The Problem: Why scraping LinkedIn leads is so painful LinkedIn is the holy grail of B2B prospecting. But when it comes to extracting data at scale, reality kicks in: Copy-pasting profile info manually is time-consuming and error-prone. Traditional scraping methods depend on cookies, browser hacks, or proxy juggling. They break constantly. Your sales and marketing teams need structured, reliable lead data — yesterday. The result? Incomplete databases, poor segmentation, and lost opportunities. The Solution: Apify + LinkedIn Profile Batch Scraper (No Cookies) Apify provides a cookie-free, scalable, and reliable way to scrape LinkedIn leads at scale. With the LinkedIn Profile Details Batch Scraper + EMAIL (No Cookies) actor, you get clean datasets in JSON or CSV format, including: Basic info: full name, headline, current company, profile URL, location, follower count. Work experience: roles, companies, dates, seniority. Education: schools, degrees, timeframes. Influence signals: creator/influencer flags and number of followers. Additional enrichment: projects, certifications, languages (if publicly available). 👉 Example: Satya Nadella — Chairman & CEO at Microsoft, 11.5M followers, education at Booth School of Business + Manipal Institute. Neal Mohan — CEO at YouTube, 2.1K connections, Stanford grad. Imagine importing structured data like this directly into Salesforce, HubSpot, or Pipedrive — ready for segmentation and outreach. Step-by-Step: How to Scrape LinkedIn Leads 1. From Apify Console (Quick Test) Open the LinkedIn Profile Batch Scraper (No Cookies) actor. Input LinkedIn profile URLs or public identifiers. Run → download results in JSON or CSV. 2. With Python (Automation at Scale) from apify_client import ApifyClientimport csvfrom datetime import datetimeclient = ApifyClient("<YOUR_API_TOKEN>")run_input = { "profileUrls": [ "https://www.linkedin.com/in/satyanadella", "https://www.linkedin.com/in/neal-mohan" ]}run = client.actor("apimaestro/linkedin-profile-batch-scraper-no-cookies-required").call(run_input=run_input)dataset_id = run["defaultDatasetId"]items = list(client.dataset(dataset_id).iterate_items())def row_from_item(it): bi = it.get("basic_info", {}) or {} loc = (bi.get("location") or {}) return { "full_name": bi.get("fullname"), "headline": bi.get("headline"), "company_current": bi.get("current_company"), "city": loc.get("city"), "country": loc.get("country"), "followers": bi.get("follower_count"), "linkedin_url": bi.get("profile_url"), }rows = [row_from_item(it) for it in items]out_file = f"leads_linkedin_{datetime.utcnow().strftime('%Y%m%d-%H%M%S')}.csv"with open(out_file, "w", newline="", encoding="utf-8") as f: w = csv.DictWriter(f, fieldnames=list(rows[0].keys())) w.writeheader() for r in rows: w.writerow(r)print("Dataset:", f"https://console.apify.com/storage/datasets/{dataset_id}")print("CSV ready:", out_file) With just a few lines of Python, you turn LinkedIn into a lead automation engine: Bulk scrape 100 or 100K profiles. Export leads directly to your CRM. Run on a schedule (daily, weekly, monthly). Business Benefits of Scraping LinkedIn Leads with Apify Faster prospecting: Spend less time searching, more time closing deals. Better segmentation: Filter by role, company, location, or influence. Consistent data: Structured JSON/CSV that plugs into any CRM. Scalability: From a few profiles to thousands — no extra complexity. Want to stop scraping profiles one by one and start working with datasets of high-quality LinkedIn leads? 👉 Try it now with Apify: LinkedIn Profile Batch Scraper (No Cookies) And if you want to go further — building a full lead automation machine that runs 24/7, feeds your CRM, and scores leads automatically —  📩 Contact me at [email protected] Let’s turn LinkedIn into your best-performing lead engine. How to Scrape LinkedIn Leads (Without Cookies) and Build a Lead Machine with Apify was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this storyThe Problem: Why scraping LinkedIn leads is so painful LinkedIn is the holy grail of B2B prospecting. But when it comes to extracting data at scale, reality kicks in: Copy-pasting profile info manually is time-consuming and error-prone. Traditional scraping methods depend on cookies, browser hacks, or proxy juggling. They break constantly. Your sales and marketing teams need structured, reliable lead data — yesterday. The result? Incomplete databases, poor segmentation, and lost opportunities. The Solution: Apify + LinkedIn Profile Batch Scraper (No Cookies) Apify provides a cookie-free, scalable, and reliable way to scrape LinkedIn leads at scale. With the LinkedIn Profile Details Batch Scraper + EMAIL (No Cookies) actor, you get clean datasets in JSON or CSV format, including: Basic info: full name, headline, current company, profile URL, location, follower count. Work experience: roles, companies, dates, seniority. Education: schools, degrees, timeframes. Influence signals: creator/influencer flags and number of followers. Additional enrichment: projects, certifications, languages (if publicly available). 👉 Example: Satya Nadella — Chairman & CEO at Microsoft, 11.5M followers, education at Booth School of Business + Manipal Institute. Neal Mohan — CEO at YouTube, 2.1K connections, Stanford grad. Imagine importing structured data like this directly into Salesforce, HubSpot, or Pipedrive — ready for segmentation and outreach. Step-by-Step: How to Scrape LinkedIn Leads 1. From Apify Console (Quick Test) Open the LinkedIn Profile Batch Scraper (No Cookies) actor. Input LinkedIn profile URLs or public identifiers. Run → download results in JSON or CSV. 2. With Python (Automation at Scale) from apify_client import ApifyClientimport csvfrom datetime import datetimeclient = ApifyClient("<YOUR_API_TOKEN>")run_input = { "profileUrls": [ "https://www.linkedin.com/in/satyanadella", "https://www.linkedin.com/in/neal-mohan" ]}run = client.actor("apimaestro/linkedin-profile-batch-scraper-no-cookies-required").call(run_input=run_input)dataset_id = run["defaultDatasetId"]items = list(client.dataset(dataset_id).iterate_items())def row_from_item(it): bi = it.get("basic_info", {}) or {} loc = (bi.get("location") or {}) return { "full_name": bi.get("fullname"), "headline": bi.get("headline"), "company_current": bi.get("current_company"), "city": loc.get("city"), "country": loc.get("country"), "followers": bi.get("follower_count"), "linkedin_url": bi.get("profile_url"), }rows = [row_from_item(it) for it in items]out_file = f"leads_linkedin_{datetime.utcnow().strftime('%Y%m%d-%H%M%S')}.csv"with open(out_file, "w", newline="", encoding="utf-8") as f: w = csv.DictWriter(f, fieldnames=list(rows[0].keys())) w.writeheader() for r in rows: w.writerow(r)print("Dataset:", f"https://console.apify.com/storage/datasets/{dataset_id}")print("CSV ready:", out_file) With just a few lines of Python, you turn LinkedIn into a lead automation engine: Bulk scrape 100 or 100K profiles. Export leads directly to your CRM. Run on a schedule (daily, weekly, monthly). Business Benefits of Scraping LinkedIn Leads with Apify Faster prospecting: Spend less time searching, more time closing deals. Better segmentation: Filter by role, company, location, or influence. Consistent data: Structured JSON/CSV that plugs into any CRM. Scalability: From a few profiles to thousands — no extra complexity. Want to stop scraping profiles one by one and start working with datasets of high-quality LinkedIn leads? 👉 Try it now with Apify: LinkedIn Profile Batch Scraper (No Cookies) And if you want to go further — building a full lead automation machine that runs 24/7, feeds your CRM, and scores leads automatically —  📩 Contact me at [email protected] Let’s turn LinkedIn into your best-performing lead engine. How to Scrape LinkedIn Leads (Without Cookies) and Build a Lead Machine with Apify was originally published in Coinmonks on Medium, where people are continuing the conversation by highlighting and responding to this story

from apify_client import ApifyClient
import csv
from datetime import datetime

client = ApifyClient("<YOUR_API_TOKEN>")

run_input = {
    "profileUrls": [
        "https://www.linkedin.com/in/satyanadella",
        "https://www.linkedin.com/in/neal-mohan"
    ]
}

run = client.actor("apimaestro/linkedin-profile-batch-scraper-no-cookies-required").call(run_input=run_input)

dataset_id = run["defaultDatasetId"]
items = list(client.dataset(dataset_id).iterate_items())

def row_from_item(it):
    bi = it.get("basic_info", {}) or {}
    loc = (bi.get("location") or {})
    return {
        "full_name": bi.get("fullname"),
        "headline": bi.get("headline"),
        "company_current": bi.get("current_company"),
        "city": loc.get("city"),
        "country": loc.get("country"),
        "followers": bi.get("follower_count"),
        "linkedin_url": bi.get("profile_url"),
    }

rows = [row_from_item(it) for it in items]

out_file = f"leads_linkedin_{datetime.utcnow().strftime('%Y%m%d-%H%M%S')}.csv"
with open(out_file, "w", newline="", encoding="utf-8") as f:
    w = csv.DictWriter(f, fieldnames=list(rows[0].keys()))
    w.writeheader()
    for r in rows:
        w.writerow(r)

print("Dataset:", f"https://console.apify.com/storage/datasets/{dataset_id}")
print("CSV ready:", out_file)