testing — 5-10 first to validate output, then scale up.
• Mode 3 / Mode 4 are cheaper per discovery: 1 search call returns 25-100 slim rows for the same $0.030/row that Mode 1 / Mode 2 charge per fetch.
• Filters don't reduce billing but reduce noise — apply industryFilter + countryFilter for tighter targeting BEFORE pasting URLs into a Mode 1 follow-up pass.

Performance

• Large Mode 1 batches (500+) take 2-3 minutes; the actor processes profiles serially to respect upstream rate limits. Live status page shows progress.
• Mode 3 polling waits up to 60s for upstream search to complete — most queries return in 5-10s. If polling times out, reduce searchLeadCount.

Data quality

• LinkedIn profiles update daily; running the same URL twice in a week may return slightly different follower_count / headline.
• "N/A" is intentional — always-emitted columns let you write IF(field == "N/A", ...) instead of null-checking.
• Search results (Mode 3 / Mode 4) are slim by design — feed URLs into Mode 1 / Mode 2 for full enrichment.

⚖️ Disclaimer

This actor scrapes only publicly available LinkedIn data. You are responsible for using the data in accordance with LinkedIn's Terms of Service, applicable privacy laws (GDPR, CCPA), and ethical-use guidelines. The actor does not scrape private profiles, paid LinkedIn features (Sales Navigator), or content behind login walls.