# LinkedIn New Job Posting Monitor and Alerts (`redfoxxie/linkedin-new-jobs-monitor-alerts`) Actor Monitor public LinkedIn job searches and deliver only newly discovered postings with persistent deduplication, remote and recency filters, source health, and bounded pay-per-alert runs. - **URL**: https://apify.com/redfoxxie/linkedin-new-jobs-monitor-alerts.md - **Developed by:** [João Ferreira](https://apify.com/redfoxxie) (community) - **Categories:** Jobs, Automation, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $1.00 / 1,000 new linkedin job alerts 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 web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows. 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/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.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/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 This independent product is not affiliated with, sponsored by, or endorsed by LinkedIn. ### Monitor new public LinkedIn jobs without paying for duplicates LinkedIn New Job Posting Monitor and Alerts turns repeatable public job searches into a deduplicated alert feed. Configure one or more keyword and location searches, schedule the Actor, and receive only postings that the same monitor has not delivered before. It is designed for recruiting pipelines, personal job alerts, niche job boards, labor-market research, and automation workflows that need new records rather than the same search results on every run. The Actor reads LinkedIn public guest job-search pages. It requires no LinkedIn login, cookies, account credentials, profile access, or personal-data scraping. Returned records contain public job-card fields such as job ID, title, company, location, posting time, canonical job URL, and the search that found the posting. ### Persistent monitoring Set `monitorId` to a stable name and run the Actor on a schedule. A named key-value store retains a bounded list of previously delivered public job IDs. Repeated results are suppressed, while genuinely new IDs become alerts. Different monitor IDs keep independent histories, so a remote Python search can run alongside a local data-engineering search without mixing state. `previewOnly=true` is the default. Preview mode fetches live public results and reports source health without charging custom events or changing deduplication state. After validating the query, switch preview off for scheduled monitoring. The Actor uses limited-permission storage access and does not request broad account permissions. ### Search controls Each run accepts up to 20 keyword and location searches. Filters include posts from the past day, week, or month and a remote-only option. Runtime and output are bounded by `maxResults` and `maxAlerts`, with hard limits of 500. If one query fails while another succeeds, successful results are returned with explicit partial-source diagnostics. If every public query fails, the run fails visibly instead of silently returning an empty result. ### Pricing The pricing model is **Pay per event + usage**. There is **no start fee**. Each newly delivered posting charges the custom event `new_linkedin_job_alert` at **$0.001**. Duplicate postings are not charged as new alerts. Apify platform usage is charged separately to the runner under the platform's normal compute and storage rules. Preview mode does not emit the paid custom event. ### Output and scheduling Use the `alerts` dataset view for a compact table of posting time, title, company, location, URL, keywords, and job ID. The run summary records fetched jobs, new jobs, suppressed duplicates, and per-search source status. For continuous monitoring, create an Apify schedule with the same `monitorId`; the first paid run establishes state and later runs emit only unseen postings. Public endpoints can change without notice. This Actor reports source failures, limits request volume, stores only bounded deduplication state, and is intended for lawful use of public information. Users remain responsible for complying with applicable terms, laws, and internal data policies. # Actor input Schema ## `searches` (type: `array`): Up to 20 keyword and location searches to monitor together. ## `postedWithin` (type: `string`): Only request postings from this recent time range. ## `remoteOnly` (type: `boolean`): Restrict results to jobs LinkedIn marks as remote. ## `maxResults` (type: `integer`): Maximum public postings inspected in one run. ## `maxAlerts` (type: `integer`): Maximum newly discovered jobs emitted and charged in one run. ## `monitorId` (type: `string`): Stable identifier used to retain deduplication state across scheduled runs. ## `previewOnly` (type: `boolean`): Fetch and show results without charging events or updating deduplication state. ## Actor input object example ```json { "searches": [ { "keywords": "Data engineer", "location": "Europe" } ], "postedWithin": "day", "remoteOnly": true, "maxResults": 100, "maxAlerts": 50, "monitorId": "remote-python-europe", "previewOnly": true } ```` # Actor output Schema ## `jobs` (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("redfoxxie/linkedin-new-jobs-monitor-alerts").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("redfoxxie/linkedin-new-jobs-monitor-alerts").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 redfoxxie/linkedin-new-jobs-monitor-alerts --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=redfoxxie/linkedin-new-jobs-monitor-alerts", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn New Job Posting Monitor and Alerts", "description": "Monitor public LinkedIn job searches and deliver only newly discovered postings with persistent deduplication, remote and recency filters, source health, and bounded pay-per-alert runs.", "version": "0.1", "x-build-id": "aNeAhk9qweH5bawpa" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/redfoxxie~linkedin-new-jobs-monitor-alerts/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-redfoxxie-linkedin-new-jobs-monitor-alerts", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/redfoxxie~linkedin-new-jobs-monitor-alerts/runs": { "post": { "operationId": "runs-sync-redfoxxie-linkedin-new-jobs-monitor-alerts", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/redfoxxie~linkedin-new-jobs-monitor-alerts/run-sync": { "post": { "operationId": "run-sync-redfoxxie-linkedin-new-jobs-monitor-alerts", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "searches" ], "properties": { "searches": { "title": "Job searches", "minItems": 1, "maxItems": 20, "type": "array", "description": "Up to 20 keyword and location searches to monitor together.", "items": { "type": "object", "properties": { "keywords": { "title": "Keywords", "description": "Role, skill, or query to search for.", "type": "string", "editor": "textfield", "example": "Data engineer", "maxLength": 200 }, "location": { "title": "Location", "description": "City, country, or region; leave blank for a broad search.", "type": "string", "editor": "textfield", "example": "Europe", "maxLength": 200 } }, "required": [ "keywords" ] }, "default": [ { "keywords": "Python developer", "location": "Portugal" } ] }, "postedWithin": { "title": "Posted within", "enum": [ "day", "week", "month" ], "type": "string", "description": "Only request postings from this recent time range.", "default": "day" }, "remoteOnly": { "title": "Remote jobs only", "type": "boolean", "description": "Restrict results to jobs LinkedIn marks as remote.", "default": false }, "maxResults": { "title": "Maximum jobs fetched", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum public postings inspected in one run.", "default": 100 }, "maxAlerts": { "title": "Maximum new alerts", "minimum": 1, "maximum": 500, "type": "integer", "description": "Maximum newly discovered jobs emitted and charged in one run.", "default": 100 }, "monitorId": { "title": "Monitor state ID", "maxLength": 60, "type": "string", "description": "Stable identifier used to retain deduplication state across scheduled runs.", "default": "default" }, "previewOnly": { "title": "Free preview", "type": "boolean", "description": "Fetch and show results without charging events or updating deduplication state.", "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 } } } } } } } } } } ```