# LinkedIn Profile & Company Data Extractor (`second_coming/linkedin-data-extractor`) Actor Extract company employee lists, decision-maker profiles, job postings, and company page data from LinkedIn. B2B lead generation tool for sales teams and recruiters. - **URL**: https://apify.com/second\_coming/linkedin-data-extractor.md - **Developed by:** [Richard P](https://apify.com/second_coming) (community) - **Categories:** Lead generation, Business - **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 & Company Data Extractor An [Apify Actor](https://apify.com/actors) for extracting publicly available data from LinkedIn company pages and public profiles. Uses JSON-LD structured data, meta tags, and HTML parsing — no login required. ### Features #### Company Data Extraction - **Company Name** — extracted from JSON-LD, meta tags, and HTML - **Industry** — parsed from the company about page - **Company Size** — employee count or size range - **Headquarters** — location from about section - **Website** — from JSON-LD `sameAs` or visible links - **Description** — company summary - **Followers** — follower count from interaction statistics - **Specialties** — comma/semicolon-separated list #### Profile Data Extraction - **Name & Headline** — from JSON-LD Person schema, meta tags, HTML - **Location** — geographic location - **About** — profile summary text - **Experience** — work history (company, title, dates, description) - **Education** — schools, degrees, dates - **Connections Count** — if publicly visible #### Job Listing Extraction - For each company, fetches job postings from LinkedIn's public jobs search - Extracts: title, location, posted date, description, employment type - Configurable limit (default 20, max 50) ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | `companyUrls` | `string[]` | `["https://www.linkedin.com/company/microsoft/"]` | LinkedIn company page URLs | | `profileUrls` | `string[]` | `[]` | LinkedIn public profile URLs | | `includeJobs` | `boolean` | `true` | Whether to fetch job listings | | `maxJobs` | `integer` | `20` | Max job listings per company (max 50) | #### Example Input ```json { "companyUrls": [ "https://www.linkedin.com/company/microsoft/", "https://www.linkedin.com/company/google/" ], "profileUrls": [ "https://www.linkedin.com/in/sundarpichai/" ], "includeJobs": true, "maxJobs": 10 } ```` ### Output Each record is pushed to the Apify dataset. Records have a `type` field distinguishing companies from profiles. #### Company Record Fields | Field | Type | Description | |-------|------|-------------| | `type` | `string` | `"company"` | | `companyName` | `string` | Company name | | `linkedinUrl` | `string` | LinkedIn URL | | `slug` | `string` | URL slug | | `industry` | `string` | Industry | | `companySize` | `string` | Employee count / range | | `headquarters` | `string` | HQ location | | `website` | `string` | Company website | | `description` | `string` | Company description | | `followers` | `integer` | LinkedIn followers | | `specialties` | `string[]` | Specialty tags | | `jobs` | `object[]` | Job listings (if requested) | | `scrapedAt` | `string` | ISO 8601 timestamp | #### Profile Record Fields | Field | Type | Description | |-------|------|-------------| | `type` | `string` | `"profile"` | | `name` | `string` | Full name | | `headline` | `string` | Profile headline | | `location` | `string` | Location | | `about` | `string` | About section | | `experience` | `object[]` | Work experience | | `education` | `object[]` | Education | | `connectionsCount` | `integer` | Connection count | | `linkedinUrl` | `string` | LinkedIn URL | | `scrapedAt` | `string` | ISO 8601 timestamp | #### Job Entry Fields (inside `jobs` array) | Field | Type | Description | |-------|------|-------------| | `title` | `string` | Job title | | `location` | `string` | Job location | | `postedDate` | `string` | Date posted | | `description` | `string` | Job description | | `employmentType` | `string` | Full-time, part-time, etc. | | `seniority` | `string` | Seniority level | ### Technical Approach 1. **JSON-LD** — primary data source. Parse `script[type="application/ld+json"]` tags for `Organization`, `Corporation`, `Person`, and `JobPosting` schemas. 2. **Meta tags** — secondary source. Extract OG and standard meta tags. 3. **Visible HTML** — tertiary source. Regex patterns and CSS selectors for fields not in structured data. 4. **Rate limiting** — 3.5s delay between requests with exponential backoff on 429 responses. 5. **Google Cache fallback** — if the direct LinkedIn fetch fails, tries `webcache.googleusercontent.com`. 6. **Graceful abort** — listens for the Apify `ABORTING` event to stop mid-crawl. ### Limitations > **Important**: LinkedIn is extremely protective of its data. This actor only extracts what is **publicly visible** without authentication. Many profiles and data fields require a LinkedIn login to access. - **Profile details** (experience, education, about) are often only partially visible without login. Profiles with "public" visibility settings yield the most data. - **Job listings** are fetched from the public jobs search page. Some companies may require login to view jobs. - **HTML selectors** for experience/education sections are fragile — LinkedIn changes class names frequently. JSON-LD parsing is far more reliable. - **Rate limiting** — aggressive scraping triggers LinkedIn's rate limits. The actor implements delays and backoff. - **No login/cookie support** — this actor never authenticates. For authenticated scraping, consider using the Apify LinkedIn Actors with built-in session management. ### Local Development ```bash ## Install dependencies pip install -r requirements.txt ## Run locally with Apify CLI apify run --purge ``` ### Deployment ```bash apify push ``` ### Input Schema See `.actor/input_schema.json` for the full input specification. # Actor input Schema ## `companyUrls` (type: `array`): LinkedIn company page URLs to scrape (e.g., https://www.linkedin.com/company/microsoft/) ## `profileUrls` (type: `array`): LinkedIn public profile URLs to scrape (e.g., https://www.linkedin.com/in/sundarpichai/) ## `includeJobs` (type: `boolean`): Whether to fetch job listings for each company from LinkedIn's public jobs search ## `maxJobs` (type: `integer`): Maximum number of job listings to extract per company (max 50) ## Actor input object example ```json { "companyUrls": [ "https://www.linkedin.com/company/microsoft/" ], "profileUrls": [], "includeJobs": true, "maxJobs": 20 } ``` # Actor output Schema ## `results` (type: `string`): Dataset URL containing scraped LinkedIn data # 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 = { "companyUrls": [ "https://www.linkedin.com/company/microsoft/" ], "profileUrls": [] }; // Run the Actor and wait for it to finish const run = await client.actor("second_coming/linkedin-data-extractor").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 = { "companyUrls": ["https://www.linkedin.com/company/microsoft/"], "profileUrls": [], } # Run the Actor and wait for it to finish run = client.actor("second_coming/linkedin-data-extractor").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "companyUrls": [ "https://www.linkedin.com/company/microsoft/" ], "profileUrls": [] }' | apify call second_coming/linkedin-data-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=second_coming/linkedin-data-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Profile & Company Data Extractor", "description": "Extract company employee lists, decision-maker profiles, job postings, and company page data from LinkedIn. B2B lead generation tool for sales teams and recruiters.", "version": "0.0", "x-build-id": "KFH3hJiZL2IzbQ7T9" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/second_coming~linkedin-data-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-second_coming-linkedin-data-extractor", "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/second_coming~linkedin-data-extractor/runs": { "post": { "operationId": "runs-sync-second_coming-linkedin-data-extractor", "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/second_coming~linkedin-data-extractor/run-sync": { "post": { "operationId": "run-sync-second_coming-linkedin-data-extractor", "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": { "companyUrls": { "title": "Company URLs", "type": "array", "description": "LinkedIn company page URLs to scrape (e.g., https://www.linkedin.com/company/microsoft/)", "items": { "type": "string", "pattern": "linkedin\\.com/company/" } }, "profileUrls": { "title": "Profile URLs", "type": "array", "description": "LinkedIn public profile URLs to scrape (e.g., https://www.linkedin.com/in/sundarpichai/)", "items": { "type": "string", "pattern": "linkedin\\.com/in/" } }, "includeJobs": { "title": "Include Job Postings", "type": "boolean", "description": "Whether to fetch job listings for each company from LinkedIn's public jobs search", "default": true }, "maxJobs": { "title": "Max Jobs per Company", "minimum": 1, "maximum": 50, "type": "integer", "description": "Maximum number of job listings to extract per company (max 50)", "default": 20 } } }, "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 } } } } } } } } } } ```