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How to Scrape LinkedIn & 8 Best LinkedIn Scrapers in 2024

Updated on Apr 5
8 min read
Written by
Gulbahar Karatas
Gulbahar Karatas
Gulbahar Karatas
Gülbahar is an AIMultiple industry analyst focused on web data collection, applications of web data and application security.

She is a frequent user of the products that she researches. For example, she is part of AIMultiple's web data benchmark team that has been annually measuring the performance of top 9 web data infrastructure providers.

She previously worked as a marketer in U.S. Commercial Service.

Gülbahar has a Bachelor's degree in Business Administration and Management.
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LinkedIn scraping involves using automated tools or scripts to extract valuable data such as user profiles, job listings, and company information from the LinkedIn platform. The extracted data can be used for various purposes, including lead generation, talent sourcing, and competitor analysis.

LinkedIn scrapers are specialized tools designed to automate the data extraction process and enhance its efficiency. There are numerous LinkedIn scrapers available, catering to diverse needs and skill levels.

This article explains the different techniques for scraping LinkedIn, best practices for efficient data extraction, and best LinkedIn scrapers. This information will help you make an informed decision in choosing the right LinkedIn scraping tool that aligns with your specific requirements.

The best LinkedIn scraping tools of 2024

The list is sorted in an ascending order based on the lowest price level, with the exception of the products of the article’s sponsors which are linked to sponsor websites.

LinkedIn scrapersTypePricingFree trial
Bright DataProxy-based$500
NetNutProxy-basedCustom offering
Linked HelperCookie-based$15
Meet AlfredCookie-based$59

* The prices listed in the comparison table are derived from the basic package offered by each vendor.

Types of LinkedIn scraping tools

1. Proxy-based LinkedIn scrapers

Web scraping tools such as Bright Data has their own proxy infrastructure including IPs and servers. They use proxy servers to access LinkedIn in order to extract data, distributing requests across several IP addresses or LinkedIn profiles.

This is the right approach for high volume, reliable LinkedIn scraping because:

  • Fast: Since the scraper relies on multiple profiles while scraping the data, it can scrape faster
  • Reliable: If a profile or IP gets banned by the target website, the provider switches to another one to continue its operations.
  • Safe: The person ordering the scraping doesn’t need to their individual account and therefore there is no risk of your profile being banned.

Cookie-based tools such as PhantomBuster use your browser cookie to extract data.1 They are used for low-volume, non-critical data collection especially if users are already a customer of these automation tools and will not incur additional costs to use them.

PhantomBuster, like many other automation tools, needs to “act” as you to perform tasks on social networks on your behalf:

  • When you are logged into LinkedIn, the website sets a session cookie in your browser (which is unique to your session).
  • You need to pass this cookie to the LinkedIn scraper.
  • Then, the scraper leverages your session cookie from the social network to collect data, make connection requests and collect data. You can automate personalized tasks on LinkedIn, like sending connection requests and liking posts.

This approach is:

  • Slow: Since it emulates human behavior, the scraping activities are slower compared to tools that use their own infrastructure. They are not suitable for large-scale data extraction tasks.
  • Risky: If LinkedIn detects suspicious activity, you could face temporary restrictions or a permanent ban from LinkedIn.

3. Browser-extension LinkedIn scrapers

Browser-extension tools work directly within the browser. They can be activated while you’re browsing LinkedIn. These tools are ideal for smaller scraping tasks. The risk of using browser extension scrapers is dependence on browser. If the browser updates or changes, the extension tools can break.

What is a LinkedIn scraper?

A LinkedIn scraping tool is software or script that can directly access LinkedIn using a web browser. LinkedIn scrapers are designed to crawl public LinkedIn profiles and extract data from the platform, including names, job titles, company names, industries, and skills.

8 best LinkedIn scraping tools

With different LinkedIn scraping tools available in the market, selecting the right one can be a challenging task. Each tool offers a unique combination of features, and pricing to cater to different needs. We provide a comprehensive overview of the top 6 LinkedIn scraping tools, outlining their key features and advantages.

1. Bright Data

Bright Data is a data collection platform that provides businesses and individuals with various web scraping and proxy services. They have a dedicated web scraper for LinkedIn that extracts and parses LinkedIn public data.


  • Pre-made web scraper templates: Pre-made web scraper templates enable users to adapt existing code to their specific requirements and applications. They eliminate the requirement to write web scraper code from scratch. You can tailor LinkedIn scraper to target specific data points.
  • Built-in debug tools: Built-in debug tools help developers identify errors in a past crawl and fix them in their code.
  • Browser scripting in JavaScript: Allow users to handle scripts and parsing codes that execute within a web browser with simple procedural JavaScript.
  • Interactive preview: You can monitor your code as you build it, enabling users identify and debug errors early in the development process.
  • Geo-location emulation: Emulate users in different geo-locations with built-in fingerprinting and CAPTCHA solving. It helps users minimize the risk of being blocked and overcome geo-restrictions.


  • Starting price: $500/mo
  • Trial: Available

2. Nimble

Nimble is a web data gathering platform providing various scraping APIs tailored for specific purposes such as SERP, E-commerce, Maps, and a general Web API. These APIs come equipped with integrated residential proxies, both dedicated and rotating. The Web API, in particular, offers tools like page interactions and parsing templates. This functionality is especially beneficial for handling sites in niches like E-commerce, SERP, and Maps, which may not be as easily accessible with other APIs.

Nimble API offers three modes of data delivery:

  1. Real-time: Data is gathered and instantly sent back to the user.
  2. Cloud: The gathered data is transferred to a cloud storage option of the user’s choice.
  3. Push/Pull: The data collected is held on Nimble’s servers, and users can access it through a given URL for downloading.


  • Batch processing: Allows users to submit a large number of URLs in a single request, handling up to 1,000 URLs.
  • Built-in proxies: Every request executed by Nimble APIs is processed via a proxy supplied by Nimble IP.
  • Custom parsing templates: Comes with integrated support for tables, JSON output, and custom objects. It is important to keep track of any alterations in the structure of the source webpage, as these changes can impact the effectiveness of parsing templates. Nimble does not handle the updating of these custom parsing templates.
  • Page interactions: Enables users to execute various actions on a webpage while collecting data. These actions include clicking, typing, and scrolling. Page Interactions operate synchronously, meaning that these operations are executed in a sequential order, one after another with a maximum time limit of 60 seconds.


  • Starting price: $500/mo
  • Trial: Available

3. NetNut

NetNut provides an API for social media scraping, enabling users to extract real-time and requested data from platforms like LinkedIn. This social scraping tool is compatible with proxies and features automated proxy rotation to optimize data collection.


  • Live and on-demand data scraping: Supports real-time and scheduled data extraction features.
  • Automatic IP rotation: Rotates proxies per each session by default.

4. Dripify

Dripify is a LinkedIn automation tool that helps sales professionals automate various tasks on LinkedIn. They provide a LinkedIn scraper that enables users to access lead data available on LinkedIn and export the collected data to a CSV file.


  • Local IP-address: Provides unique IP address from users local region, enabling users to access websites as if they were located in different geographical regions.
  • Human behavior simulation: Imitates the actions of a real user when interacting with LinkedIn (Figure 6). It adds random time delays between requests and simulates user clicks on links or buttons to help you appear more like a genuine user.  

Figure 6: Methods for simulating human behavior while navigating LinkedIn platform


  • Starting price: $59/user/mo
  • Trial: Available


Lyne’s LinkedIn scraping tool enables sales and marketing teams to extract prospect data from LinkedIn Sales Navigator search and scrape LinkedIn search results.


  • Chrome Extension: Offers users to use the LinkedIn scraper as a Chrome extension. You can automatically scrape public data from LinkedIn profiles.
  • CSV export: Export scraped data in CSV format.
  • Email Validation: Validates whether an email address is valid and properly formatted.
  • CRM Sync: Synchronizes data between a CRM platform and LinkedIn scraper.


  • Starting price: $39/mo
  • Trial: Available

6. PhantomBuster

PhantomBuster offers a LinkedIn profile scraper and a company scraper to scrape public data from the platform.


  • Updated LinkedIn data: You can set up the LinkedIn scraping tool to launch repeatedly to extract data daily.
  • Firefox and Chrome extension: The linkedIn data scraper is available as extension.
  • Cloud-based: Runs on the remote servers, allowing users extract data LinkedIn without using local resources.


  • Starting price: $59/mo
  • Trial: Available for 14 days

7. Meet Alfred

Meet Alfred is a LinkedIn automation platform that provides a LinkedIn scraper to extract data from user and company profiles.


  • CRM integration: Connecting your CRM platform with the LinkedIn scraper, allowing users to update data between connected systems automatically. 
  • CSV format: Extract data from LinkedIn Sales Navigator, people or company profiles and download it as a CSV file.


  • Starting price: $59/mo
  • Trial: Available

8. Linked Helper

Linked Helper is a LinkedIn automation tool that allows sales teams to scrape LinkedIn automatically to streamline lead generation and LinkedIn outreach.


Scrape data from the linkedIn and sales navigator accounts, including emails and message history, and export it to CSV or send it to a 3rd party service.


  • Starting price: $15/mo
  • Trial: 14-day free trial

Which LinkedIn data can you scrape?

Social media web scraping can pose privacy concerns, you should

  • avoid collecting private or sensitive data, such as professional email addresses and phone numbers.
  • adhere to ethical practices and examine the target site’s terms of service.

That being said, the following are some examples of data that are technically possible to be scraped from LinkedIn:

  1. LinkedIn profile data: Using a specific account name or URL, you can extract public data from LinkedIn profile URLs, such as names, headlines, profile pictures, and location (Figure 1).

Figure 1: The output of a scraped LinkedIn profile account by a URL

Bright Data's LinkedIn Scraper help businesses and individuals extract publicly available data from LinkedIn.
Source: Bright Data
  1. LinkedIn job listings: LinkedIn job listings include information such as job description, required qualifications, responsibilities and title (Figure 2).

Figure 2: Sample output of a scraped LinkedIn job listings using a LinkedIn scraping tool

Bright Data's LinkedIn Scraper enable users to scrape LinkedIn job listings.
Source: Bright Data
  1. LinkedIn posts: LinkedIn scrapers allow users to extract text and image data from posts, including the owner’s URL, publication date, and comments (Figure 3). Scraped post data can be used for lead generation, brand sentiment, and market research.

Figure 3: The output of a scraped LinkedIn post by a LinkedIn scraping tool

Bright Data's LinkedIn Scraper helps users to extract data from LinkedIn posts by a URL or query.
Source: Bright Data
  1. LinkedIn search results: LinkedIn search results include information about companies or profiles (Figure 4). You can scrape LinkedIn search results using keywords or URLs, and the platform will return results based on the keywords or filters you apply.

Figure 4: Sample output of a scraped LinkedIn search result

Source: Bright Data
  1. LinkedIn group: Extract public LinkedIn group data such as the number of members, the content of a discussion or question posted in the group, and group members’ names and profile URLs.

How does a LinkedIn scraper work?

LinkedIn scraping mainly involves two steps: fetching the public profile web page and extracting data from it. However, it is important to note that using a LinkedIn scraper might violate LinkedIn’s terms of service. Consider the ethical and legal concerns before scraping data from LinkedIn. Here’s a general overview of how a LinkedIn scraper data from LinkedIn profiles:

  1. The LinkedIn scraper requires a list of target LinkedIn profile URLs or specific search queries like job title to begin the scraping process.
  2. The scraper sends HTTP requests to the target URLs.
  3. The LinkedIn data scraper crawl web pages to fetch HTML content of the target pages for later processing. Fetching is the process of downloading of a page.
  4. After retrieving the HTML content, the LinkedIn data extractor parses the content to identify relevant data points. To parse HTML and XML documents, you can use browser automation tools such as Beautiful Soup or lxml. They built a parse tree for parsed web pages, allowing users to extract data from HTML.
  5. After locating the relevant elements, the scraper extracts the desired data.
  6. The scraped data may require cleaning and structuring to remove irrelevant information.
  7. The scraped data is then saved in a preferred format, such as Excel, JSON or CSV format.

Legality of scraping LinkedIn data may vary depending on the specific circumstances and your jurisdiction. To clarify any uncertainties, consult with legal counsel to understand the laws and regulations relevant to your jurisdiction. Scraping personally identifiable information or exposing the scraped data to the public are likely to be illegal in most jurisdictions.

hiQ Labs, a data analytics company, used automated bots to scrape publicly available information from LinkedIn profiles in 2019. LinkedIn sued hiQ Labs, stating that the company’s scraping activities violated both the Computer Fraud and Abuse Act (CFAA) and LinkedIn’s terms of service.2 The Ninth Circuit ruled in favor of hiQ Labs, stating that the company’s scraping activities did not violate the CFAA because the data was publicly available. However, hiQ Labs was observed to have breached LinkedIn’s terms by scraping and creation of fake accounts. Before this case could set precedent with a final ruling, the parties reached a settlement so the issue is not yet resolved.3

Here are some best practices for scraping data from LinkedIn in compliance with LinkedIn’s terms of service:

  1. Consult your legal advisor.
  2. Review LinkedIn terms of service.
  3. Adhere to the rules outlined in the robots.txt (Figure 5).

Figure 5: LinkedIn’s robots.txt file

Before scraping data from LinkedIn, make sure to check the robots.txt file.
Source: LinkedIn4
  1. Stick to data that is publicly available. Avoid scraping personal, sensitive, or private LinkedIn data.
  2. Use extracted data responsibly and ethically.
  3. Or use LinkedIn APIs to access data legally.

Transparency statement:

AIMultiple works with many B2B tech companies. AIMultiple’s sponsors are provided links within the main body of AIMultiple’s articles.

More on social media scraping

Check out our data-driven list of web scrapers for help choosing the right tool, and get in touch with us:

Find the Right Vendors
Gulbahar Karatas
Gülbahar is an AIMultiple industry analyst focused on web data collection, applications of web data and application security. She is a frequent user of the products that she researches. For example, she is part of AIMultiple's web data benchmark team that has been annually measuring the performance of top 9 web data infrastructure providers. She previously worked as a marketer in U.S. Commercial Service. Gülbahar has a Bachelor's degree in Business Administration and Management.

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