All-in one Linkedin Scraper

What does All-in-One LinkedIn Scraper do?

No cookies. No login. No risk to your LinkedIn account. 8 modes, pay-per-result from $1/1K. Free email discovery. AI-ready via MCP.

Scrape LinkedIn profiles, companies, jobs, posts, employees, and people-search results — all without ever providing a li_at cookie. Most competitors require your authenticated session, which means a permanent ban risk. This scraper works 100% without authentication by using public LinkedIn pages, the public Jobs API, and 5 search engines in parallel. Optionally provide a cookie for 5× richer profiles (experience, education, skills, certifications, languages) and 100× more company employees.

Why choose this over 5 separate LinkedIn scrapers?

Feature	This actor	dev_fusion	harvestapi	curious_coder
Cookies required	❌ No	❌ No	❌ No	✅ Yes
Modes covered	8 (profiles, companies, jobs, posts, search, search-profiles, profile-complete, company-employees)	1 (profiles)	5 (separate actors)	1-2
Free email discovery (11 patterns + MX validation)	✅ Included	❌ Add-on $10/1K	❌ Add-on $10/1K	❌ —
Profile pricing	$4/1K	$10/1K	$4-10/1K	$4-25/mo
Jobs pricing (with recruiter intel)	$1/1K	n/a	n/a	$1/1K
Output schema fields surfaced (profiles)	26 incl. connections / followers / verified / influencer	12	22	16
Reliability layer (admission gate + SERP early-warning + 8-country retry)	✅	—	—	—
AI-ready via MCP	✅	—	—	—

What's NEW (build 0.1.432, 2026-04-26)

• 6 free fields surfaced — connections_count, follower_count, picture_url, premium, verified, influencer on every profile-style mode (parsed from Voyager payloads we already fetch — zero cost increase)
• open_profile boolean on Search, Search Profiles, Company Employees — InMail-receptivity signal for outreach
• id (LinkedIn URN) on every record — clean joins across modes
• Jobs: job_poster_* + expire_at — recruiter intel + posting expiry (when authenticated)
• Posts: share_urn + content_attributes — automation hooks + structured content type detection
• Reliability hybrid — Apify maxConcurrency=8 + per-cookie admission gate + 5-engine SERP early-warning + exponential retry. Burst-resistant; no more 0% delivery under high concurrency.
• Lower compute cost — default memory dropped to 256 MB (was 512). ~50% lower compute on cookieless modes.
• SERP cache-hit telemetry — every run logs cache hit-rate so you can tune your batch sizes.

What's New (2026-04-24)

• Company Employees delivery 2-5× higher. Mode 8 now combines three authenticated discovery paths when a cookie is available (faceted people-search HTML + /company/<slug>/people/ tab + 5-engine SERP). Previously capped at ~3-10 employees per company; now returns 20-100+ depending on company size and rate-limit window.
• Jobs salary field populated on 50-70% of listings (was ~10%). The scraper now extracts compensation ranges from the job description text ($160K - $215K, $120,000 to $180,000, $160-215K/yr patterns) when LinkedIn doesn't surface them in a dedicated salary element.
• Search Profiles now returns all matching people. The completeness gate was relaxed so discovery-mode records (name + url + headline/location/experience) publish to the dataset instead of being filtered out. A canonical-slug dedupe prevents the same person being enriched multiple times via different URL formats.
• SERP resilience: 5 search engines in parallel. Brave + Yahoo + Google + Ecosia + Startpage. Ecosia and Startpage are EU-friendly meta-search engines that rarely rate-limit, so Search / Search Profiles / Company Employees keep working on days when Brave returns 429. Cross-engine field union merges headline/location from whichever engine has the richer snippet.
• Voyager-redacted records filtered automatically. When a cookie owner isn't network-connected to a search result, LinkedIn redacts the name to "LinkedIn Member" and returns a URN-format URL like /in/ACr___.... These records are now dropped in favor of the SERP pass, which provides human-readable names and navigable slugs.
• Cost + margin estimate logged per run. Every run ends with a Cost + margin estimate log line: { delivered, apifyCostUSD_est, revenueUSD, estMarginPct }. When estimated margin falls below 50%, a WARN is emitted so you can spot cost regressions without manual audit.
• Mode 8 email discovery off by default. Email discovery per employee is CPU-heavy (~15-20 seconds each); on a 25-employee run it would exceed the default timeout. If you need emails for discovered employees, run the profiles mode on the returned URLs in a follow-up call — this keeps each mode's runtime and cost predictable.

What's New (2026-04-24)

• Company Employees delivery 2-5× higher. Mode 8 now combines three authenticated discovery paths when a cookie is available (faceted people-search HTML + /company/<slug>/people/ tab + 5-engine SERP). Previously capped at ~3-10 employees per company; now returns 20-100+ depending on company size and rate-limit window.
• Jobs salary field populated on 50-70% of listings (was ~10%). The scraper now extracts compensation ranges from the job description text ($160K - $215K, $120,000 to $180,000, $160-215K/yr patterns) when LinkedIn doesn't surface them in a dedicated salary element.
• Search Profiles now returns all matching people. The completeness gate was relaxed so discovery-mode records (name + url + headline/location/experience) publish to the dataset instead of being filtered out. A canonical-slug dedupe prevents the same person being enriched multiple times via different URL formats.
• SERP resilience: 5 search engines in parallel. Brave + Yahoo + Google + Ecosia + Startpage. Ecosia and Startpage are EU-friendly meta-search engines that rarely rate-limit, so Search / Search Profiles / Company Employees keep working on days when Brave returns 429. Cross-engine field union merges headline/location from whichever engine has the richer snippet.
• Voyager-redacted records filtered automatically. When a cookie owner isn't network-connected to a search result, LinkedIn redacts the name to "LinkedIn Member" and returns a URN-format URL like /in/ACr___.... These records are now dropped in favor of the SERP pass, which provides human-readable names and navigable slugs.
• Cost + margin estimate logged per run. Every run ends with a Cost + margin estimate log line: { delivered, apifyCostUSD_est, revenueUSD, estMarginPct }. When estimated margin falls below 50%, a WARN is emitted so you can spot cost regressions without manual audit.
• Memory default raised to 512 MB. Prevents out-of-memory kills on concurrent enrichment workloads (Search Profiles, Profile Complete). Memory is auto-scaled — you're not charged for memory you don't use on small runs.
• Mode 8 email discovery off by default. Email discovery per employee is CPU-heavy (~15-20 seconds each); on a 25-employee run it would exceed the default timeout. If you need emails for discovered employees, run the profiles mode on the returned URLs in a follow-up call — this keeps each mode's runtime and cost predictable.

✨ Premium Residential Proxy — Included (Smart Routing)

Built-in smart proxy routing — no setup, no configuration, no extra cost.

LinkedIn's anti-bot systems aggressively block datacenter IPs on profile-heavy endpoints, burning single-use li_at cookies and returning authwall redirects. We solve this with smart tier routing — premium residential proxy is used automatically on the modes that need it, while public modes use cheaper datacenter IPs to keep pricing at $1/1K.

How Smart Routing works

The scraper routes each mode automatically based on LinkedIn's endpoint requirements:

Mode	Proxy used	Why
Profiles	Residential	LinkedIn rejects datacenter IPs with authwall redirects
Search Profiles	Residential	Voyager GraphQL requires authenticated IP
Profile Complete	Residential	Voyager + profile pages need residential
Posts (with cookie)	Residential (sticky)	Voyager feed API requires fresh residential session
Company Employees (with cookie)	Residential (sticky)	Voyager paginated search
Companies	Datacenter	Public company page endpoint
Jobs	Datacenter	Public LinkedIn Jobs API
Search	Datacenter	SERP-based (Brave/Yahoo/Google)

Why this matters

• ✅ ~95% success rate on profile scraping (vs ~40% on datacenter-only scrapers)
• ✅ li_at cookie sessions protected — sticky residential proxy per Voyager session minimizes cookie burn
• ✅ Full Voyager GraphQL data — experience, education, skills, certifications, languages, endorsements
• ✅ Zero authwall errors on profile endpoints (common with datacenter IPs)
• ✅ You only pay per result returned — failed scrapes are never billed
• ✅ Balanced cost — Companies, Jobs, Search stay on datacenter to keep pricing at $1/1K

Override options

You can customize the proxy via the proxyConfig input (Apify Proxy editor):

• Default — Apify RESIDENTIAL group for profile modes, datacenter for public modes
• Custom RESIDENTIAL groups — Specify country codes (e.g. US, DE, NL) to match your li_at origin
• Bring your own proxy — Paste proxy URLs in proxyUrls

FAQ

Do I need a LinkedIn cookie? For enhanced profile data (experience, education, skills, posts, full search) yes — li_at cookie. Companies, Jobs, Search work without cookies.

Why do my cookies burn so fast on other scrapers? LinkedIn uses IP reputation to tie cookie sessions to login origin. Single li_at cookies used from datacenter IPs get burned after one session. Sticky residential proxy minimizes this by routing each Voyager session through a consistent residential IP.

Can I speed up scraping by reducing proxy usage? No — the current routing is already optimized. Disabling residential on profile modes will dramatically increase failure rates.

Getting Started

1. Click "Try for free" at the top of this page
2. Choose a scraping mode (Profiles, Companies, Jobs, Posts, Search, Search Profiles, Profile Complete, or Company Employees)
3. Paste LinkedIn URLs or enter a search query
4. Click Start — results appear in the Dataset tab within seconds
5. Download as JSON, CSV, or Excel — or connect via API, n8n, Make, or Zapier

No cookies needed. No login required. Just paste and scrape.

Easiest Way to Start: Paste a URL

Just paste any LinkedIn URL into the "LinkedIn URLs" field and hit Start. The scraper auto-detects the mode:

URL Pattern	Auto-Detected Mode
linkedin.com/in/williamhgates	Profiles
linkedin.com/company/microsoft	Companies
linkedin.com/posts/microsoft_...	Posts

For Jobs and Search modes, enter a keyword in the "Search Query" field instead.

8 Scraping Modes

Mode	Description	Best For
Profiles	Full public profile data: experience, education, about, posts	Lead enrichment, recruiting
Companies	Company pages: industry, size, HQ, website, specialties, followers	Market research, B2B targeting
Jobs	Job search with filters: type, experience, location, salary	Job market analysis, recruiting
Posts	Posts with engagement, hashtags, video detection, content type	Content analysis, social listening
Search	Find LinkedIn profiles by keywords and location (5 engines in parallel: Brave + Yahoo + Google + Ecosia + Startpage)	Prospecting, lead discovery
Search Profiles	Search by person name → find ALL matching profiles → fully scrape each one (14+ fields)	Finding all people with a given name
Profile Complete	Profile + all posts + engagement data in one result	Full person intelligence
Company Employees	Find people who work at a company via three parallel discovery paths: LinkedIn's authenticated people-search HTML (with cookie), /company/<slug>/people/ tab scrape (with cookie), and 5-engine SERP fallback. Returns 20-100+ employees per company.	B2B lead lists, org mapping

Standard vs Enhanced Mode

Every mode works out of the box without any cookie or login. For 4 modes, you can optionally provide your LinkedIn li_at cookie to unlock significantly richer data — at the same price per result.

What You Get Without a Cookie (Standard Mode)

All 8 modes work using search engine results (Brave, Yahoo, Google) and public LinkedIn pages. This is the default, zero-risk option:

• Profiles: Name, headline, location, photo, current company/title
• Companies: Full company page (industry, size, HQ, website, specialties, followers)
• Jobs: Full job details (title, salary, description, applicants) — no cookie needed
• Posts: Full post content with engagement metrics, hashtags, video detection
• Search: Profile discovery via 5-engine parallel SERP (Brave + Yahoo + Google + Ecosia + Startpage — EU-friendly engines added for resilience on rate-limit days)
• Search Profiles: Search by name + basic enrichment per profile
• Profile Complete: Profile + posts (basic fields)
• Company Employees: ~20-50 employees discovered via 5-engine SERP — more employees per company when Brave returns rich snippet data; fewer on rate-limit days when fallback engines carry the load

What You Unlock With a Cookie (Enhanced Mode)

Paste your li_at cookie to unlock LinkedIn's internal API. Same price per result — you just get more data.

Mode	Standard (no cookie)	Enhanced (with cookie)	Improvement
Profiles	6 fields (name, headline, location, company, title, photo)	20+ fields (+experience, education, skills, certifications, languages, about, industry)	5x more fields
Profile Complete	Basic profile + posts	Full profile + about section + structured posts with engagement	5x more fields
Search Profiles	SERP discovery + basic enrichment	Voyager search (structured results) + full enrichment with experience/education	Better discovery + richer data
Company Employees	~20-50 employees from 5-engine SERP	20-100+ employees via LinkedIn's authenticated people-search HTML + /company/<slug>/people/ scrape + SERP fallback (multi-path discovery)	2-5x more results, improved field accuracy
Companies	Full data	No change	—
Jobs	Full data	No change	—
Posts	Full data	No change	—
Search	Full data	No change	—

How to Get Your LinkedIn Cookie (2 minutes)

Your li_at cookie is what LinkedIn uses to keep you logged in. Here's how to find it:

Step 1: Open linkedin.com in Google Chrome and make sure you're logged in.

Step 2: Right-click anywhere on the page and select "Inspect" (or press F12 on Windows / Cmd+Option+I on Mac).

Step 3: In the panel that opens, click the "Application" tab at the top. If you don't see it, click the >> arrows to find it.

Step 4: In the left sidebar, click the arrow next to "Cookies" to expand it, then click on https://www.linkedin.com.

Step 5: You'll see a table of cookies. Look for the one named li_at in the "Name" column. Click on it.

Step 6: Double-click the "Value" field next to li_at to select it. It's a long string starting with AQEDAQ.... Copy it (Ctrl+C / Cmd+C).

Step 7: Paste it into the "LinkedIn Cookie" field (also called cookie or li_at) in this scraper's input settings.

Tip: The cookie value is long (150+ characters). Make sure you copy the entire value — don't cut it short.

Privacy: Your cookie is sent directly to LinkedIn's API — it is never stored on our servers and is discarded after the run completes.

Cookie Expiration & Error Handling

LinkedIn cookies (li_at) expire or get invalidated when:

• Time-based expiration — cookies typically last 1-3 months
• IP rotation — LinkedIn may invalidate a cookie when it's used from a different IP address than where it was created
• Multiple concurrent uses — using the same cookie in multiple scraper runs simultaneously can trigger invalidation
• LinkedIn security rotation — LinkedIn periodically rotates session cookies

When your cookie expires, the scraper will fail fast with a clear error message:

"Your LinkedIn li_at cookie has expired or been invalidated by LinkedIn. Please log into LinkedIn in your browser and copy a fresh li_at cookie value."

The scraper does NOT silently degrade with an expired cookie — it stops immediately so you don't waste compute on incomplete results. Simply refresh your cookie and re-run.

Is My Account Safe?

• The scraper uses human-like delays (randomized with Box-Muller distribution) between requests
• Session rotation prevents pattern detection
• Byte-exact Chrome TLS + HTTP/2 fingerprint via node-tls-client (the Voyager session uses bogdanfinn's tls-client to match Chrome's HTTP/2 SETTINGS; prior Impit-based implementations leaked a mismatched fingerprint that LinkedIn could flag at warmup)
• Requests look identical to normal browser activity
• We've processed thousands of profiles without any account issues
• If you're concerned, use a secondary LinkedIn account

Slug Validation Pre-Flight

When you provide a LinkedIn profile URL, the scraper runs one authenticated Voyager search before the main scrape loop. The goal is to catch genuinely unreachable input (empty slugs, zero search hits) without getting in the way of legitimate celebrity / short-form slugs that LinkedIn's name-search doesn't round-trip cleanly.

When the input is unreachable — the scraper emits a stub record with optional suggestions so you can reconcile bad input:

{
  "mode": "profile_complete",
  "url": "https://www.linkedin.com/in/this-slug-does-not-exist",
  "slug": "this-slug-does-not-exist",
  "error": "invalid_slug",
  "reason": "no_search_results",
  "suggestions": [],
  "scraped_at": "2026-04-22T04:00:00.000Z"
}

When the input is ambiguous (e.g. williamhgates, elonmusk, marybarra — valid slugs whose derived name like "William Hgates" doesn't surface the owner in Voyager's top 10) — the scraper does not block. It proceeds with the normal scrape path (direct profile fetch + retries) and returns the full profile.

Slug validation requires an li_at cookie (it uses LinkedIn's authenticated search). Set validateSlugs: false to skip the pre-flight if you want to save one Voyager search call per profile.

Dry-Run Mode (Preview Cost Before Spending)

Add dryRun: true to any input to validate the configuration and get a cost estimate without making any LinkedIn requests or billing events. The run exits in a few seconds with a single report row written to the dataset:

{
  "mode": "dry_run",
  "requested_mode": "profile_complete",
  "dryRun": true,
  "input_valid": true,
  "estimated_events": 2,
  "estimated_cost_usd": 0.011,
  "price_per_event_usd": 0.0055,
  "inputs": {
    "urls_count": 2,
    "has_search_query": false,
    "max_results": 50,
    "has_cookie": true,
    "cookie_pool_size": 0,
    "discover_emails": true,
    "validate_slugs": true
  },
  "warnings": [],
  "note": "No HTTP calls made. Remove `dryRun: true` from input to execute the real run."
}

Useful for previewing large runs before launching them, catching misconfigured inputs (missing cookie on Posts mode, maxResults above soft cap, etc.), or integrating into CI/CD scenarios that validate spend before green-lighting a scheduled scrape.

Cookie-Pool Rotation (Power Users)

Provide an array of li_at cookies as cookies: ["AQED...", "AQED..."] to rotate them across profile batches during a single run. This effectively multiplies your daily Voyager quota — LinkedIn's soft ~100-profiles-per-day-per-cookie ceiling becomes ~300 with three cookies. Rotation is per batch, never per request, so the cookie + Evomi IP pairing stays intact. If LinkedIn invalidates a cookie mid-run (returns li_at=delete me), the scraper auto-advances to the next cookie; if the whole pool is exhausted the run falls back to SERP + anonymous scraping. Leave cookies empty to use the single-cookie cookie input as before.

When to Use Which Mode

Your Goal	Recommended Mode	Cookie Needed?
Quick lead list with names + titles	Standard (no cookie)	No
Full profile with experience + skills for recruiting	Enhanced	Yes
Job market research	Standard	No
Find all employees at a target company	Enhanced	Yes (for 100+)
Monitor someone's LinkedIn posts	Standard	No
Deep person intelligence report	Enhanced (Profile Complete)	Yes

Smart Proxy Routing

The scraper automatically uses the right proxy for each mode — no configuration needed:

• Profiles, Search Profiles, Profile Complete — residential proxy (required because LinkedIn blocks datacenter IPs on profile pages). Proxy cost is included in the per-result price.
• Companies, Jobs, Posts, Search, Company Employees — datacenter proxy (cheaper, works fine for public pages and API calls).

Pricing — Pay Per Result, No Monthly Fee

Standard Modes

Mode	Price / result	Price / 1,000	Fields	Best For
Jobs	$0.001	$1.00	16 fields	Job market research, recruiting pipelines
Posts	$0.0015	$1.50	14 fields	Content monitoring, social listening
Companies	$0.002	$2.00	15 fields	B2B market research, competitor tracking
Search	$0.002	$2.00	9 fields	Lead discovery, prospecting
Profiles	$0.004	$4.00	15 fields (incl. residential proxy)	Lead enrichment, recruiting
Search Profiles	$0.004	$4.00	15 fields (incl. residential proxy)	Name search + full enrichment

Premium Tier

Mode	Price / result	Price / 1,000	What's Included	Best For
Profile Complete	$0.0055	$5.50	19 fields + posts + engagement (incl. residential proxy)	Full person intelligence, influencer analysis

Why Profile Complete? Buying Profile + Posts separately costs $5.50/1K and requires two runs + manual matching. Profile Complete gives you everything in one result for $5.50/1K — one run, one output, zero hassle.

Apify Subscription Discounts

Higher Apify subscription plans get automatic discounts on all modes:

Apify Plan	Discount Tier	Discount
Free / Starter	Standard	—
Scale	Bronze	5% off
Business	Silver	10% off
Enterprise	Gold	15% off

Cost examples:

• 100 job listings: $0.10
• 500 LinkedIn profiles: $1.25
• 200 company pages: $0.40
• 100 complete person profiles with posts: $0.40

You only pay for results delivered. Platform compute costs are included.

Spend guardrails. To prevent accidental multi-dollar bills from a mistyped maxResults, high-unit-price modes have soft caps that require explicit opt-in above the threshold (confirmLargeRun: true): Profile Complete 100, Profiles / Search Profiles 200, Company Employees 250, Posts 500. Low-unit-price modes (Jobs, Search, Companies) have no soft cap and accept up to maxResults: 1000 directly.

Why This LinkedIn Scraper?

• No cookies needed — all 8 modes work without login. Optional cookie unlocks 5x richer data (experience, education, skills) and 10x more company employees
• 8 modes in one actor — profiles, companies, jobs, posts, search, search profiles, profile complete, company employees — one integration to maintain
• Cheapest on Apify — see comparison below
• HTTP-only architecture — node-tls-client (bogdanfinn) for authenticated Voyager calls with byte-exact Chrome TLS + HTTP/2 SETTINGS fingerprint, plus Impit for anonymous SERP and public-page fetches (no bloated browser)
• 384 MB memory — runs on minimal resources, keeping your compute costs low
• Smart proxy routing — datacenter for most modes, residential only when needed (~30% cost savings)
• Human-like behavior — randomized delays with Box-Muller distribution jitter
• MCP-compatible — works with AI agents (Claude, GPT, Cursor) out of the box

How We Compare

Feature	This Scraper	Dev Fusion	HarvestAPI	ApiMaestro	Bebity
Profiles / 1K	$4.00	$10.00	$4.00	$5.00	$29/mo flat
Jobs / 1K	$1.00	—	$1.00	$5.00	$29.99/mo flat
Posts / 1K	$1.50	—	$2.00	$5.00	—
Companies / 1K	$2.00	$8.00	—	$5.00	$29/mo flat
Search / 1K	$2.00	—	$4.00	$5.00	—
Cookies required	No	No	No	No	Unclear
Modes in one actor	8	1	1 (15+ separate actors)	1 (10+ separate actors)	2
Email discovery	Free (+ optional Hunter.io)	Built-in	+$6/1K add-on	Built-in	No
Memory	384 MB	256 MB	256 MB	128–256 MB	4096 MB
Success rate	95%+ (field-level accuracy validated against 9 PDF ground-truth profiles and 10 live Chrome profiles)	99.9%	99.9%	99.9%	82%

Key advantages:

• One actor, 8 modes — competitors split into 10–15 separate actors, each requiring its own integration. We give you everything in one.
• Competitive pricing — profiles at $4.00/1K (vs $5–$10/1K elsewhere), jobs at $1/1K
• Free email discovery — most competitors charge $6–$10/1K extra for email enrichment
• Lightweight — 384 MB HTTP-only means lower compute costs vs browser-based scrapers (Bebity uses 4096 MB)

MCP Integration for AI Agents

This scraper works with AI agents via the Model Context Protocol (MCP). Connect it to Claude Desktop, Cursor, GPT, or any MCP-compatible client.

Setup:

1. Go to mcp.apify.com
2. Add "All-in-One LinkedIn Scraper" to your MCP server
3. Ask your AI: "Find 20 software engineers in San Francisco on LinkedIn"

Example prompts for your AI agent:

• "Scrape the LinkedIn profile of Bill Gates"
• "Search LinkedIn for product managers in New York"
• "Get the latest job postings for data scientist in London"
• "Get the complete profile with posts for Satya Nadella"

Works with Claude Desktop, Cursor, GPT via MCP, and any other MCP-compatible AI client.

Integrations

n8n

1. Add the Apify node in your n8n workflow
2. Select "All-in-One LinkedIn Scraper" as the actor
3. Configure the mode and input parameters
4. Connect the output to your CRM, Google Sheets, or database

Make.com (Integromat)

1. Add the Apify module to your scenario
2. Select "Run Actor" and choose this scraper
3. Map the JSON output fields to your downstream modules
4. Use for automated lead enrichment, job monitoring, or CRM syncing

Zapier

1. Create a new Zap with Apify as the trigger or action
2. Select "Run Actor" and configure with this scraper's actor ID
3. Map output fields to Google Sheets, HubSpot, Salesforce, or Slack
4. Trigger on schedule or from a webhook

REST API & SDKs

Use the Apify API, JavaScript SDK, or Python SDK for programmatic access. See the Python example below.

---

Mode 1: Profiles

Scrape public LinkedIn profile data including experience, education, skills, and recent posts.

Input Parameters

Parameter	Type	Required	Description	Values
mode	string	Yes	Scraping mode	"profiles"
urls	string[]	Yes	LinkedIn profile URLs	linkedin.com/in/username format
maxResults	integer	No	Maximum profiles to return	1–1000, default: 25
proxyConfig	object	No	Proxy settings	Default: Apify RESIDENTIAL
minDelay	integer	No	Min delay between requests (ms)	500–30000, default: 2000
maxDelay	integer	No	Max delay between requests (ms)	1000–60000, default: 5000

Input Example

{
  "mode": "profiles",
  "urls": [
    "https://www.linkedin.com/in/williamhgates",
    "https://www.linkedin.com/in/satlokomern"
  ],
  "maxResults": 25
}

Output Fields

Field	Type	Description	Example
mode	string	Always "profiles"	"profiles"
name	string	Full name	"Bill Gates"
headline	string	Profile headline	"Co-chair, Gates Foundation"
location	string	Location from profile	"Seattle, Washington"
url	string	LinkedIn profile URL	"https://www.linkedin.com/in/williamhgates"
current_title	string	Current job title	"Co-chair"
current_company	string	Current employer	"Gates Foundation"
experience	array	Full work history with dates	[{title, company, dates, location}]
education	array	Education with degrees	[{school, degree, dates}]
skills	array	Professional skills	["Strategy", "Leadership", ...]
certifications	array	Professional certifications	[{name, issuer, dates}]
languages	array	Languages spoken	[{name, proficiency}]
location_normalized	object	Structured {city, region, country, raw} derived from location	{"city":"Seattle","region":"WA","country":"United States","raw":"Seattle, WA"}
highest_degree	string|null	Highest-ranked degree code from education[] (PhD>MD>JD>MBA>MS>MA>MEng>LLM>Master>BS>BA>BEng>LLB>Bachelor>Associate>HighSchool). null when no degree parseable.	"MS"
open_to_work	boolean|null	true if profile shows the "Open to Work" badge; null if the profile doesn't expose it	true
hiring	boolean|null	true if profile shows the "Hiring" badge; null if the profile doesn't expose it	false
email	string	Discovered email address	"bill@gatesfoundation.org"
email_source	string	How email was found	"company-website" or "guessed"
scraped_at	string	ISO timestamp	"2026-03-19T00:00:00.000Z"

Experience entries are enriched with parsed date fields alongside the raw dates string: start_date (ISO 8601, month precision), end_date (or null when current), duration_months (integer), and current (boolean). Example: {"title":"Chair","company":"Gates Foundation","dates":"2000 - Present · 26 yrs 4 mos","start_date":"2000-01-01","end_date":null,"duration_months":315,"current":true}.

With cookie (optional): When you provide an li_at cookie, the scraper uses LinkedIn's Voyager API to fetch full experience, education, skills, certifications, and languages via GraphQL section queries. Without a cookie, these fields are extracted from search engine results (more limited).

Email Discovery: Enabled by default. Uses a 5-layer pipeline: JSON-LD extraction, company website scraping (14 paths), search engine email search, MX-validated pattern guessing (11 patterns including underscore, reversed, hyphenated, and initials), and optional Hunter.io verification (premium).

Hunter.io Integration (Premium): Provide your own hunterApiKey for verified email delivery. Hunter.io verifies guessed email patterns ($0.01/verification) and uses its Email Finder database for higher accuracy. Without it, emails are verified via free MX record checks only.

Output Example

{
  "mode": "profiles",
  "name": "Dr. William Soliman",
  "headline": "Help Raise the Bar in Life Sciences | Founder, CEO @ ACMA",
  "location": "New York",
  "url": "https://www.linkedin.com/in/drwilliamsoliman",
  "current_title": "Founder, CEO",
  "current_company": "Accreditation Council for Medical Affairs (ACMA)",
  "email": "william.soliman@acma-health.com",
  "email_source": "guessed",
  "experience": [
    {
      "title": "Founder, CEO",
      "company": "Accreditation Council for Medical Affairs (ACMA) · Self-employed",
      "dates": "Feb 2015 - Present · 11 yrs 2 mos",
      "location": "Greater New York City Area"
    },
    {
      "title": "Founder/CIO",
      "company": "White Manna Capital Partners · Self-employed",
      "dates": "Jul 2025 - Present · 9 mos",
      "location": null
    }
  ],
  "education": [
    {
      "school": "Columbia University",
      "degree": "PhD",
      "dates": null
    },
    {
      "school": "Columbia University",
      "degree": "Master's Degree",
      "dates": null
    }
  ],
  "skills": ["Biotechnology Industry", "Pharmaceuticals", "Medical Science", "New Business Development", "Entrepreneurship"],
  "certifications": [],
  "languages": [],
  "scraped_at": "2026-03-19T13:10:14.167Z"
}

Real test result from Build 0.1.179 — 20 experience positions, 5 education entries, 24 skills extracted via GraphQL section queries.

Input Parameters (Profiles Mode)

Parameter	Type	Required	Description
mode	string	Yes	"profiles"
urls	string[]	Yes	LinkedIn profile URLs
cookie	string	No	li_at cookie for Voyager API (enables experience/education/skills)
discoverEmails	boolean	No	Find email addresses (default: true)
hunterApiKey	string	No	Hunter.io API key for premium email verification
maxResults	integer	No	Max profiles (default: 25)

{
  "mode": "profiles",
  "urls": ["https://www.linkedin.com/in/drwilliamsoliman"],
  "cookie": "your_li_at_cookie_here",
  "discoverEmails": true,
  "hunterApiKey": "optional_hunter_key"
}

Use Cases

• Lead enrichment: Enrich CRM contacts with LinkedIn profile data (headline, company, location)
• Recruiting: Screen candidates by experience, education, and current role
• Sales prospecting: Identify decision-makers and their current titles
• Competitor intelligence: Track key people at competitor companies

How to Run

Apify Console:

1. Go to All-in-One LinkedIn Scraper on Apify
2. Select Profiles mode
3. Paste LinkedIn profile URLs
4. Click Start

Python:

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("get-leads/linkedin-scraper").call(run_input={
    "mode": "profiles",
    "urls": [
        "https://www.linkedin.com/in/williamhgates",
        "https://www.linkedin.com/in/satyanadella"
    ],
    "maxResults": 25
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['name']} — {item['headline']} — {item['location']}")

---

Mode 2: Companies

Scrape public LinkedIn company pages including industry, size, headquarters, website, specialties, and recent posts.

Input Parameters

Parameter	Type	Required	Description	Values
mode	string	Yes	Scraping mode	"companies"
urls	string[]	Yes	LinkedIn company URLs	linkedin.com/company/name format
maxResults	integer	No	Maximum companies to return	1–1000, default: 25
proxyConfig	object	No	Proxy settings	Default: Apify RESIDENTIAL
minDelay	integer	No	Min delay between requests (ms)	500–30000, default: 2000
maxDelay	integer	No	Max delay between requests (ms)	1000–60000, default: 5000

Input Example

{
  "mode": "companies",
  "urls": [
    "https://www.linkedin.com/company/microsoft",
    "https://www.linkedin.com/company/google"
  ],
  "maxResults": 25
}

Output Fields

Field	Type	Description	Example
mode	string	Always "companies"	"companies"
name	string	Company name	"Microsoft"
description	string	Company description	"Every company has a mission..."
url	string	LinkedIn company URL	"https://www.linkedin.com/company/microsoft"
website	string	Company website	"https://www.microsoft.com"
logo	string	Company logo URL	"https://media.licdn.com/..."
industry	string	Industry	"Software Development"
company_size	string	Employee range	"10,001+ employees"
employee_count	string	Employee count string	"10,001+ employees"
headquarters	string	HQ location	"Redmond, Washington"
company_type	string	Company type	"Public Company"

| specialties | string | Specialties list | "Cloud, AI, Enterprise Software" | | followers | integer | LinkedIn followers | 27657016 | | recent_posts | array | Last 3 posts | [{text, url, date}] | | scraped_at | string | ISO timestamp | "2026-02-27T00:00:00.000Z" |

Output Example

{
  "mode": "companies",
  "name": "Microsoft",
  "description": "Every company has a mission. What's ours? To empower every person and every organization on the planet to achieve more.",
  "url": "https://www.linkedin.com/company/microsoft",
  "website": "https://www.microsoft.com",
  "logo": "https://media.licdn.com/dms/image/...",
  "industry": "Software Development",
  "company_size": "10,001+ employees",
  "employee_count": "10,001+ employees",
  "headquarters": "Redmond, Washington",
  "company_type": "Public Company",
  "specialties": "Business Software, Developer Tools, Cloud Computing, AI",
  "followers": 27657016,
  "recent_posts": [
    {
      "text": "We're excited to announce...",
      "url": "https://www.linkedin.com/posts/microsoft_...",
      "date": "2026-02-25"
    }
  ],
  "scraped_at": "2026-02-27T00:43:22.000Z"
}

Use Cases

• Market research: Analyze companies by industry, size, and location
• B2B targeting: Build company lists for outbound sales campaigns
• Competitor tracking: Monitor competitor companies' follower growth and posts
• Investment research: Screen companies by size, industry, and founding year

Python Example

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("get-leads/linkedin-scraper").call(run_input={
    "mode": "companies",
    "urls": [
        "https://www.linkedin.com/company/microsoft",
        "https://www.linkedin.com/company/google"
    ]
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['name']} — {item['industry']} — {item['followers']} followers")

---

Mode 3: Jobs

Search LinkedIn's public job listings with filters for job type, experience level, time posted, work arrangement, and location.

Input Parameters

Parameter	Type	Required	Description	Values
mode	string	Yes	Scraping mode	"jobs"
searchQuery	string	Yes	Job search keywords	e.g. "software engineer", "data scientist"
location	string	No	Location filter	e.g. "San Francisco", "United States"
maxResults	integer	No	Maximum jobs to return	1–1000, default: 25
timePosted	string	No	Filter by posting date	"24h", "week", "month"
jobType	string	No	Filter by job type	"full-time", "part-time", "contract", "temporary", "volunteer", "internship"
experienceLevel	string	No	Filter by experience	"internship", "entry", "associate", "mid-senior", "director", "executive"
workType	string	No	Filter by work arrangement	"onsite", "remote", "hybrid"
proxyConfig	object	No	Proxy settings	Default: Apify RESIDENTIAL
minDelay	integer	No	Min delay between requests (ms)	500–30000, default: 2000
maxDelay	integer	No	Max delay between requests (ms)	1000–60000, default: 5000

Input Example

{
  "mode": "jobs",
  "searchQuery": "software engineer",
  "location": "San Francisco",
  "maxResults": 50,
  "jobType": "full-time",
  "experienceLevel": "mid-senior",
  "workType": "remote",
  "timePosted": "week"
}

Output Fields

Field	Type	Description	Example
mode	string	Always "jobs"	"jobs"
job_id	string	LinkedIn job ID	"3847291056"
title	string	Job title	"Senior Software Engineer"
company	string	Company name	"Google"
company_url	string	Company LinkedIn URL	"https://www.linkedin.com/company/google"
location	string	Job location	"San Francisco, CA (Remote)"
posted_date	string	When posted	"2 days ago"
salary	string	Salary range (if listed — parsed from both dedicated salary elements AND description text for patterns like $160K - $215K, $120,000 to $180,000, $160-215K/yr). Typically populated on 50-70% of listings; some jobs genuinely don't disclose salary.	"$180,000 - $250,000"
benefits	string	Benefits info	"Health insurance, 401k"
is_promoted	boolean	Promoted listing	false
url	string	Job listing URL	"https://www.linkedin.com/jobs/view/3847291056"
applicants	string	Number of applicants	"87 applicants"
description	string	Full job description	"We are looking for..."
description_html	string	HTML job description	"<p>We are looking for...</p>"
seniority_level	string	Seniority level	"Mid-Senior level"
employment_type	string	Employment type	"Full-time"
job_function	string	Job function	"Engineering, Information Technology"
industries	string	Industries	"Technology, Software"
company_logo	string	Company logo URL	"https://media.licdn.com/..."
scraped_at	string	ISO timestamp	"2026-02-27T00:00:00.000Z"

Output Example

{
  "mode": "jobs",
  "job_id": "3847291056",
  "title": "Senior Software Engineer",
  "company": "Google",
  "company_url": "https://www.linkedin.com/company/google",
  "location": "San Francisco, CA (Remote)",
  "posted_date": "2 days ago",
  "salary": "$180,000 - $250,000",
  "benefits": null,
  "is_promoted": false,
  "url": "https://www.linkedin.com/jobs/view/3847291056",
  "applicants": "87 applicants",
  "description": "We are looking for a Senior Software Engineer to join our Cloud team...",
  "seniority_level": "Mid-Senior level",
  "employment_type": "Full-time",
  "job_function": "Engineering, Information Technology",
  "industries": "Technology, Internet",
  "company_logo": "https://media.licdn.com/dms/image/...",
  "scraped_at": "2026-02-27T00:43:22.000Z"
}

Use Cases

• Job market analysis: Track hiring trends by role, location, and industry
• Recruiting intelligence: Monitor competitor job postings and salary ranges
• Salary benchmarking: Collect salary data across roles and locations
• Job board aggregation: Feed LinkedIn jobs into your own job board or newsletter

Python Example

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("get-leads/linkedin-scraper").call(run_input={
    "mode": "jobs",
    "searchQuery": "data scientist",
    "location": "New York",
    "maxResults": 100,
    "jobType": "full-time",
    "timePosted": "week"
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['title']} at {item['company']} — {item['salary']} — {item['location']}")

---

Mode 4: Posts

Extract posts from LinkedIn profiles and company pages with engagement data, hashtags, and video/media detection. Uses the Voyager feed API with pagination to fetch 10-50+ posts per profile. Company pages hit the unified q=feed&organizationActor=urn:li:fs_normalized_company:… endpoint and return the same 10-20 posts LinkedIn's web client shows; profile posts use profileUpdatesV2?q=memberShareFeed. Post dates that Voyager omits are reconstructed from the activity URN's snowflake timestamp.

Cookie required: Posts mode requires a valid li_at cookie. Profile posts use the Voyager profileUpdatesV2 endpoint with pagination. Provide the cookie via the cookie input parameter.

Input Parameters

Parameter	Type	Required	Description	Values
mode	string	Yes	Scraping mode	"posts"
urls	string[]	Yes	Profile or company URLs	/in/username or /company/name
cookie	string	Yes	LinkedIn li_at cookie	Your session cookie value
maxResults	integer	No	Maximum posts to return	1–1000, default: 25
startDate	string	No	Filter posts from this date	"YYYY-MM-DD" format, e.g. "2026-01-01"
endDate	string	No	Filter posts until this date	"YYYY-MM-DD" format, e.g. "2026-04-01"
proxyConfig	object	No	Proxy settings	Default: Apify RESIDENTIAL
minDelay	integer	No	Min delay between requests (ms)	500–30000, default: 2000
maxDelay	integer	No	Max delay between requests (ms)	1000–60000, default: 5000

Input Example

{
  "mode": "posts",
  "urls": [
    "https://www.linkedin.com/in/williamhgates",
    "https://www.linkedin.com/company/microsoft"
  ],
  "cookie": "YOUR_LI_AT_COOKIE",
  "maxResults": 20,
  "startDate": "2026-01-01",
  "endDate": "2026-04-01"
}

Output Fields

Field	Type	Description	Example
mode	string	Always "posts"	"posts"
author	string	Post author name	"Bill Gates"
author_url	string	Author's LinkedIn URL	"https://www.linkedin.com/in/williamhgates"
text	string	Post content (up to 5000 chars)	"AI is transforming education..."
url	string	Direct post URL	"https://www.linkedin.com/posts/williamhgates_..."
date	string	Publication date	"2026-02-20"
content_type	string	"text", "image", or "video"	"video"
hashtags	array	Extracted hashtags	["#ai", "#education"]
likes	integer	Number of likes/reactions	45000
comments_count	integer	Number of comments	1200
shares_count	integer	Number of shares/reposts	350
media_url	string	Attached image URL	"https://media.licdn.com/..."
video_url	string	Attached video URL (if detected)	"https://www.linkedin.com/video/..."
scraped_at	string	ISO timestamp	"2026-02-27T00:00:00.000Z"

Output Example

{
  "mode": "posts",
  "author": "Bill Gates",
  "author_url": "https://www.linkedin.com/in/williamhgates",
  "text": "AI is transforming education in ways we couldn't have imagined... #ai #education #technology",
  "url": "https://www.linkedin.com/posts/williamhgates_ai-education-technology-activity-123456",
  "date": "2026-02-20",
  "content_type": "image",
  "hashtags": ["#ai", "#education", "#technology"],
  "likes": 45000,
  "comments_count": 1200,
  "shares_count": 350,
  "media_url": "https://media.licdn.com/dms/image/...",
  "video_url": null,
  "scraped_at": "2026-02-27T00:43:22.000Z"
}

Use Cases

• Content analysis: Analyze what topics perform best on LinkedIn
• Social listening: Monitor what companies and influencers are posting about
• Competitive intelligence: Track competitor announcements and messaging
• Influencer research: Identify high-performing content creators in your industry

Python Example

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("get-leads/linkedin-scraper").call(run_input={
    "mode": "posts",
    "urls": ["https://www.linkedin.com/in/williamhgates"],
    "maxResults": 10
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['author']}: {item['text'][:100]}... ({item['likes']} likes)")

---

Mode 5: Search

Find LinkedIn profiles by keywords and location using a 5-engine parallel search: Brave, Yahoo, Google, Ecosia, and Startpage all queried simultaneously for maximum speed and reliability. LinkedIn Voyager API adds structured data (headline, location, images) when a cookie is provided. Results are merged and deduplicated across all engines — fields from one engine fill in gaps left by another (cross-engine field union by canonical URL).

Input Parameters

Parameter	Type	Required	Description	Values
mode	string	Yes	Scraping mode	"search"
searchQuery	string	Yes	Search keywords	e.g. "product manager", "CEO SaaS"
location	string	No	Location filter	e.g. "New York", "London"
maxResults	integer	No	Maximum results to return	1–1000, default: 25
proxyConfig	object	No	Proxy settings	Default: Apify RESIDENTIAL
minDelay	integer	No	Min delay between requests (ms)	500–30000, default: 2000
maxDelay	integer	No	Max delay between requests (ms)	1000–60000, default: 5000

Input Example

{
  "mode": "search",
  "searchQuery": "product manager",
  "location": "New York",
  "maxResults": 25
}

Output Fields

Field	Type	Description	Example
mode	string	Always "search"	"search"
name	string	Person's name	"Jane Smith"
headline	string	LinkedIn headline	"Product Manager at Stripe"
location	string	Parsed location	"New York, NY"
url	string	LinkedIn profile URL	"https://www.linkedin.com/in/janesmith"
current_title	string	Parsed current title	"Product Manager"
current_company	string	Parsed current company	"Stripe"
source	string	Data source used	"voyager", "serp"
scraped_at	string	ISO timestamp	"2026-02-27T00:00:00.000Z"

Output Example

{
  "mode": "search",
  "name": "Francesca Giordano",
  "headline": "Senior Product Manager at New York & Company",
  "location": "New York, NY",
  "url": "https://www.linkedin.com/in/francesca-giordano-08283691",
  "current_title": "Senior Product Manager",
  "current_company": "New York & Company",
  "source": "voyager",
  "scraped_at": "2026-04-03T09:14:22.000Z"
}

Use Cases

• Prospecting: Find potential customers by role and location
• Lead discovery: Build targeted outreach lists for sales teams
• Recruiting: Source candidates matching specific criteria
• Market sizing: Estimate the number of professionals in a role/location

Python Example

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("get-leads/linkedin-scraper").call(run_input={
    "mode": "search",
    "searchQuery": "CTO SaaS",
    "location": "San Francisco",
    "maxResults": 50
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['name']} — {item['headline']} — {item['url']}")

---

Mode 6: Search Profiles (Search by Name + Full Enrichment)

Enter a person's name and the scraper finds ALL matching LinkedIn profiles, then fully scrapes each one with title, company, location, headline, and optional email discovery. Combines the discovery power of Search mode with the enrichment depth of Profiles mode.

How It Works

1. Discovery: Searches LinkedIn via Voyager API + 5 search engines in parallel (Brave + Yahoo + Google + Ecosia + Startpage) to find all profiles matching the name. Canonical-slug dedupe prevents wasting enrichment budget on the same person appearing under different URL formats.
2. Enrichment: For each discovered profile, runs the full enrichment pipeline (SERP extraction, Voyager lookup, 999 metadata)
3. Email Discovery (optional): Scrapes employer websites for email addresses

Input Parameters

Parameter	Type	Required	Description	Values
mode	string	Yes	Scraping mode	"search_profiles"
searchQuery	string	Yes	Person's name to search	e.g. "Sunny Savita", "Naveen Jain"
location	string	No	Location filter	e.g. "New York", "India"
maxResults	integer	No	Maximum profiles to return	1–1000, default: 25
cookie	string	No	LinkedIn li_at cookie for Voyager API	Enables structured search results
discoverEmails	boolean	No	Find email addresses	Default: true
proxyConfig	object	No	Proxy settings	Default: Apify RESIDENTIAL

Input Example

{
  "mode": "search_profiles",
  "searchQuery": "Sunny Savita",
  "maxResults": 10,
  "discoverEmails": true
}

Output Fields

Same fields as Profiles mode (14+ fields per profile):

Field	Type	Description	Example
mode	string	Always "search_profiles"	"search_profiles"
name	string	Full name	"Sunny Savita"
headline	string	LinkedIn headline	"Senior Generative AI Engineer"
location	string	Location	"Bangalore Urban"
url	string	LinkedIn profile URL	"https://www.linkedin.com/in/sunny-savita"
current_title	string	Current job title	"Senior Generative AI Engineer"
current_company	string	Current employer	"PwC India"
experience	array	Full work history (entries enriched with parsed start_date / end_date / duration_months / current per entry)	[{title, company, dates, start_date, end_date, duration_months, current, location}]
education	array	Education history	[{school, degree, dates}]
skills	array	Professional skills	["Python", "Machine Learning"]
location_normalized	object	Structured {city, region, country, raw} derived from location	{"city":"Bangalore","region":null,"country":"India","raw":"Bangalore Urban"}
highest_degree	string|null	Highest-ranked degree code from education[] (PhD>MD>JD>MBA>MS>MA>MEng>LLM>Master>BS>BA>BEng>LLB>Bachelor>Associate>HighSchool); null when no degree parseable	"MS"
open_to_work	boolean|null	true if profile shows the "Open to Work" badge; null if the profile doesn't expose it	true
hiring	boolean|null	true if profile shows the "Hiring" badge; null if the profile doesn't expose it	false
email	string	Discovered email (if available)	"sunny.savita@pwc.com"
email_source	string	How email was found	"company-website"
scraped_at	string	ISO timestamp	"2026-03-19T00:00:00.000Z"

Output Example

[
  {
    "mode": "search_profiles",
    "name": "Sunny Savita",
    "headline": "Senior Generative AI Engineer",
    "location": "Bangalore Urban",
    "url": "https://www.linkedin.com/in/sunny-savita",
    "current_title": "Senior Generative AI Engineer",
    "current_company": "PwC India",
    "scraped_at": "2026-03-19T07:40:01.000Z"
  },
  {
    "mode": "search_profiles",
    "name": "Sunny Savita",
    "headline": "Senior Generative AI Engineer",
    "location": "Agra",
    "url": "https://www.linkedin.com/in/sunny-savita-8a80b620",
    "current_title": "Senior Generative AI Engineer",
    "current_company": "PwC India",
    "scraped_at": "2026-03-19T07:40:01.000Z"
  }
]

Use Cases

• Finding all people with a given name: Enter "Naveen Jain" and get every Naveen Jain on LinkedIn with full profile data
• Lead research: Search for a contact by name when you don't have their LinkedIn URL
• Recruiting: Find all candidates matching a specific name, then filter by title/company/location
• Due diligence: Verify a person's LinkedIn presence by searching their name

Python Example

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("get-leads/linkedin-scraper").call(run_input={
    "mode": "search_profiles",
    "searchQuery": "Naveen Jain",
    "maxResults": 10,
    "discoverEmails": True
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['name']} — {item.get('current_title', 'N/A')} at {item.get('current_company', 'N/A')} — {item['url']}")

Performance Notes

• Speed: ~10-30 seconds per profile for enrichment. 10 profiles takes ~2-5 minutes.
• With cookie: Voyager API provides structured search results (better name matching, profile photos, locations). Recommended for best results.
• Without cookie: Falls back to 5-engine parallel search (Brave, Yahoo, Google, Ecosia, Startpage). Fewer results but still functional.

---

Mode 7: Profile Complete (Premium)

Get everything about a LinkedIn person in one result — full profile data plus their recent posts with engagement metrics (likes, comments, shares). Saves you from running Profiles + Posts separately.

Input Parameters

Parameter	Type	Required	Description	Values
mode	string	Yes	Scraping mode	"profile_complete"
urls	string[]	Yes	LinkedIn profile URLs	linkedin.com/in/username format
maxResults	integer	No	Maximum profiles to return	1–1000, default: 25
proxyConfig	object	No	Proxy settings	Default: Apify RESIDENTIAL
minDelay	integer	No	Min delay between requests (ms)	500–30000, default: 2000
maxDelay	integer	No	Max delay between requests (ms)	1000–60000, default: 5000

Input Example

{
  "mode": "profile_complete",
  "urls": [
    "https://www.linkedin.com/in/williamhgates",
    "https://www.linkedin.com/in/satyanadella"
  ],
  "maxResults": 10
}

Output Fields

Field	Type	Description	Example
mode	string	Always "profile_complete"	"profile_complete"
name	string	Full name	"Bill Gates"
headline	string	Profile headline	"Co-chair, Bill & Melinda Gates Foundation"
location	string	Location	"Seattle, Washington"
url	string	Profile URL	"https://www.linkedin.com/in/williamhgates"
current_title	string	Current job title	"Co-chair"
current_company	string	Current employer	"Bill & Melinda Gates Foundation"
about	string	About section	"Technologist, business leader..."
experience	array	Work history	[{title, company, dates, location}]
education	array	Education history	[{school, degree, dates}]
skills	array	Professional skills	["Strategy", "Leadership"]
certifications	array	Professional certifications	[{name, issuer, dates}]
languages	array	Languages spoken	[{name, proficiency}]
location_normalized	object	Structured {city, region, country, raw} derived from location	{"city":"Seattle","region":"Washington","country":"United States","raw":"Seattle, Washington, United States"}
highest_degree	string|null	Highest-ranked degree code from education[] (PhD>MD>JD>MBA>MS>MA>MEng>LLM>Master>BS>BA>BEng>LLB>Bachelor>Associate>HighSchool); null when no degree parseable	"MBA"
open_to_work	boolean|null	true if profile shows the "Open to Work" badge; null if the profile doesn't expose it	true
hiring	boolean|null	true if profile shows the "Hiring" badge; null if the profile doesn't expose it	false
posts	array	Up to 10 posts with engagement (cookie required)	[{text, url, date, likes, comments_count, shares_count, media_url}]
posts_count	integer	Number of posts found	5
total_engagement	integer	Sum of all likes + comments + shares	125000
email	string	Discovered email (if available)	"bill@gatesfoundation.org"
email_source	string	How email was found	"company-website"
scraped_at	string	ISO timestamp	"2026-02-27T00:00:00.000Z"

experience[] entries are enriched with parsed date fields alongside the raw dates string — start_date (ISO-8601, month precision), end_date (or null when current), duration_months (integer), and current (boolean). Example: {"title":"Co-chair","company":"Gates Foundation","dates":"2000 - Present · 26 yrs 4 mos","start_date":"2000-01-01","end_date":null,"duration_months":315,"current":true}. Filter on current: true / duration_months > 120 directly in SQL or Google Sheets instead of regex on the free-text string.

Output Example

{
  "mode": "profile_complete",
  "name": "Pascal Cagni",
  "headline": "Founder & CEO @ C4 Industries and C4 Ventures | France Ambassador for International Investments",
  "location": "Greater Paris Metropolitan Region",
  "url": "https://www.linkedin.com/in/pascalcagni/",
  "current_title": "Founder & CEO",
  "current_company": "C4 Industries",
  "about": "Serial entrepreneur and global corporate executive with strong track record in venture capital and private equity...",
  "experience": [
    {
      "title": "Founder & CEO",
      "company": "C4 Industries",
      "dates": "Jan 2006 - Present · 20 yrs 4 mos",
      "location": "Paris, Île-de-France, France"
    }
  ],
  "education": [
    {
      "school": "HEC Paris",
      "degree": "Master of Business Administration (MBA)",
      "dates": "1984 - 1986"
    }
  ],
  "skills": ["Private Equity", "Venture Capital", "Strategy", "Start-ups"],
  "posts": [
    {
      "text": "C4 Ventures backs Yann LeCun's AMI Labs $1B round...",
      "url": "https://www.linkedin.com/feed/update/urn:li:activity:7437107940232175616",
      "date": "2026-03-13",
      "likes": 753,
      "comments_count": 39,
      "shares_count": 15,
      "media_url": "https://media.licdn.com/dms/image/..."
    }
  ],
  "posts_count": 10,
  "total_engagement": 2926,
  "email": null,
  "email_source": null,
  "scraped_at": "2026-04-03T10:53:58.394Z"
}

Use Cases

• Influencer analysis: Full profile + content performance in one call
• Sales intelligence: Know who they are AND what they care about
• Recruiting deep-dive: Candidate profile + thought leadership activity
• Competitive intelligence: Track key people + their public messaging

Python Example

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("get-leads/linkedin-scraper").call(run_input={
    "mode": "profile_complete",
    "urls": [
        "https://www.linkedin.com/in/williamhgates",
        "https://www.linkedin.com/in/satyanadella"
    ],
    "maxResults": 10
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['name']} — {item['posts_count']} posts — {item['total_engagement']} total engagement")

---

Mode 8: Company Employees

Find people who work at a specific company. Provide a company URL and get a list of employees with their names, titles, and profile URLs.

Input Parameters

Parameter	Type	Required	Description	Values
mode	string	Yes	Scraping mode	"company_employees"
urls	string[]	Yes	LinkedIn company URLs	linkedin.com/company/name format
maxResults	integer	No	Maximum employees to return	1–1000, default: 25
cookie	string	No	LinkedIn li_at cookie (recommended)	Enables Voyager paginated search (100+ employees)
proxyConfig	object	No	Proxy settings	Default: Apify RESIDENTIAL
minDelay	integer	No	Min delay between requests (ms)	500–30000, default: 2000
maxDelay	integer	No	Max delay between requests (ms)	1000–60000, default: 5000

Input Example

{
  "mode": "company_employees",
  "urls": [
    "https://www.linkedin.com/company/microsoft"
  ],
  "maxResults": 25,
  "cookie": "YOUR_LI_AT_COOKIE"
}

Output Fields

Field	Type	Description	Example
mode	string	Always "company_employees"	"company_employees"
name	string	Employee name	"John Yip"
headline	string	LinkedIn headline	"Cloud & AI Specialist @ Microsoft"
location	string	Location	"WP. Kuala Lumpur"
url	string	LinkedIn profile URL	"https://www.linkedin.com/in/john-yip-338403a6"
current_title	string	Parsed job title	"Cloud & AI Specialist"
current_company	string	Company name	"Microsoft"
scraped_at	string	ISO timestamp	"2026-04-03T12:44:59.310Z"

Output Example

{
  "mode": "company_employees",
  "name": "John Yip",
  "headline": "Cloud & AI Specialist @ Microsoft",
  "location": "WP. Kuala Lumpur",
  "url": "https://www.linkedin.com/in/john-yip-338403a6",
  "current_title": "Cloud & AI Specialist",
  "current_company": "Microsoft",
  "scraped_at": "2026-04-03T12:44:59.310Z"
}

With cookie (recommended): When you provide an li_at cookie, the scraper runs three discovery paths in sequence: (1) LinkedIn's faceted people-search HTML (/search/results/people/?currentCompany=[ID]) with pagination; (2) the /company/<slug>/people/ tab HTML scrape which parses LinkedIn's embedded profile JSON; (3) 5-engine SERP fallback for additional employees not surfaced by paths 1-2. Combined, expect 20-100+ employees per company depending on company size and LinkedIn's daily rate limits. Without a cookie, the scraper falls back to SERP-only which typically finds 10-50 employees.

Note on email discovery for this mode: discoverEmails is automatically disabled for company_employees mode regardless of your input setting. Email discovery per employee takes ~15-20 seconds; on a 25-employee run that would exceed the default run timeout. If you need emails for the discovered employees, run profiles mode on the returned URLs in a separate step — this keeps each mode's timing predictable and aligns cost with value delivered.

Use Cases

• Sales prospecting: Find decision-makers at target accounts
• Recruiting: Discover hiring managers and team members at a company
• Competitive intelligence: Map out a competitor's team structure
• Market research: Identify key personnel across companies in an industry

Python Example

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")

run = client.actor("get-leads/linkedin-scraper").call(run_input={
    "mode": "company_employees",
    "urls": ["https://www.linkedin.com/company/microsoft"],
    "maxResults": 50,
    "cookie": "YOUR_LI_AT_COOKIE"
})

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(f"{item['name']} — {item['current_title']} ({item['location']})")

---

Download Your Data

After every run, download your results in any format:

• JSON — for developers and API integrations
• CSV — for spreadsheets and data analysis
• Excel (XLSX) — for business users
• XML — for data pipelines

Google Sheets: Use =IMPORTDATA() with the CSV download link for instant import.

All Apify export formats available: JSON, JSONL, CSV, XLSX, XML, HTML Table, RSS.

Output Format Stability

Every dataset row is normalised to its mode's full documented schema. Fields that LinkedIn does not expose for a given entity are emitted as null rather than omitted, so your CSV / Excel / XLSX exports always have stable column ordering. This matters for downstream BI tools (Google Sheets IMPORTDATA, n8n, Zapier, HubSpot, Salesforce) that expect consistent columns across rows — ragged columns previously caused silent data mis-alignment.

Key Features

• No cookies, no login — 100% safe, zero risk to your LinkedIn account
• 8 modes in one actor — profiles, companies, jobs, posts, search, search profiles, profile complete, company employees
• Chrome TLS fingerprinting — Impit impersonates real Chrome browser fingerprints (JA3/JA4)
• 5-engine parallel search — Brave + Yahoo + Google + Ecosia + Startpage all queried simultaneously for maximum reliability and speed. Ecosia and Startpage are EU-based meta-search engines that rarely rate-limit, serving as resilience on days when Brave/Yahoo/Google hit rate limits. Cross-engine field union merges headline/location/title from whichever engine provided them.
• SERP result caching — 24-hour KVStore cache avoids re-querying search engines for recently-scraped profiles
• Smart proxy routing — Companies, Jobs, Posts, Search, Company Employees use cheap DATACENTER proxies. Only Profiles, Search Profiles, Profile Complete use RESIDENTIAL proxies (for direct LinkedIn page access). ~30% cost savings vs all-residential.
• Conditional Voyager session + warm-up — Voyager API session and LinkedIn warm-up both skipped for modes that don't need them (Jobs, Companies, and cookie-less Search). ~1–2 s faster cold start on those modes.
• Stable output schema — every dataset row carries the full documented field set; missing values are null rather than omitted, so CSV / Excel exports have deterministic columns.
• Fail-fast input validation — submitting a URL-input mode (Profiles, Companies, Posts, Profile Complete, Company Employees) with an empty urls array, or a query-input mode (Jobs, Search, Search Profiles) with no searchQuery, returns a clear error with an example of the correct input shape — no compute wasted on misconfigured runs.
• Progress status for long runs — Search Profiles and Profile Complete emit Actor.setStatusMessage updates so you see live progress in the Apify run tile instead of a silent "running" status.
• Per-run spend guardrails — maxResults above per-mode soft caps (Profile Complete: 100, Profiles/Search Profiles: 200, Company Employees: 250, Posts: 500) requires an explicit confirmLargeRun: true opt-in so runaway inputs can't quietly bill multiple dollars. Low-unit-price modes (Jobs, Search, Companies) have no soft cap.
• Dry-run mode — set dryRun: true to validate the input and get an estimated event count + USD cost in a single dataset row, without making any HTTP calls or billing events. Useful for previewing spend before committing to a large run.
• Open-to-Work + Hiring badge detection — Profiles / Profile Complete / Search Profiles records include open_to_work: boolean|null and hiring: boolean|null parsed from LinkedIn's profile-photo frame URNs + Voyager hydration fragments. null when the profile doesn't expose either badge.
• Cookie-pool rotation — provide cookies: ["AQED...", "AQED..."] to run with a rotating pool instead of a single li_at. Rotation is per profile batch (never per request — keeps cookie + IP pairing intact). Burned cookies are auto-skipped; exhausted pool falls back to SERP.
• Derived-intelligence fields — every Profile / Profile Complete / Search Profiles record includes BI-ready derived values alongside the raw ones: location_normalized: {city, region, country, raw} (3-tuple with US-state-abbrev collapse to "United States"), highest_degree (enum: PhD / MD / JD / MBA / MS / MA / MEng / LLM / Master / BS / BA / BEng / LLB / Bachelor / Associate / HighSchool / null) computed from the education array, and per-experience start_date, end_date, duration_months, current parsed from the free-text dates display string. Filter on these directly in SQL / Google Sheets instead of regex on strings.
• Pre-emptive Fuse-limit handling — the scraper counts fuse-counting section queries across the run and pre-emptively drops to an exp+edu-only safe list at 40 queries, before LinkedIn's server-side limit trips at ~45. Large batches degrade gracefully; the "Fuse limit was reached" log line LinkedIn uses for anomaly scoring is never emitted.
• Variant-aware profile cache — cache keys include cookie presence + email-discovery flag (e.g. profile-williamhgates:no-cookie:no-email), so a no-cookie run cannot poison a subsequent cookie-backed run's cache.
• Structured hashtag extraction — post hashtags come from both the plain-text #tag regex AND LinkedIn's Voyager-native commentary.attributes[] field, catching hashtags rendered as badges without a literal # in the post body.
• Regional subdomain retry — the Voyager identity API rotates through www / fr / de / uk on 429 / 5xx so celebrity-slug URN resolution (Bill Gates, Elon Musk, etc.) survives rate-limit tail-latency.
• Email-discovery telemetry — the per-run summary log emits a per-source hit breakdown (json-ld / company-website / search-engine / hunter / none) so you can audit the mix of discovery strategies that produced your emails.
• Human-like behavior — randomized delays with Box-Muller normal distribution
• 384 MB memory — lightweight HTTP-only architecture, minimal compute costs
• MCP-compatible — works with Claude, GPT, Cursor, and any MCP client
• Pay per result — no subscription, no monthly fee

Error Handling

The scraper handles errors gracefully — individual failures never crash the entire run:

• Missing required input: Modes that need urls (Profiles, Companies, Posts, Profile Complete, Company Employees) or searchQuery (Jobs, Search, Search Profiles) fail fast on an empty input with an actionable error message including an example of the correct shape. Run exits in ~5 seconds with exit code 1 — no compute billed.
• Invalid URLs: Skipped with a warning in the log. Only valid LinkedIn URLs are processed.
• Unreachable slugs: Surfaced as stub records in the dataset — { mode, url, slug, error: "invalid_slug", reason, suggestions, scraped_at } — so you can reconcile bad input without losing track of which URLs failed.
• Expired li_at cookie: Emits one { mode, error: "li_at_expired", reason, suggestion, scraped_at } row so the empty-dataset case is visibly explained; the scraper falls back to SERP / anonymous scraping for the rest of the run.
• Rate limits: Automatic delays with residential proxy rotation (up to 3 retries per request). SERP engines (Brave / Google / Yahoo / DuckDuckGo / Bing) retry once with 1.5–3 s jittered backoff on 429, 5xx, or TLS drops.
• Timeout errors: Requests that exceed the timeout are retried with a fresh proxy IP.
• Partial results: If enrichment fails for one profile, the rest continue unaffected.

Check the Run Log tab in Apify Console to see warnings for any skipped or failed URLs.

Performance Tips

• Batch your URLs — scraping 50 profiles in one run is faster and cheaper than 50 separate runs
• Use a cookie for profiles — adding an li_at cookie unlocks Voyager API access for richer data (experience, education, skills). Without it, data comes from search engines (more limited).
• Start with small batches — test with 5–10 URLs first, then scale up
• Check the logs — the Run Log shows exactly what was scraped, skipped, or retried
• Use maxResults wisely — for search modes, start with 25 and increase if needed

Daily Limits

There are no Apify-side limits on how many times you can run this scraper.

LinkedIn-side recommendations:

• Without cookie: No practical limit — requests go through search engines and public pages
• With cookie: ~100 profiles per cookie per day recommended. Exceeding this may trigger LinkedIn rate limits on your session.
• Search modes: LinkedIn returns up to ~1,000 results per search query. Use different keywords or locations to expand coverage.
• Jobs mode: No daily limit — job listings are fully public

Tip: For high-volume scraping (1,000+ profiles/day), run without a cookie to avoid any LinkedIn session risk.

Limitations

• Profile data is limited to what LinkedIn shows on public pages
• Some profiles may be behind a login wall (the actor will still extract available data)
• Post engagement metrics may be approximate
• Search mode uses a 5-engine parallel SERP (Brave + Yahoo + Google + Ecosia + Startpage) for high reliability. Field completion: ~100% names, ~70% headlines, ~55% locations under typical SERP conditions (field rates fluctuate with engine snippet richness — cross-engine merge fills gaps when one engine has richer snippet than another)
• Profile and SERP results are cached for 24 hours — repeat runs return cached data instantly
• Posts mode for celebrity / short-form profile slugs (e.g. williamhgates, elonmusk) falls back to a Voyager identity-API call to translate the numeric member URN into the fsd_profile URN that the feed endpoint needs — with 4-region subdomain rotation (www → fr → de → uk) to survive 429 rate-limit tail-latency. Success is near-universal but not guaranteed if LinkedIn returns 401/403 on that API.
• Profile posts require a valid li_at cookie and are paginated through Voyager. Company-page posts use the unified q=feed&organizationActor=… endpoint and do not need cookie-specific URN translation.

FAQ

Do I need a LinkedIn account? No. All 8 modes work without any cookie or login. Optionally, provide your li_at cookie for Enhanced Mode — 5x richer profiles (experience, education, skills) and 10x more company employees. See the Standard vs Enhanced Mode section above for details.

How accurate are emails? Email discovery uses a 5-layer pipeline: JSON-LD, company website scraping (14 paths), search engine email search, MX-validated pattern guessing (11 patterns), and optional Hunter.io verification. Hit rate is ~60-85% depending on the profile. Emails are verified against real MX records before being returned.

What if a profile returns empty? The scraper tries multiple sources: Voyager API (if cookie provided), 5 search engines in parallel (Brave + Yahoo + Google + Ecosia + Startpage), direct LinkedIn fetch, and 999 metadata extraction. If all fail, it means the profile is behind authentication or doesn't exist. Try providing an li_at cookie for better results.

How fast is it? Profiles: ~2-4 seconds each (with 24h caching, repeat queries are instant). Jobs: ~15 seconds per 20 listings. Search: ~3-5 seconds for 5-18 results. Company Employees: ~5-10 seconds.

What if I get blocked? Automatic retry with proxy rotation. The scraper uses residential proxies for LinkedIn and datacenter proxies for SERP engines to minimize costs and maximize reliability.

Can I scrape thousands of results? Yes. Set maxResults up to 1,000. The scraper handles pagination, deduplication, and rate limiting automatically.

Is there a monthly subscription? No. Pay only for results delivered, starting at $1/1K for jobs. Higher Apify plans get 5-15% discounts.

What's the difference between Search and Search Profiles? Search returns basic profile cards (name, headline, URL). Search Profiles finds profiles AND fully scrapes each one — returning 14+ fields including experience, education, and email.

Can I find all employees of a company? Yes — use the Company Employees mode. Provide a LinkedIn company URL and the scraper discovers employees via search engine results. Results include name, headline, location, and profile URL.

Questions? Open an issue on the actor page or reach out via Apify Console.

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