We benchmarked the best LinkedIn scraper tools using 9,000 requests across posts, profiles, and job listings. This guide covers two main areas:
- Comparison of the top LinkedIn data scrapers based on success rate, speed, and pricing
- Python tutorial for extracting LinkedIn posts, profiles, companies, and jobs.
This guide will help you whether you’re choosing a scraper for your team or developing your own LinkedIn scraper in Python.
Pricing for the top LinkedIn scraping tools
* Bright Data LinkedIn Scraper’s pricing is determined by the number of records you collect. The specific rate per record varies, depending on your chosen subscription plan.
LinkedIn scraper APIs benchmark results
This chart compares the daily success rates of LinkedIn scraper APIs based on live tests conducted every 15 minutes:
Potential reasons behind the performance differences
Our benchmark results show clear performance gaps between Apify and Octoparse across both success rate and response time. The following factors likely explain these differences:
1. Differences in API architecture:
Apify
- Likely uses a more LinkedIn-aware crawling architecture optimized for social network layouts.
- It’s consistent selector handling better accommodates small HTML or layout changes, which aligns with its stable success rates.
Octoparse
- Rely on a more generic scraping engine, maybe less when LinkedIn layout changes occur.
2. Response time optimization:
Apify
- Maintains extremely low response times (≈10–12 ms) throughout the benchmark. The tool offers optimized request routing.
Octoparse
- Response times range from ~35 ms to over 60 ms, indicating:
- Heavier processing overhead
- Potential lack of caching
1. Proxy-based LinkedIn scrapers
Proxy-based LinkedIn scrapers use their own proxy infrastructure, including IP addresses and servers, to extract LinkedIn data at scale. These APIs send requests through a managed proxy network.
This is the right approach for high-volume, reliable LinkedIn scraping because:
- Fast: Since the scraper relies on multiple profiles while scraping the data, it can scrape faster
- Reliable: If the target website bans a profile or IP address, the provider switches to another one to continue operations.
- Safe: The person ordering the scraping doesn’t need to use their own account, so there is no risk of your profile being banned.
Bright Data provides a dedicated LinkedIn Web Scraper API designed for structured data extraction from public LinkedIn pages. The API suite includes Profiles API, Post API, and Company API, each optimized for accuracy and compliance. The platform also offers LinkedIn datasets tailored to specific LinkedIn use cases.
Features:
- Discovery: You can get data from LinkedIn using a specific keyword, such as first and last name, date filters, or job location.
- Real-time scraping: Enabling users to get the most current information available on LinkedIn.
- Built-in proxy support: The LinkedIn APIs include built-in proxy support.
Pricing:
- Starting price: $499/mo
- Trial: 20 free API calls
Apply the code API25 at checkout to get a 25% discount on Bright Data’s LinkedIn Scraper:
Apify provides a range of pre-built Actors tailored for web scraping on LinkedIn. Popular LinkedIn scraping tools include Job Scraper, Sales Navigator Scraper, Company Scraper, Profile Scraper, Post Scraper, and Ads Scraper.
Features:
- Customizable Actors (Pre-built Web Scrapers): A marketplace of LinkedIn scrapers built by community developers, each adaptable to specific scraping needs.
- Automation: Link multiple Actors or integrate with external tools via APIs. Each Actor supports connections to the MCP server, enabling users to manage and communicate with various scraping Actors.
Pricing:
- Starting price (mo): $25. There are four pricing options available: rental, pay-per-result, pay-per-event, and pay-per-usage.
- Trial: Available, duration depends on the scraper.
Proxycurl
Proxycurl is a developer-oriented LinkedIn scraping API that provides structured data for profiles, companies, jobs, and employees. Unlike browser automation tools, Proxycurl uses a REST API to return fresh, normalized JSON data ready for analytics.
Features:
- Dedicated LinkedIn Profile, Company, Employee, and Job endpoints.
- Usage-based pricing for flexible, scalable data collection.
- Offers real-time and cached data options for cost control.
Pricing:
- Starts at $10/month on a pay-per-call model.
- Free tier with limited requests available for testing.
ScraperAPI is a managed proxy and web-scraping infrastructure that includes dedicated support for LinkedIn data extraction. Rather than providing a pre-built LinkedIn dataset, it handles the scraping process, including IP rotation, CAPTCHA solving, and header management.
Features
- Concurrent request support: scale thousands of requests per minute.
- LinkedIn-specific templates for profiles, companies, and job pages.
Pricing
- Starts at $49/month for 100,000 API credits.
- Free trial with 5,000 requests.
Dripify
Dripify is a LinkedIn automation tool that helps sales professionals automate tasks on the platform. They provide a LinkedIn scraper that enables users to access LinkedIn lead data and export it to a CSV file.
Features:
- Local IP address: Provides a unique IP address from the user’s local region, enabling users to access websites as if they were located in different geographical areas.
- Human behavior simulation: Imitates the actions of a real user when interacting with LinkedIn. It adds random time delays between requests and simulates user clicks on links or buttons to help you appear more like a genuine user.
Pricing:
- Starting price: $59/user/mo
- Trial: Available
Linked Helper is a desktop-based LinkedIn automation platform that also includes LinkedIn scraping functionality. The tool doesn’t require proxies by default, but it allows users to set up proxies per LinkedIn account manually.
Features:
- Automated Profile Connecting: Visits LinkedIn profiles and sends personalized messages.
- Data Scraping: Offers a data extractor to gather data from LinkedIn profiles and from Sales Navigator. You can get the collected data in CSV format.
- Built-in CRM: All contacts are stored in an integrated CRM within Linked Helper. If you do use an external CRM, you can send data to these CRMs.
Pricing:
- Starting price (mo): $15 (standard), limiting specific outreach actions, such as messaging up to 20 event attendees per day
- Trial: 14-day free trial
2. Cookie-based LinkedIn scrapers
Cookie-based tools use your browser cookie to extract data. . They are used for low-volume, non-critical data collection, especially if users are already customers of these automation tools and will not incur additional costs.
These automation tools need to “act” on your to perform tasks on social networks:
- When you are logged into LinkedIn, the website sets a session cookie in your browser (which is unique to your session).
- You need to pass this cookie to the LinkedIn scraper.
- Then, the scraper leverages your session cookie from the social network to make connection requests and collect data. You can automate personalized tasks on LinkedIn, like sending connection requests and liking posts.
This approach is:
- Slow: Since it emulates human behavior, scraping is slower than with tools that use their own infrastructure. They are not suitable for large-scale data extraction tasks.
- Risky: If LinkedIn detects suspicious activity, you could face temporary restrictions or a permanent ban from LinkedIn.
PhantomBuster offers a LinkedIn profile scraper and a company scraper to scrape public data from the platform.
Features:
- Updated LinkedIn data: You can set up the LinkedIn scraping tool to launch repeatedly to extract data daily.
- Firefox and Chrome extension: The LinkedIn data extractor is available as an extension.
- Cloud-based: Runs on the remote servers, allowing users to extract data from LinkedIn without using local resources.
Pricing:
- Starting price: $59/mo
- Trial: Available for 14 days
3. Browser-extension LinkedIn scrapers
Browser extension tools work directly within the browser. They can be activated while you’re browsing LinkedIn. These tools are ideal for smaller scraping tasks. The risk of using browser extension scrapers is dependent on the browser. If the browser updates or changes, the extension tools can break.
Snov.io
Snov.io is a Chrome extension sales engagement platform that provides solutions across the outreach cycle. Snov.io’s LinkedIn Email Finder mechanically extracts email addresses from a LinkedIn profile or search results page.
It is vital to note that Snov.io is not a dedicated LinkedIn scraping tool; it can only scrape email addresses. You may collect emails in bulk from LinkedIn People Search pages and LinkedIn Sales Navigator search results.
LinkedIn automation providers such as PhantomBuster, Linked Helper, and Dripify provide pre-built scripts. If your organization requires LinkedIn automation but lacks an email solution, Snov.io may be sufficient. The free plan (50 credits) is generous.
Features:
- Email Finder: Discovers email addresses based on name, company, and domain inputs. Snov.io also offers Chrome extensions for generating leads (“click-and-collect”). You can extract emails from LinkedIn and Google search engines.
- Email Verifier: Offers a 7-tier email verification tool, verifying addresses with 98% accuracy. Keep in mind that verification consumes credits, one credit per verification.
Pricing:
- Starting price (mo): $39 (1,000 credits)
- Trial: 50 credits monthly
FindThatLead is a cloud-based B2B lead-generation and email-verification platform. The platform offers a Chrome extension that lets users extract email addresses from LinkedIn profiles and websites. It is not a free solution and requires credits from your own FindThatLead account.
Features:
- Email Finder & Verifier: You can receive emails from LinkedIn and other websites that include additional information, such as your name, email address, and work title.
- Email Sender & Drip Campaigns: Email Sender is a free tool that allows you to personalize messages for each recipient.
Pricing:
- Starting price (mo): $49 (2000 email credits)
- Trial: 50 email credits, including the Chrome extension.
Evaboot
Evaboot is a Chrome-based automation tool that exports lead data directly from LinkedIn Sales Navigator. Instead of scraping through proxies, it leverages your own Sales Navigator session to collect and clean visible lead data. But it is not suitable for large-scale scraping or automated scheduling.
Features
- Native Sales Navigator integration: Pulls names, job titles, company names, industries, and locations from Sales Navigator search results.
- Data cleaning: Automatically removes duplicates, broken links, and incomplete profiles.
Pricing:
- Starts at $49/month with a 7-day free trial.
- Offers pay-per-export options for small teams.
How to Scrape LinkedIn Data with Python
Learn how to perform LinkedIn scraping using Python and the Bright Data API. This tutorial demonstrates how to programmatically extract LinkedIn posts, profiles, jobs, and company data.
Each example follows the same pattern: you send the target LinkedIn URL to Bright Data’s LinkedIn Scraper API and receive structured data (JSON or CSV) in return.
Prerequisites
You only need a few setup steps to get started:
- Python 3.x is installed on your system
- requests library (pip install requests)
- Bright Data account with the LinkedIn dataset enabled
How to scrape LinkedIn posts
Step 1: Trigger the scraping job
Send a LinkedIn post URL to the Bright Data API endpoint to start the scraping process. The same pattern applies to profile, job, and company scraping later in this guide.
This Python script sends a POST request to the Bright Data LinkedIn Scraper API to initiate the scraping job. We authenticate using our API key and specify the dataset ID. “
Each LinkedIn post URL is passed as a JSON object and sent to the API, which handles proxy rotation, CAPTCHA solving, and request validation in the background. The API returns a unique snapshot ID, which you’ll use later to retrieve the scraped LinkedIn data.
Step 2: Retrieve the scraped data
Use the snapshot ID returned by the trigger job. Secrets and endpoints are read from env vars only.
This script retrieves the scraped LinkedIn data using the snapshot ID returned by the trigger job. It polls the Bright Data API to check the job status until the scraping process completes.
The API response can be either a single JSON object (with status) or multiple JSON objects in NDJSON format. For NDJSON responses, parse each line and extract the post records; for single JSON responses, check the status field: if it’s “building”, wait a few seconds and retry until it becomes “done”. Once finished, you can extract and display the structured LinkedIn post data.
How to scrape LinkedIn jobs with Python
Learn how to scrape LinkedIn job listings using Python and the LinkedIn Scraper API. You can extract structured job data, including titles, companies, locations, posting dates, and job descriptions, directly from LinkedIn job URLs.
This approach is ideal for building job boards, recruitment analytics, or salary research tools.
Step 1: Trigger the scraping job
The script below sends a POST request to Bright Data’s API to start a LinkedIn job scraping task. Each job URL is passed to the LinkedIn_jobs dataset, which automatically handles proxy rotation and LinkedIn’s anti-bot protection.
This script starts the LinkedIn job scraping process by sending a POST request to the Bright Data API. We authenticate using our API key and specify the LinkedIn Jobs dataset ID.
The search criteria define which roles to scrape. For example, software engineers in hybrid positions or data analysts in remote roles across New York.
The API returns a snapshot ID that can be used to retrieve the results once scraping is complete. Because all scraping tasks run on Bright Data’s cloud infrastructure, the process continues even if you close your Python script.
Step 2: Wait and retrieve results
Wait 5-10 minutes for the scraping to complete, then use this script to retrieve the data:
Once the LinkedIn job-scraping process completes, we retrieve the structured data using the snapshot ID returned by the trigger job. The response is typically in NDJSON format, where each line represents a separate job listing.
We parse each entry and extract key information, including job title, company name, location, employment type, and posting date. For single JSON responses, the script checks the status field and waits until it equals “done”, ensuring that all LinkedIn job data is fully processed. The script also uses .get() with default values to gracefully handle any missing fields.
How to scrape LinkedIn profile pages
You might want to scrape LinkedIn profiles for several legitimate use cases. For example, analyzing employees from a specific company, enriching a recruitment database, or processing a list of LinkedIn profile URLs collected from a networking event.
Step 1: Trigger the scraping job
This script sends a POST request to Bright Data’s API to start scraping the specified LinkedIn profiles. We authenticate with our API token and provide the dataset ID (available in your Bright Data dashboard under the LinkedIn People dataset).
The profile URLs are formatted as dictionary objects and sent to the API, which processes them and returns a snapshot ID for retrieving the data later. The try-except block handles the response and displays the snapshot ID or any errors.
Step 2: Retrieve the scraped LinkedIn profile data
Use the snapshot ID returned in Step 1 to poll the LinkedIn Scraper API until the job finishes, then parse the response.
The API may return NDJSON (one JSON object per line) or a single JSON object with a status field. Your script handles both: it checks the status (“building”, “running”, “ready”, “done”), waits when needed, and prints structured LinkedIn profile data once available.
How to scrape LinkedIn company data
You can use a LinkedIn company scraper to extract public company data, including names, industries, sizes, locations, and employee counts. If you don’t already have company URLs, you can generate them using a Google Search API query like site:linkedin.com/company/ [industry or keyword].
Step 1: Trigger the scraping job
We authenticate using our API token and include the dataset ID from the Bright Data dashboard. The LinkedIn company URLs are converted into the required JSON format and submitted to the API for processing.
Once the request is accepted, the API returns a snapshot ID that we’ll use later to retrieve the scraped company data. Basic error handling ensures the script either displays the snapshot ID or logs any request issues for debugging.
Step 2: Retrieve the scraped data
Once the scraping job is triggered, use the snapshot ID to check the status and retrieve the data.
This script fetches the scraped company data using the snapshot ID from the previous step. It continuously polls the API and supports multiple response formats.
First, it validates the HTTP status code to detect any errors. Then it tries to parse the response, which can come in two formats: JSONL (newline-delimited JSON objects) or a standard JSON object with status information.
LinkedIn scraper APIs benchmark methodology
The benchmark periodically sends requests to predefined LinkedIn profiles and company pages to measure data retrieval consistency and latency. A total of 100 profile URLs and 100 company URLs are requested at fixed intervals, and results are aggregated daily.
Requests run every 15 minutes with a 60-second timeout to ensure regular sampling while minimizing rate limiting from LinkedIn.
A request is considered successful if the response includes LinkedIn-specific fields such as “linkedin_id”, “headline”, “company_name”, or “industry”. Success is validated in two steps:
- First by scanning for these identifiers,
- And then rechecking for partially formatted content if no direct match is found. This dual process reduces false negatives caused by minor layout or formatting changes.
In our benchmark, we used the following dedicated APIs, explicitly designed to extract data from LinkedIn. To learn more, see our benchmark methodology for scraping APIs.
* : is listed for reference, but it was not used in our LinkedIn scraping benchmark.
FAQs about LinkedIn scrapers
CTO
Sedat is a technology and information security leader with experience in software development, web data collection 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 an advisor to C-level executives and board members of corporations with high-traffic and mission-critical technology operations like payment infrastructure.
– Has extensive business acumen alongside his technical expertise.
Researched by
Gulbahar Karatas
Industry Analyst
Gülbahar is an AIMultiple industry analyst focused on web data collection, applications of web data and application security.
