🚀 Fast LinkedIn Jobs Scraper (No Cookies, Pay-Per-Result)

Scrape real-time, comprehensive LinkedIn job postings into clean, structured JSON, CSV, or Excel — no LinkedIn account, no cookies, and no login required.

Designed for recruiters, job seekers, researchers, and AI workflow builders who need high-velocity, reliable job market data without account ban risks.

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✨ Key Features

• 100% Guest-Session Based: No LinkedIn login, password, or session cookies required. Zero risk of account restriction or ban.
• Pay-Per-Result Pricing: Pay only for the jobs you actually collect (from $0.70 to $1.00 per 1,000 results). No expensive monthly recurring subscription.
• Under 10 Applicants Filter: Target early-bird job opportunities before hundreds of candidates flood the application queue.
• Easy Apply Filter: Separate 1-click LinkedIn applications from third-party external career portals (Workday, Greenhouse, Lever).
• Dual Description Extraction: Extract both clean plain-text description (optimized for LLMs and AI agent tokens) and formatted descriptionHtml (with bolding and bullet lists intact).
• Agent-Ready Architecture: Designed specifically to plug directly into n8n, Claude Code, Make, and LangChain without custom cleanup.

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💼 Popular Use Cases

• Autonomous Job Hunting: Feed real-time postings directly into automated cover letter pipelines, match evaluators, and auto-notification bots.
• Recruitment & Headhunting: Track hiring surges, monitor competitor talent needs, and uncover newly opened roles across regions.
• Niche Job Boards: Automatically populate industry-specific job boards and career newsletters with fresh, deduplicated listings.
• Labor Market Analytics: Analyze tech stack adoption trends, salary benchmarks, and remote-work distributions across European and global markets.

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📥 Input Parameters

Parameter	Type	Default	Description
titles (Required)	Array of Strings	["Analytics Engineer", "BI Developer"]	Target job titles, skills, or tech stacks to search for.
locations	Array of Strings	["European Union"]	Target countries, cities, or economic regions (e.g., Netherlands, Germany, London).
rows	Integer	10	Maximum number of job postings to collect per keyword/location pair (1 to 500).
publishedAt	String	"r604800"	Recency filter: "" (Any Time), "r86400" (Past 24h), "r604800" (Past 7d), "r2592000" (Past Month).
contractTypes	Array of Strings	["F"]	Employment type: F (Full-Time), C (Contract/Freelance), P (Part-Time), I (Internship).
under10Applicants	Boolean	false	When true, filters strictly for roles with fewer than 10 applicants.
easyApply	Boolean	false	When true, returns only jobs supporting 1-click LinkedIn Easy Apply.
proxyConfiguration	Object	{ "useApifyProxy": true }	Automatically routes through Apify proxy pools to prevent rate-limiting at scale.

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📤 Sample Output Data

Every extracted job record provides comprehensive, structured metadata ready for spreadsheets, databases, or AI processing:

{
  "id": "4482521949",
  "title": "Senior Analytics Engineer",
  "companyName": "Tech Scaleup",
  "location": "Amsterdam, North Holland, Netherlands",
  "jobUrl": "https://www.linkedin.com/jobs/view/4482521949",
  "postedTime": "18 hours ago",
  "applicantsCount": "Be among the first 25 applicants",
  "salary": "€75,000 - €95,000 / yr",
  "seniorityLevel": "Mid-Senior level",
  "employmentType": "Full-time",
  "jobFunction": "Engineering, Information Technology",
  "applyType": "EASY_APPLY",
  "description": "We are seeking a Senior Analytics Engineer to lead our modern data stack. You will architect scalable ELT pipelines and partner with stakeholders...",
  "descriptionHtml": "<div class=\"show-more-less-html__markup\"><p>We are seeking a <strong>Senior Analytics Engineer</strong>...</p></div>",
  "scrapingInfo": {
    "queryTitle": "Analytics Engineer",
    "queryLocation": "Netherlands"
  }
}

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🤖 AI Agent & Automation Integration

This Actor is optimized for agentic workflows. Because it outputs pre-cleaned text descriptions alongside structured metadata, language models (Gemini, Claude, GPT-4o) can parse, evaluate, and act on listings without HTML preprocessing or token bloat.

1. LangChain / Python Agent Tool

Call this scraper directly as a tool inside your LangChain or autonomous Python agents:

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

# Run the scraper actor programmatically
run = client.actor("zinnen/fast-linkedin-jobs-scraper").call(
    run_input={
        "titles": ["Analytics Engineer", "BI Lead"],
        "locations": ["Netherlands", "Germany"],
        "publishedAt": "r86400",
        "rows": 5,
        "contractTypes": ["F"],
    }
)

# Fetch dataset items directly into your LLM pipeline
jobs = client.dataset(run["defaultDatasetId"]).list_items().items
for job in jobs:
    print(f"Role: {job['title']} at {job['companyName']} | Link: {job['jobUrl']}")

2. n8n Autonomous Pipelines

Use this Actor as the trigger for automated career pipelines:

• Scrape: Pull the latest postings daily via the native Apify n8n node.
• Evaluate: Feed the clean description into an AI Agent node (Gemini/Ollama) to compute match scores and filter sponsorship viability.
• Act: Auto-generate tailored PDF cover letters and log results to Google Sheets, Notion, or Slack alerts.

3. Claude Code & Model Context Protocol (MCP)

Add this scraper to your local Claude Code or MCP agent environment by referencing your Apify API token:

$claude mcp add apify -- apify-mcp-server

Ask Claude Code:

"Use the fast-linkedin-jobs-scraper tool to find the 5 newest Analytics Engineer jobs in Europe posted in the past 24 hours, and analyze their required technical stacks."

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❓ Frequently Asked Questions

Do I need to provide a LinkedIn account or login cookies?

No. This scraper operates 100% on public guest endpoints. You never need to supply login credentials, session cookies, or personal LinkedIn credentials.

How do proxies work?

By default, the Actor automatically engages Apify's residential and datacenter proxy rotation pools. This bypasses IP rate-limits and bot-detection challenges even when scraping high volumes.

What does Pay-Per-Result mean?

Instead of paying a rigid $30–$50 monthly subscription, you only pay for the exact volume of job items your Actor successfully writes to the dataset. Small test runs cost fractions of a cent.