# LinkedIn Job Scraper - Public Jobs, No Login (`nezha/linkedin-job-scraper`) Actor Scrape public LinkedIn job search results without login and export job title, company, location, salary, job description, apply URL, seniority, employment type, industry, and application count fields. - **URL**: https://apify.com/nezha/linkedin-job-scraper.md - **Developed by:** [nezha](https://apify.com/nezha) (community) - **Categories:** Jobs, Other - **Stats:** 7 total users, 4 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.50 / 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 Scraper - Public Jobs, No Login Extract public LinkedIn job search results without a LinkedIn login. Provide keywords, location, company filters, date filters, work arrangement, job type, and seniority filters, then export structured job records to an Apify dataset. ### What This Actor Extracts This Actor opens LinkedIn public guest job endpoints and saves job records with: - job title, job URL, and LinkedIn job ID - company name, company URL, and optional company ID lookup - job location, published time, application count, salary, benefits - employment type, seniority level, industries, job function - job description text and optional description HTML - apply type and apply URL ### Quick Start 1. Add one or more values to **Keywords**. 2. Set **Location**, for example `Los Angeles Metropolitan Area`. 3. Keep **Max Jobs** small for your first run, such as `20`. 4. Keep **Max Concurrency** around `2-4` and **Delay Between Requests** above zero. 5. Run the Actor and export the dataset as CSV, Excel, JSON, or via API. ### Example Input ```json { "MainKeys": ["Controller"], "Location": "Los Angeles Metropolitan Area", "Published_at": "Past week", "Remote_or_OnSite": ["Remote", "Hybrid"], "Job_Type": ["Full time"], "Experience_Level": ["Entry level", "Associate"], "maxItems": 20, "maxListPages": 5, "maxConcurrency": 3, "maxRetries": 3, "requestDelaySecs": 2, "includeDescriptionHtml": true, "includeNoResultRecords": true, "resolveCompanyIds": true } ```` ### Output Preview | title | companyName | location | publishedAt | contractType | jobUrl | | --- | --- | --- | --- | --- | --- | | Controller | Example Company | Los Angeles, CA | 1 week ago | Full-time | https://www.linkedin.com/jobs/view/... | ### Stability Recommendations Public LinkedIn pages can throttle aggressive traffic. The Actor is configured for stability rather than maximum speed: - low default concurrency - request delay with random jitter - session and cookie persistence - retries and failed request records - basic detection for rate-limit, sign-in, verification, and blocked pages Use Apify Proxy when you need consistent network routing, and start with small test runs before increasing `maxItems`. ### Notes This Actor works with public LinkedIn job pages that are available without login. It does not log into LinkedIn, bypass access controls, or solve verification challenges. # Actor input Schema ## `MainKeys` (type: `array`): Job keywords to search on LinkedIn public jobs, for example Controller, Python Engineer, Sales Manager, or Data Analyst. ## `Location` (type: `string`): LinkedIn location text. The Actor resolves it through LinkedIn's public GEO typeahead when possible. Empty input defaults to United States. ## `Company_Title` (type: `array`): Optional LinkedIn company names. The Actor resolves each name through LinkedIn's public COMPANY typeahead and applies the resulting f\_C filter. ## `Company_ID` (type: `array`): Optional LinkedIn company IDs. Use this when you already know the exact f\_C values and want to avoid company-name lookup. ## `Published_at` (type: `string`): Optional LinkedIn date filter. It maps to f\_TPR in LinkedIn public job search. ## `Remote_or_OnSite` (type: `array`): Optional work arrangement filters. Supported values: Remote, On site, Hybrid. They map to LinkedIn f\_WT values. ## `Job_Type` (type: `array`): Optional job type filters. Supported values: Full time, Part time, Contract, Temporary, Volunteer. They map to LinkedIn f\_JT values. ## `Experience_Level` (type: `array`): Optional seniority filters. Supported values: Internship, Entry level, Associate, Mid Senior level, Director. They map to LinkedIn f\_E values. ## `maxItems` (type: `integer`): Maximum number of job records to save in one run. ## `maxListPages` (type: `integer`): Maximum LinkedIn search result pages to scan per keyword. Each public list page usually contains up to 10 jobs. ## `maxConcurrency` (type: `integer`): Concurrent HTTP requests. Keep this low for public LinkedIn pages; 2-4 is usually a safer range than high parallelism. ## `maxRetries` (type: `integer`): How many times to retry a failed list or detail request before storing it in FAILED\_REQUESTS. ## `requestDelaySecs` (type: `integer`): Polite delay before each HTTP request. The Actor also adds a small random jitter. ## `includeDescriptionHtml` (type: `boolean`): Store the job description HTML in descriptionHtml. Disable to reduce dataset size. ## `includeNoResultRecords` (type: `boolean`): When enabled, a keyword/location with no first-page matches saves one empty row with error\_message set. ## `resolveCompanyIds` (type: `boolean`): When enabled, each detail record attempts to resolve the company ID by company name. Disable for fewer auxiliary requests. ## `proxyConfiguration` (type: `object`): Optional Apify proxy configuration. Use stable country/session settings and modest concurrency for more reliable public LinkedIn runs. ## Actor input object example ```json { "MainKeys": [ "Controller" ], "Location": "Los Angeles Metropolitan Area", "Published_at": "Past week", "maxItems": 100, "maxListPages": 100, "maxConcurrency": 3, "maxRetries": 3, "requestDelaySecs": 2, "includeDescriptionHtml": true, "includeNoResultRecords": true, "resolveCompanyIds": true } ``` # Actor output Schema ## `dataset` (type: `string`): Structured public LinkedIn job records. ## `outputSummary` (type: `string`): Input settings, request settings, and crawl statistics. ## `failedRequests` (type: `string`): List or detail requests that failed after retries. # 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 = { "MainKeys": [ "Controller" ], "Location": "Los Angeles Metropolitan Area" }; // Run the Actor and wait for it to finish const run = await client.actor("nezha/linkedin-job-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 = { "MainKeys": ["Controller"], "Location": "Los Angeles Metropolitan Area", } # Run the Actor and wait for it to finish run = client.actor("nezha/linkedin-job-scraper").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 '{ "MainKeys": [ "Controller" ], "Location": "Los Angeles Metropolitan Area" }' | apify call nezha/linkedin-job-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=nezha/linkedin-job-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "LinkedIn Job Scraper - Public Jobs, No Login", "description": "Scrape public LinkedIn job search results without login and export job title, company, location, salary, job description, apply URL, seniority, employment type, industry, and application count fields.", "version": "0.1", "x-build-id": "La0pc6gVWZaN2Aom1" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/nezha~linkedin-job-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-nezha-linkedin-job-scraper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/nezha~linkedin-job-scraper/runs": { "post": { "operationId": "runs-sync-nezha-linkedin-job-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/nezha~linkedin-job-scraper/run-sync": { "post": { "operationId": "run-sync-nezha-linkedin-job-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "MainKeys" ], "properties": { "MainKeys": { "title": "Keywords", "type": "array", "description": "Job keywords to search on LinkedIn public jobs, for example Controller, Python Engineer, Sales Manager, or Data Analyst.", "items": { "type": "string" } }, "Location": { "title": "Location", "type": "string", "description": "LinkedIn location text. The Actor resolves it through LinkedIn's public GEO typeahead when possible. Empty input defaults to United States." }, "Company_Title": { "title": "Company Names", "type": "array", "description": "Optional LinkedIn company names. The Actor resolves each name through LinkedIn's public COMPANY typeahead and applies the resulting f_C filter.", "items": { "type": "string" } }, "Company_ID": { "title": "Company IDs", "type": "array", "description": "Optional LinkedIn company IDs. Use this when you already know the exact f_C values and want to avoid company-name lookup.", "items": { "type": "string" } }, "Published_at": { "title": "Published At", "enum": [ "Any time", "Past month", "Past week", "Past 24 hours" ], "type": "string", "description": "Optional LinkedIn date filter. It maps to f_TPR in LinkedIn public job search.", "default": "Past week" }, "Remote_or_OnSite": { "title": "Remote or On-site", "type": "array", "description": "Optional work arrangement filters. Supported values: Remote, On site, Hybrid. They map to LinkedIn f_WT values.", "items": { "type": "string" } }, "Job_Type": { "title": "Job Type", "type": "array", "description": "Optional job type filters. Supported values: Full time, Part time, Contract, Temporary, Volunteer. They map to LinkedIn f_JT values.", "items": { "type": "string" } }, "Experience_Level": { "title": "Experience Level", "type": "array", "description": "Optional seniority filters. Supported values: Internship, Entry level, Associate, Mid Senior level, Director. They map to LinkedIn f_E values.", "items": { "type": "string" } }, "maxItems": { "title": "Max Jobs", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of job records to save in one run.", "default": 100 }, "maxListPages": { "title": "Max List Pages", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum LinkedIn search result pages to scan per keyword. Each public list page usually contains up to 10 jobs.", "default": 100 }, "maxConcurrency": { "title": "Max Concurrency", "minimum": 1, "maximum": 10, "type": "integer", "description": "Concurrent HTTP requests. Keep this low for public LinkedIn pages; 2-4 is usually a safer range than high parallelism.", "default": 3 }, "maxRetries": { "title": "Retries Per Request", "minimum": 1, "maximum": 10, "type": "integer", "description": "How many times to retry a failed list or detail request before storing it in FAILED_REQUESTS.", "default": 3 }, "requestDelaySecs": { "title": "Delay Between Requests", "minimum": 0, "maximum": 30, "type": "integer", "description": "Polite delay before each HTTP request. The Actor also adds a small random jitter.", "default": 2 }, "includeDescriptionHtml": { "title": "Include Description HTML", "type": "boolean", "description": "Store the job description HTML in descriptionHtml. Disable to reduce dataset size.", "default": true }, "includeNoResultRecords": { "title": "Save No-result Rows", "type": "boolean", "description": "When enabled, a keyword/location with no first-page matches saves one empty row with error_message set.", "default": true }, "resolveCompanyIds": { "title": "Resolve Company IDs", "type": "boolean", "description": "When enabled, each detail record attempts to resolve the company ID by company name. Disable for fewer auxiliary requests.", "default": true }, "proxyConfiguration": { "title": "Proxy Configuration", "type": "object", "description": "Optional Apify proxy configuration. Use stable country/session settings and modest concurrency for more reliable public LinkedIn runs." } } }, "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 } } } } } } } } } } ```