# Linkedin Employee Extractor (`chirpy_polygon/linkedin-employee-extractor`) Actor Extract employee profiles from LinkedIn company people search using authenticated requests (requires session cookies) - **URL**: https://apify.com/chirpy\_polygon/linkedin-employee-extractor.md - **Developed by:** [Bhojraj Pilaniya](https://apify.com/chirpy_polygon) (community) - **Categories:** Lead generation, Automation, Developer tools - **Stats:** 154 total users, 24 monthly users, 95.8% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## 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/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 Employee Extractor & API This project is unofficial and not affiliated with LinkedIn. A lightweight LinkedIn employee extractor that uses your session cookies to fetch people profiles (no browser automation needed). ### Features - **Authenticated Requests**: Uses `li_at` and `JSESSIONID` cookies. - **Fast & Lightweight**: No Selenium/Puppeteer overhead. - **API Server**: Built-in HTTP server to trigger scrapes remotely. - **Pagination Support**: Fetch all available results for a company/role. ### Setup #### 1. Install dependencies ```bash pip install -r requirements.txt ```` #### 2. Get your LinkedIn cookies 1. Open **Chrome** and go to [linkedin.com](https://www.linkedin.com) 2. Make sure you're **logged in** 3. Open **DevTools** (`F12` or `Cmd+Option+I`) 4. Go to **Application** tab → **Cookies** → `https://www.linkedin.com` 5. Copy these two values: - **`li_at`**: Your session token. - **`JSESSIONID`**: Your CSRF token (remove quotes if present, e.g., `ajax:...`). #### 3. Configure `.env` Create a `.env` file based on the example: ```env GEO_URN="102713980" # India (optional) DELAY_BETWEEN_REQUESTS=2 PORT=8000 ``` ### Usage #### Option 1: Run the API Server (Recommended) Start the server: ```bash python server.py ``` Send a scraping request: ```bash curl -X POST http://localhost:8000/scrape \ -H "Content-Type: application/json" \ -d '{ "company_url": "https://www.linkedin.com/company/google", "tags": ["software engineer"], "max_pages": 0, "li_at": "YOUR_LI_AT_COOKIE", "jsessionid": "YOUR_JSESSIONID_COOKIE" }' ``` > **Note**: If you provide `li_at` and `jsessionid` in the request body, they will override the environment variables. #### Option 2: Run as CLI Script Edit `.env` with your target `COMPANY_URLS` and `TAGS`, then run: ```bash python scraper.py ``` ### Deployment #### Docker Build and run the container: ```bash docker build -t linkedin-scraper . docker run -p 8000:8000 --env-file .env linkedin-scraper ``` #### Cloud (Render/Railway/Heroku) 1. **Push to GitHub** (see instructions below). 2. Connect your repo to a cloud provider. 3. Add your **Environment Variables** (`LI_AT`, `JSESSIONID`, etc.) in the cloud dashboard. 4. Deploy! # Actor input Schema ## `dryRun` (type: `boolean`): If enabled, the Actor finishes quickly without extracting data. Disable to run extraction. ## `companyUrls` (type: `array`): List of LinkedIn company URLs (e.g., https://www.linkedin.com/company/google) ## `tags` (type: `array`): List of keywords or job titles to search for within the company. ## `li_at` (type: `string`): Your LinkedIn 'li\_at' session cookie. Required for authentication. ## `jsessionid` (type: `string`): Your LinkedIn 'JSESSIONID' cookie (CSRF token). Required for authentication. ## `geoUrn` (type: `string`): Optional location filter ID (e.g., '102713980' for India). Leave empty for global. ## `searchMode` (type: `string`): 'title' searches for strict job titles. 'keywords' searches broadly. ## `maxPages` (type: `integer`): Maximum number of pages to scrape per keyword (10 results per page). Set 0 for unlimited. ## `delay` (type: `number`): Delay between requests to avoid rate limiting. ## Actor input object example ```json { "dryRun": true, "companyUrls": [ "https://www.linkedin.com/company/google" ], "tags": [ "Software Engineer" ], "searchMode": "title", "maxPages": 5, "delay": 2 } ``` # 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 = { "companyUrls": [ "https://www.linkedin.com/company/google" ], "tags": [ "Software Engineer" ] }; // Run the Actor and wait for it to finish const run = await client.actor("chirpy_polygon/linkedin-employee-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/google"], "tags": ["Software Engineer"], } # Run the Actor and wait for it to finish run = client.actor("chirpy_polygon/linkedin-employee-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/google" ], "tags": [ "Software Engineer" ] }' | apify call chirpy_polygon/linkedin-employee-extractor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=chirpy_polygon/linkedin-employee-extractor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Linkedin Employee Extractor", "description": "Extract employee profiles from LinkedIn company people search using authenticated requests (requires session cookies)", "version": "1.0", "x-build-id": "EwBIXfUPKiTXgEVc1" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/chirpy_polygon~linkedin-employee-extractor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-chirpy_polygon-linkedin-employee-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/chirpy_polygon~linkedin-employee-extractor/runs": { "post": { "operationId": "runs-sync-chirpy_polygon-linkedin-employee-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/chirpy_polygon~linkedin-employee-extractor/run-sync": { "post": { "operationId": "run-sync-chirpy_polygon-linkedin-employee-extractor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "dryRun": { "title": "Dry Run (for Store test)", "type": "boolean", "description": "If enabled, the Actor finishes quickly without extracting data. Disable to run extraction.", "default": true }, "companyUrls": { "title": "Company URLs", "type": "array", "description": "List of LinkedIn company URLs (e.g., https://www.linkedin.com/company/google)", "items": { "type": "string" } }, "tags": { "title": "Keywords / Job Titles", "type": "array", "description": "List of keywords or job titles to search for within the company.", "items": { "type": "string" } }, "li_at": { "title": "li_at Cookie", "type": "string", "description": "Your LinkedIn 'li_at' session cookie. Required for authentication." }, "jsessionid": { "title": "JSESSIONID Cookie", "type": "string", "description": "Your LinkedIn 'JSESSIONID' cookie (CSRF token). Required for authentication." }, "geoUrn": { "title": "Geo URN", "type": "string", "description": "Optional location filter ID (e.g., '102713980' for India). Leave empty for global." }, "searchMode": { "title": "Search Mode", "enum": [ "title", "keywords" ], "type": "string", "description": "'title' searches for strict job titles. 'keywords' searches broadly.", "default": "title" }, "maxPages": { "title": "Max Pages per Tag", "minimum": 0, "type": "integer", "description": "Maximum number of pages to scrape per keyword (10 results per page). Set 0 for unlimited.", "default": 5 }, "delay": { "title": "Delay (seconds)", "minimum": 0.5, "type": "number", "description": "Delay between requests to avoid rate limiting.", "default": 2 } } }, "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 } } } } } } } } } } ```