# Linkedin Job Scrapper (`falcon_74/linkedin-job-scrapper`) Actor Search and export data: Specific Jobs with Specific Location and Time Range - **URL**: https://apify.com/falcon\_74/linkedin-job-scrapper.md - **Developed by:** [falcon 74](https://apify.com/falcon_74) (community) - **Categories:** Jobs, Automation, Social media - **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $2.00 / 1,000 results This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. 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 Public Job Scraper Extract rich, detailed job postings from LinkedIn without needing to log in. This stealthy, Python-based scraper bypasses LinkedIn's strict anti-bot protections using residential proxy rotation and human-like delays, ensuring your data extraction runs smoothly and reliably. ### 🚀 What can this Actor do? This scraper allows you to search for jobs on LinkedIn just like a human would, and extracts both the high-level search results and the deep details from individual job posting pages. * **No login required:** Scrapes the public-facing jobs portal to eliminate the risk of your personal LinkedIn account getting banned. * **Deep data extraction:** It doesn't just scrape the search page. It visits every single job link to pull specific criteria like Seniority Level, Employment Type, and Industry. * **Built for stealth:** Automatically utilizes Apify Residential Proxies and randomizes request delays to mimic natural human browsing and avoid IP bans. * **Time-based filtering:** Filter jobs posted in the past 24 hours, the past week, or the past month. ### 💼 Use Cases * **Recruitment & HR:** Monitor competitor hiring trends or find open roles in your industry. * **Market Research:** Analyze the demand for specific skills and job functions across different cities or countries. * **Job Boards & Aggregators:** Populate your own platform with fresh, highly-detailed job listings. ### 💰 Pricing This Actor uses Apify's **Pay-per-Event** pricing model. You only pay for successful data extraction, making it highly cost-effective and predictable. * **Start Event:** `$0.00005` per run (Essentially free to start). * **Per Result:** `$20.00 per 1,000 results` (Just $0.02 per successfully extracted job). > **Pro Tip:** You can set a **Max Charge Limit** in the Apify console before starting your run. The Actor will automatically stop scraping once it hits your defined budget! ### ⚙️ Input Configuration The Actor is designed to be simple to use. Here are the parameters you can configure: | Field | Description | Example | | :--- | :--- | :--- | | **Job Title or Keywords** | The specific role or skills you are looking for. | `Software Engineer`, `Data Analyst` | | **Location** | The geographic location. Can be a city, country, or "Remote". | `London`, `United States`, `Remote` | | **Date Posted** | Filter by when the job was published. Options are: *Any time, Past 24 hours, Past week, Past month*. | `Past 24 hours` | *Note: Because this scraper uses LinkedIn's public endpoint to keep you safe from login bans, custom date ranges (like "last 12 hours") are not supported by LinkedIn. Please use the exact options provided in the input dropdown.* ### 📊 Output Format The Actor pushes the extracted data into the Apify default dataset. You can download the results in JSON, CSV, Excel, or HTML. Here is an example of a single extracted job item in JSON format: ```json { "title": "Software Engineer, Backend", "company": "DoorDash", "location": "London, England, United Kingdom", "time_published": "2 hours ago", "url": "[https://www.linkedin.com/jobs/view/software-engineer-backend-at-doordash-1234567890](https://www.linkedin.com/jobs/view/software-engineer-backend-at-doordash-1234567890)", "experience_level": "Mid-Senior level", "employment_type": "Full-time", "job_function": "Engineering and Information Technology", "industry": "Software Development" } # Actor input Schema ## `keywords` (type: `string`): What job are you looking for? (e.g., Software Engineer) ## `location` (type: `string`): Where? (e.g., London, United States, Remote) ## `date_limit` (type: `string`): Filter jobs by when they were published (Note: LinkedIn public page only supports these three intervals) ## Actor input object example ```json { "keywords": "software engineer", "location": "London", "date_limit": "all" } ```` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("falcon_74/linkedin-job-scrapper").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 = {} # Run the Actor and wait for it to finish run = client.actor("falcon_74/linkedin-job-scrapper").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 '{}' | apify call falcon_74/linkedin-job-scrapper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=falcon_74/linkedin-job-scrapper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Linkedin Job Scrapper", "description": "Search and export data: Specific Jobs with Specific Location and Time Range", "version": "0.0", "x-build-id": "ueZ7yVYsHItQnoVBJ" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/falcon_74~linkedin-job-scrapper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-falcon_74-linkedin-job-scrapper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/falcon_74~linkedin-job-scrapper/runs": { "post": { "operationId": "runs-sync-falcon_74-linkedin-job-scrapper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/falcon_74~linkedin-job-scrapper/run-sync": { "post": { "operationId": "run-sync-falcon_74-linkedin-job-scrapper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keywords", "location", "date_limit" ], "properties": { "keywords": { "title": "Job Title or Keywords", "type": "string", "description": "What job are you looking for? (e.g., Software Engineer)", "default": "software engineer" }, "location": { "title": "Location", "type": "string", "description": "Where? (e.g., London, United States, Remote)", "default": "London" }, "date_limit": { "title": "Date Posted", "enum": [ "all", "r86400", "r604800", "r2592000" ], "type": "string", "description": "Filter jobs by when they were published (Note: LinkedIn public page only supports these three intervals)", "default": "all" } } }, "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 } } } } } } } } } } ```