# LinkedIn Job Radar Actor (`vjkhush/linkedin-job-radar-actor`) Actor Search LinkedIn jobs by keywords and locations. Get full descriptions, seniority level, employment type, and industries — no login required. - **URL**: https://apify.com/vjkhush/linkedin-job-radar-actor.md - **Developed by:** [Richard v](https://apify.com/vjkhush) (community) - **Categories:** AI, Automation, Jobs - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $3.50 / 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 Job Radar Search LinkedIn jobs by keywords and locations. Get full descriptions, seniority level, employment type, and industries — no login required. ### What it does - Searches LinkedIn's public job board using keyword + location combinations - Visits each job page to extract the full description, seniority level, employment type, job function, industries, and apply URL - Filters results by location, company name, or remote/hybrid work - Supports multiple keywords and locations in a single run ### Output fields Each result includes: | Field | Example | |---|---| | `job_id` | `4195830261` | | `title` | `Software Engineer` | | `company` | `adesso SE` | | `location` | `Düsseldorf, North Rhine-Westphalia, Germany` | | `posted_at` | `2 days ago` | | `description` | Full job description (2,000–7,000+ chars) | | `seniorityLevel` | `Mid-Senior level` | | `employmentType` | `Full-time` | | `jobFunction` | `Engineering and Information Technology` | | `industries` | `IT Services and IT Consulting` | | `applyUrl` | Direct apply link | | `url` | Canonical LinkedIn job URL | | `source` | `linkedin` | | `scraped_at` | ISO timestamp | > Turn off **Scrape job details** to skip the detail page visit. You'll get titles, companies, locations, and URLs much faster — but no full description or metadata. ### Built-in filters - **Location** — strict post-scrape matching ensures only jobs in your target city appear (LinkedIn's own location filter is loose) - **Company name** — substring match on company field - **Remote-first** — keeps only remote/hybrid listings based on keyword detection - **Date window** — last 24 hours or last 3 days ### Example input #### Quick search (5 results with full details) ```json { "keywords": ["Software Engineer"], "locations": ["Düsseldorf"], "maximumItems": 5, "scrapeDetails": true } ```` #### Multi-keyword monitoring run ```json { "keywords": [ "cyber security", "information security", "soc analyst", "security engineer" ], "locations": ["Germany", "Berlin", "Munich"], "dateFilter": "LAST_3_DAYS", "maximumItems": 200, "maxListingPages": 20, "proxyConfiguration": { "useApifyProxy": true, "groups": ["RESIDENTIAL"], "countryCode": "DE" } } ``` #### Fast listing-only scan (no detail pages) ```json { "keywords": ["Data Analyst"], "locations": ["London"], "maximumItems": 100, "scrapeDetails": false, "dateFilter": "LAST_24_HOURS" } ``` ### Performance tips - Default settings (50 items, detail scraping ON) finish in ~2 minutes - Turn off **Scrape job details** for faster runs when you only need titles and links - Each listing page has ~25 results — set **Listing page cap** accordingly - Residential proxies are used by default for stability - The actor gracefully stops before the Apify timeout, so partial results are always saved ### Date filter `dateFilter` is intentionally capped to: - `LAST_24_HOURS` — jobs posted in the last 24 hours - `LAST_3_DAYS` — jobs posted in the last 3 days (default) No option older than 3 days is supported. ### Use cases - Job market monitoring with n8n, Make, or Zapier - Feed into Google Sheets or Airtable for tracking - Telegram/Slack alerts for new postings - Salary and hiring trend analysis # Actor input Schema ## `keywords` (type: `array`): Add one or more role/skill keywords. ## `keyword` (type: `string`): Optional fallback keyword. Comma-separated values are also supported. ## `locations` (type: `array`): City, region, or country names. ## `location` (type: `string`): Optional fallback location. Comma-separated values are supported. ## `scrapeDetails` (type: `boolean`): Visit each job page to get the full description, seniority level, employment type, and industries. Slower but much more data. ## `dateFilter` (type: `string`): Maximum supported recency is 3 days. ## `maximumItems` (type: `integer`): Maximum output records. Each keyword+location combo crawls listing pages, so higher values take longer. ## `maxListingPages` (type: `integer`): Maximum listing pages to crawl per keyword+location combo. Each page has ~25 results. ## `maxConcurrency` (type: `integer`): Crawler concurrency. ## `maxRequestRetries` (type: `integer`): Retries per request before failing. ## `minListingDelayMs` (type: `integer`): Minimum pause before parsing list pages. ## `maxListingDelayMs` (type: `integer`): Maximum pause before parsing list pages. ## `companyName` (type: `string`): Optional company name filter. ## `remoteWork` (type: `boolean`): Keep only jobs that look remote/hybrid. ## `proxyConfiguration` (type: `object`): Proxy setup. Residential proxies recommended for stability. ## Actor input object example ```json { "keywords": [ "Cyber Security" ], "keyword": "", "locations": [ "Germany" ], "location": "", "scrapeDetails": true, "dateFilter": "LAST_3_DAYS", "maximumItems": 50, "maxListingPages": 5, "maxConcurrency": 1, "maxRequestRetries": 3, "minListingDelayMs": 700, "maxListingDelayMs": 2200, "companyName": "", "remoteWork": false } ``` # Actor output Schema ## `jobListings` (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("vjkhush/linkedin-job-radar-actor").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("vjkhush/linkedin-job-radar-actor").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 vjkhush/linkedin-job-radar-actor --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=vjkhush/linkedin-job-radar-actor", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Job Radar Actor", "description": "Search LinkedIn jobs by keywords and locations. Get full descriptions, seniority level, employment type, and industries — no login required.", "version": "0.1", "x-build-id": "IFGNNc7zwFCkX7J7n" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/vjkhush~linkedin-job-radar-actor/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-vjkhush-linkedin-job-radar-actor", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/vjkhush~linkedin-job-radar-actor/runs": { "post": { "operationId": "runs-sync-vjkhush-linkedin-job-radar-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/vjkhush~linkedin-job-radar-actor/run-sync": { "post": { "operationId": "run-sync-vjkhush-linkedin-job-radar-actor", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "maximumItems" ], "properties": { "keywords": { "title": "Keywords (multi)", "type": "array", "description": "Add one or more role/skill keywords.", "default": [ "Cyber Security" ], "items": { "type": "string" } }, "keyword": { "title": "Single keyword (optional)", "type": "string", "description": "Optional fallback keyword. Comma-separated values are also supported.", "default": "" }, "locations": { "title": "Locations (multi)", "type": "array", "description": "City, region, or country names.", "default": [ "Germany" ], "items": { "type": "string" } }, "location": { "title": "Single location (optional)", "type": "string", "description": "Optional fallback location. Comma-separated values are supported.", "default": "" }, "scrapeDetails": { "title": "Scrape job details", "type": "boolean", "description": "Visit each job page to get the full description, seniority level, employment type, and industries. Slower but much more data.", "default": true }, "dateFilter": { "title": "Posting date window", "enum": [ "LAST_24_HOURS", "LAST_3_DAYS" ], "type": "string", "description": "Maximum supported recency is 3 days.", "default": "LAST_3_DAYS" }, "maximumItems": { "title": "Target results", "minimum": 1, "maximum": 10000, "type": "integer", "description": "Maximum output records. Each keyword+location combo crawls listing pages, so higher values take longer.", "default": 50 }, "maxListingPages": { "title": "Listing page cap", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum listing pages to crawl per keyword+location combo. Each page has ~25 results.", "default": 5 }, "maxConcurrency": { "title": "Parallel pages", "minimum": 1, "maximum": 20, "type": "integer", "description": "Crawler concurrency.", "default": 1 }, "maxRequestRetries": { "title": "Retry budget", "minimum": 1, "maximum": 10, "type": "integer", "description": "Retries per request before failing.", "default": 3 }, "minListingDelayMs": { "title": "Min page pause (ms)", "minimum": 0, "maximum": 10000, "type": "integer", "description": "Minimum pause before parsing list pages.", "default": 700 }, "maxListingDelayMs": { "title": "Max page pause (ms)", "minimum": 0, "maximum": 20000, "type": "integer", "description": "Maximum pause before parsing list pages.", "default": 2200 }, "companyName": { "title": "Company contains", "type": "string", "description": "Optional company name filter.", "default": "" }, "remoteWork": { "title": "Remote-first only", "type": "boolean", "description": "Keep only jobs that look remote/hybrid.", "default": false }, "proxyConfiguration": { "title": "Proxy mode", "type": "object", "description": "Proxy setup. Residential proxies recommended for stability." } } }, "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 } } } } } } } } } } ```