# Linkedin Jobs Scraper (`crawlerforge/linkedin-jobs-scraper`) Actor Powerful LinkedIn job scraper that extracts detailed listings in seconds. Collect titles, companies, descriptions, and dates automatically β€” ideal for market research, job analytics, or HR insights. Fast, reliable, and easy to use on Apify. - **URL**: https://apify.com/crawlerforge/linkedin-jobs-scraper.md - **Developed by:** [CrawlerForge](https://apify.com/crawlerforge) (community) - **Categories:** Automation, Jobs, Lead generation - **Stats:** 21 total users, 0 monthly users, 100.0% runs succeeded, 2 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 Jobs Scraper ![Scrapify Spotlight](resources/spotlight.png) Extract job listings directly from **LinkedIn** with full details β€” titles, companies, locations, posting dates, descriptions, applicants count, and more. --- ### πŸš€ Features βœ… **Fast & reliable** β€” Handles 50–200 job listings in just a few minutes. βœ… **Detailed output** β€” Includes job descriptions, applicants, and posting dates. βœ… **Configurable** β€” Set keyword, location, max jobs, and batch size for parallel scraping. βœ… **CSV & JSON export** β€” Automatically saves data in Apify Dataset and CSV file. βœ… **Automatic login** β€” Logs into LinkedIn and reuses saved sessions for faster runs. --- ### 🧩 Input parameters | Parameter | Type | Description | |------------|------|-------------| | `keyword` | string | Keyword to search for (e.g., β€œdata engineer”). | | `location` | string | Location of the job search (e.g., β€œFrance”). | | `maxJobs` | integer | Maximum number of job posts to scrape (default: 50). | | `fetchDetails` | boolean | Whether to open each job and extract details (description, applicants, date, etc.). | | `batchSize` | integer | Number of job detail pages opened in parallel. Recommended: 3 for optimal performance. | --- ### πŸ“Š Output data Each job entry includes the following fields: ```json { "title": "Data Engineer", "company": "Company Name", "location": "Paris, Île-de-France, France", "url": "https://www.linkedin.com/jobs/view/123456789", "description": "Full job description text...", "applicants": "34", "postedDate": "2025-10-21T09:15:00.000Z", "postedDateRaw": "il y a 1 semaine", "companyLogo": "https://media.licdn.com/logo.png", "scrapedAt": "2025-10-28T10:00:00.000Z" } ```` *** ### 🧰 Use cases β€’ Market research for job demand across industries.\ β€’ Tracking hiring trends and company growth.\ β€’ Competitive analysis of hiring patterns.\ β€’ Feeding job data into dashboards or AI tools.\ β€’ Lead generation for recruiters or consultants. *** # Actor input Schema ## `keyword` (type: `string`): Keyword to search for (e.g., 'Data Engineer'). ## `location` (type: `string`): Location of the job search (e.g., 'France', 'New York'). ## `maxJobs` (type: `integer`): Maximum number of job posts to scrape. ## `fetchDetails` (type: `boolean`): If enabled, the scraper will open each job and extract full details (description, applicants, date, etc.). ## Actor input object example ```json { "keyword": "data engineer", "location": "France", "maxJobs": 50, "fetchDetails": true } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("crawlerforge/linkedin-jobs-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ 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); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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 = {} # Run the Actor and wait for it to finish run = client.actor("crawlerforge/linkedin-jobs-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{}' | apify call crawlerforge/linkedin-jobs-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=crawlerforge/linkedin-jobs-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Linkedin Jobs Scraper", "description": "Powerful LinkedIn job scraper that extracts detailed listings in seconds. Collect titles, companies, descriptions, and dates automatically β€” ideal for market research, job analytics, or HR insights. Fast, reliable, and easy to use on Apify.", "version": "1.1", "x-build-id": "Ki5Hx5kJYlcieOBaT" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/crawlerforge~linkedin-jobs-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-crawlerforge-linkedin-jobs-scraper", "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/crawlerforge~linkedin-jobs-scraper/runs": { "post": { "operationId": "runs-sync-crawlerforge-linkedin-jobs-scraper", "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/crawlerforge~linkedin-jobs-scraper/run-sync": { "post": { "operationId": "run-sync-crawlerforge-linkedin-jobs-scraper", "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", "required": [ "keyword", "location" ], "properties": { "keyword": { "title": "Job keyword", "type": "string", "description": "Keyword to search for (e.g., 'Data Engineer').", "default": "data engineer" }, "location": { "title": "Job location", "type": "string", "description": "Location of the job search (e.g., 'France', 'New York').", "default": "France" }, "maxJobs": { "title": "Maximum number of jobs", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum number of job posts to scrape.", "default": 50 }, "fetchDetails": { "title": "Fetch job details", "type": "boolean", "description": "If enabled, the scraper will open each job and extract full details (description, applicants, date, etc.).", "default": true } } }, "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 } } } } } } } } } } ```