Proxycurl was how most developers pulled LinkedIn data.
Reliable, well-documented, widely trusted.
Then LinkedIn sued them in early 2025 and by mid-year the service was gone. If you were building on Proxycurl, you got cut off overnight.
That's probably why you're here. We put together this guide covering 7 linkedin scraping api options that actually work in 2026.
Here’s the full list of the top LinkedIn scraping APIs for 2026 that we’ll cover today:
- Z Real-Time LinkedIn Scraper API
- Bright Data LinkedIn Scraper
- Core Signal
- Unipile LinkedIn Scraper API
- Skraap API
- ScraperAPI
- Apify LinkedIn Profile Scraper
LinkedIn Scraping API: Why Developers actually need one
LinkedIn holds the most accurate professional database on the internet. Job titles, company headcounts, hiring signals, career histories, skill sets. You can't get that depth and freshness anywhere else.
The official LinkedIn API exists, but it's locked behind a partner program. Unless you're building something LinkedIn has specifically approved, you're not getting in. Not a rumor. Their published policy.
So if we need LinkedIn data for a product, we need a third-party scraping API. And the legal question everyone asks first has a real answer.
In 2022, the US Ninth Circuit Court ruled in hiQ Labs v. LinkedIn that scraping publicly available data does not violate the Computer Fraud and Abuse Act. LinkedIn's Terms of Service still prohibit scraping, and they will block accounts that do it at scale. But using a managed scraping API that handles anti-detection and proxy rotation on your behalf puts the technical risk management on the provider, not on you.
Top 7 LinkedIn Scraping APIs of 2026
1. Z Real-Time LinkedIn Scraper API
Let's start with the one we'd recommend trying first.
The Z Real-Time LinkedIn Scraper API on API Market gives you full-funnel access to LinkedIn data through a single API. Most linkedin scraping tools specialize in one data type: profiles only, or jobs only, or company pages only. Z API covers all of it.
You get structured access to profiles, companies, jobs, posts, groups, and the LinkedIn Ad Library. There are also enrichment and metadata endpoints for audience segmentation by industry, job function, skills, education, location, and more. If you are building a product that needs to go wide across LinkedIn data types, you do not need to stitch together multiple APIs.
Key Features:
- Profile data including experiences, education, skills, languages, licenses, honors, posts, comments, and reactions
- Company profiles, employee data, hiring activity, and company posts with engagement metrics
- Open job listings and full job detail endpoints
- Post details, comments, replies, reposts, group profiles, and group posts
- LinkedIn Ad Library access for ad research and competitive intelligence
- Search across people, companies, jobs, posts, groups, courses, events, products, schools, and services
- Metadata endpoints for segmentation by industry, job function, and geography
Pricing Structure:
You get a 7-day free trial with 50 API units and no credit card required, so you can test all 67 endpoints before spending anything.
After that, the entry plan is $9/month for 3,000 API units on a hard cap (no surprise overages).
The $19/month plan gives you 10,000 units with overage at $0.007 per extra unit.
The $49/month plan covers 30,000 units ($0.005/extra), $199/month covers 150,000 units ($0.004/extra), and $499/month covers 500,000 units ($0.003/extra). Unused units reset each billing cycle.
Real Use Cases:
Say you are building a recruiting platform that needs to surface candidates matching specific skill combinations. You call the people search endpoint with the parameters you need, get a structured list back, and enrich each result with the full profile endpoint. That entire pipeline runs in code. No browser, no manual copy-paste, no VA doing LinkedIn research by hand.
For a B2B sales team, the logic is direct. You want to target companies in a specific vertical that are actively posting sales roles, which often signals they are growing fast enough to need new tools. You pull company profiles, filter by hiring activity from the jobs endpoint, and feed the output into your outreach sequence automatically.
The ad library endpoint is one of the more underused data sources available in a linkedin scraper python workflow. If you do competitive intelligence, knowing what your competitors advertise on LinkedIn, who they target, and what messaging they run is real, actionable signal. Very few APIs give you that data structured and clean.
2. Bright Data LinkedIn Scraper
Bright Data is the enterprise-grade option, and it shows in both the infrastructure depth and the price point.
They offer separate scraper APIs for Profiles, Companies, and Posts, each optimized independently for accuracy and completeness. Data comes back as JSON, NDJSON, or CSV and gets delivered via webhook or direct API response. If you are running a data pipeline that needs structured LinkedIn output at high volume with serious uptime requirements, Bright Data has the infrastructure for it.
Key Features:
- Dedicated scraper APIs for Profiles, Companies, and Posts with separate optimized endpoints
- JSON, NDJSON, and CSV delivery options via webhook or direct API response
- Scheduled recurring crawls for automatic LinkedIn data refreshes at set intervals
- 150 million-plus residential IPs across 195 countries for geo-targeted data collection
- 99.99% uptime reliability backed by enterprise SLAs
- Built-in proxy rotation and anti-detection at the infrastructure level
Pricing Structure:
On pay-as-you-go, Bright Data's LinkedIn Scraper API starts at $1.50 per 1,000 records with no monthly commitment and no charge for failed requests. If you commit to a monthly subscription, pricing drops to $0.75 to $0.98 per 1,000 records depending on volume. A free trial includes 20 API calls to test the service before you commit. For high-volume enterprise agreements, you work directly with their sales team on a custom arrangement.
Real Use Cases:
Large sales intelligence platforms that resell LinkedIn enrichment data as part of a broader product use Bright Data as their backend data layer. You build the product, they handle the collection and delivery at volume.
If you are running thousands of profile checks per day across multiple geographies, Bright Data's geo-targeting lets you pull localized data with accuracy. A market research team running European talent supply analysis can specify country-level sources to get regionally accurate results rather than defaulting to US-skewed data.
3. Coresignal Professional network data
CoreSignal takes a completely different approach from most options on this list. Rather than scraping LinkedIn in real time, they maintain a continuously refreshed database of professional network data and give you API access to it directly.
We are talking 792 million-plus employee records and 103 million-plus company records, with 695 million records refreshed every month. If your use case is large-scale data access, B2B intelligence, or historical trend analysis, this is built for that.
Key Features:
- 792 million-plus employee records covering profiles, career histories, skills, and education
- 103 million-plus company records with firmographic and technographic data
- Jobs API for open job postings and historical hiring activity
- Up to 5 years of historical data for trend analysis and forecasting
- Credit-based access: 1 Collect credit per profile retrieved or enriched, 1 Search credit per query
- Average API response time of 176ms across all endpoints
- Bulk dataset downloads available alongside real-time API access
Pricing Structure:
CoreSignal offers a 14-day free trial with Search and Collect credits included, no credit card needed. After that, the Starter plan is $49/month. The Pro plan is $800/month, and the Premium plan is $1,500/month. For high-volume enterprise requirements, custom pricing is available. Each plan includes both Search and Collect credits, and one credit retrieves or enriches one profile.
Real Use Cases:
If you are building a recruiting platform that needs to match candidates by skills, location, and experience across millions of profiles, CoreSignal's pre-structured data means you skip the scraping infrastructure entirely. You query their API, get clean structured JSON back, and move straight to building.
Market research teams that track hiring trends across industries over time get real value from the historical data layer. Instead of scraping a snapshot today and comparing it to a snapshot you took six months ago, CoreSignal gives you five years of hiring activity already indexed and queryable.
For B2B sales intelligence tools that need to enrich CRM records with current company and employee data, the Company Enrichment API handles that without your team building and maintaining a scraper that breaks every time LinkedIn changes its structure.
4. Unipile LinkedIn Scraper API
Unipile takes a completely different approach from every other API on this list. Rather than charging per request or per profile, they charge per linked account.
You connect LinkedIn accounts to Unipile, then access those accounts' data programmatically. The pricing works out to $49 per month for up to 10 linked accounts, with additional accounts added at around $5 each per month. No credit systems, no per-request costs.
Key Features:
- Profile scraping from Classic LinkedIn, Sales Navigator, and LinkedIn Recruiter search types
- Inbox and messaging data: messages, contacts, and full conversation history
- Bulk profile URL scraping with structured JSON output
- CRM-ready output format designed for direct integration without transformation
- No per-request pricing: pay for connected accounts, not volume of API calls
- Multi-platform support across email, LinkedIn, and WhatsApp under one account
Pricing Structure:
Unipile starts at 49 euros per month, covering up to 10 linked accounts. Each additional account costs approximately 5 euros per month. A free trial period is available. The account-based model favors high-volume use from a small number of accounts. If you need data from a wide variety of sources without connecting accounts directly, this model works against you.
Real Use Cases:
Unipile is the right choice when your use case is messaging-heavy alongside data extraction. Sales teams that want to programmatically manage LinkedIn outreach, sync conversations to a CRM, and track message history across rep accounts get real value here.
If you are building a LinkedIn automation tool for a sales team where a handful of reps want to manage connection requests and follow-up sequences programmatically, Unipile's account model makes more sense than paying per API call.
One thing worth flagging before you build on this: if your primary need is bulk profile data extraction at scale rather than messaging workflows, a per-request API is more efficient. You would be paying for accounts rather than actual data volume.
5. Skrapp
Skrapp is built specifically for one thing: finding and verifying business email addresses from LinkedIn profiles. If your team's primary need is building a targeted outreach list with verified emails rather than raw profile data, Skrapp is the tool designed for that workflow.
It runs as a Chrome extension that sits inside LinkedIn and Sales Navigator. You browse a list or search result, and Skrapp pulls verified email addresses in real time at up to 25 profiles per second.
Key Features:
- Chrome extension integrates directly with LinkedIn and LinkedIn Sales Navigator
- Extract up to 2,500 verified business emails per operation at 25 profiles per second
- 92% average email verification accuracy across all industries
- Access to a continuously refreshed database of 200 million-plus prospects
- Company domain search to find employee email addresses by organization
- Bulk lead export to CSV, with list management built in
- API access available for developers who want to integrate email lookup into their own systems
Pricing Structure:
Skrapp offers a free plan with 100 email credits per month for one user, no credit card required. The Professional plan is $39/month (or $30/month billed annually) for 1,000 email credits per month and 2 users. The Enterprise plan is $349/month (or $262/month billed annually) for 50,000 email credits per month and 5 users. Each credit returns one verified email address.
Real Use Cases:
Sales teams running outbound campaigns on LinkedIn use Skrapp to skip the manual copy-paste step. You open a Sales Navigator saved list, run Skrapp across it, and get a CSV of verified emails ready to import into your outreach tool. What would take hours of manual work runs in minutes.
If you are doing account-based outreach and already know which companies you want to target, the domain search feature lets you pull employee email addresses by organization without even needing to find individual LinkedIn profiles first.
For teams that cannot justify a $300/month lead database subscription but need reliable emails for outreach, Skrapp at $39/month hits a practical price point while maintaining accuracy that competes with tools costing significantly more.
6. ScraperAPI
ScraperAPI is a managed proxy and scraping infrastructure layer built specifically for developers. You do not get pre-structured LinkedIn data out of the box. What you get is a reliable way to send requests to LinkedIn without getting blocked, and you control everything about how you parse and store the results.
Key Features:
- IP rotation on every request using residential, mobile, and datacenter proxies
- Built-in CAPTCHA solving with no additional configuration
- ultra_premium=true parameter for LinkedIn-specific anti-bot bypass
- output_format=markdown endpoint returns LinkedIn pages as clean structured Markdown for LLM pipelines
- Works directly with Python (requests + BeautifulSoup) and Node.js without browser automation
- Targets LinkedIn's hidden job search API endpoints for public job listing extraction
- 5,000 free credits on signup, no credit card required
Pricing Structure:
ScraperAPI gives you a 7-day trial with 5,000 free credits. The Hobby plan is $49/month for 100,000 credits. Startup is $149/month for 1 million credits, and Business is $299/month for 3 million credits.
LinkedIn uses ultra-premium routing: 30 credits per request without JavaScript rendering, or 75 credits per request with rendering. On the Hobby plan's 100,000 credits with rendering on, you get roughly 1,333 LinkedIn page loads. Run the math against your expected volume before picking a plan.
Real Use Cases:
If you are building a LinkedIn job scraper in Python and want to skip building a proxy management system from scratch, ScraperAPI handles that in one line. You point your requests at their endpoint instead of LinkedIn directly, and IP rotation, header management, and CAPTCHA bypassing happen automatically on their infrastructure.
The output_format=markdown feature is particularly useful for teams feeding LinkedIn profile data into LLM workflows. Instead of parsing raw HTML yourself, you get a clean Markdown version of the profile ready to pass to Gemini, GPT, or any other model. Pull a profile, send it to the LLM with a prompt, and get a structured summary back without any HTML cleanup in between.
For developers already running multi-site scraping pipelines, ScraperAPI works as a drop-in proxy layer. You keep your existing parsing code and just route your LinkedIn requests through their endpoint.
7. Apify LinkedIn Profile Scraper
Apify runs a marketplace of scraping actors, and LinkedIn has several dedicated ones. The pricing model is different from every other option on this list: you pay per result, not per month.
This makes Apify interesting for developers who have variable usage patterns. If you need 50,000 profiles one month and 5,000 the next, you are not locked into a flat monthly rate either way.
Key Features:
- Multiple LinkedIn scraper actors for different data types: profiles, companies, jobs, and posts
- No LinkedIn account or cookies required on most actors
- Pay-per-result model with no monthly minimum and no expiring credits
- Outputs in JSON, CSV, and XML for direct export or dataset storage
- Cloud execution: actors run on Apify's infrastructure with no server management needed
- Separate job scraper actor available for public LinkedIn job listings
Pricing Structure:
Apify's LinkedIn profile scrapers typically run between $2 and $3 per 1,000 profiles depending on which actor you use. One of the most-used actors charges $3 per 1,000 profiles with no monthly commitment. Post scraping costs less, around $1 per 1,000 posts on some actors. The platform offers a free trial, and there is no minimum deposit or subscription required to get started.
Real Use Cases:
Developers running one-off research projects prefer Apify because you are not paying a flat monthly subscription for something you might use twice a year. Need 10,000 LinkedIn profiles for a market sizing project? Call the actor, pay for the run, done.
For teams with unpredictable scraping volumes, the pay-per-use model keeps costs honest. You are not carrying a $149 subscription into a month where you barely touched the API.
Pricing Overview of the Best LinkedIn Scraping APIs
Before you commit to a plan, map the pricing model to how you will actually use it. The same $49/month entry point covers very different things depending on the tool.
CoreSignal's $49/month Starter gives you a credit pool for querying 792 million pre-collected records. Skrapp's $39/month Professional gives you 1,000 verified email credits per month.
ScraperAPI's $49/month Hobby gives you 100,000 proxy credits, but LinkedIn costs 30 to 75 credits per request depending on rendering mode. At 75 credits per request with rendering, that is about 1,333 LinkedIn page loads for the month. Same entry price, very different effective usage. Know what you are actually buying before you commit.
- Z Real-Time LinkedIn Scraper API: Free 7-day trial with 50 API units, no credit card needed. Plans start at $9/month (3,000 units) up to $499/month (500,000 units). 67 endpoints, all LinkedIn data types covered.
- Bright Data: Pay-as-you-go at $1.50/1K records (no monthly commitment). Monthly subscription drops to $0.75-$0.98/1K records. Free trial includes 20 API calls.
- CoreSignal: 14-day free trial with Search and Collect credits. Starter $49/month, Pro $800/month, Premium $1,500/month. 1 credit = 1 profile collected or enriched.
- Unipile: Starts at €49/month for up to 10 linked accounts. Additional accounts at ~€5 each. Free trial available.
- Skrapp: Free plan with 100 email credits/month. Professional $39/month (1,000 credits, 2 users). Enterprise $349/month (50,000 credits, 5 users). Annual billing drops costs further.
- ScraperAPI: 7-day trial with 5,000 free credits. Hobby $49/month (100K credits), Startup $149/month (1M credits), Business $299/month (3M credits). LinkedIn: 30 credits/request (no render) or 75 credits/request (with render).
- Apify: $2 to $3 per 1,000 profiles depending on actor. No monthly minimum. Pure pay-as-you-go.
Conclusion
All seven of these LinkedIn scraping APIs work. Each one fits a different situation, and we broke them down exactly for that reason.
If you need messaging and inbox data alongside profile scraping, Unipile's account-based model is built for that. If you want maximum infrastructure scale and enterprise-grade reliability, Bright Data and Oxylabs are the ones to evaluate.
If your scraping volume is unpredictable month to month, Apify's pay-per-result model keeps your costs predictable. ScraperAPI and Scrapingdog both remove the anti-detection headache so you can focus on your pipeline rather than fighting LinkedIn's blocking systems.
But if you want a single linkedin scraping api that covers profiles, companies, jobs, posts, groups, ads, and search across all of LinkedIn's public data types without stitching together multiple providers, the Z Real-Time LinkedIn Scraper API on API Market is the one to start with. It is the only option on this list that gives you full-funnel LinkedIn data access through one integration.
Start your free trial here: Z Real-Time LinkedIn Scraper API on API Market
FAQs About LinkedIn Scraping APIs
Q1. Is scraping LinkedIn data with an API legal?
Scraping publicly available LinkedIn data is not illegal under US federal law. The US Ninth Circuit Court ruled in hiQ Labs v. LinkedIn (2022) that scraping public data does not violate the Computer Fraud and Abuse Act. LinkedIn's Terms of Service still prohibit it, and they actively block at scale, but using a managed scraping API shifts the technical risk to the provider's infrastructure.
Q2. What happened to Proxycurl?
Proxycurl was one of the most widely used LinkedIn data APIs before 2025. In January 2025, LinkedIn filed a lawsuit against them, and by mid-2025 the service shut down entirely. The core issue was that Proxycurl built their service using fake LinkedIn accounts to access data at scale, which gave LinkedIn clear legal grounds to act. If you are searching for a LinkedIn scraping API right now, the Proxycurl shutdown is almost certainly what pushed you to start looking.
Q3. How do I choose the right LinkedIn scraping API for my use case?
Start with what data you actually need. Profiles and company pages are covered by most options here. If you also need jobs, posts, ad library, or cross-type search, your options narrow fast. Then think about scale: predictable high volume favors subscriptions, variable volume favors Apify's pay-per-result model. Finally, decide if you want pre-parsed structured data or raw HTML you process yourself.
