# demo scraper (`aligned_safe/demo-scraper`) Actor ..... - **URL**: https://apify.com/aligned\_safe/demo-scraper.md - **Developed by:** [Vaidik Dubey](https://apify.com/aligned_safe) (community) - **Categories:** Jobs, Lead generation, Developer tools - **Stats:** 0 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $50.00 / 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 A fast and reliable LinkedIn job scraper that works **without authentication**. Scrapes job listings with optional filters for location, experience level, job type, and more. ### ✨ Features - ✅ **No Authentication Required** — Works with public LinkedIn job listings - ✅ **Fast Scraping** — 60+ jobs per page in ~2 minutes - ✅ **Smart Deduplication** — Skips previously scraped jobs - ✅ **Multiple Filters** — Location, experience, workplace type, job type, etc. - ✅ **Reliable Data** — Consistent results across runs - ✅ **CSV & JSON Output** — Export in your preferred format ### 📊 Output Format Returns jobs with the following fields: - **job_id** — LinkedIn job ID - **job_title** — Job title/role name - **company_name** — Hiring company - **job_location** — Location (city, region, or "Remote") - **posted_date** — When the job was posted - **num_applicants** — Number of applicants (when available) - **job_url** — Direct LinkedIn job link - **scraped_at** — Timestamp of scraping ### 🎯 Input Parameters #### Required | Parameter | Type | Description | Example | |-----------|------|-------------|---------| | **keyword** | string | Job title or keyword | "Full Stack Engineer" | | **location** | string | Job location | "Singapore" / "Remote" | #### Optional | Parameter | Type | Default | Description | |-----------|------|---------|-------------| | **pages** | integer | 1 | Pages to scrape (1-20). 1 page ≈ 25-60 jobs | | **workplace** | string | - | Filter: `remote`, `hybrid`, `onsite` | | **experience** | string | - | Filter: `internship`, `entry`, `associate`, `mid-senior`, `director`, `executive` | | **jobType** | string | - | Filter: `full-time`, `part-time`, `contract`, `temporary`, `volunteer` | | **easyApply** | boolean | false | Show only Easy Apply jobs | | **under10Applicants** | boolean | false | Show only jobs with <10 applicants | ### 💡 Example Usage #### Search 1: Basic Search (3 pages) ```json { "keyword": "Python Developer", "location": "Singapore", "pages": 3 } ```` **Result:** ~180 Python developer jobs in Singapore (≈6 minutes) #### Search 2: Filtered Search ```json { "keyword": "Data Scientist", "location": "Remote", "pages": 2, "experience": "mid-senior", "jobType": "full-time" } ``` **Result:** ~120 remote mid-senior Data Scientist jobs (full-time only) #### Search 3: Niche Search ```json { "keyword": "Product Manager", "location": "New York", "pages": 1, "easyApply": true, "under10Applicants": true } ``` **Result:** Product Manager jobs in NYC with <10 applicants and Easy Apply ### ⏱️ Performance | Scope | Time | Est. Jobs | |-------|------|-----------| | 1 page | ~2 min | 25-60 | | 3 pages | ~6 min | 75-180 | | 5 pages | ~10 min | 125-300 | | 10 pages | ~20 min | 250-600 | | 20 pages | ~40 min | 500-1200 | *Times vary based on network speed and LinkedIn response time* ### 💰 Pricing This actor uses Actor compute time based on: - **Memory:** 1024 MB - **Runtime:** ~2 min per page **Estimated cost:** - 1 page search: $0.05-0.10 - 5 pages search: $0.25-0.50 - 20 pages search: $1.00-2.00 *Actual pricing depends on Apify's compute costs in your region* ### 🔧 Technical Details - **Language:** Python 3.11 - **Browser:** Playwright (Chromium) - **Parser:** BeautifulSoup4 - **Export:** CSV (default) / JSON (via Apify dataset) ### 📝 Notes - Jobs are deduplicated using job\_id + company\_name + title hash - Salary data is not available on public LinkedIn listing pages - Some fields may be empty if LinkedIn doesn't display them - Resume mode: Running the same search twice won't re-scrape existing jobs ### 🆘 Support For issues or feature requests, check Apify documentation or create an issue in the repository. *** **Version:** 1.0.0\ **Last Updated:** 2026-07-03 # Actor input Schema ## `keyword` (type: `string`): Job title or keyword to search for (e.g., 'Full Stack Engineer', 'Python Developer') ## `location` (type: `string`): Job location (e.g., 'Singapore', 'Remote', 'New York', 'London') ## `pages` (type: `integer`): Number of pages to scrape. 1 page = ~25-60 jobs. (Max 20 pages) ## `workplace` (type: `string`): Filter by workplace type ## `experience` (type: `string`): Filter by experience level required ## `jobType` (type: `string`): Filter by job type ## `easyApply` (type: `boolean`): Show only jobs with Easy Apply option ## `under10Applicants` (type: `boolean`): Show only jobs with less than 10 applicants ## Actor input object example ```json { "keyword": "Full Stack Engineer", "location": "Singapore", "pages": 1, "workplace": "", "experience": "", "jobType": "", "easyApply": false, "under10Applicants": false } ``` # 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("aligned_safe/demo-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 = {} # Run the Actor and wait for it to finish run = client.actor("aligned_safe/demo-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 '{}' | apify call aligned_safe/demo-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=aligned_safe/demo-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "demo scraper", "description": ".....", "version": "1.0", "x-build-id": "YeaHGQpfKdFJL4EB2" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/aligned_safe~demo-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-aligned_safe-demo-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/aligned_safe~demo-scraper/runs": { "post": { "operationId": "runs-sync-aligned_safe-demo-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/aligned_safe~demo-scraper/run-sync": { "post": { "operationId": "run-sync-aligned_safe-demo-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keyword", "location" ], "properties": { "keyword": { "title": "Job Keyword", "type": "string", "description": "Job title or keyword to search for (e.g., 'Full Stack Engineer', 'Python Developer')" }, "location": { "title": "Location", "type": "string", "description": "Job location (e.g., 'Singapore', 'Remote', 'New York', 'London')" }, "pages": { "title": "Number of Pages", "minimum": 1, "maximum": 20, "type": "integer", "description": "Number of pages to scrape. 1 page = ~25-60 jobs. (Max 20 pages)", "default": 1 }, "workplace": { "title": "Workplace Type", "enum": [ "", "remote", "hybrid", "onsite", "on-site" ], "type": "string", "description": "Filter by workplace type", "default": "" }, "experience": { "title": "Experience Level", "enum": [ "", "internship", "entry", "associate", "mid-senior", "director", "executive" ], "type": "string", "description": "Filter by experience level required", "default": "" }, "jobType": { "title": "Job Type", "enum": [ "", "full-time", "part-time", "contract", "temporary", "volunteer", "internship" ], "type": "string", "description": "Filter by job type", "default": "" }, "easyApply": { "title": "Easy Apply Only", "type": "boolean", "description": "Show only jobs with Easy Apply option", "default": false }, "under10Applicants": { "title": "Under 10 Applicants", "type": "boolean", "description": "Show only jobs with less than 10 applicants", "default": false } } }, "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 } } } } } } } } } } ```