# LinkedIn Profile Scraper + emails(No cookies) (`spectre_scrape/linkedin-profile-scraper-emails-no-cookies`) Actor Scrape detailed LinkedIn profile data including experience, education, skills, and more. No cookies or login required from the user. Fast and reliable. And get there work emails with it. - **URL**: https://apify.com/spectre\_scrape/linkedin-profile-scraper-emails-no-cookies.md - **Developed by:** [Spectre](https://apify.com/spectre_scrape) (community) - **Categories:** Lead generation, Social media - **Stats:** 7 total users, 3 monthly users, 98.9% runs succeeded, 1 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $1.00 / 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/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. 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. Actors are written with capital "A". ## 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. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## LinkedIn Profile Extractor and B2B Email Enricher Our powerful tool allows you to gather deep, structured data from LinkedIn profiles at lightning speed. You can collect complete data from the experience, education, skills, and certifications sections of any public profile without triggering platform restrictions by leveraging an array of secure, rotating session cookies. Optionally, our tool features a state-of-the-art **Multi-Method Email Discovery** engine. This is ideal for lead generation, recruitment, and high-level networking. We perform comprehensive validation checks, including native profile regex scanning, internal contact info harvesting, and advanced SMTP/MX probing to ensure the email addresses are valid and deliverable. Adaptive run-time: if you toggle the email search off, the scraper flies through profiles almost instantly. #### Key Benefits * **Cost-Efficient Toggle**: Run pure data extraction at maximum speed, or toggle on the B2B Email Enricher only when you need deep prospecting. * **Fast response times & multi-threaded concurrency**. * **Real-time scraping - fresh data bypassing local caching**. #### How It Works Simply provide one of the following: * List of LinkedIn profile URLs (e.g., `https://www.linkedin.com/in/demishassabis`) * List of LinkedIn public identifiers (e.g., `demishassabis`) **Email Search (Toggle)** By selecting the "Enrich Email (B2B)" mode in your Input configuration, you can enable our multi-layer pipeline to discover hidden B2B emails. The scraper will intelligently extract explicit emails. *Important Note: While our tool employs 5 separate fallback layers to guarantee the highest possible email discovery probability, no tool can extract an email for 100% of profiles if the data does not exist or the corporate firewall rigidly drops all traffic.* #### Data You'll Receive * Profile summary, headline, and total follower counts * Current and previous work experience (with complete descriptions) * Educational qualifications * Certifications, Languages, and Volunteer experiences * Top skills extracted directly from the profile * Built-in `email` and `emailConfidence` scoring (e.g., `contact_info`, `smtp_accepted`, `pattern_guess`, `no_mx`) #### Sample Output Data Here is a sample output showing the exact schema of the extracted data: ```json [ { "publicIdentifier": "demishassabis", "profileUrl": "https://www.linkedin.com/in/demishassabis", "fullName": "Demis Hassabis", "firstName": "Demis", "lastName": "Hassabis", "headline": "Co-Founder & CEO, Google DeepMind", "summary": "Co-Founder & CEO of Google DeepMind - working on AGI, responsible for AI breakthroughs such as AlphaGo, the first program to beat the world champion at the game of Go; and AlphaFold, which cracked the 50-year grand challenge of protein structure prediction and was recognised with the 2024 Nobel Prize in Chemistry. Revolutionising drug discovery at Isomorphic Labs. Ultimately trying to understand the fundamental nature of reality.", "location": "United Kingdom", "industryName": "", "profilePicture": "https://media.licdn.com/dms/image/v2/D4E03AQHDlCnuxugKuA/profile-displayphoto-shrink_100_100/B4EZSKW58BHAAU-/0/1737488048398?e=1781136000&v=beta&t=hHpxLWJ9hhYLM-k-aTwZGFcC7fe7nbNk1mYsuUXeUpk", "backgroundImage": "https://media.licdn.com/dms/image/v2/D4D16AQGKHrdjpqvDCA/profile-displaybackgroundimage-shrink_350_1400/profile-displaybackgroundimage-shrink_350_1400/0/1715180022856?e=1781136000&v=beta&t=_mN12QnUMv7MOZTMsAQ7QaLvl-Vom8lDpoIXaesyOZk", "connectionCount": null, "followerCount": null, "email": "demis.hassabis@google.com", "emailConfidence": "contact_info", "experience": [ { "title": "Co-Founder & CEO", "companyName": "Google DeepMind", "companyLinkedInUrl": "https://www.linkedin.com/company/1594050", "location": null, "startDate": "2010", "endDate": null, "duration": null, "description": null }, { "title": "Founder & CEO", "companyName": "Isomorphic Labs", "companyLinkedInUrl": "https://www.linkedin.com/company/82385765", "location": null, "startDate": "2021", "endDate": null, "duration": null, "description": null }, { "title": "Research Fellow", "companyName": "Gatsby Computational Neuroscience Unit, UCL", "companyLinkedInUrl": "https://www.linkedin.com/company/4171", "location": null, "startDate": "2009", "endDate": "2010", "duration": null, "description": null }, { "title": "Early Gaming Career", "companyName": "Elixir Studios, Lionhead Studios, Bullfrog Productions", "companyLinkedInUrl": null, "location": null, "startDate": "1992", "endDate": "2005", "duration": null, "description": "Worked on the AI and design for pioneering simulation games such as Theme Park (1994), Black & White (2001), and Republic: the Revolution (2003)" } ], "education": [ { "schoolName": "University College London, U. of London", "schoolLinkedInUrl": "https://www.linkedin.com/school/12682", "degree": "PhD", "fieldOfStudy": "Cognitive Neuroscience", "startDate": null, "endDate": null, "description": null }, { "schoolName": "University of Cambridge", "schoolLinkedInUrl": "https://www.linkedin.com/school/12691", "degree": "BA", "fieldOfStudy": "Computer Science", "startDate": null, "endDate": null, "description": null } ], "skills": [ { "name": "Mathematical Modeling" }, { "name": "Algorithms" }, { "name": "Video Games" }, { "name": "Cognitive Neuroscience" }, { "name": "Statistics" }, { "name": "Computer Vision" }, { "name": "Game Development" }, { "name": "C++" }, { "name": "Optimization" }, { "name": "Computer Science" }, { "name": "Game Design" }, { "name": "Applied Mathematics" }, { "name": "Artificial Intelligence" }, { "name": "Matlab" }, { "name": "Pattern Recognition" }, { "name": "Programming" }, { "name": "Image Processing" }, { "name": "Machine Learning" }, { "name": "Software Engineering" }, { "name": "Computer Games" } ], "languages": [], "certifications": [], "volunteerExperiences": [], "scrapedAt": "2026-05-26T04:53:55.121Z" } ] ```` #### Exporting and API As a standard Apify Actor, the tool automatically stores results in a dataset. You can easily export this data in various formats such as CSV, JSON, XLS, etc., or access the data on-demand via the Apify API. #### Support and Feedback We continuously enhance our tools based on user feedback. If you encounter technical issues or have suggestions for improvement, please open an Issue in the Apify Console. *** **Disclaimer**: This Actor is an independent tool and is not affiliated with, endorsed by, or sponsored by LinkedIn Corporation. LinkedInยฎ is a registered trademark of LinkedIn Corporation. All trademarks are property of their respective owners. Please ensure your usage complies with applicable data privacy laws (e.g., GDPR, CCPA). # Actor input Schema ## `urls` (type: `array`): List of LinkedIn profile URLs to scrape (e.g., https://www.linkedin.com/in/jamiedimon). ## `publicIdentifiers` (type: `array`): List of LinkedIn public identifiers (the last part of the profile URL, e.g., 'demishassabis'). Use this if you already have identifiers extracted. ## `enrichEmail` (type: `boolean`): Enable advanced SMTP probing and pattern guessing to automatically resolve B2B contact emails. Turning this off makes the scraper significantly faster. ## `maxConcurrency` (type: `integer`): Maximum number of parallel requests. Higher values are faster but increase the risk of rate limiting. ## Actor input object example ```json { "urls": [ "https://www.linkedin.com/in/jamiedimon", "https://www.linkedin.com/in/demishassabis" ], "enrichEmail": true, "maxConcurrency": 5 } ``` # Actor output Schema ## `results` (type: `string`): The scraped array of LinkedIn profile objects (Experience, Education, Skills, etc). # 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/in/jamiedimon", "https://www.linkedin.com/in/demishassabis" ] }; // Run the Actor and wait for it to finish const run = await client.actor("spectre_scrape/linkedin-profile-scraper-emails-no-cookies").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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/in/jamiedimon", "https://www.linkedin.com/in/demishassabis", ] } # Run the Actor and wait for it to finish run = client.actor("spectre_scrape/linkedin-profile-scraper-emails-no-cookies").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "urls": [ "https://www.linkedin.com/in/jamiedimon", "https://www.linkedin.com/in/demishassabis" ] }' | apify call spectre_scrape/linkedin-profile-scraper-emails-no-cookies --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=spectre_scrape/linkedin-profile-scraper-emails-no-cookies", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Profile Scraper + emails(No cookies)", "description": "Scrape detailed LinkedIn profile data including experience, education, skills, and more. No cookies or login required from the user. Fast and reliable. And get there work emails with it.", "version": "1.0", "x-build-id": "xhmh8gO8hIfkN04W1" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/spectre_scrape~linkedin-profile-scraper-emails-no-cookies/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-spectre_scrape-linkedin-profile-scraper-emails-no-cookies", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/spectre_scrape~linkedin-profile-scraper-emails-no-cookies/runs": { "post": { "operationId": "runs-sync-spectre_scrape-linkedin-profile-scraper-emails-no-cookies", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/spectre_scrape~linkedin-profile-scraper-emails-no-cookies/run-sync": { "post": { "operationId": "run-sync-spectre_scrape-linkedin-profile-scraper-emails-no-cookies", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "urls": { "title": "Profile URLs", "type": "array", "description": "List of LinkedIn profile URLs to scrape (e.g., https://www.linkedin.com/in/jamiedimon).", "items": { "type": "string" } }, "publicIdentifiers": { "title": "Public Identifiers", "type": "array", "description": "List of LinkedIn public identifiers (the last part of the profile URL, e.g., 'demishassabis'). Use this if you already have identifiers extracted.", "items": { "type": "string" } }, "enrichEmail": { "title": "Enrich Email (B2B)", "type": "boolean", "description": "Enable advanced SMTP probing and pattern guessing to automatically resolve B2B contact emails. Turning this off makes the scraper significantly faster.", "default": true }, "maxConcurrency": { "title": "Max Concurrency", "minimum": 1, "maximum": 20, "type": "integer", "description": "Maximum number of parallel requests. Higher values are faster but increase the risk of rate limiting.", "default": 5 } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```