10 Best LinkedIn Profile Scrapers in 2026 [Don't Miss]
TL;DR
A "LinkedIn profile scraper" is one of three product types, not one category. Browser-automation tools (PhantomBuster, Waalaxy) drive a logged-in session; developer APIs (Apify actors, general crawling infrastructure) fetch and parse public pages programmatically; enrichment platforms (Apollo.io, Lusha, Coresignal) sell pre-collected contact and company records instead of scraping live.
Automated LinkedIn scraping violates LinkedIn's User Agreement, and that risk did not go away after hiQ Labs v. LinkedIn. The case ended in a November 2022 settlement after a district court found hiQ had breached LinkedIn's terms — account bans and contract claims remain a real exposure even though the earlier CFAA "hacking" theory was narrowed in scrapers' favor.
Proxycurl, a well-known LinkedIn data API, shut down in 2025. Any roundup still recommending it is out of date; this list excludes it and points to currently operating alternatives instead.
Nstdata Crawl ranks first here as infrastructure, not as a purpose-built LinkedIn parser. It supplies the fetching, JavaScript rendering, and proxy layer at pay-per-successful-fetch pricing, but a team still writes its own profile field-extraction logic on top — unlike the other nine tools, which ship that parsing already built.
Nine other tools cover the rest of the market: PhantomBuster and Waalaxy for browser-driven automation, Apify for a pay-per-result developer API, Evaboot and Skrapp.io for Sales Navigator export and email finding, TexAu and Apollo.io for multi-source enrichment platforms, and Lusha and Coresignal for verified contact or bulk workforce datasets.
Billing model varies by category more than by vendor. Automation tools mostly charge monthly subscriptions gated by execution time or seats; API and enrichment tools mostly charge per credit or per successfully matched record, with a metered free tier in most cases.
Recruiting, sales prospecting, and market research all draw on the same public-profile data, just filtered differently. Recruiters and sourcers need structured work history and skills; sales teams need verified contact details layered on top of a profile; researchers need aggregate counts across many companies rather than individual contact info.
Automated scraping of LinkedIn violates LinkedIn's own User Agreement, which prohibits using bots, scrapers, or other automated methods to access or copy any part of the platform — that contractual restriction applies regardless of whether the data scraped is publicly visible. The clearest evidence of that risk is the litigation history in hiQ Labs v. LinkedIn, the case most roundups cite as settled law in favor of scrapers when it is not fully settled that way.
The Ninth Circuit ruled in 2019, and again in April 2022 after the Supreme Court sent the case back for reconsideration under its Van Buren decision, that accessing publicly available LinkedIn profiles does not amount to "hacking" under the Computer Fraud and Abuse Act. That narrowed one legal theory against scrapers. But in November 2022, a district court separately found that hiQ had breached LinkedIn's User Agreement by scraping it after being sent a cease-and-desist, and the two companies settled rather than litigate that contract claim further. The practical result: the CFAA no longer criminalizes scraping public data on its own, but LinkedIn's terms still prohibit it, and LinkedIn can still enforce that prohibition through account suspension, IP blocking, and a breach-of-contract claim.
For any commercial use of LinkedIn data, three things follow from that history. First, treat scraping LinkedIn directly (versus using data that a third-party enrichment vendor already collected and licenses to you) as a ToS violation with real account-ban risk, not a solved legal question. Second, apply GDPR or CCPA-consistent data handling to anything that identifies a named individual — a documented lawful basis, a defined retention period, and a way to honor deletion requests — since scraped profile data is personal data under both frameworks regardless of how it was obtained. Third, get a legal review before any commercial deployment that scrapes LinkedIn at volume; the analysis above is a summary of public case history, not legal advice for a specific use case.
What is a LinkedIn profile scraper?
A LinkedIn profile scraper is any tool that extracts structured data — name, headline, work history, education, skills — from LinkedIn profile pages, either by automating a browser session or by fetching and parsing the page programmatically. The category splits into three working shapes. Browser-automation tools log in as a real member and click, scroll, and export the way a person would, which is why LinkedIn's own detection systems treat unusual session behavior (rapid profile views, non-human click timing) as the trigger for a ban rather than the mere presence of public data. Developer-facing scraping APIs and general crawling infrastructure fetch pages without a persistent logged-in session, returning raw HTML or structured JSON that the caller parses into fields. Enrichment platforms take a third path entirely: they maintain a pre-collected database of profile and company records (sourced from public web crawling, licensed data partners, or user-contributed contact networks) and match a query against that database instead of visiting LinkedIn live for every request.
How these tools were evaluated
Every entry below was checked against its own product page for six things: what data fields it actually returns, whether it scopes itself to public profile data only, what export or integration format it supports, how it behaves at scale (rate limits, ban risk, reliability), how it frames compliance, and its billing model.
Data fields covered — whether the tool returns full work history and skills or only contact details layered on top of a profile.
Public-data scope — whether the product's own materials describe it as working from public pages versus requiring a logged-in session against connections-only content.
Export and integration format — CSV, JSON/API, or a CRM-native integration.
Reliability at scale — how the vendor itself describes handling rate limits, retries, and anti-bot/rate-limit challenges from the target site.
Compliance posture — whether the vendor states a public-data-only scope, GDPR/CCPA language, or a data-sourcing method it can defend.
Billing model — subscription, per-record credit, or pay-per-successful-fetch, described here without specific dollar figures since rates change.
Build your own public-data pipeline
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1. Nstdata Crawl: Best for a custom, compliant scraping pipeline
Nstdata Crawl is an AI-oriented web crawling API, not a purpose-built LinkedIn scraper — that distinction matters more here than in most categories on this list. The Nstdata Crawl product page describes it as turning any URL into Markdown, cleaned HTML, raw page data, a screenshot, or a PDF through a single REST call, bundling JavaScript rendering, Nstdata's own proxy-backed fetching, automatic retries, and both single-page and site-level crawling behind one API with official Node.js, Python, and Go SDKs. Because it has no LinkedIn-specific parsing built in, a team pointing it at public LinkedIn pages still has to write the logic that turns rendered HTML into named fields like job title or company — the same work a purpose-built tool like Apify's LinkedIn actor already did. That tradeoff is the honest reason it ranks first as infrastructure rather than as the easiest LinkedIn-specific option: teams that already run other scraping targets get one fetching layer for all of them instead of a LinkedIn-only tool sitting next to a separate general-purpose scraper.
Format flexibility — a single request can return Markdown for LLM ingestion, raw HTML for custom parsing, or a screenshot for visual verification, so one integration serves both a data pipeline and a QA process.
Proxy-backed rendering with anti-bot/rate-limit handling — JavaScript rendering and Nstdata's own proxy pool (the same underlying Residential Lite Proxies network Nstdata sells standalone) are included in the base request, so a fetch against a JavaScript-heavy page or one guarded by rate-limit challenges doesn't need a separate headless-browser stack.
Pay-per-successful-fetch billing — a request is billable once a response comes back with no network error, including 404 and 403 responses, and only a system-side failure to retrieve content goes unbilled; bandwidth is metered separately.
Site-level crawling with explicit boundaries — maxDepth, maxPages, and include/exclude URL rules are required inputs for a site crawl rather than optional extras, which keeps a crawl job from wandering into search, pagination, or login URLs by accident.
2. PhantomBuster: Best for no-code LinkedIn action sequences
PhantomBuster is a browser-automation platform that runs pre-built "Phantoms" — scripted sequences that visit LinkedIn pages using a member's own logged-in session cookie and export what the session can see. It covers profile visits, connection-request sending, and post/comment scraping through a no-code sequence builder rather than a written script, which is why it shows up in nearly every LinkedIn tooling roundup as the default browser-automation option. Because each Phantom runs against one member's real session, LinkedIn's own rate limits apply directly: pushing volume too fast is the most commonly cited cause of temporary restrictions in independent PhantomBuster reviews, not a flaw specific to the tool. It bills on a subscription tied to execution time rather than per record, so cost scales with how long sequences run rather than how many profiles they touch.
3. Apify LinkedIn Profile Scraper: Best for a pay-per-result developer API
Apify's community-built LinkedIn Profile Scraper actor extracts a defined set of public profile fields — name, headline, location, work history, education, skills, certifications, and recommendations — in bulk without requiring a LinkedIn login cookie. It runs on Apify's actor platform at a flat pay-per-usage rate of roughly $10 per 1,000 results, with a free tier capped at ten runs a day and ten profiles per run through the UI only; unlimited API access and mobile-number lookup require a paid plan. Because it operates without session cookies, it avoids the single-account rate-limit ceiling that browser-automation tools run into, but it also means the actor is doing its own fetching and parsing independent of the member using it, so its continued reliability depends on the actor's maintainer keeping pace with LinkedIn's page structure.
4. Evaboot: Best for cleaning and exporting Sales Navigator search results
Evaboot is a Chrome extension built specifically to export LinkedIn Sales Navigator search results into a clean CSV, rather than to scrape individual profiles on demand. It runs a cleaning pass on the exported list — normalizing capitalization, stripping legal suffixes from company names, removing emoji — and filters out entries that fail basic quality checks before the list ever reaches a spreadsheet, then finds and verifies a professional email address for each remaining contact. Evaboot bills through a credits system and positions itself against both traditional LinkedIn scrapers and contact databases like ZoomInfo on the strength of that cleaning step, citing over 3,500 client organizations. It only works from an active Sales Navigator search, so it fits a list-building workflow more than a single-profile lookup.
5. Waalaxy: Best for solo founders and small teams running LinkedIn outreach campaigns
Waalaxy is a LinkedIn and email outreach automation platform built around campaign sequences rather than raw data export. It automates connection requests and follow-up messages across LinkedIn and email, includes an email finder, and syncs prospect lists into a CRM like HubSpot or Pipedrive, or into 2,000+ other apps through Zapier or Make. The vendor pitches it at users who have never run LinkedIn outreach before, emphasizing setup speed over configurability, with more than 200,000 users and a 14-day free trial ahead of its subscription plans. Because sequences act on the member's own session the same way PhantomBuster's do, the same session-based rate-limit exposure applies, and Waalaxy is closer to an outreach tool that happens to extract profile data than a scraper built for bulk export.
6. TexAu: Best for waterfall enrichment across many data providers
TexAu is a go-to-market automation platform that sources leads from LinkedIn Sales Navigator through its UI, then enriches, scores, and pushes them into a CRM or outbound tool — or exposes the same pipeline through a REST API and an MCP server for developers wiring it into Claude, Cursor, or a custom agent. Its defining feature is a "waterfall" enrichment model: a single lookup cascades across multiple underlying data providers (including Apollo, Hunter, Snov, and RocketReach among its 32 integrations) until one returns a match, and TexAu only charges the credit if a match actually comes back. New accounts start with 50 free credits, and there's no per-seat licensing or annual commitment required to keep using it. That waterfall design makes match rates less dependent on any single provider's coverage, at the cost of being one step further removed from a live LinkedIn fetch than a direct scraper.
7. Apollo.io: Best for prospecting and enrichment in one platform
Apollo.io is a sales intelligence platform built around a maintained database of more than 240 million contacts and 30 million companies, rather than a tool that scrapes an individual LinkedIn profile on request. It layers 65+ data points per lead — firmographic, demographic, and intent signals — on top of contact records, refreshing roughly 150 million contacts and verifying about 72 million emails a month, and embeds directly into a Chrome extension, Salesforce, and AI assistants like ChatGPT and Claude so reps can enrich a lead without leaving their existing workflow. Apollo bills through monthly or annual subscription plans with a free tier to start. Because it works from its own pre-built database rather than a live LinkedIn session, it's a fit for teams that want prospecting and enrichment together rather than teams that specifically need a fresh scrape of a named profile.
8. Lusha: Best for verified phone and email enrichment
Lusha is a B2B contact-intelligence platform that supplies verified phone numbers and email addresses for people and companies, positioned to fill in the details a LinkedIn profile itself doesn't publish. It reports 98% email accuracy and 86% phone accuracy across a database of 290 million-plus contacts, offers a Chrome extension that customers describe as integrating directly into a LinkedIn browsing workflow, and carries SOC 2 Type II certification alongside explicit GDPR and CCPA compliance messaging. Billing runs through tiered plans with a free-to-start option and no card required for the entry tier. Lusha is an enrichment layer next to LinkedIn browsing rather than a scraper that exports profile fields at scale, which is exactly the audience it targets.
9. Coresignal: Best for bulk workforce and company datasets
Coresignal sells structured company, employee, and jobs data as flat-file datasets or a queryable API, covering more than 70 million company profiles, 907 million professional profiles, and 475 million deduplicated job postings, aggregated from public web sources rather than scraped from LinkedIn exclusively. The company states its data comes from "only publicly available, strictly business-related" sources and that it does not collect data from behind login walls. Access is priced either as a batch flat-file purchase or through API queries, with a self-service dashboard offering up to 100 free preview records. Coresignal fits market research, workforce analytics, and AI-agent data feeds that need broad coverage across many companies at once far more than it fits looking up one named individual's current LinkedIn profile.
10. Skrapp.io: Best for a lightweight LinkedIn email finder
Skrapp.io is a Chrome extension pair — a LinkedIn Email Finder and a Sales Navigator Email Finder — built to turn a profile or a Sales Navigator list into a verified business email address in one click, backed by a database the vendor says covers more than 200 million contacts and 40 million companies at a claimed 97% verification accuracy. It bills on a credit system, with a free tier of 50 credits and no card required, and paid plans starting at a low monthly fee for higher volume and team features. Skrapp.io does the least of any tool on this list beyond email lookup — it isn't built to pull full work history or export bulk profile fields — which makes it a narrow, purpose-fit choice rather than a general scraper.
Side-by-side: what each tool actually returns
Tool
Full work history/skills
Verified contact (phone/email)
Requires LinkedIn login session
Export/integration
Nstdata Crawl
Only if you build the parser
No (infrastructure only)
No
API (Markdown/JSON/HTML/screenshot/PDF)
PhantomBuster
Yes
No
Yes
CSV, API
Apify LinkedIn actor
Yes
Optional (paid tier)
No
JSON/API
Evaboot
Partial (Sales Nav fields)
Yes (email)
Yes (Sales Nav session)
CSV
Waalaxy
Partial
Yes (email)
Yes
CRM sync, CSV
TexAu
Partial
Yes (waterfall match)
Yes (for sourcing step)
API, CRM sync
Apollo.io
Firmographic + limited history
Yes (email, phone)
No (own database)
CRM sync, API
Lusha
No
Yes (email, phone)
No (own database)
Chrome extension, CRM sync
Coresignal
Yes
No
No (own database)
Flat file, API
Skrapp.io
No
Yes (email only)
Partial (extension reads page)
CSV, extension
Which one should you pick?
Choose based on what you already have and what you're missing, not on which tool ranks highest overall. A team that already runs scraping infrastructure for other sites and wants LinkedIn folded into the same pipeline should look at general crawling infrastructure like Nstdata Crawl, accepting the extra parsing work in exchange for one system instead of several. A team with no scraping infrastructure that wants full profile fields fast should reach for Apify's actor or a browser-automation tool like PhantomBuster, trading the lower setup effort for either per-record cost or session-based rate-limit exposure. A sales or recruiting team that mainly needs a verified email or phone number next to a profile it's already looking at should pick an enrichment tool — Lusha, Apollo.io, or Skrapp.io — rather than a scraper, since the enrichment vendor already absorbed the collection and matching work. A researcher who needs breadth across thousands of companies rather than depth on one profile fits Coresignal's dataset model better than any single-profile tool on this list.
Common use cases for LinkedIn profile data
Recruiting and sourcing — pulling structured work history and skills for candidates who already have a public profile, to build a searchable internal pipeline instead of manually copying fields from each page.
Sales prospecting and lead generation — matching a target account list against public profile and firmographic data, then layering a verified contact method on top before outreach.
Market and workforce research — aggregating headcount, title, and tenure signals across many companies at once to track hiring trends or benchmark a market, which favors a dataset like Coresignal's over any single-profile lookup tool.
Conclusion
The right LinkedIn profile scraper depends on whether the job is building a data pipeline, running an outreach campaign, or buying access to already-collected contact records — those are three different products, not three tiers of the same one. Whatever tool a team picks, the legal exposure described above doesn't change with the vendor: automated LinkedIn scraping still breaches LinkedIn's terms, and any commercial use of the resulting personal data still needs a documented lawful basis and a legal review sized to that specific use case.
Q: Is it legal to scrape public LinkedIn profiles?
Scraping public LinkedIn profiles is not "hacking" under the Computer Fraud and Abuse Act after the Ninth Circuit's rulings in hiQ Labs v. LinkedIn, but it still breaches LinkedIn's User Agreement, which prohibits automated access regardless of whether the data is public — the case ended in a 2022 settlement after a court found exactly that kind of breach, so account-ban and contract-claim risk remains real.
Q: Will LinkedIn ban my account for using a scraping tool?
Yes, that's the most common consequence — LinkedIn's systems flag unusual session behavior like rapid profile views or non-human click timing on tools that use a logged-in session, and independent reviews of browser-automation tools consistently cite account restriction as the main practical risk, more so than any legal action.
Q: What's the difference between a LinkedIn scraper and a data enrichment tool?
A scraper (PhantomBuster, Apify's actor) fetches data from LinkedIn itself, live or near-live, while an enrichment tool (Apollo.io, Lusha, Coresignal) matches a query against a database the vendor already built from public sources, so it never touches LinkedIn directly on your behalf.
Q: Is Proxycurl still a usable option for LinkedIn data?
No — Proxycurl shut down in 2025, and any current roundup listing it as active is out of date; teams that relied on it need to migrate to an alternative such as the API and enrichment options covered above.
Q: Can Nstdata Crawl scrape LinkedIn profiles out of the box?
Not out of the box — Nstdata Crawl fetches and renders any public page, including LinkedIn's, but it does not ship prebuilt LinkedIn field-extraction the way a purpose-built tool like Apify's actor does, so a team using it against LinkedIn writes its own parsing logic on top of Crawl's fetch and render layer.
Q: Do I need a paid plan to try any of these tools?
Most have a free tier or trial — Apify's actor, TexAu, Apollo.io, Lusha, and Skrapp.io all offer a limited free allotment, Waalaxy offers a 14-day trial, and Nstdata Crawl offers a free tier of the Crawl API — but full-volume use of any of them requires a paid plan.
Q: What should I do with LinkedIn profile data after collecting it?
Apply the same handling any personal data requires: document a lawful basis for holding it, set a retention period, honor deletion or opt-out requests, and confirm with legal counsel that the specific collection method and downstream use comply with GDPR, CCPA, or the applicable jurisdiction's law before using it commercially.
Marcus Chen
Sep. 9th 2026
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