Best LinkedIn Profile Scrapers of 2026 [No-Code edition]

⚡ 30-Second Summary

1. I tested and compared best LinkedIn profile scrapers that are actually usable in 2026, focusing on real-world scale, safety, data quality, and cost.
2. Lobstr.io is the most affordable option at scale, starting around $5 per 1k profiles with verified emails.
3. It offers rich profile data, email enrichment with under 3% bounce rate, strong multi-account scaling, and a safety-first scraping approach.
4. The only real tradeoff with Lobstr.io is speed.
5. PhantomBuster is better suited for LinkedIn automation than pure data extraction.
6. It offers fewer profile data points and unpredictable per-minute pricing, but works reasonably well for outreach-driven workflows.
7. Apify stands out for speed and safety thanks to its no-cookie approach and broad data coverage.
8. However, it lacks consistency, often struggles at scale, and does not properly validate emails.
9. This guide breaks down which tool makes sense based on what you care about most, cost, automation, safety, or raw speed.

I’ve already shown you how to scrape LinkedIn profiles, with code and without code, using lobstr.io.

But those guides missed one thing… choice.

And honestly, finding a dedicated LinkedIn profile scraper that works at scale, stays affordable, and doesn’t put your account at risk is harder than it should be.

If you search Google or ask any AI about LinkedIn profile scrapers, you’ll quickly realize it’s a mess.

You’ll get generic LinkedIn scrapers that aren’t built for profile scraping, confusing APIs, and Chrome extensions that don’t scale and eventually get your account banned.

So I went out and tested tools that are actually built for scraping LinkedIn profiles that offer top notch scalability, data quality, safety, speed, at an affordable cost.

Here are my top 3 picks that’d ace the job without denting your wallet.

But before we jump into the detailed comparison, let’s address the obvious question first… is it even legal to scrape LinkedIn profiles?

Is it legal to scrape LinkedIn profiles?

Short answer… Yes, if you don’t do stupid things.

LinkedIn’s terms don’t allow scraping, but that’s a platform rule, not a criminal law. Scraping publicly available LinkedIn profile data is generally considered legal.

You get into trouble when you scrape recklessly, use shady practices, or break data privacy laws, not because scraping itself is illegal.

In practical terms, you’re on safe ground as long as:

1. You respect rate limits ✅
2. You don’t use fake or compromised accounts ❌
3. You don’t collect private or sensitive information ❌
4. You don’t resell or publicly monetize the data without consent ❌
5. You store the data responsibly and follow local privacy laws like GDPR ✅

Now, since we’re not going to jail for scraping LinkedIn, let’s talk about the scrapers.

How did I choose the best LinkedIn profile scrapers?

What I needed to understand next was simple… what actually goes wrong when people use these LinkedIn profile scrapers.

So I went looking for pain points.

I dug through community posts, user reviews, and discussions around different scrapers to see where people were struggling and shortlisted the 5 most common pain points:

1. Data quality
2. Safety
3. Affordability
4. Scalability
5. Integrations and data export
6. Speed

Now since, I had to test all the tools based on these 5 pain points so…

For data quality, I looked at what each tool offers beyond basic profile info. Things like education, work history, skills, certifications, and whether email enrichment is included.

For safety, I focused on how scrapers handle LinkedIn account risk.

Since most tools rely on your own account, I checked for things like custom limit settings, multiple account support, cookie handling, and overall ban-risk mitigation.

For scalability, multiple account support matters a lot. If a scraper doesn’t let you use more than one account, you’re stuck with LinkedIn’s default per-account limits, which kills scale.

For affordability, I simplified pricing down to cost per 1000 profiles, both with and without emails. I checked both entry level and scale level costing.

For integrations and data export, I checked how many native integrations a tool offers, what workflow automation tools are supported natively.

Plus I also checked how many data export formats are supported along with the basic CSV file export.

For speed, I measured profiles per minute. Slow scrapers break workflows, no matter how cheap they look on paper.

And since, we’re talking about no-code tools, customer support and ease of use are also important factors.

Next, I went hunting for tools.

I searched Google, read listicles, went through Reddit threads, and checked AI recommendations. Most of what came up was noise.

What I kept finding were:

1. Generic LinkedIn, visual, or general-purpose scrapers
2. Chrome extensions
3. B2B databases and enrichment tools
4. Sales Navigator scrapers
5. Profile scraper APIs

Generic and visual scrapers don’t offer deep profile data, don’t scale well, and often increase ban risk. Chrome extensions suffer from the same problems.

B2B databases and enrichment tools don’t actually scrape LinkedIn profiles. They give you data from their own databases, which is often outdated and missing context.

Sales Navigator scrapers require an active Sales Navigator subscription, which adds extra cost when affordability is already a major pain point.

Profile scraper APIs are for nerds. If you don’t code or just want a straightforward solution, these aren’t what you’re looking for. I’ll cover them in a separate article.

Since this list is strictly no-code, I filtered everything else out.

Based on that, here’s my list of the best LinkedIn profile scrapers for 2026.

Best LinkedIn Profile Scrapers of 2026

1. Lobstr.io

User rating: 5/5 ⭐⭐⭐⭐⭐

Lobstr.io is a French web scraping platform that offers 20+ ready-made, no-code scrapers across different domains, including a dedicated LinkedIn Profile Scraper.

Features

1. 70+ meaningful data points per LinkedIn profile
2. Full profile coverage including education, work history, skills, and interests
3. Enrichment with verified work emails
4. Multi-account management for safer scaling
5. Built-in rate limit and cookie management
6. Parallel data collection using multiple LinkedIn accounts
7. Scheduling to monitor profile changes over time
8. Export to CSV, JSON, Google Sheets, Amazon S3, or SFTP
9. API access with developer and vibe-coder friendly documentation

Data

Lobstr.io goes well beyond basic name, title, and company scraping. You’re getting proper profile context, not shallow lead lists.

| id | object | squid |
| run | background_picture_url | connection_degree |
| connections_url | description | education_1 |
| education_1.grade | education_1.end_year | education_1.activities |
| education_1.school_urn | education_1.start_year | education_1.description |
| education_1.school_logo | education_1.school_name | education_1.field_of_study |
| education_10 | education_2 | education_3 |
| education_4 | education_5 | education_6 |
| education_7 | education_8 | education_9 |
| educations | educations[].grade | educations[].end_year |
| educations[].activities | educations[].school_urn | educations[].start_year |
| educations[].description | educations[].school_logo | educations[].school_name |
| educations[].field_of_study | email | email_status |
| featured_1 | featured_2 | featured_3 |
| featured_4 | featured_5 | first_name |
| full_name | functions | functions.email |
| functions.email.filling_date | headline | industry |
| interests_companies | interests_companies[].company_id | interests_companies[].company_url |
| interests_companies[].company_logo | interests_companies[].is_following | interests_companies[].follower_count |
| interests_groups | interests_groups[].group_id | interests_groups[].group_url |
| interests_groups[].group_logo | interests_groups[].group_name | is_creator |
| job_1 | job_1.title | job_1.end_year |
| job_1.location | job_1.end_month | job_1.start_year |
| job_1.company_url | job_1.description | job_1.start_month |
| job_1.company_logo | job_1.company_name | job_10 |
| job_10.title | job_10.end_year | job_10.location |
| job_10.end_month | job_10.start_year | job_10.company_url |
| job_10.description | job_10.start_month | job_10.company_logo |
| job_10.company_name | job_2 | job_2.title |
| job_2.end_year | job_2.location | job_2.end_month |
| job_2.start_year | job_2.company_url | job_2.description |
| job_2.start_month | job_2.company_logo | job_2.company_name |
| job_3 | job_3.title | job_3.end_year |
| job_3.location | job_3.end_month | job_3.start_year |
| job_3.company_url | job_3.description | job_3.start_month |
| job_3.company_logo | job_3.company_name | job_4 |
| job_4.title | job_4.end_year | job_4.location |
| job_4.end_month | job_4.start_year | job_4.company_url |
| job_4.description | job_4.start_month | job_4.company_logo |
| job_4.company_name | job_5 | job_5.title |
| job_5.end_year | job_5.location | job_5.end_month |
| job_5.start_year | job_5.company_url | job_5.description |
| job_5.start_month | job_5.company_logo | job_5.company_name |
| job_6 | job_6.title | job_6.end_year |
| job_6.location | job_6.end_month | job_6.start_year |
| job_6.company_url | job_6.description | job_6.start_month |
| job_6.company_logo | job_6.company_name | job_7 |
| job_7.title | job_7.end_year | job_7.location |
| job_7.end_month | job_7.start_year | job_7.company_url |
| job_7.description | job_7.start_month | job_7.company_logo |
| job_7.company_name | job_8 | job_8.title |
| job_8.end_year | job_8.location | job_8.end_month |
| job_8.start_year | job_8.company_url | job_8.description |
| job_8.start_month | job_8.company_logo | job_8.company_name |
| job_9 | job_9.title | job_9.end_year |
| job_9.location | job_9.end_month | job_9.start_year |
| job_9.company_url | job_9.description | job_9.start_month |
| job_9.company_logo | job_9.company_name | jobs |
| jobs[].title | jobs[].end_year | jobs[].location |
| jobs[].end_month | jobs[].start_year | jobs[].company_url |
| jobs[].description | jobs[].start_month | jobs[].company_logo |
| jobs[].company_name | last_name | location |
| mutual_connections_text | mutual_connections_url | native_id |
| num_connections | num_followers | open_to_work |
| picture_url | public_identifier | sales_nav_url |
| scraping_time | skills | subscribers |
| url | vmid |  |
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Lobstr.io’s email discovery does not depend on emails being publicly visible on LinkedIn profiles. It uses a dedicated enrichment and validation system to find work emails reliably.

In practice, with Lobstr.io’s email finder, you surely get a bounce rate between 1 to 3%, even at scale.

Pricing

Lobstr.io runs on a monthly subscription model, ranging from $20 to $500 per month, with a fixed number of credits included in each plan.

To make pricing easier to understand, here’s the simplified cost:

1. Free plan allows you to extract data from 100 profiles per month
2. $2 per 1,000 profiles without email enrichment
3. $20 per 1,000 profiles with email enrichment

At scale, pricing drops further:

1. $0.5 per 1,000 profiles
2. $5 per 1,000 profiles with emails

Safety and scalability

Account safety is where Lobstr.io clearly stands out.

You can sync multiple LinkedIn accounts and distribute scraping across them, which keeps you within LinkedIn’s limits while still collecting data at scale.

Default limits already align with what LinkedIn enforces, but you can go even safer by setting your own daily limits.

You can also resync accounts, remove them, or manually add cookies directly from the dashboard.

Speed

By default, Lobstr.io scrapes 1 profile per minute per account. It’s not fast. I won’t pretend it is. Even I find it slow.

But that limit exists for a reason… going faster puts your LinkedIn account at risk.

But you can still improve speed with concurrency. If you use multiple accounts with multiple slots, the workload is divided.

For example, using 3 accounts with 3 slots lets you scrape 3 profiles at the same time, effectively tripling your throughput.

Data export and integrations

Lobstr.io lets you export the dataset as CSV or JSON, which already covers most basic workflows.

If you want automation, you can set up automated exports to Google Sheets, Amazon S3, or SFTP, or even receive the data directly via email.

For more advanced workflows, Lobstr.io has a native Make.com integration, which opens up access to 3,000+ 3rd-party apps.

And if you need something custom, there’s a developer-friendly API that lets you plug the scraper directly into your own systems.

Ease of use

Lobstr.io offers a simple and easy to use interface. Every key feature is just one click away. You can launch a scraper in just 4 simple steps (in less than 100 seconds).

If you’re confused, there’s a proper knowledge base that explains every feature. There are also in-depth blog guides for each scraper.

For API users, the documentation is developer friendly and vibe coder friendly, with clear examples.

Customer Support

Well, I won’t say anything about customer support. Our reviews already say everything you need to know.

Most reviews would tell you 1 thing: customer support is handled by technical people who understand the product.

Best for

Lobstr.io is for people who need LinkedIn profile data at scale without risking account bans. It’s a good fit if you care about data depth, safety, and predictable costs.

It works especially well for lead generation, market research, enrichment pipelines, and recurring profile monitoring where safety matters more than raw speed.

2. PhantomBuster

User rating: 4.5/5 ⭐⭐⭐⭐

It’s less of a pure scraper and more of an automation toolbox built around LinkedIn.

Features

1. 60+ LinkedIn profile data points plus email enrichment
2. Automations for lead collection, enrichment, and outreach
3. Built-in workflow system to chain multiple automations
4. Export data to CSV, JSON, and Google Sheets
5. Built-in scheduling
6. Multiple LinkedIn account management
7. Team access
8. Native integrations with CRMs, outreach, and enrichment tools

Data

| URL | companyIndustry | companyName |
| companyWebsite | firstName | lastName |
| linkedinCompanyUrl | linkedinCompanySlug | linkedinCompanyId |
| linkedinDescription | linkedinFollowersCount | linkedinHeadline |
| linkedinIsHiringBadge | linkedinIsOpenToWorkBadge | linkedinJobDateRange |
| linkedinJobDescription | linkedinJobLocation | linkedinJobTitle |
| linkedinPreviousCompanySlug | linkedinPreviousJobDateRange | linkedinPreviousJobDescription |
| linkedinPreviousJobLocation | linkedinPreviousJobTitle | linkedinPreviousSchoolUrl |
| linkedinPreviousSchoolCompanySlug | linkedinPreviousSchoolDateRange | linkedinPreviousSchoolDegree |
| linkedinPreviousSchoolDescription | linkedinPreviousSchoolName | linkedinProfileId |
| linkedinProfileSlug | linkedinProfileUrl | linkedinProfileUrn |
| linkedinProfileImageUrn | linkedinProfileImageUrl | linkedinSchoolUrl |
| linkedinSchoolCompanySlug | linkedinSchoolDateRange | linkedinSchoolDegree |
| linkedinSchoolDescription | linkedinSchoolName | linkedinSkillsLabel |
| location | previousCompanyName | connectionDegree |
| refreshedAt | mutualConnectionsUrl | connectionsUrl |
| linkedinConnectionsCount | profileUrl | linkedinCompanyName |
| linkedinCompanyDescription | linkedinCompanyTagline | linkedinCompanyFollowerCount |
| linkedinCompanyWebsite | linkedinCompanyEmployeesCount | linkedinCompanySize |
| linkedinCompanyHeadquarter | linkedinCompanyIndustry | linkedinCompanyFounded |
| scraperProfileId | scraperFullName | linkedinCompanySpecialities |
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Pricing

PhantomBuster’s pricing is where things get tricky.

Instead of charging per profile, it charges per minute. Yup… you’re paying for the time it takes the scraper to collect the data instead of actual data collected by the scraper.

During testing, scraping speed ranged from 10 profiles per minute to as slow as 1 profile every 2 minutes. That makes it hard to predict cost reliably.

Using a conservative and more realistic average of 2 profiles per minute, the math looks like this:

1. Around $28.75 per 1,000 profiles at entry levels
2. Drops to roughly $12.2 per 1,000 profiles at scale

Email enrichment is not included in this cost. PhantomBuster sells enrichment separately using email credits.

These are attempt credits, not verified email credits. Failed attempts still consume credits, which adds hidden cost very quickly.

Safety and scalability

PhantomBuster supports syncing multiple LinkedIn accounts, but it operates close to LinkedIn’s default limits.

You can’t define your own custom limits to stay extra safe, which means you’re often scraping right at the edge. That’s fine for small runs, but risky at scale.

To be fair, during testing I didn’t experience account restrictions. Still, the lack of granular control makes this a concern for long-term, high-volume scraping.

Speed

Speed is too unpredictable.

In some runs, PhantomBuster scraped up to 10 profiles per minute. In others, it slowed down to 1 profile per minute or worse.

Speed drops further when email enrichment or company data collection is enabled. This variability is the main reason pricing becomes hard to justify at scale.

Data export and integrations

Integrations are one of PhantomBuster’s strengths.

You can export data directly to CSV and connect natively with CRMs like HubSpot, Salesforce, and Pipedrive, along with outreach and enrichment tools.

There is an API available, but the documentation is poor and not beginner-friendly, which limits its usefulness for custom setups.

Ease of use

PhantomBuster is generally easy to use.

There are video tutorials built directly into the dashboard that walk you through each automation step by step, which lowers the learning curve.

You can organize LinkedIn leads into lists and even import profiles directly from your CRM, which is convenient if your data already lives there.

One thing I didn’t like… you can’t directly paste LinkedIn profile URLs in bulk or upload a simple TXT file.

You have to push them through Google Sheets first. It’s not a deal breaker, but it’s unnecessary friction if you’re used to quick inputs like me.

Customer support

Customer support reviews are mixed. Some users report quick responses, others complain about unprofessional or shallow replies.

In my experience, support felt a bit laid back and not very technical.

You have to deal with an AI with RAG knowledge and generic answers before you can talk to a human to get your problem solved.

Best for

PhantomBuster is a good fit if you want LinkedIn automation beyond scraping, things like lead collection, enrichment, and outreach chained together.

For raw LinkedIn profile data at scale, it’s too costly.

3. Apify

User rating: 4.6/5 ⭐⭐⭐⭐

There’s no Apify-maintained LinkedIn profile scraper, but the community-built Mass LinkedIn Profile Scraper is one of the more popular options on the platform.

Features

1. No cookies required
2. 90+ LinkedIn profile data points
3. Email and phone collection when publicly available
4. Scheduling for profile monitoring
5. Multiple export options and integrations
6. Agentic AI and MCP compatibility
7. Developer-friendly API with clean documentation

Data

| linkedinUrl | firstName | lastName |
| fullName | headline | connections |
| followers | email | mobileNumber |
| jobTitle | jobStartedOn | jobLocation |
| jobStillWorking | companyName | companyIndustry |
| companyWebsite | companyLinkedin | companyFoundedIn |
| companySize | currentJobDuration | currentJobDurationInYrs |
| topSkillsByEndorsements | addressCountryOnly | addressWithCountry |
| addressWithoutCountry | profilePic | profilePicHighQuality |
| backgroundPic | linkedinId | isPremium |
| isVerified | isJobSeeker | isRetired |
| isCreator | isInfluencer | isCurrentlyEmployed |
| about | publicIdentifier | linkedinPublicUrl |
| openConnection | urn | totalRecommendationsReceived |
| totalRecommendationsGiven | birthday | associatedHashtag |
| firstRoleYear | totalExperienceYears | experiencesCount |
| experiences | experiences[].companyId | experiences[].companyUrn |
| experiences[].companyLink1 | experiences[].companyName | experiences[].companySize |
| experiences[].companyWebsite | experiences[].companyIndustry | experiences[].logo |
| experiences[].title | experiences[].jobDescription | experiences[].jobStartedOn |
| experiences[].jobEndedOn | experiences[].jobLocation | experiences[].jobStillWorking |
| experiences[].jobLocationCountry | experiences[].employmentType | experiences[].subtitle |
| experiences[].caption | experiences[].metadata | updates |
| skills | skills[].title | creatorWebsite |
| profilePicAllDimensions | educations | educations[].companyId |
| educations[].companyUrn | educations[].companyLink1 | educations[].logo |
| educations[].title | educations[].subtitle | educations[].description |
| educations[].grade | educations[].period | educations[].period.startedOn |
| educations[].period.endedOn | licenseAndCertificates | honorsAndAwards |
| languages | volunteerAndAwards | verifications |
| promos | highlights | projects |
| publications | patents | courses |
| testScores | organizations | volunteerCauses |
| interests | recommendationsReceived | recommendations |
| peopleAlsoViewed |  |  |
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The data itself is quite rich, but there’s an important caveat. This scraper does not enrich or validate emails.

It only collects emails and phone numbers if they are publicly visible on LinkedIn profiles.

Because of that, email bounce rate is uncertain and email availability is highly dependent on the profiles being scraped.

Pricing

Apify’s pricing plans range from $30 to $1,000 per month.

This specific scraper costs $10 per 1,000 profiles.

It does not qualify for Apify Store discounts, so the rate stays fixed across all plans. So no matter how much you scale, the cost per 1,000 profiles remains the same.

Safety and scalability

From a safety perspective, this is the safest option on the list.

You’re not using your own LinkedIn account, so there’s no risk of account limits or bans. On paper, that also makes it look highly scalable.

In practice, scalability is somewhat questionable.

While reviewing user feedback and reported issues, I noticed a pattern.

Once output sizes go beyond 1,000 profiles, the scraper often struggles. Crashes, partial outputs, null fields, and incorrect data are common complaints.

So yes, it’s safe. But large-scale reliability is questionable.

Speed

Speed is one of the main reasons I shortlisted this scraper.

It can scrape up to 40 profiles per minute, which is significantly faster than other scrapers on the list.

Data export and integrations

You can export results to various formats like JSON, CSV, Excel, XML, RSS, and HTML tables.

And thanks to Apify’s integration ecosystem, this scraper plugs easily into workflow tools, AI tools, and a wide range of third-party applications.

You can also use it directly inside your own applications through Apify’s API, which is well-documented and clean.

Ease of use

It’s relatively easy to use.

The interface looks a bit nerdy, but the workflow itself is simple. You add profile URLs, configure the run, and start collecting data.

That said, if you’re not technical, Apify can feel rough around the edges.

Timeout errors, resource limits, and memory-related issues can be confusing if you’ve never dealt with this kind of environment before.

Customer support

Since this scraper is community-maintained, you don’t get access to Apify’s official support for it.

Support is limited to contacting the developer via email or opening an issue. In both cases, responses usually take more than 48 hours, and resolution isn’t guaranteed.

Best for

Apify is best if you want fast LinkedIn profile scraping without risking your own account and you’re comfortable dealing with occasional instability.

It’s not ideal if you need consistent large-scale runs, guaranteed data quality, or responsive support.

So out of these 3 top LinkedIn profile scrapers, which one is best for you?

Which LinkedIn profile scraper is best for your use case?

1. Cost +scalability + safety = Lobstr.io
2. Automation + outreach = PhantomBuster
3. Speed + no accounts = Apify

If you want reliable lead generation at scale, where pricing makes sense, emails actually deliver, and accounts stay alive… Lobstr.io is the best fit.

It’s slower by design, but safe, predictable, and built for production use.

If your priority is LinkedIn automation and outreach, not deep profile data… PhantomBuster works well.

You’re paying for workflows and chaining actions, not for rich or cost-efficient data extraction.

If you want speed and zero account risk, especially for research or one-off runs… Apify stands out.

It’s fast and safe, but consistency and email quality become issues once you scale.

Now before wrapping up, let me answer some FAQs.

FAQs

Why is Evaboot not in the list?

Evaboot is not a LinkedIn profile scraper. It works exclusively with LinkedIn Sales Navigator search results, not basic LinkedIn profiles.

If your use case is scraping profile without an active Sales Navigator subscription, Evaboot simply doesn’t fit.

Can I use Apollo for scraping LinkedIn profile data?

Not really. Apollo is not a real-time scraping tool. Apollo relies on its own internal data sources, not real time scraping of LinkedIn profiles.

That means data freshness varies, coverage is inconsistent, and you don’t control what gets updated or when.

What does LinkedIn profile data enrichment mean?

LinkedIn profile data enrichment means enhancing raw profile information with additional, useful data.

This usually includes:

1. Adding verified work emails
2. Filling missing fields like company details, roles, or seniority
3. Turning basic profile data into enriched data you can actually use for outreach

The goal is simple… convert profile information into qualified leads, not just scraped names and headlines.

Conclusion