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 scraping benchmark

Read our methodology for details about the LinkedIn benchmark.

• Success rate: Bright Data, Oxylabs and Decodo each completed 70% of the 1,000 LinkedIn post requests, so success rate does not separate the top three. Nimble completed 64%.
• Speed: Bright Data averaged 7 seconds per request, the fastest of the four providers. Nimble averaged 10 seconds, Oxylabs 11 and Decodo 12.
• Parsed data: Bright Data was the only provider that returned structured JSON, with 39 metadata fields per LinkedIn post. Oxylabs, Decodo and Nimble returned raw HTML.

The chart below shows the daily success rate of each LinkedIn scraper API, measured by live tests that run every 15 minutes:

LinkedIn scraper API comparison

What data you can scrape from LinkedIn

Bright Data was the only provider in our LinkedIn scraping benchmark that returned structured JSON. Oxylabs, Decodo and Nimble returned raw HTML, so they had no parsed fields to count. The table below groups every field Bright Data returned for a LinkedIn post by category:

Besides posts, Bright Data’s LinkedIn scraper APIs collect profiles, company pages and job listings. Each page type has its own endpoint and field set. Example fields:

• 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 scraper APIs

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 Bright Data’s “LinkedIn posts, collect by URL” API. The API returned 39 parsed JSON fields per LinkedIn post on average, while Oxylabs, Decodo and Nimble returned raw HTML with no parsed fields. Bright Data averaged 7 seconds per request, the fastest in the benchmark, against 10 to 12 seconds for the other three providers. Its 70% success rate matched Oxylabs and Decodo.

We called Oxylabs through its Web Scraper API in Realtime mode, with the universal source and HTML rendering enabled. The API returns the rendered HTML page without parsed fields, so LinkedIn post data has to be extracted 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

PhantomBuster, Linked Helper and Dripify run on your own LinkedIn account instead of on dedicated scraping infrastructure. These tools suit low-volume, non-critical collection, especially if you already pay for one and want to avoid a separate scraping cost.

Each tool connects to your LinkedIn account differently:

• 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.

All three tools share two trade-offs:

• Slow: session-based tools emulate human behavior, so they run slower than tools on dedicated scraping infrastructure and do not suit large-scale extraction.
• Account risk: if LinkedIn flags the activity, the connected account can face temporary restrictions or a permanent ban.

PhantomBuster is a cloud automation platform whose LinkedIn Profile Scraper and LinkedIn Company Scraper collect public data from LinkedIn profiles and company pages. PhantomBuster calls each automation a Phantom.

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.

PhantomBuster’s Profile Scraper pulls data through API calls instead of opening the profile page. As a result, LinkedIn registers no profile view, and the scraper returns only the last two roles in a profile’s work history. The Profile Scraper does not collect endorsements.

Linked Helper is a desktop LinkedIn automation platform with scraping built in. Linked Helper campaigns run on your own computer. A paid cloud storage tier keeps the data on Linked Helper’s servers, while the automation still runs on your computer.

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 Linked Helper has processed, with tags and notes. Direct integrations cover HubSpot, Salesforce, Pipedrive and Zoho, and webhooks reach the rest through Zapier, Make or n8n.

Linked Helper needs no proxy when you run your own LinkedIn account from your own computer. Linked Helper recommends a dedicated proxy per account once you move to a VPS, work across countries or manage client accounts.

Dripify

Dripify is a cloud-based LinkedIn outreach automation tool for sales teams, recruiters and founders. Dripify’s scraper collects lead data and exports it to CSV. Beyond LinkedIn, Dripify also offers 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.

Dripify’s CSV export is available from the Pro plan up. The Basic plan does not include it.

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Email-finder and extension tools

Snov.io, FindThatLead and Evaboot run inside the browser and collect email addresses and lead data instead of full LinkedIn records. These tools suit small lead lists, and a browser update or a LinkedIn layout change can stop them from working.

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.

Snov.io collects only email addresses from LinkedIn, so it fits teams that already run LinkedIn automation and need to add emails to their leads.

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 claims 98%+ accuracy. This is the vendor’s own figure and has not been independently audited.

FindThatLead is a cloud-based B2B lead generation and email verification platform. The FindThatLead Chrome extension extracts email addresses from LinkedIn profiles and other websites, and lookups draw on your 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 uses no email credits, but your plan caps daily send volume and the number of sequences.

Evaboot

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

Features:

• Native Sales Navigator integration: exports 39 columns per lead, including names, job titles, company names, industries, locations, headline, summary, connection count and company size.
• 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.

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.

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

202620252024

1. Sep 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.

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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