# Linkedin Company Profile Scraper (`scrapeverse/linkedin-company-profile-scraper`) Actor The LinkedIn Company Profile Scraper is a powerful and efficient tool designed to extract valuable information from LinkedIn company profiles with ease. Whether you're a market researcher, sales professional, or just curious about a company's background. - **URL**: https://apify.com/scrapeverse/linkedin-company-profile-scraper.md - **Developed by:** [ScrapeVerse](https://apify.com/scrapeverse) (community) - **Categories:** Lead generation, Social media - **Stats:** 528 total users, 51 monthly users, 98.9% runs succeeded, 93 bookmarks - **User rating**: 4.29 out of 5 stars ## Pricing from $5.00 / 1,000 results This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event ## What's an Apify Actor? An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md. ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run. For examples already wired to this Actor's own input schema, see the [API](#api) section below. Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`). # README ## LinkedIn Company Profile Scraper A **linkedin company** profile scraper is the fastest way to collect public business information without spending hours clicking through pages. Instead of opening every **linkedin company** profile manually, copying details, and building spreadsheets by hand, you simply paste the URLs and let the automation do the heavy lifting. Within minutes, you receive structured, clean, analysis-ready data from each **linkedin company** page. Think of it as converting any public **linkedin company** listing into an instant database. *** ### Why use a LinkedIn Company Profile Scraper? If you work in sales, recruitment, investing, partnerships, or market intelligence, information from a **linkedin company** page is incredibly valuable. The problem is time. Manual collection is slow, repetitive, and error-prone. A **linkedin company** scraper helps you: - build prospect lists - analyze markets - track competitors - enrich CRM records - discover hiring trends - monitor company growth You get standardized output from every **linkedin company** profile, ready for Excel, Google Sheets, BI tools, or APIs. No more copy-paste. No more messy formatting. *** ### How the LinkedIn Company scraper works It’s simple. You submit a list of **linkedin company** URLs. The actor visits each **linkedin company** page and extracts the publicly available data. Behind the scenes: - URLs are read from the `urls` input - each **linkedin company** profile is processed - structured results are stored in the dataset Start → run → download. *** ### What are the constraints? The scraper only collects data visible on a public **linkedin company** profile. ✔ No login ✔ No private information ✔ No hidden data For example, LinkedIn usually shows only a preview of employees. The scraper can access exactly what is displayed publicly for that **linkedin company**. If a visitor can see it, the tool can extract it. *** ### Supported input URL format Currently supported → **slug-based linkedin company URLs** ✅ `https://www.linkedin.com/company/apifytech` ❌ `https://linkedin.com/company/10038644` If you only have IDs, convert them first using the **[Linkedin Company Profile Id To Slug Finder](https://apify.com/sanjeta/linkedin-company-profile-id-to-slug-finder)**. *** ### 📦 What company insights can you pull from LinkedIn? | Field | What it means | How people use it | | ------------------------------ | --------------------------------------- | ----------------------------------------------- | | **company_name** | Official company name on LinkedIn | CRM enrichment, matching, deduplication | | **universal_name_id** | Unique slug from the LinkedIn URL | Stable ID, joins, profile building | | **company_id** | Numeric LinkedIn organization ID | Internal mapping, automation | | **linkedin_internal_id** | Extra LinkedIn reference (may be empty) | Advanced integrations | | **logo_image_url** | Company logo | Dashboards, lead previews, UI | | **background_cover_image_url** | Banner image | Brand monitoring, marketing analysis | | **tagline** | Short mission / positioning line | Personalization, AI classification | | **about** | Full description of the company | LLM enrichment, summarization, intent detection | | **industry** | Primary industry category | Market segmentation, filtering | | **industries** | Additional industries | Vertical routing | | **type** | Ownership type (private/public/etc.) | Investor & enterprise research | | **founded** | Year the company started | Stage & maturity modeling | | **specialties** | Declared expertise keywords | Capability search, competitor analysis | | **headquarters** | Main office location | Territory assignment | | **location** | General visible region | Geo enrichment | | **locations\[]** | Detailed office information | Expansion & footprint research | | **website** | Official domain | Tech lookup, contact discovery | | **company_size** | Employee range | Lead qualification | | **company_size_on_linkedin** | Count of profiles linked | Growth tracking | | **follower_count** | Displayed follower number | Social proof | | **follower_count_num** | Numeric followers | Analytics & trends | | **employees\[]** | List of visible employees | Decision maker discovery, recruiting | | **updates\[]** | Recent posts & activity | Intent data, trigger outreach | | **similar_companies\[]** | LinkedIn recommended peers | Competitor & TAM expansion | | **affiliated_companies\[]** | Related organizations | Corporate structure mapping | | **inputURL** | Source page | Data lineage | | **status** | Extraction result | Pipeline reliability | ##### Sample Output ``` [{ "company_name": "Apify", "universal_name_id": "apify", "logo_image_url": "https://media.licdn.com/dms/image/v2/D4D0BAQF7OnC-r1njIA/company-logo_200_200/B4DZwd3LFrHEAI-/0/1770027528966/apify_logo?e=2147483647&v=beta&t=3P-kpqqDp-xRunnoe7qQwvZ3DazE1KnOdCiVlTREoJQ", "background_cover_image_url": "https://media.licdn.com/dms/image/v2/D4D3DAQHvzCPj3SIuwg/image-scale_191_1128/B4DZwd25f3J0Ac-/0/1770027457587/apify_cover?e=2147483647&v=beta&t=LINhTZoj6pz0QZOMBRAR4zsAmibBHWaR_XR-qn7SmLU", "linkedin_internal_id": "", "industry": "Technology, Information and Internet", "location": "", "follower_count": "21,408", "tagline": "", "company_size_on_linkedin": 191, "about": "Apify is the world's largest marketplace of tools for web scraping, data extraction, and automation tools. \n\nUsers extract data from social media, e-commerce sites, search engines, and maps for competitive analysis, lead generation, and product research. Apify integrates with n8n, Make, Zapier, and LangChain, and its MCP support lets AI agents dynamically discover and use tools from the marketplace without custom code. Apify handles infrastructure, proxies, and scaling, so users focus on the data, not the pipeline.\n\nTrusted by enterprises like Siemens, Intercom, Groupon, and Microsoft, as well as 10,000+ customers around the world 🌎", "website": "https://apify.com/", "industries": "", "company_size": "51-200 employees", "headquarters": "Prague", "type": "Privately Held", "founded": "2016", "specialties": "Web scraping, Browser automation, AI agents, API integration, Data pipelines, No-code tools, Actor marketplace, and Developer platform", "company_id": 10608457, "locations": [ { "is_hq": true, "office_address_line_1": "Vodickova 704/36", "office_address_line_2": "Prague, 11100, CZ", "office_location_link": "https://www.bing.com/maps?where=Vodickova+704%2F36+Prague+11100+CZ&trk=org-locations_url" }, { "is_hq": false, "office_address_line_1": "San Francisco, CA, US", "office_address_line_2": "", "office_location_link": "https://www.bing.com/maps?where=San+Francisco+CA+US&trk=org-locations_url" } ], "employees": [ { "employee_photo": "https://media.licdn.com/dms/image/v2/C4E03AQHmtgVOIg7GeQ/profile-displayphoto-shrink_100_100/profile-displayphoto-shrink_100_100/0/1638120354738?e=2147483647&v=beta&t=-JjUdsDqMPboRwz07ODwvK0971jMiFrZlt38Tl8ipz0", "employee_name": "Honza Javorek", "employee_position": "Tvořím junior.guru, které pomáhá juniorním programátorům", "employee_profile_url": "https://cz.linkedin.com/in/honzajavorek?trk=org-employees" }, { "employee_photo": "https://media.licdn.com/dms/image/v2/D4E03AQGVYzvGHp9zhw/profile-displayphoto-shrink_100_100/profile-displayphoto-shrink_100_100/0/1688982177437?e=2147483647&v=beta&t=RZOrroNiBfyCRXLYrJGiJfMIhpA9OO-MjINurO_LwIU", "employee_name": "Jan Čurn", "employee_position": "", "employee_profile_url": "https://www.linkedin.com/in/jancurn?trk=org-employees" }, { "employee_photo": "https://media.licdn.com/dms/image/v2/C4E03AQFbR0qzQd-6eA/profile-displayphoto-shrink_100_100/profile-displayphoto-shrink_100_100/0/1516309144835?e=2147483647&v=beta&t=7Y2jjcvg3MWTreeC2ojO7B5pmmvToO2r3S5zCGylqnA", "employee_name": "Jakub Balada", "employee_position": "Co-founder at Apify", "employee_profile_url": "https://cz.linkedin.com/in/jbalada?trk=org-employees" }, { "employee_photo": "https://media.licdn.com/dms/image/v2/C4E03AQFZbIYP7gY9Sg/profile-displayphoto-shrink_100_100/profile-displayphoto-shrink_100_100/0/1630875589640?e=2147483647&v=beta&t=Pa6gHOuM4nqo9mL8tujZBw8Sms0--R1q4VZw15OVW5c", "employee_name": "Simona Baxa", "employee_position": "Head of People at Apify", "employee_profile_url": "https://cz.linkedin.com/in/simonabaxa?trk=org-employees" } ], "updates": [ { "text": "Monitoring video performance across YouTube, TikTok, Facebook, and Instagram used to eat up hours of manual work.\n\nMindvalley built a system that does it automatically, and predicts which ads will perform before spending a dollar.\nThe setup: Apify scrapers pull video data (hooks, thumbnails, engagement, posting times), then AI analyzes patterns.\n\n💡 Key insight: the first 5 seconds make or break everything.\n\nCheck out the full case study in the comments 👇", "articlePostedDate": "9h", "totalLikes": "5" }, { "text": "🚨 Web scraping in 2026 is getting more expensive - and AI is still surprisingly underused.\n\nIn December 2025, Apify and The Web Scraping Club surveyed hundreds of web scraping professionals to understand what’s changed, what’s working, and what’s coming next.\n\nA few highlights from the State of Web Scraping Report 2026 👇\n\n💸 Costs are rising fast\n• 66% increased proxy usage\n• 58% are spending more on proxies\n• 62%+ report higher infrastructure costs due to tougher anti-bot defenses\n\n🤖 AI: skepticism first, adoption next\n• 54% don’t use AI in their scraping workflows (yet)\n• But 66% plan to try AI-assisted scraping tools\n• Among current AI users, 100% plan to increase usage\n\nThe signal is clear. Once developers adopt AI for scraping, they don’t go back.\n\n\nDownload the free report in the comments 👇", "articlePostedDate": "18h", "totalLikes": "11" }, { "text": "THE FUTURE BELONGS TO BUILDERS\n\nThe timing is actually insane. Apify announced the winners yesterday (Feb 4th), and today is my birthday. I will gladly take the regional prize for the Apify $1M Challenge as a birthday gift.\n\nHuge shout-out to the entire Apify crew for building a world-class competition.\n\nThey have one of the strongest developer ecosystems in the game right now. \n\nKeep a close eye on them in 2026.", "articlePostedDate": "1d", "totalLikes": "30" }, { "text": "🏆 Beyond the grand prizes of the Apify $1M Challenge, we’re also recognizing standout developers from across the Apify community!\n\nThese winners stood out through long-term impact, rapid momentum, and regional representation, helping the challenge grow into a global effort.\n\nTap through to meet them 💚 →\n\nBuilt once, growing over time. Their work shows how consistent building on Apify can turn into lasting impact and recurring rewards.\n\n#Apify1MChallenge", "articlePostedDate": "1d", "totalLikes": "12" }, { "text": "Scraping 10,000 Amazon products with an AI agent used to mean 500 batches of 20 and constant timeout anxiety.\n\nApify MCP server now supports async execution. Start an Actor, get a run ID, let the agent handle other work, and fetch results when ready.\n\nDocs in comments 👇", "articlePostedDate": "1d", "totalLikes": "9" }, { "text": "Reviews are structured customer research hiding in plain sight - what people notice, what they hate, the exact language they use.\n\nJoe Nilsen built a Google Sheet that pulls Amazon reviews by ASIN, star rating, and keyword filters. No code, runs straight from the sheet menu, powered by Apify.\n\nGrab it below 👇Amazon sellers have a goldmine in their accounts and never touch it.\n\nReviews are not \"feedback.\"\n\nThey are a searchable, structured map of:\n\n- what customers actually notice\n- what they hate\n- what language they use\n- what competitors are getting away with\n\nWe built a Google Sheet that scrapes reviews without having the user touch any code.\n\n 1. Add ASINs.\n 2. Select star ratings.\n 3. Add keyword filters.\n 4. Run it from a menu inside the sheet.\n\nPowered by Apify, so it scales.\n\nI'm giving the sheet away to anyone who wants it.\n\nReviews are the cheapest customer research you will ever get.\n\nYou don’t need better ideas.\n\nYou need better extraction.\n\nDive in.", "articlePostedDate": "1d\n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n 2d", "totalLikes": "4" }, { "text": "The journey led somewhere big 🚀\n\nAfter months of building at the edge of the AI frontier, these creators went the furthest.\n\nIntroducing the Grand winners of the Apify $1M Challenge → the builders whose work stood out, scaled up, and helped push automation forward.\n\n💙 Click through to meet the winners and their top submitted Actors.\n\nP.S. Their Actors are 100% worth trying, find them in the comments.\n\n#Apify1MChallenge", "articlePostedDate": "2d", "totalLikes": "18" }, { "text": "Starting in 1 hour! ⏰ \n\nWe’re announcing the winners of the Apify $1M Challenge live 🏆 Join us to see the top Actors and a special update for all participating devs.\n\nRSVP → https://luma.com/6c1493t0The Apify $1M Challenge is coming to an end - and on February 4, we're announcing the winners live 🏆\n\nTune in to see which developers and Actors came out on top, and stick around for a special announcement about updated rewards for all participating developers.\n\nWhether you competed, missed out, or just want to discover some of the best new Actors on the platform, this one's worth your time.\n\n🗓️ February 4\n⏰ 5 pm CET / 8 am PST / 9:30 pm IST\n\nRSVP below in the comments ☑️", "articlePostedDate": "2d\n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n 1w", "totalLikes": "1" }, { "text": "As we conclude the Apify $1M Challenge, I would like to invite everyone to an online celebration with the community and us. ❤️ \n\nWe will announce the Grand Prize winners, our regional winners, and a special surprise for everyone who participated in this challenge.\n\nRegister now( https://luma.com/6c1493t0 ), and see you on Feb 4th :) \n\nWith love, Apify team. 🎉", "articlePostedDate": "1w", "totalLikes": "29" }, { "text": "When people hear the story of Apify, they often focus on its origins: two developers in Prague, frustrated by the limitations of the modern web, building a tool to solve their own problem. \n\nIt’s a great startup story, and it’s only the beginning of what matters.\n\nApify is no longer just a product born out of developer frustration. It has become the world’s largest marketplace for AI-ready data collection tools, and a critical infrastructure layer for companies that want to move faster, work smarter, and build better products.\n\n\n𝗥𝗘𝗔𝗗 𝗧𝗛𝗘 𝗗𝗘𝗧𝗔𝗜𝗟𝗦 👉 https://lnkd.in/dDNtAC3z\n\n\nJakub Balada Jan Čurn Petra Chocholova Magda Rýdová Ondra Urban Dusan Antos Nicolas Bowles Daniel Čížek Marek Trunkát Richard \"Cameron\" Hawes Uri Gankin 📈 Theo Vasilis Tomáš Jindra Christos Seraphim Stamopoulos Kirill Turin Dávid Lukáč Dmitry Chepurnikh Natasha Lekh Ales Wilk Jan Zenisek Myrsini Koukiasa Zuzka Pelechova Simona Liptáková Michelle Macias Mendez Václav Králíček \n\n\n#Startups #Founders #AI #Marketplace #StartupStory #Data #Tools", "articlePostedDate": "2d", "totalLikes": "68" } ], "similar_companies": [ { "link": "https://cz.linkedin.com/company/itsmakehq?trk=similar-pages", "name": "Make", "summary": "Software Development", "location": "Prague, Praha 8" }, { "link": "https://de.linkedin.com/company/n8n?trk=similar-pages", "name": "n8n", "summary": "Software Development", "location": "Berlin, BE" }, { "link": "https://www.linkedin.com/company/grow-with-clay?trk=similar-pages", "name": "Clay", "summary": "Software Development", "location": "New York, NY" }, { "link": "https://www.linkedin.com/company/instantlyapp?trk=similar-pages", "name": "Instantly.ai", "summary": "Software Development", "location": "Sheridan, WY" }, { "link": "https://fr.linkedin.com/company/phantombuster-official?trk=similar-pages", "name": "PhantomBuster", "summary": "Technology, Information and Internet", "location": "Paris, Île-de-France" }, { "link": "https://es.linkedin.com/company/zenrows?trk=similar-pages", "name": "ZenRows", "summary": "Technology, Information and Internet", "location": "" }, { "link": "https://www.linkedin.com/company/serperapi?trk=similar-pages", "name": "Serper", "summary": "Information Services", "location": "" }, { "link": "https://uk.linkedin.com/company/trigify?trk=similar-pages", "name": "Trigify.io", "summary": "Software Development", "location": "" }, { "link": "https://mk.linkedin.com/company/heyreachio?trk=similar-pages", "name": "HeyReach.io", "summary": "Software Development", "location": "Skopje, North Macedonia" }, { "link": "https://au.linkedin.com/company/smartlead-ai?trk=similar-pages", "name": "Smartlead", "summary": "Software Development", "location": "Sydney, Nsw" } ], "affiliated_companies": [], "inputURL": "https://www.linkedin.com/company/apify", "follower_count_num": 21408, "status": "success" }] ``` *** ### When should you use a LinkedIn Company scraper? Whenever your workflow requires visiting multiple **linkedin company** profiles. Common scenarios: ✔ outbound sales ✔ recruitment research ✔ VC scouting ✔ agency prospecting ✔ competitor monitoring ✔ market mapping If humans are clicking profile after profile, automation will multiply productivity. *** ### Why teams switch to this scraper Most tools promise data. This one focuses on **reliable linkedin company extraction at scale** while keeping costs predictable. You can run bulk jobs, plug the output into your systems, and trust the format every time. For growing teams, that reliability matters more than flashy features. *** ### 🆚 LinkedIn Company Scraper – Feature Comparison | Feature / Capability | **Our LinkedIn Company Scraper** | Typical Other Scrapers | | -------------------------------------- | ---------------------------------------------------- | ----------------------- | | **Data Accuracy** | ⭐ **High – optimized & continuously maintained** | Unstable / inconsistent | | **Success Rate** | 🚀 **Very High – reliable extraction every run** | Varies / often fails | | **Cost Efficiency** | 💰 **Lowest Cost – optimized resource usage** | Moderate to High | | **Speed / Performance** | ⚡ **High Speed – fast extraction & processing** | Slow to Moderate | | **Public Data Coverage** | 📊 **Large – deep LinkedIn company extraction** | Partial / limited | | **Similar Companies List** | 🔍 **Yes** | Rare / minimal | | **Affiliate / Related Companies List** | 🤝 **Yes** | Rare or unavailable | *** ### 💬 FAQ #### 1) How do I find LinkedIn company URLs as input? You can automate that part too using [LinkedIn Company URL - Mass Finder](https://apify.com/sanjeta/linkedin-company-url---mass-finder) Stop wasting time manually searching for LinkedIn company pages. Provide your company list and get their matching LinkedIn URLs in minutes. It’s the fastest way to prepare bulk input for your scraping workflow. *** #### 2) How do I extract a LinkedIn company profile? The safest and most common approach is to collect information that is publicly visible on linkedin company pages. You can use specialized automation tools or Apify actors built for this purpose. Typically, you provide the company URL (or first discover it using a finder actor), configure the fields you want (about, industry, size, website, followers, etc.), and export the results to CSV or JSON for further use in sales, research, or enrichment workflows. *** #### 3) Can I extract data from Sales Navigator or Recruiter platforms? Proceed carefully. Accessing those environments may involve contractual or legal risks. Many teams stay focused on publicly visible **linkedin company** data. *** #### 4) In which formats can I export the results? Most workflows support CSV for spreadsheets and JSON for integrations, analytics, or engineering pipelines. *** #### 5) Can this be combined with other Apify actors? Yes. It can run independently or as part of a larger system. Matching schemas between actors makes automation seamless. *** #### 6) Can scraped data help me design a better company page? Definitely. Reviewing multiple **linkedin company** profiles can inspire positioning, messaging, and keyword strategies. Just make sure the final content is original. Here’s an additional FAQ written in the same tone and structure: *** #### 7) Why we don’t support setting cookies? Adding cookies allows LinkedIn to associate activity with your personal account. If scraping is done in the wrong way, that account can be restricted or permanently blocked. We promote collecting only publicly available data, ensuring your personal identity is never involved in the process. *** ## You Might Also Like Discover More Actors to Enhance Your Workflow #### [LinkedIn Company URL - Mass Finder](https://apify.com/sanjeta/linkedin-company-url---mass-finder) Stop wasting time manually searching for LinkedIn company pages. With LinkedIn Mass Company URL Finder, input your company list and get instant access to their LinkedIn URLs. Automate your search and streamline your workflow today! #### [Linkedin User Profile Url - Mass Finder](https://apify.com/sanjeta/linkedin-user-profile-url---mass-finder) LinkedIn User Profile URL - Mass Finder automates locating LinkedIn profiles by inputting names, location or designations. It provides instant URLs, saving time for market research, lead generation, and recruitment. It enhances efficiency and streamlines workflows. #### [Linkedin Jobs Scrapper](https://apify.com/sanjeta/linkedin-jobs-scrapper) Get detailed and insightful data just by entering few information, with no need to worry about cookies or authentication. # Actor input Schema ## `urls` (type: `array`): Linkedin Company pages slug based urls ie.\['https://www.linkedin.com/company/apifytech' , 'https://www.linkedin.com/company/linkedin'], Id based urls currently not supported i.e.'https://linkedin.com/company/10038644' ## `proxy` (type: `object`): Select proxies to be used by your crawler. ## `debugSecret` (type: `string`): or maintainers ## Actor input object example ```json { "urls": [ "https://www.linkedin.com/company/cloudester/", "https://www.linkedin.com/company/revo-karmahub-software-and-services-pvt-ltd./", "https://www.linkedin.com/company/prometheas-technologies/", "https://www.linkedin.com/company/prakashsoftware/", "https://www.linkedin.com/company/nividasoftware/" ], "proxy": { "useApifyProxy": true }, "debugSecret": "" } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "urls": [ "https://www.linkedin.com/company/cloudester/", "https://www.linkedin.com/company/revo-karmahub-software-and-services-pvt-ltd./", "https://www.linkedin.com/company/prometheas-technologies/", "https://www.linkedin.com/company/prakashsoftware/", "https://www.linkedin.com/company/nividasoftware/" ], "proxy": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("scrapeverse/linkedin-company-profile-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "urls": [ "https://www.linkedin.com/company/cloudester/", "https://www.linkedin.com/company/revo-karmahub-software-and-services-pvt-ltd./", "https://www.linkedin.com/company/prometheas-technologies/", "https://www.linkedin.com/company/prakashsoftware/", "https://www.linkedin.com/company/nividasoftware/", ], "proxy": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("scrapeverse/linkedin-company-profile-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}") for item in client.dataset(run.default_dataset_id).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "urls": [ "https://www.linkedin.com/company/cloudester/", "https://www.linkedin.com/company/revo-karmahub-software-and-services-pvt-ltd./", "https://www.linkedin.com/company/prometheas-technologies/", "https://www.linkedin.com/company/prakashsoftware/", "https://www.linkedin.com/company/nividasoftware/" ], "proxy": { "useApifyProxy": true } }' | apify call scrapeverse/linkedin-company-profile-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com/?tools=fetch-actor-details,scrapeverse/linkedin-company-profile-scraper" } } } ``` The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer ` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations). ## OpenAPI specification Download the OpenAPI definition: https://api.apify.com/v2/actors/3rgDeYgLhr6XrVnjs/builds/uxg8VPIYvSuQ02Eit/openapi.json