# LinkedIn Jobs Scraper: $0.50 per 1,000 Jobs, No Login (`fit_melon/linkedin-jobs-scraper`) Actor Scrape public LinkedIn job offers without an account: title, company, location, date, full description, seniority, employment type, salary when shown, applicants and company details. Search with keywords and a location, or paste search URLs. $0.50 per 1,000 jobs, all included. - **URL**: https://apify.com/fit_melon/linkedin-jobs-scraper.md - **Developed by:** [Melon Data](https://apify.com/fit_melon) (community) - **Categories:** Jobs, Lead generation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing $0.50 / 1,000 jobs 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/actors/running/actors-in-store.md#pay-per-event ## What's an Apify Actor? An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes. 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. Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md. ## 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. Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run. For examples already wired to this Actor's own input schema, see the [API](#api) section below. Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`). # README ## LinkedIn Jobs Scraper: $0.50 per 1,000 jobs, no login Get public LinkedIn job offers as clean, structured data: title, company, location, date posted, full description (plain text and safe HTML), seniority level, employment type, industries, applicants, salary when shown, apply type and, if you want them, the company's public details (industry, size, website, address, specialties, followers…). Search with **keywords + a location**, or paste **LinkedIn job search URLs**. One item per job, no duplicates. - **Public data only.** The actor reads the job pages LinkedIn shows to any logged-out visitor. No LinkedIn account, no login, no cookies, no CAPTCHA solving. - **No personal data fields.** Recruiter / job poster names, photos and profile links are never collected. Job descriptions are delivered as the employer wrote them (see the FAQ). - **One price, everything included.** **$0.50 per 1,000 jobs, all included**: description, criteria and company details are in the price. No start fee, no paid options. ### Why this LinkedIn jobs scraper? Measured on 2026-10-27, on the same LinkedIn search ("data engineer", Berlin, past week, 100 jobs with company details), run at the same moment with two of the most used LinkedIn jobs scrapers on Apify: | | This actor | curious_coder | valig | |---|---|---|---| | Jobs delivered | 100 | 99 | 100 | | Same jobs as ours | – | 99 of 99 | 100 of 100 | | Run time, 100 jobs **with company details** | **78.6 s** | 137.2 s | 10.1 s, without company details (75 s one hour earlier) | | Items with a recruiter / poster name or profile link | **0** | 16 | 16 | - **Same jobs as the others.** On that search, every job the other two scrapers returned was also in our results. - **Faster with company details in our test:** 100 jobs with company details in 78.6 s, against 137.2 s for curious_coder started at the same moment (one run each; LinkedIn's speed varies during the day). On a large search: 1,000 jobs with descriptions in about 225 s (one run, United States). - **No recruiter or employee fields.** No recruiter, job poster or employee data is extracted, and there were 0 LinkedIn profile links in 2,220 benchmark jobs (descriptions included). - **Clean text + safe HTML.** `description` is plain text with paragraphs and `- ` bullets (no blank lines between bullets, no leftover HTML entities). `descriptionHtml` keeps only simple formatting tags (paragraphs, line breaks, lists, bold, italics, underline, headings, quotes), with no links and no attributes (0 tags with attributes in 2,220 jobs). - **Numbers ready to filter:** applicants as min / max / exact, salary as min / max / currency / period, company size as min / max, members on LinkedIn, followers, founded year. ISO dates, plus the hour when LinkedIn gives it. - **No start fee.** A 20-job run costs $0.01. You pay per delivered job, never more than your run's spending limit. - **Filters are free for what they reject.** Jobs rejected by your filters and jobs that no longer accept applications are not delivered and not charged. Duplicates are never charged. - **Past LinkedIn's ~1,000 limit.** A LinkedIn search shows at most about 1,000 positions. When a search reaches it, the actor also runs it on the past month, week and day (London test: 791 distinct jobs from the plain search, 2,010 with the date windows). - **Moving from another scraper?** Its input names are accepted, and `outputFormat` adds its output field names next to ours (see the [migration guide](#migration-guide)). ### How to use it 1. Type **keywords** (job title, skill, company) and a **location** (city, region or country, as on LinkedIn), or paste one or more **LinkedIn job search URLs** (search on linkedin.com/jobs, copy the address bar). 2. Set **Maximum jobs per search** (0 = all the jobs LinkedIn shows). 3. Optional: filters, company details, HTML description. 4. Click **Start**, then download the results as JSON, CSV, Excel, XML or HTML, or fetch them through the API. ### Input examples **Keywords + location** ```json { "keywords": "data analyst", "location": "United Kingdom", "maxJobs": 200 } ``` **Several keywords and locations** (JSON input: every keyword is searched in every location) ```json { "titles": ["data engineer", "analytics engineer"], "locations": ["Berlin", "Munich"], "maxJobs": 100 } ``` **LinkedIn search URLs** (keywords, location, date posted and company filters of the URL are used) ```json { "searchUrls": [ "https://www.linkedin.com/jobs/search?keywords=data%20engineer&location=Berlin&f_TPR=r604800" ], "maxJobs": 100, "includeCompanyDetails": true } ``` **Filters** ```json { "keywords": "product manager", "location": "United States", "datePosted": "pastWeek", "contractTypes": ["F", "C"], "experienceLevels": ["4", "5"], "onlyWithSalary": true, "excludeRecruitingAgencies": true, "titleExclude": ["intern"], "maxJobs": 500 } ``` | Filter | Applied by | |---|---| | `datePosted` (past 24 hours / week / month), `companyIds` | LinkedIn | | `contractTypes` (employment type), `experienceLevels`, `easyApply`, `onlyWithSalary`, `excludeRecruitingAgencies` | this actor, on each job page | | `companyName`, `companyExclude`, `titleInclude`, `titleExclude` | this actor, on the search list | LinkedIn does not apply employment type, experience level or Easy Apply to logged-out visitors, so the actor checks them on each job page. A filtered search reads more job pages and takes longer, but **you pay only for the jobs that match**. **Only new jobs** (scheduled runs: each run delivers, and charges, only the jobs this search did not deliver before) ```json { "keywords": "data engineer", "location": "Berlin", "datePosted": "past24Hours", "onlyNewJobs": true, "maxJobs": 0 } ``` Each search keeps the job IDs it delivered (IDs only) in a key-value store of your own Apify account (`linkedin-jobs-seen` by default, `seenJobsStoreName` to choose another). Known jobs are skipped before their job page is read. With `datePosted` the list to read stays short; without it, a run may go deep into older jobs to fill `maxJobs`. **Input names of other scrapers** (same run, written for another actor) ```json { "urls": ["https://www.linkedin.com/jobs/search?keywords=nurse&location=Toronto"], "limitPerSource": 50, "scrapeCompany": true } ``` ### Output example One item per job (illustrative values, description shortened). With company details on: ```json { "jobId": "4400000001", "jobUrl": "https://www.linkedin.com/jobs/view/4400000001", "title": "Senior Data Analyst", "companyName": "Northwind Analytics", "companyUrl": "https://www.linkedin.com/company/northwind-analytics", "companyId": "1234567", "companyLogo": "https://media.licdn.com/dms/image/v2/.../company-logo_100_100/...", "location": "London, England, United Kingdom", "postedAt": "2026-10-27", "postedAtTimestamp": "2026-10-27T08:00:00.000Z", "postedAtPrecision": "hour", "postedTimeAgo": "6 hours ago", "applicantsCount": 200, "applicantsCountMin": 201, "applicantsCountMax": null, "applicantsCountIsExact": false, "applicantsText": "Over 200 applicants", "seniorityLevel": "Mid-Senior level", "employmentType": "Full-time", "industries": "IT Services and IT Consulting", "salary": "£55,000.00/yr - £65,000.00/yr", "salaryMin": 55000, "salaryMax": 65000, "salaryCurrency": "GBP", "salaryPeriod": "year", "salarySource": "employer", "salaryAdditionalCompensation": null, "applyType": "external", "jobInsight": "Actively Hiring", "benefitsCount": null, "description": "About the role\n\nYou will turn our product data into decisions.\n\nWhat you will do:\n\n- Build dashboards for the product teams\n- Own the weekly business review\n- Work with data engineers on new pipelines", "descriptionHtml": "

About the role

You will turn our product data into decisions.

What you will do:

", "companyIndustry": "IT Services and IT Consulting", "companySize": "501-1,000 employees", "companySizeMin": 501, "companySizeMax": 1000, "companyEmployeesOnLinkedIn": 742, "companyWebsite": "https://www.northwind-analytics.example", "companyDomain": "northwind-analytics.example", "companyHeadquarters": "London, England", "companyAddress": { "street": "1 Example Street", "city": "London", "region": "England", "postalCode": "EC1A 1AA", "country": "GB" }, "companyCountry": "GB", "companyType": "Privately Held", "companyFoundedYear": 2009, "companySpecialties": ["Analytics", "Data engineering", "Business intelligence"], "companySlogan": "Data you can act on", "companyFollowers": 18450, "companyDescription": "Northwind Analytics helps retailers understand their customers.", "searchKeywords": "data analyst", "searchLocation": "London", "searchUrl": "https://www.linkedin.com/jobs/search?keywords=data+analyst&location=London", "scrapedAt": "2026-10-27T14:05:12.481Z" } ``` The dataset has two ready-made views: **Overview** (the job table) and **Companies** (jobs with their company details). ### Output fields | Field | Type | Description | |---|---|---| | `jobId` | string | LinkedIn job ID | | `jobUrl` | string | Public job page (www.linkedin.com, no tracking parameters) | | `title` | string | Job title | | `companyName` | string | Company name | | `companyUrl` | string | Company's LinkedIn page | | `companyId` | string | LinkedIn company ID (usable in the `companyIds` filter) | | `companyLogo` | string | Company logo URL | | `location` | string | Job location as shown by LinkedIn | | `postedAt` | string | Date posted, ISO `YYYY-MM-DD` | | `postedAtTimestamp` | string or null | ISO time, rounded to the hour or minute, when LinkedIn gives it ("6 hours ago") | | `postedAtPrecision` | string | `day`, `hour` or `minute` | | `postedTimeAgo` | string | LinkedIn's relative text ("1 week ago") | | `applicantsCount` | number or null | Applicants number shown by LinkedIn (200 for "Over 200") | | `applicantsCountMin` / `applicantsCountMax` | number or null | Bounds ("Over 200" = 201 / null; "Be among the first 25" = 0 / 24) | | `applicantsCountIsExact` | boolean or null | true when LinkedIn shows an exact number | | `applicantsText` | string | LinkedIn's text ("97 applicants") | | `seniorityLevel` | string | Internship, Entry level, Associate, Mid-Senior level, Director, Executive, Not Applicable | | `employmentType` | string | Full-time, Part-time, Contract, Temporary, Internship, Volunteer, Other | | `industries` | string | Job industries | | `salary` | string or null | Pay range as shown | | `salaryMin` / `salaryMax` | number or null | Pay range as numbers | | `salaryCurrency` | string or null | ISO currency code (USD, EUR, GBP…) | | `salaryPeriod` | string or null | `year`, `month`, `week`, `day` or `hour` | | `salarySource` | string or null | `employer` when LinkedIn says the employer provided the range | | `salaryAdditionalCompensation` | array or null | Extras listed with the pay (bonus, equity…) | | `applyType` | string | `easyApply` (apply on LinkedIn) or `external` (on the company's site) | | `jobInsight` | string or null | LinkedIn's note on the card ("Actively Hiring", "Be an early applicant") | | `benefitsCount` | number or null | Number of benefits shown on the card | | `description` | string | Full description, clean plain text (with `includeDescription`) | | `descriptionHtml` | string | Same, as safe HTML (with `includeDescriptionHtml`) | | `companyIndustry` | string | Company industry (with `includeCompanyDetails`) | | `companySize` | string | Size as shown ("501-1,000 employees") | | `companySizeMin` / `companySizeMax` | number or null | Size as numbers ("10,001+" = 10001 / null) | | `companyEmployeesOnLinkedIn` | number or null | Members who list the company on LinkedIn | | `companyWebsite` | string | Company website | | `companyDomain` | string | Website domain (`example.com`) | | `companyHeadquarters` | string | Headquarters as shown | | `companyAddress` | object | `street`, `city`, `region`, `postalCode`, `country` | | `companyCountry` | string | ISO country code of the address | | `companyType` | string | Privately Held, Public Company, Nonprofit… | | `companyFoundedYear` | number or null | Year founded | | `companySpecialties` | array or null | Specialties list | | `companySlogan` | string or null | Company tagline | | `companyFollowers` | number or null | LinkedIn followers | | `companyDescription` | string | Company "About" text | | `searchKeywords` / `searchLocation` | string | The search that found the job | | `searchUrl` | string | That search on LinkedIn | | `scrapedAt` | string | ISO time of the scrape | Company fields appear only when `includeCompanyDetails` is on. With it on (200 jobs, Germany), industry was filled for 94.5 % of jobs, size 93.5 %, website 95 %, country 86.5 %. A missing value means the company page did not show it. With `includeDescription` off, the actor reads only the search list: `companyId`, the applicants fields, `seniorityLevel`, `employmentType`, `industries`, `salarySource`, `salaryAdditionalCompensation`, `applyType` and the description fields are then null or absent, and `salary` comes from the search list only. Run statistics (jobs per search, requests, blocks, timings) are saved in the run's key-value store as `RUN_STATS`. ### Pricing **$0.50 per 1,000 jobs ($0.0005 per job), all included**: description, criteria, salary and company details. No start fee, no paid option. You pay only for jobs delivered to your dataset; filtered-out jobs, closed jobs and duplicates are free, and the actor never goes beyond the spending limit you set for the run. Price comparison, per 1,000 jobs, Starter plan tier, measured 2026-10-27 from the public Store pricing: | Actor | Per 1,000 jobs | Start fee per run | Paid options | |---|---|---|---| | **This actor** | **$0.50** | none | none, all included | | curious_coder/linkedin-jobs-scraper | $1.00 | $0.00005 | none | | valig/linkedin-jobs-scraper | $0.36 | $0.0009 | none | | bebity/linkedin-jobs-scraper | $1.25 | $0.00005 | company & hiring-contact insights +$0.90, company website +$0.80 | | cheap_scraper/linkedin-job-scraper | $0.60 | $0.005 | AI job analysis +$0.50 | Prices of other actors can change: check their Store pages. ### Limits and FAQ **Do I need a LinkedIn account or cookies?** No. The actor reads only what LinkedIn shows to logged-out visitors. It never logs in, never sends cookies and never gets around the sign-in page. **What is not available logged out?** - **Workplace filter (on-site / remote / hybrid):** LinkedIn ignores it for logged-out visitors, so the actor does not send it and says so in the log. Remote or hybrid is often written in the title or description. - **Sorting** (most recent / most relevant): ignored by LinkedIn logged out; jobs come in LinkedIn's default order. Use `datePosted` for fresh jobs. - **External apply URL:** the public page only says whether you apply on LinkedIn (Easy Apply) or on the company's site; the company's link is behind the sign-in, so `applyType` is given, not the URL. - **Job function:** not shown on public job pages. **How precise is the date?** `postedAt` is the day LinkedIn shows. For recent jobs LinkedIn also says "6 hours ago": the actor then fills `postedAtTimestamp` to the hour (7 % of jobs on an any-time search, 22 % on a past-week search). **How many jobs show a salary?** Only those whose employer published one: 5 % (Germany, 200 jobs), 10.4 % (ten countries, 1,000 jobs) and 15.5 % (United States, 1,000 jobs) in our tests. `onlyWithSalary` keeps only those. **Is the description complete?** Yes, delivered as the employer wrote it (100 % of benchmark jobs had one). It may contain the employer's own contact details (a hiring email address or phone number) when the employer put them in the text. The actor never extracts people's data into separate fields. **How many jobs can I get per search?** LinkedIn shows at most about 1,000 positions per search (fewer distinct jobs, since its list repeats some jobs between pages). With `splitSearch` on (default), the actor also runs the search on the past month, week and day to reach more jobs, and removes duplicates. To go further, split by location, keywords or company. **How fast is it?** About 225 s for 1,000 jobs with descriptions in our tests, 78.6 s for 100 jobs with company details. Speed varies with LinkedIn's load: the actor uses 18 parallel requests by default and never more than 6 requests per second, and slows down by itself when LinkedIn asks it to. This moderate rate is on purpose. **What if LinkedIn blocks a request?** The actor retries with another IP, waits when LinkedIn asks, and reads a suspicious empty page again before treating it as the end of the list. In the 1,000-job benchmark, 0.3 % of responses were blocks and the run still delivered all 1,000 jobs. **Is there a free trial?** Apify's free plan gives monthly platform credit you can use on this actor. **Is scraping LinkedIn legal?** The actor collects only job data that LinkedIn shows publicly to logged-out visitors, and extracts no recruiter or employee data (descriptions are delivered as the employer wrote them). You are responsible for how you use the data, in line with the laws that apply to you (for example GDPR) and LinkedIn's terms. **Is this an official LinkedIn product?** No. This actor is not affiliated with, endorsed by or sponsored by LinkedIn Corporation. "LinkedIn" is a trademark of LinkedIn Corporation and is used here only to describe the public website the job data comes from. ### Integrations - **API:** run the actor and get the items in one call: ```bash curl -X POST "https://api.apify.com/v2/acts/fit_melon~linkedin-jobs-scraper/run-sync-get-dataset-items?token=" \ -H "Content-Type: application/json" \ -d '{"keywords":"data analyst","location":"London","maxJobs":50}' ``` Or use the Apify client for [JavaScript](https://docs.apify.com/api/client/js) or [Python](https://docs.apify.com/api/client/python). - **Make, n8n, Zapier:** use the Apify app / node, choose this actor and pass the same JSON input. - **Webhooks:** get a call when a run succeeds or fails, to load the dataset into your system. - **Schedules:** run the same search every hour or every day (for example with `datePosted: "past24Hours"`) to follow new jobs. Turn on `onlyNewJobs` to get and pay only for jobs the search did not deliver before (or give `skipJobIds` yourself, Advanced section). - **Exports:** JSON, CSV, Excel, XML, HTML, or straight to Google Sheets through the integrations. ### Migration guide Coming from another LinkedIn jobs scraper? In most cases you only change the actor ID in Make, n8n, Zapier or your code. **Input names accepted** | Our field | Also accepted | |---|---| | `searchUrls` | `urls`, `startUrls`, `searchUrl`, `url` | | `keywords` | `titles`, `title`, `keyword` | | `location` | `locations` | | `maxJobs` | `count`, `rows`, `limit`, `limitPerSource`, `maxRowsPerUrl`, `maxItems`, `maxResults` | | `datePosted` | `publishedAt` (also LinkedIn codes `r86400`, `r604800`, `r2592000`) | | `contractTypes` | `contractType`, `jobType`, `jobTypes`, `employmentType`, `employmentTypes` | | `experienceLevels` | `experienceLevel`, `seniorityLevel` | | `easyApply` | `filterEasyApply` | | `onlyWithSalary` | `requireSalaryInfo` | | `companyIds` | `companyId` | | `companyName` | `companyInclude`, `companyNames` | | `titleExclude` | `jobTitleExclude` | | `includeCompanyDetails` | `scrapeCompany`, `companyProfile`, `enrichCompanyData` | | `proxyConfiguration` | `proxy` | Values in words also work in the other scrapers' input names (`"full-time"`, `"contract"`, `"entry level"`, `"week"`…). Known input fields of those scrapers that we do not support are named in the run log. **Output field names: `outputFormat`** | Value | Adds next to our fields | |---|---| | `auto` (default) | detects the scraper your input was written for (from input names only it uses) | | `standard` | nothing, our names only | | `curious_coder` | `id`, `link`, `companyLinkedinUrl`, `inputUrl`, `descriptionText`, `companyEmployeesCount`; `companyAddress` is given in curious_coder's schema.org shape (`streetAddress`, `addressLocality`…) instead of ours | | `valig` | `id`, `url`, `postedDate`, `experienceLevel`, `contractType`, `workType` and `sector` (the job industry), `applicationsCount` | Differences to check in your mappings: `applicantsCount` is a number here; `applyType` is `easyApply` / `external`. Recruiter / job poster fields of other scrapers are never delivered (they contain personal data). *Not affiliated with LinkedIn. LinkedIn is a trademark of LinkedIn Corporation.* ### Other actors by fit_melon - [Google Maps Scraper](https://apify.com/fit_melon/google-maps-scraper-cheap): businesses and places from Google Maps. ### Feedback Found a missing field or a wrong value? Open an issue on the actor's **Issues** tab with the run ID. It helps us fix it. # Changelog This Actor's version history is a separate document: https://apify.com/fit_melon/linkedin-jobs-scraper/changelog.md # Actor input Schema ## `keywords` (type: `string`): What you would type in LinkedIn's job search box, e.g. 'data engineer' or 'nurse'. Leave empty to get all jobs of a location. ## `location` (type: `string`): City, region or country, as on LinkedIn: 'London', 'Paris, Île-de-France, France', 'Germany', 'United States'. Leave empty for worldwide. ## `searchUrls` (type: `array`): Search on linkedin.com/jobs (logged out or not), copy the URL from the address bar and paste it here. Its keywords, location, date posted and company filters are used. One search per URL. ## `maxJobs` (type: `integer`): Jobs to deliver for each search (each URL, or each keywords + location). 0 = all the jobs LinkedIn shows. You pay only for delivered jobs, never for duplicates. ## `datePosted` (type: `string`): Only jobs posted in this period. ## `contractTypes` (type: `array`): Only these employment types (as shown on the job page). Empty = all. ## `experienceLevels` (type: `array`): Only these seniority levels (as shown on the job page). Jobs marked 'Not Applicable' by the employer are left out when a level is chosen. Empty = all. ## `easyApply` (type: `boolean`): Only jobs you apply to on LinkedIn (Easy Apply), not on the company's website. ## `onlyWithSalary` (type: `boolean`): Keep only jobs whose LinkedIn page shows a pay range (from 5 % in Germany to 15.5 % in the United States in our tests). ## `excludeRecruitingAgencies` (type: `boolean`): Skip jobs posted in the 'Staffing and Recruiting' industry. ## `companyIds` (type: `array`): LinkedIn company IDs (the f_C value of a LinkedIn search URL, e.g. 1066442). Applied by LinkedIn. ## `companyName` (type: `array`): Keep only jobs whose company name contains one of these words (not case sensitive). ## `companyExclude` (type: `array`): Skip jobs whose company name contains one of these words, e.g. recruiting agencies. ## `titleInclude` (type: `array`): Keep only jobs whose title contains one of these words (not case sensitive). ## `titleExclude` (type: `array`): Skip jobs whose title contains one of these words, e.g. 'senior' or 'intern'. ## `includeDescription` (type: `boolean`): Full job description (clean text with paragraphs and lists, plus safe HTML), seniority level, employment type, industries, applicants, salary and apply type (one job page per job). Off = only what the search list shows: faster. ## `includeDescriptionHtml` (type: `boolean`): Adds descriptionHtml (safe HTML: paragraphs, lists, bold, italics, headings; no link, no attribute) next to the plain-text description. Off = items about half the size. ## `includeCompanyDetails` (type: `boolean`): Public company page: industry, size (text and numbers), website and domain, address and country, headquarters, type, founded year, specialties, slogan, followers, members on LinkedIn, description. Included in the price; a little slower (one page per company). ## `outputFormat` (type: `string`): Moving from another LinkedIn jobs scraper? Adds its main field names next to ours (id, link/url, descriptionText, postedDate, contractType…), so most Make, n8n or Zapier mappings keep working. Recruiter fields are never delivered. 'Auto' detects it from input fields only that scraper uses. ## `splitSearch` (type: `boolean`): LinkedIn shows at most about 1,000 positions per search (often about 800 distinct jobs). When a search reaches it, the same search is also run on the past month, week and day to reach more jobs. Duplicates are removed. ## `onlyNewJobs` (type: `boolean`): Each search remembers the jobs it delivered (job IDs only, in a key-value store of your Apify account) and skips them in later runs: you get and pay only for jobs you did not get before. Combine with 'Date posted' (past 24 hours or past week) so each run reads a short list instead of going deep into older jobs. ## `seenJobsStoreName` (type: `string`): Name of the key-value store of your account that keeps the delivered job IDs (letters, digits, hyphens). Use another name to start from scratch, or one name per project. ## `geoId` (type: `string`): Optional LinkedIn location ID (geoId of a LinkedIn search URL), used instead of the location text. ## `skipJobIds` (type: `array`): LinkedIn job IDs you already have (jobId of earlier runs): they are neither delivered nor charged. Useful for scheduled runs. ## `maxConcurrency` (type: `integer`): Job pages fetched in parallel (at most 6 requests per second in any case). The actor also slows down by itself when LinkedIn asks it to. ## `residentialFallback` (type: `boolean`): When LinkedIn blocks the datacenter proxies, retry some requests through residential proxies (included in the price, limited per run). ## `proxyConfiguration` (type: `object`): Apify Proxy (datacenter) is used by default and is included in the price. ## Actor input object example ```json { "keywords": "software engineer", "location": "London", "maxJobs": 100, "datePosted": "anyTime", "easyApply": false, "onlyWithSalary": false, "excludeRecruitingAgencies": false, "includeDescription": true, "includeDescriptionHtml": true, "includeCompanyDetails": false, "outputFormat": "auto", "splitSearch": true, "onlyNewJobs": false, "seenJobsStoreName": "linkedin-jobs-seen", "maxConcurrency": 18, "residentialFallback": true, "proxyConfiguration": { "useApifyProxy": true } } ``` # Actor output Schema ## `jobs` (type: `string`): All scraped jobs (one item per job), Overview table. ## `companies` (type: `string`): Jobs with their company details (when company details are included). ## `runStats` (type: `string`): Run statistics: jobs per search, requests, blocks, timings. # 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": "software engineer", "location": "London", "proxyConfiguration": { "useApifyProxy": true } }; // Run the Actor and wait for it to finish const run = await client.actor("fit_melon/linkedin-jobs-scraper").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": "software engineer", "location": "London", "proxyConfiguration": { "useApifyProxy": True }, } # Run the Actor and wait for it to finish run = client.actor("fit_melon/linkedin-jobs-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}") for item in client.dataset(run.default_dataset_id).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": "software engineer", "location": "London", "proxyConfiguration": { "useApifyProxy": true } }' | apify call fit_melon/linkedin-jobs-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "type": "http", "url": "https://mcp.apify.com/?tools=fetch-actor-details,fit_melon/linkedin-jobs-scraper" } } } ``` The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer ` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations). ## OpenAPI specification Download the OpenAPI definition: https://api.apify.com/v2/actors/eCWRycpBXaNUfR0Qm/builds/eCCqHtUy5A9DXkvr1/openapi.json