The Best LinkedIn Scrapers

We benchmarked four LinkedIn scraping APIs with 4,000 requests, sending each provider the same 1,000 LinkedIn post URLs. For every request we measured success rate, completion time, and the number of parsed metadata fields returned.

LinkedIn scraper API comparison

LinkedIn scraping benchmark

Read our methodology for details about the LinkedIn benchmark.

This chart compares the daily success rates of LinkedIn scraper APIs based on live tests conducted every 15 minutes:

What data you can scrape from LinkedIn

Bright Data was the only provider in LinkedIn scraping benchmark that returned structured JSON. The other three returned raw HTML, so there were no fields to count on their side. We collected every field Bright Data returned for a LinkedIn post and grouped them by category:

Posts are one of several LinkedIn page types you can collect. Profiles, company pages and job listings each have their own endpoint and their own field set:

• Profiles: name, position, about, experience, education, current_company, followers, connections, languages and honors_and_awards.
• Company pages: name, about, industries, specialties, company_size, employees_in_linkedin, headquarters, locations, website and alumni_information.
• Job listings: job_title, company_name, job_location, job_summary, job_seniority_level, job_employment_type, job_function, job_num_applicants, base_salary and apply_link.

LinkedIn scrapers

Bright Data offers ten scraper APIs for LinkedIn, covering four content types:

• People profiles: collect by URL, plus a people search that discovers profiles by first and last name.
• Company information: collect by URL.
• Job listings: collect by job URL, discover by keyword, or discover by company URL.
• Posts: collect by URL, or discover by company URL, profile URL, or article URL.

Bright Data also provides four ready-to-use datasets in its Dataset Marketplace:

• LinkedIn people profiles: for tracking talent movement, sourcing candidates, lead generation, and alternative investment research.
• LinkedIn company information: for competitive analysis, CRM enrichment, and ecosystem mapping.
• LinkedIn job listings: job titles, company names, locations, and application details, for job market analysis and recruitment strategy.
• LinkedIn posts: post text, hashtags, engagement metrics, images, and video, for content trend analysis and engagement research.

We benchmarked the “LinkedIn posts, collect by URL” API. Metadata is counted as structured JSON fields, so only vendors that return parsed data score on it. Bright Data returned 39 fields per post on average. The other four vendors return raw HTML and no parsed fields. Bright Data was also the fastest, completing in 7 seconds on average against 10 to 12 for the others. Its 70% success rate matched Oxylabs and Decodo.

Oxylabs was called through its Web Scraper API in real time mode, with the universal source and HTML rendering enabled. It returns the rendered page, so LinkedIn post data is extracted from the HTML with CSS selectors.

On LinkedIn, Oxylabs reached a 70% success rate and averaged 11 seconds per request.

For Decodo we used the Scraper API with headless rendering and the premium proxy pool. The response is the page itself rather than parsed fields, so the post data has to be pulled out of the markup. Decodo’s LinkedIn success rate was 70%, with requests averaging 12 seconds.

Nimble ran through its Web API with rendering turned on and HTML as the output format. Reading a post therefore means parsing the returned page. Nimble completed 64% of LinkedIn requests, averaging 10 seconds each.

Session-based LinkedIn automation tools

These tools drive your own LinkedIn account rather than running on their own scraping infrastructure. They suit low-volume, non-critical collection, especially if you already pay for the tool and would rather not add a separate scraping cost.

How they connect to your account differs:

• PhantomBuster reads the session cookie LinkedIn sets when you log in, then runs the automation from its own cloud.
• Linked Helper runs as a desktop app and drives the LinkedIn session already open on your machine.
• Dripify takes your LinkedIn credentials and runs from its cloud.

The trade-offs are the same in all three cases:

• Slow: because they emulate human behavior, they are slower than tools running on dedicated scraping infrastructure, and unsuited to large-scale extraction.
• Risky: if LinkedIn flags the activity, you face temporary restrictions or a permanent ban on the account you connected.

PhantomBuster offers a LinkedIn profile scraper and a company scraper for public data on the platform.

Features:

• Scheduled runs: Phantoms can be set to launch on a recurring basis, from once a day to several times within working hours.
• Browser extension: available for both Chrome and Firefox. The extension connects your LinkedIn session in one click; it is not where the automations run.
• Cloud-based: Phantoms execute on PhantomBuster’s servers, so collection does not tie up your own machine.

Two limits worth knowing before you pick it: the Profile Scraper pulls data through API calls rather than opening the page, so no profile view is registered and only the last two roles are returned. Endorsements are not collected.

Linked Helper is a desktop LinkedIn automation platform with scraping built in. Campaigns execute on your own computer. A cloud storage tier is also sold, where the data sits on Linked Helper’s servers while the automation still runs locally.

Features:

• Automated profile connecting: visits profiles and sends personalized invitations, with spintax, custom variables and if-then-else conditions.
• Data scraping: extracts from LinkedIn, Sales Navigator and Recruiter, with CSV export. Collecting a profile returns basic fields; visiting it returns job history, skills and the rest.
• Built-in CRM: a plug-in you enable, storing every profile and company the tool has touched, with tags and notes. Direct integrations cover HubSpot, Salesforce, Pipedrive and Zoho, and webhooks reach the rest through Zapier, Make or n8n.

Proxies are optional. Running your own account from your own computer needs none. A dedicated proxy per account is recommended once you move to a VPS, work across countries, or manage client accounts.

Dripify

Dripify automates LinkedIn outreach for sales teams, recruiters and founders. Its scraper collects lead data and exports it to CSV. The product has since broadened past LinkedIn into multichannel sequences, email automation and data enrichment.

Features:

• Local IP address: assigns your account a dedicated IP from your own region, so LinkedIn sees activity from a location consistent with where you actually are.
• Human behavior simulation: page views and button clicks with randomized delays between them, plus daily action limits you can tune.

Note that CSV export starts at the Pro plan and is not available on Basic.

Email-finder and extension tools

These run inside the browser and target email and lead capture rather than full-scale scraping. They suit small tasks and break when the browser or the page layout changes.

Snov.io

Snov.io is a lead generation and outreach platform whose Chrome extension automatically pulls email addresses from LinkedIn. It works on profile pages, on LinkedIn and Sales Navigator search results, and on event pages, one at a time or in bulk.

It is not a dedicated LinkedIn scraping tool. What it collects is email addresses, so it fits an organization that already has LinkedIn automation in place and needs the email layer.

Features:

• Email Finder: locates addresses from a name, company and domain. The Chrome extension pulls them from LinkedIn and from Google search results.
• Email Verifier: a seven-step check covering syntax, gibberish, domain, MX record, SMTP, mailbox existence and catch-all. Snov.io reports 98%+ accuracy, which is its own figure rather than an audited one. Verification costs one credit per address.

FindThatLead is a cloud-based B2B lead generation and email verification platform. Its Chrome extension extracts email addresses from LinkedIn profiles and from any website, drawing on your own FindThatLead credit balance.

Features:

• Email Finder and Verifier: returns the address along with name, role, phone and social profiles, plus company-level data such as location and technologies. A credit is spent only on a successful match.
• Email Sender and drip campaigns: sending consumes no email credits, though daily send volume and the number of sequences are capped by your plan.

Evaboot

Evaboot is a Chrome extension that exports lead data from LinkedIn Sales Navigator and finds emails for those leads. It runs on your own Sales Navigator session, so an active paid seat is required.

Features:

• Native Sales Navigator integration: exports names, job titles, company names, industries and locations, along with headline, summary, connection count and company size, across 39 columns.
• Data cleaning: normalizes names, job titles and company names, stripping emoji and honorifics.
• Lead filtering: checks whether each lead genuinely matches the Sales Navigator filters you searched with, and flags the ones that do not.
• Email finder and verifier: finds professional emails and checks deliverability.

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LinkedIn scraper benchmark methodology

We benchmarked four providers on 1,000 LinkedIn post URLs.

Each URL was requested once per provider, one URL per request. Requests were not batched, which keeps response time attributable to a single URL, and they ran sequentially from a single cloud server.

Responses were stored and scored in a separate pass rather than judged during the run. That keeps a parsing fix from requiring a fresh set of requests, and it means every number below can be re-derived from the saved responses.

A response counts as successful when it carries the post’s data. HTTP 404 counts as a success, since the page is gone and the provider reported that correctly. Any status outside 200 to 399 is a failure. Several APIs answer 200 while the page they fetched returned 403, so we read that inner status separately and apply the same rule to it.

For providers returning JSON, at least two of the provider’s own documented fields must arrive with a value of the expected type. Two rather than one, because a provider echoing back the input URL would otherwise pass on a single field. For providers returning HTML, at least one of the CSS selectors written for LinkedIn posts must match. Selectors were written and verified against captured responses before the run.

Deleted posts are excluded from provider scores. Providers phrase a dead link differently: most return LinkedIn’s own notice, others report an internal crawl error. Once two or more providers independently report a URL as unavailable, the remaining providers are not penalized for returning no content from it.

Metadata is counted as structured JSON fields, so the count reflects what a provider returns already parsed. Providers that return raw HTML leave the parsing to you and are not scored on this measure.

Completion time is the average across valid responses only. A provider that fails quickly should not read as a fast provider.

FAQs

Cite this benchmark

Pick the format that matches where you're publishing. Pasting the link version into your CMS preserves the backlink.

@misc{dogan2026,
  author = {Dogan, Sedat and Şipi, Nazlı},
  title  = {{The Best LinkedIn Scrapers}},
  year   = {2026},
  month  = sep,
  howpublished    = {\url{https://aimultiple.com/linkedin-scrapers}},
  note   = {AIMultiple. Retrieved September 2, 2026}
}

Changelog

18 updates

All18General14Methodology4

1. 2026Sep 2

   Added a "What data you can scrape from LinkedIn" section detailing fields by page type.

2. Sep 2

   Replaced the LinkedIn scraper benchmark methodology with a four-provider test on 1,000 post URLs.

3. Jun 10

   Removed the Proxycurl entry from the proxy-based LinkedIn scrapers section.

4. Feb 9

   Added a section comparing Apify and Bright Data to the introduction.

5. 2025Nov 25

   Added a chart to the "LinkedIn scraper APIs benchmark results" section.

6. Oct 24

   Added Evaboot and Python code examples to the article.

7. Aug 4

   Added NetNut B2B Data Scraper API to the list of LinkedIn scrapers.

8. Apr 24

   Updated the 'Best LinkedIn scraping tools' section.

9. Apr 2

   Added Apify as a product in the Pricing for the top LinkedIn scraping tools section.

10. Feb 27

    Removed the Benchmark Methodology section.

11. Feb 17

    Removed Zyte from the list of LinkedIn scraping tools.

12. Jan 9

    Added Zyte to the list of LinkedIn scraping tools.

13. Jan 2

    Added three business use cases of LinkedIn data to the article.

14. 2024Dec 12

    Removed the section "6 best LinkedIn scraping tools".

15. Oct 10

    Removed the Benchmark Methodology section.

16. Oct 10

    Added a Benchmark Methodology section.

17. Sep 23

    Updated the description of Bright Data in the summary table.

18. Sep 23

    Expanded the methodology section with details on benchmarked scrapers and test parameters.

Sedat Dogan

CTO

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Sedat is a technology and information security leader with 20 years of experience in software development, network infrastructure and cybersecurity. Sedat:
- Has 20 years of experience as a white-hat hacker and development guru, with extensive expertise in programming languages and server architectures.
- Is a board advisor at a VC investing in early-stage technology firms and at Ödeal, a regional digital payment platform serving 125,000 merchants.
- Has led the technology infrastructure and cybersecurity of seven national elections, and has been recognized in the cybersecurity Hall of Fame by global technology leaders including Twitter.

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Researched by

Nazlı Şipi

AI Researcher

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Nazlı is a data analyst at AIMultiple. She has prior experience in data analysis across various industries, where she worked on transforming complex datasets into actionable insights.

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