Best Open-Source LinkedIn Scrapers (2026)

Open-source scrapers are free, hackable, and great for developers, but two things bite people: most public listicles are out of date, and the riskiest projects are the ones that log in as you. This page fixes the first problem with live GitHub data and is honest about the second.

↑ Live data from the GitHub API, refreshed automatically. ⚠ = no commits in over a year.

How to read this table

• Stars signal popularity, not quality, but a healthy star count usually means more eyes on bugs.
• Last commit is the one most listicles hide. A repo that hasn’t been touched in over a year (⚠) will likely break the next time LinkedIn changes its markup.
• License matters if you’re building a product. Copyleft licenses (GPL/AGPL) carry obligations; MIT is the most permissive.

The table above is generated from the GitHub API at build time, so it reflects reality rather than whenever someone last edited a blog post.

The trade-off no one mentions: your account

Most open-source profile scrapers work by driving a browser logged in as you. That’s the highest account-ban risk pattern there is. The job-focused scrapers are safer because they target public listings without a login.

• Safer (public, no login): JobSpy, py-linkedin-jobs-scraper.
• Higher risk (uses your login): joeyism/linkedin_scraper, drissbri/linkedin-scraper, pratik-dani/LinkedIn-Scraper.

If protecting your account matters, read how to avoid a ban, or skip self-hosting entirely and use a managed API like Bright Data that pulls public data without your account.

When open source is (and isn’t) the right call

Choose open source when you’re a developer, you want $0 cost, you need full control, and you can maintain the code as LinkedIn changes.

Choose a managed tool when you can’t risk your account, you need reliability and scale, or you’d rather not babysit a Selenium script. Compare both sides in the directory, or let the finder pick for you.