# LinkedIn Jobs Scraper Unlimited (`mukeshrana90/linkedin-jobs-scraper-unlimited`) Actor Scrape LinkedIn job listings with no result cap. Extracts full job details including title, company, location, description, and more. - **URL**: https://apify.com/mukeshrana90/linkedin-jobs-scraper-unlimited.md - **Developed by:** [Mukesh Kumar](https://apify.com/mukeshrana90) (community) - **Categories:** Jobs, Lead generation, Automation - **Stats:** 3 total users, 2 monthly users, 100.0% runs succeeded, 2 bookmarks - **User rating**: 5.00 out of 5 stars ## Pricing from $0.20 / 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 Jobs Scraper Unlimited An Apify Actor that scrapes LinkedIn job listings with **no result cap**. While LinkedIn limits search results to ~1,000 per query, this actor automatically slices queries by time windows to retrieve all matching jobs. ### Features - Search by keyword, location, experience level, job type, workplace type - **Break the 1,000-result cap** via automatic query slicing - Full job detail extraction (title, company, description, seniority, etc.) - Cross-listing deduplication - TLS fingerprint spoofing via curl_cffi (appears as real Chrome) - Configurable concurrency and rate limiting - Residential proxy support via Apify Proxy ### Input | Field | Type | Default | Description | |-------|------|---------|-------------| | keywords | string | (required) | Search keywords | | location | string | "" | Job location | | geoId | string | "" | LinkedIn geographic ID | | datePosted | enum | "anyTime" | anyTime, pastMonth, pastWeek, past24Hours | | experienceLevel | array | [] | internship, entryLevel, associate, midSenior, director, executive | | jobType | array | [] | fullTime, partTime, contract, temporary, volunteer, internship | | workType | array | [] | onSite, remote, hybrid | | maxResults | int | 100 | Max jobs to scrape (0 = unlimited) | | scrapeJobDetails | bool | true | Fetch full descriptions | | maxConcurrency | int | 5 | Parallel detail requests | | minDelay | int | 1000 | Min delay between requests (ms) | | maxDelay | int | 3000 | Max delay between requests (ms) | | proxyConfig | object | Apify residential | Proxy configuration | ### Output Each job in the dataset contains: ```json { "jobId": "3912345678", "title": "Senior Python Developer", "company": "Acme Corp", "companyUrl": "https://linkedin.com/company/acme", "location": "San Francisco, CA (Remote)", "postedAt": "2 days ago", "applicantCount": "42 applicants", "workType": "Remote", "seniority": "Mid-Senior level", "employmentType": "Full-time", "description": "We are looking for...", "url": "https://www.linkedin.com/jobs/view/3912345678" } ```` ### Cost estimate With residential proxies at ~$0.50/1000 requests: - \~2 requests per job (1 search page / 25 jobs + 1 detail page) - **~$0.001 per job** at steady state ### Development ```bash pip install -e ".[dev]" make lint ## ruff check make typecheck ## mypy --strict make test ## pytest make all ## lint + typecheck + test ``` ### Legal notice This actor scrapes publicly accessible LinkedIn job listings (guest endpoints, no login required). It respects rate limits and uses residential proxies to minimize impact. No personal data beyond publicly posted job information is collected. See LinkedIn's Terms of Service for your jurisdiction's requirements. # Actor input Schema ## `keywords` (type: `string`): Job search keywords (e.g., 'python developer', 'data engineer') ## `location` (type: `string`): Job location (e.g., 'San Francisco', 'Remote') ## `companyName` (type: `array`): Filter by company names (e.g., Google, Microsoft). Leave empty for all companies. ## `companyId` (type: `array`): Filter by LinkedIn company IDs. You can find these in the company's LinkedIn URL. ## `datePosted` (type: `string`): Filter by posting date ## `maxResults` (type: `integer`): Maximum number of jobs to scrape. Set 0 for unlimited. ## `workType` (type: `array`): Filter by workplace type ## `jobType` (type: `array`): Filter by employment type ## `experienceLevel` (type: `array`): Filter by experience level ## `geoId` (type: `string`): LinkedIn geoId for precise location filtering. Overrides location text if provided. ## `scrapeJobDetails` (type: `boolean`): Fetch full job description for each listing. Slower but more data. ## `maxConcurrency` (type: `integer`): Maximum concurrent requests for detail pages ## `minDelay` (type: `integer`): Minimum delay between requests in milliseconds ## `maxDelay` (type: `integer`): Maximum delay between requests in milliseconds ## `proxyConfig` (type: `object`): Apify proxy configuration ## Actor input object example ```json { "keywords": "python developer", "location": "United States", "companyName": [ "Google", "Microsoft" ], "companyId": [ "1441", "1035" ], "datePosted": "anyTime", "maxResults": 100, "workType": [], "jobType": [], "experienceLevel": [], "scrapeJobDetails": true, "maxConcurrency": 5, "minDelay": 1000, "maxDelay": 3000, "proxyConfig": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } ``` # Actor output Schema ## `results` (type: `string`): LinkedIn job listings stored in the default dataset # 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 = { "keywords": "python developer", "location": "United States", "companyName": [ "Google", "Microsoft" ], "companyId": [ "1441", "1035" ], "datePosted": "anyTime" }; // Run the Actor and wait for it to finish const run = await client.actor("mukeshrana90/linkedin-jobs-scraper-unlimited").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 = { "keywords": "python developer", "location": "United States", "companyName": [ "Google", "Microsoft", ], "companyId": [ "1441", "1035", ], "datePosted": "anyTime", } # Run the Actor and wait for it to finish run = client.actor("mukeshrana90/linkedin-jobs-scraper-unlimited").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 '{ "keywords": "python developer", "location": "United States", "companyName": [ "Google", "Microsoft" ], "companyId": [ "1441", "1035" ], "datePosted": "anyTime" }' | apify call mukeshrana90/linkedin-jobs-scraper-unlimited --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=mukeshrana90/linkedin-jobs-scraper-unlimited", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Jobs Scraper Unlimited", "description": "Scrape LinkedIn job listings with no result cap. Extracts full job details including title, company, location, description, and more.", "version": "0.1", "x-build-id": "cnhFkm2jkWK43dnqb" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/mukeshrana90~linkedin-jobs-scraper-unlimited/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-mukeshrana90-linkedin-jobs-scraper-unlimited", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/mukeshrana90~linkedin-jobs-scraper-unlimited/runs": { "post": { "operationId": "runs-sync-mukeshrana90-linkedin-jobs-scraper-unlimited", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/mukeshrana90~linkedin-jobs-scraper-unlimited/run-sync": { "post": { "operationId": "run-sync-mukeshrana90-linkedin-jobs-scraper-unlimited", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "keywords": { "title": "Job title", "type": "string", "description": "Job search keywords (e.g., 'python developer', 'data engineer')" }, "location": { "title": "Job location", "type": "string", "description": "Job location (e.g., 'San Francisco', 'Remote')" }, "companyName": { "title": "Company name", "type": "array", "description": "Filter by company names (e.g., Google, Microsoft). Leave empty for all companies.", "items": { "type": "string" }, "default": [] }, "companyId": { "title": "Company id", "type": "array", "description": "Filter by LinkedIn company IDs. You can find these in the company's LinkedIn URL.", "items": { "type": "string" }, "default": [] }, "datePosted": { "title": "Published at", "enum": [ "anyTime", "pastMonth", "pastWeek", "past24Hours" ], "type": "string", "description": "Filter by posting date", "default": "anyTime" }, "maxResults": { "title": "Total rows", "minimum": 0, "type": "integer", "description": "Maximum number of jobs to scrape. Set 0 for unlimited.", "default": 100 }, "workType": { "title": "On-site/Remote", "type": "array", "description": "Filter by workplace type", "items": { "type": "string", "enum": [ "onSite", "remote", "hybrid" ], "enumTitles": [ "On-site", "Remote", "Hybrid" ] }, "default": [] }, "jobType": { "title": "Job type", "type": "array", "description": "Filter by employment type", "items": { "type": "string", "enum": [ "fullTime", "partTime", "contract", "temporary", "volunteer", "internship" ], "enumTitles": [ "Full-time", "Part-time", "Contract", "Temporary", "Volunteer", "Internship" ] }, "default": [] }, "experienceLevel": { "title": "Experience Level", "type": "array", "description": "Filter by experience level", "items": { "type": "string", "enum": [ "internship", "entryLevel", "associate", "midSenior", "director", "executive" ], "enumTitles": [ "Internship", "Entry Level", "Associate", "Mid-Senior", "Director", "Executive" ] }, "default": [] }, "geoId": { "title": "Geographic ID", "type": "string", "description": "LinkedIn geoId for precise location filtering. Overrides location text if provided." }, "scrapeJobDetails": { "title": "Scrape Full Job Details", "type": "boolean", "description": "Fetch full job description for each listing. Slower but more data.", "default": true }, "maxConcurrency": { "title": "Max Concurrency", "minimum": 1, "maximum": 20, "type": "integer", "description": "Maximum concurrent requests for detail pages", "default": 5 }, "minDelay": { "title": "Min Delay (ms)", "minimum": 500, "type": "integer", "description": "Minimum delay between requests in milliseconds", "default": 1000 }, "maxDelay": { "title": "Max Delay (ms)", "minimum": 1000, "type": "integer", "description": "Maximum delay between requests in milliseconds", "default": 3000 }, "proxyConfig": { "title": "Proxy Configuration", "type": "object", "description": "Apify proxy configuration", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } } } }, "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 } } } } } } } } } } ```