The 26 Best LinkedIn Scrapers in 2026 [Free & Paid]

LinkedIn scraping tools are software programs that automatically extract publicly available profile, contact, and company data from LinkedIn — so sales and marketing teams can build lead lists without manual copy-paste work. They are essential for professionals and businesses aiming to leverage LinkedIn's network of over 1 billion users, automating the data collection process and enhancing lead generation efforts.

There are plenty of free and paid options, whether on a budget or willing to invest. Here are the 26 best LinkedIn scrapers and automation tools to help you streamline your lead generation efforts and export your lead lists more easily.

Today, we'll look at:

Quick-glance tool comparison

Before diving into detailed reviews, here's a quick scan of the landscape:

This table helps you filter by budget, deployment type, and primary use case before reading the full tool breakdowns below.

26 Best LinkedIn Scraping Tools

Here are the best LinkedIn scrapers to extract the data you need:

Linkedin Scrapers Final Comparison

In this table, you will find all the information on the different Linkedin scraping tools we mentioned.

Types Of LinkedIn Scraping

LinkedIn scraping tools fall into three categories: cloud-based (runs on remote servers, no install needed), browser-based (runs via a Chrome extension), and desktop-based (runs as an installed app on your computer). Each type differs in safety profile, setup complexity, and whether it keeps running when your PC is off.

1. Cloud-Based Linkedin scraping

Cloud-based LinkedIn scraping is a method where a third-party tool runs all data extraction activity on remote servers using your LinkedIn session cookies — meaning no software is installed on your own device and the tool keeps working even when your computer is off.

Pros of Using Cloud-Based Scraping

1. Run when your PC is turned off
2. Uses proxies and VPN
3. Dedicated IP address
4. Can manage multiple accounts at the same time
5. Less likely to be forbidden by your company

Cons of Using Cloud-Based Scraping

1. It uses a different IP address than yours
2. Can use LinkedIn API a lot

2. Browser-Based Linkedin Scraping

Browser-based scraping runs its automation by simulating clicks directly on your browser and replicating human behavior. They need a Chrome Extension to function and take control over your browser.

Pros of Using Browser-Based Scraping

1. Usually less expensive (no server costs)
2. Easy to set up
3. Offers many features

Cons of Using Browser-Based Scraping

1. You need to have a browser opened for it to run
2. Other extensions can interrupt their work
3. There is a chance to lose the data due to the cache being cleared
4. Your company might prevent you from installing Chrome Extension

3. Desktop-Based Linkedin scraping

Desktop-based scraping runs directly from your desktop. It usually involves installing an app on your computer.

Pros of using Desktop-Based Scraping

1. It uses your IP address
2. Can manage multiple accounts at the same time

Cons of using Desktop-Based Scraping

1. It doesn't work if your PC is turned off
2. It is likely to be forbidden by your company as it can potentially access all your computer
3. It consumes many desktop resources, and you'd need to reduce your Mac memory usage

It's important to know that no solution is safer than the others. The most essential thing to not having your account restricted is to respect LinkedIn's limitations. So, when you choose a tool, ensure it stays within LinkedIn's recommended limitations.

LinkedIn Scraper APIs and Enrichment APIs

Beyond the cloud/browser/desktop categories above, there's a fourth tool type worth knowing: API-based LinkedIn data providers. These services (like Proxycurl, People Data Labs, Scrapingdog's LinkedIn API, and Bright Data's Dataset API) don't run automation inside your LinkedIn session. Instead, they maintain their own databases of LinkedIn profile and company data — refreshed regularly — and you query them programmatically.

When to Use an API Provider

• You need thousands of profiles per hour without bumping LinkedIn's per-account export limits.
• You're a developer building a product (recruiting SaaS, sales intel dashboard, etc.) and want to avoid managing LinkedIn sessions or browser automation.
• You want a single endpoint that returns structured JSON for any LinkedIn URL, so you can integrate LinkedIn data into your app's backend.

Main Features of LinkedIn Data APIs

• Profile enrichment by URL: send a LinkedIn profile or company URL, get back title, email guesses, employment history, skills, etc.
• Search endpoints: some APIs let you search by keywords, location, industry, etc., without using LinkedIn's UI at all.
• Batch processing: enrich 100 or 10,000 profiles in one API call rather than scraping them one by one.
• No LinkedIn session risk: because you're not automating your own LinkedIn account, you can't trigger a restriction on your personal profile.

Trade-offs

• Cost per record is higher than self-scraping. API providers charge per profile or per 1,000 credits, which can be expensive at scale.
• Data freshness varies. Some APIs crawl LinkedIn continuously; others refresh profiles every few weeks. Check the provider's SLA.
• Email accuracy is probabilistic. API providers use pattern-matching and waterfall logic to guess emails, similar to an email finder — they're not pulling verified opt-in contact info.

Example API Providers

• Proxycurl: Most widely used for LinkedIn profile and company enrichment. Offers real-time scraping and cached data options.
• People Data Labs: Aggregates data from many public sources (not just LinkedIn) into a single person or company record.
• Scrapingdog LinkedIn API: Budget-friendly option with simple REST endpoints; good for startups.
• Bright Data Dataset API: Enterprise-grade service with pre-built LinkedIn datasets you can query without scraping infrastructure.

If you're a no-code user running small lists, a browser or cloud scraper is simpler and cheaper. If you're a technical team building a product, an API provider saves you the ops burden of running browser automation and lets you scale instantly.

Is Scraping Illegal on LinkedIn?

Scraping LinkedIn is not illegal under most jurisdictions, but it does violate LinkedIn's Terms of Service. In practice, LinkedIn tolerates scraping when users stay within its published daily export limits — 80 profiles/day on a free account, up to 2,500/day with Sales Navigator plus a tool like Evaboot.

In practice, LinkedIn won't bother you if you respect the limitations they impose:

• 80 profiles exported per day with a free account
• 150 profiles exported per day with a LinkedIn Premium account.
• 1000 profiles exported per day with a Sales Navigator account.
• 2500 profiles exported per day with a Sales Navigator account + Evaboot.

They are aware that all recruiters and sales need to scrape their data. Making it illegal would make them lose too much business.

The hiQ Labs vs LinkedIn Case

In 2017, LinkedIn sued hiQ Labs — a startup that scraped public LinkedIn profiles to build employee-retention analytics. LinkedIn argued that scraping violated the Computer Fraud and Abuse Act (CFAA) and their ToS. The Ninth Circuit Court of Appeals ruled in hiQ's favor in 2019, stating that scraping publicly accessible data doesn't violate the CFAA. The case went to the Supreme Court, which vacated the ruling and sent it back for reconsideration in light of a separate CFAA case. As of 2026, the legal line is still being drawn, but the takeaway is this: scraping public profile data is legally defensible in many jurisdictions — violating LinkedIn's ToS is a contract issue, not a criminal one.

Public vs Private Data

LinkedIn profiles have two visibility tiers. Public profiles are visible to anyone, even without a LinkedIn login. Private or restricted profiles are behind LinkedIn's login wall. Tools that scrape public data (e.g., names, headlines, locations visible to the web) stand on firmer legal ground. Tools that scrape data behind the login wall (e.g., email addresses, full work history) by simulating a logged-in session are more exposed to ToS enforcement. Most scrapers in this guide fall into the latter category — they operate within your authenticated session. That's why respecting LinkedIn's rate limits is your best defense against account restrictions.

Can I Connect LinkedIn Scrapers to my CRM?

Most LinkedIn scrapers can directly send the data they extract to your CRM.

If they don't, you can always download the CSV or Excel file they provide and use the import feature of your CRM to add your leads to it.

This features is really to use on the most common CRMs like Pipedrive, Hubsport or Salesforce.

Can You Scrape LinkedIn Data with Python?

Yes, you can scrape LinkedIn data with Python using libraries like Beautiful Soup or Selenium — but you need coding skills, or you'll need to hire a developer to build and maintain the scraper for you.

You need to know how to code for that. If you don't, you can still hire a freelancer to build a custom LinkedIn scraper for your needs.

How To Use LinkedIn Scrapers For Lead Generation?

9 ways to use LinkedIn scrapers for lead generation:

1. Sales Navigator lead lists

LinkedIn Sales Navigator is by far the best B2B data provider, as LinkedIn users constantly update the data. Exporting leads from Sales Navigator is a great way to build qualified lead lists, as you get a lot of data, such as names, job titles, emails, company names, company website URLs, etc.

This information can be used to create targeted lists of potential customers for your sales team to contact.

With this data, you can create personalized messaging that speaks directly to your ideal prospects' needs and pain points.

Additionally, LinkedIn scraping allows you to identify key decision-makers within a company, focusing your efforts on those who have the power to make a purchase.

By scraping LinkedIn, you can streamline your sales process, increase your conversion rates, and ultimately drive revenue growth for your business.

2. Event Attendees

LinkedIn events are a great source of leads as you can see people who are interested in a particular topic.

If you export LinkedIn event attendees using a tool like Phantombuster, you can import them into a CSV file with their contact information and mention of the event. These events provide a great icebreaker for your prospecting messages.

3. Post likers and commenters

Same thing for LinkedIn posts. People liking and commenting on LinkedIn posts manifest an interest in a specific topic.

If you export LinkedIn post commenters or likers and then reach out to them, that provides a great icebreaker for your prospecting messages.

4. Competitors' company followers

Why bother building an audience from scratch when your competitors are already doing all the work for you?

This hack you can use to extract your competitors' company page followers. This is really good data as if someone follows a competitor on LinkedIn, you can be pretty sure that they will be interested in your products or services.

5. Group members

Like events and posts, being a member of LinkedIn groups is a sign of interest.

By exporting LinkedIn group members, you are to build a qualified lead list of people interested in a specific topic. All members joined the group for a reason: they sought answers to their problems. Try to find out if they found it or if they still need help.

6. LinkedIn contacts

You can export your LinkedIn contacts from LinkedIn settings, which will only give you a few emails.

If you want more data and emails, exporting your LinkedIn contacts using a LinkedIn scraping tool is better.

You can do it through LinkedIn or Sales Navigator search engine. It will give better, cleaner data and more emails.

7. LinkedIn search results

If you don't have a LinkedIn Sales Navigator account yet (which I recommend if you are serious about LinkedIn prospecting), you can still export your LinkedIn search results from the basic search engine.

The best tool for that is Phantombuster. But be aware that you will be limited. With a free account, you can only visit 80-100 LinkedIn profiles per day.

8. Recruiter lists

LinkedIn scraping can be a powerful tool for recruiters seeking the best candidates for their organization.

Using web scraping software, recruiters can export LinkedIn profiles containing specific keywords and job titles, making it easier to build a list of candidates with the right skills and experience.

Once the recruiter has compiled a list of potential candidates, they can use LinkedIn's messaging feature to reach out to them and measure their interest in the position. They can also upload the files into their own Applicant Tracking System.

9. Sales Navigator Account Lists

LinkedIn Sales Navigator account list scraping can be a powerful tool for conducting competition analysis. By analyzing competitors' profiles, businesses can gain insight into their strengths and weaknesses, as well as their strategies for success.

One effective approach is to use a web scraping tool to collect data on competitors' job titles, skills, and educational backgrounds. This information can then be used to identify patterns and trends that can inform a company's own hiring and training strategies.

Additionally, LinkedIn scraping can help businesses identify potential partners, customers, and suppliers by analyzing connections between competitors and other industry players.

Best Free LinkedIn Scrapers

If you're on a budget, several scrapers offer free tiers that let you test the waters before committing to a paid plan. Here's a quick shortlist:

• Evaboot: No free tier, but starts at $9/month for 100 credits — cheapest way to get clean Sales Navigator exports with emails.
• Phantombuster: 10 free leads to start; then $69/month. Good for testing event or group scraping.
• Wiza: 20 emails and 5 phone numbers per month free. Solid for small one-off exports.
• Prospeo: 75 free credits. Useful for email verification on a handful of profiles.
• GetProspect: 50 leads/month free. Decent for occasional LinkedIn or Sales Navigator scraping.
• Lix: 50 credits free on the Starter plan. Simple browser extension for profile data.
• Derrick App (Google Sheets): 200 data enrichments/month at €0. Best free option if you live in Google Sheets.

Free tiers are great for proof-of-concept, but they hit daily or monthly caps fast. If you're serious about lead gen, budget for at least a starter paid plan — you'll hit your free limit in the first week.

Free Chrome Extensions (use with caution)

Some free browser extensions scrape LinkedIn without a paid plan (e.g., LinkedIn Helper clones, no-name Chrome Web Store tools). These are risky. They often:

• Exceed LinkedIn's rate limits by default.
• Have no safety throttling, increasing the chance your account gets restricted.
• Sell or leak your exported data to third parties.

If you do use a free extension, manually throttle your activity (80 profiles/day max on free LinkedIn, 1,000/day on Sales Navigator), and never leave it running unattended.

How to Avoid Getting Your LinkedIn Account Blocked

LinkedIn flags accounts for two main reasons: excessive automation activity (too many profile views, connection requests, or messages in a short window) and use of third-party tools that violate their ToS. Scrapers fall into the latter category, but you can minimize ban risk by following these practices:

1. Stay within LinkedIn's published limits

• Free LinkedIn: 80–100 profile views per day.
• LinkedIn Premium: ~150 profile views per day.
• Sales Navigator: 1,000 profile views per day (LinkedIn's official guidance).
• Sales Navigator + Evaboot: Up to 2,500 lead exports per day (Evaboot throttles safely within LinkedIn's tolerance).

Any tool that promises "unlimited" scraping is lying — or burning accounts.

2. Use cloud-based or desktop scrapers (not always safer, but less detectable)

Browser extensions leave a clear fingerprint (extension ID, unusual click patterns). Cloud-based scrapers like Evaboot, Phantombuster, or Captain Data run from dedicated IPs and mimic human browsing more convincingly. Desktop scrapers (Linked Helper, LaGrowthMachine on desktop mode) use your real IP but require the app to be open — they're safer than extensions because they control timing granularly.

3. Avoid data-center IPs and shared proxies

If you use a cloud scraper that rotates IPs, make sure it uses residential proxies or mobile IPs, not data-center IPs. LinkedIn can spot data-center ranges instantly. Most reputable scrapers handle this for you, but if you're building your own Python scraper, invest in a residential proxy service (Bright Data, Oxylabs, etc.).

4. Warm up new accounts

Brand-new LinkedIn accounts that immediately start scraping get flagged. If you're setting up a fresh profile, spend 2–3 weeks using it manually (accept invites, comment on posts, update your profile) before connecting a scraper. This mimics organic behavior and builds trust with LinkedIn's fraud detection.

5. Don't run multiple tools on the same account simultaneously

Running Phantombuster + Dux-Soup + Waalaxy on one LinkedIn account at the same time is a fast track to a restriction. Pick one scraper or automation tool per account. If you need both data export and messaging automation, choose a tool that does both (e.g., LaGrowthMachine, MeetAlfred).

6. Check your "Who Viewed Your Profile" settings

LinkedIn tracks who views profiles. If you scrape 500 profiles in a day and your "Who Viewed Your Profile" feed shows 500 recent views, recruiters and prospects will notice the pattern. Turn on Private Mode (LinkedIn Premium feature) or use a Sales Navigator account — Sales Navigator views are anonymous by default.

7. Use a dedicated LinkedIn account for scraping (if you're risk-averse)

If your main LinkedIn profile is mission-critical (executive, high-visibility role), consider creating a second "burner" account for scraping and outreach. If it gets restricted, your primary profile stays clean. This violates LinkedIn ToS (one account per person), but it's a common workaround for agencies and sales teams.

Fatal mistakes to avoid

• Scraping while logged in from multiple devices/locations. LinkedIn flags accounts that log in from Paris and San Francisco within an hour. If you use a cloud scraper, don't manually browse LinkedIn from your laptop at the same time.
• Using cracked/pirated scraper accounts. Some tools are sold on black-hat forums with shared licenses. These accounts are pre-flagged by LinkedIn.
• Ignoring LinkedIn's email warnings. If LinkedIn sends you a "suspicious activity" email, stop all automation for 48 hours. Continuing will trigger a permanent ban.
• Buying LinkedIn accounts from third parties. Aged LinkedIn accounts sold on forums are honeypots. LinkedIn tracks account ownership via phone number and email recovery; a purchased account will get banned within weeks.
• Exporting the same lead list repeatedly. If you scrape the same Sales Navigator search 10 times in a week, LinkedIn's system notices. Vary your searches, or export once and enrich locally.

In short: respect the rate limits, use one tool at a time, and warm up your account. Most bans happen because users rush.

AI Use Cases for LinkedIn Scrapers

LinkedIn scrapers used to be the end of the workflow — you'd export a list, manually score it, and hand it to your SDRs. Now, AI agents and LLM-powered enrichment layers turn raw scraped data into ranked, personalized, ready-to-send campaigns in minutes. Here's what's newly possible in 2026:

1. Chain your scraper into an AI scoring agent

No-code AI platforms like n8n, Zapier, and Make now offer prebuilt workflows that take your Evaboot or Phantombuster CSV, pass each lead through an LLM prompt (e.g., "Does this profile match our ICP?"), and output a scored, priority-ranked list. This used to require a data team; now it's a drag-and-drop workflow.

Why now: n8n and similar platforms released native LLM nodes in 2025, and community templates for "scrape → score → CRM" are public. You can clone and deploy one in 20 minutes.

Example flow: Evaboot exports Sales Navigator list → n8n reads CSV → GPT-4 scores each lead 1–10 based on title, company size, recent funding → top 20% auto-added to HubSpot as "hot leads."

2. AI enrichment layer: full digital footprint from a scraped profile URL

You scrape a LinkedIn URL with Wiza or Findymail. An AI agent then pulls that person's recent posts, comments, GitHub repos, personal blog, and company news — and synthesizes it into a one-paragraph "why we're reaching out" intro. This is hyper-personalization at scale, using LLMs that weren't accurate enough 18 months ago.

Why now: LLMs can now reliably extract structured data from unstructured web pages (posts, bios, articles) without hallucinating. Builders are publicly sharing playbooks where GPT-4 or Claude scrapes a prospect's last 5 LinkedIn posts and writes a custom opener.

Example flow: Scrupp exports 500 leads → Zapier triggers for each lead → Custom GPT prompt scrapes their LinkedIn activity feed → Generates: "Saw your post on [topic] last week — we help companies like [their company] solve exactly that problem."

3. Feed scraped lists into AI scoring agents before outreach

You export 1,000 leads from Sales Navigator. Before you email anyone, an AI agent reads each profile (job title, company description, recent job changes) and ranks them by ICP fit — automatically. The top 100 go into your high-touch sequence; the rest into nurture.

Why now: 2026 playbooks (public Notion docs, YouTube tutorials) show SDRs chaining Evaboot exports into Clay.com or Apify AI agents that score leads in bulk. This used to require custom code; now it's a template.

Example: Captain Data exports company employees → AI agent checks if their company recently raised a Series A (via Crunchbase API) → if yes, bump them to the top of the outreach queue.

4. GPT-powered personalization inside your cold email tool

Scrapers like Zopto and Lyne.ai now ship with built-in GPT integrations. You export a Sales Navigator list, and the tool auto-writes the first line of each email by feeding the prospect's LinkedIn headline + company name into GPT. No separate copy tool needed.

Why now: Cold email tools added native LLM personalization in 2025–2026 because buyers expect it. Before, you'd export from LinkedIn, enrich in Clay, write copy in Jasper, then import to Lemlist. Now it's one click inside the scraper.

Example flow: Zopto scrapes 200 VPs of Sales → GPT writes: "Hi [Name], noticed [Company] is hiring 3 SDRs this month — we help teams like yours ramp new reps 40% faster."

5. Build an AI agent that auto-refreshes your lead lists weekly

Set up a Make.com or n8n workflow that re-runs your Sales Navigator search every Monday, exports new leads via Evaboot, enriches them with an AI scoring agent, and auto-adds the top 10% to your CRM — all while you sleep. This is the "set it and forget it" prospecting machine.

Why now: Cloud scrapers now offer API access (Evaboot API, Phantombuster API), and no-code platforms have native LinkedIn + LLM + CRM connectors. The entire stack snaps together without writing code.

Example: Every Monday at 9am, n8n triggers Evaboot to export "VP Sales, SaaS, 50–200 employees, posted in last 7 days" → GPT-4 scores each lead → top 20 auto-emailed via Lemlist.

Where to learn more

Public playbooks for AI + LinkedIn scraper workflows are popping up on YouTube, Twitter, and Notion. Search for "n8n LinkedIn scraper AI agent," "Clay.com LinkedIn enrichment," or "GPT LinkedIn outreach" to find step-by-step templates. The tools are no longer the bottleneck — the workflow design is.

Frequently asked questions

Conclusion

With LinkedIn Scraping tools you can efficiently collect and organize valuable information and seamlessly export your lead lists into your CRM or marketing platforms. This automation not only saves time, but also ensures that you have accurate and up-to-date data to drive your marketing and outreach strategies.

With the right LinkedIn scraper, you can streamline your workflow, increase productivity, and ultimately see better results from your lead generation campaigns.