# Job Scraper (`kindred_llama/job-scraper`) Actor 🚀 Multi-Site Job Scraper fetches real-time jobs from 7+ leading job boards in one run. Search by title, location, and experience, then get clean, deduplicated, structured results—perfect for recruiters, job boards, analytics, and AI-powered job search applications. - **URL**: https://apify.com/kindred\_llama/job-scraper.md - **Developed by:** [Shailesh Pawar](https://apify.com/kindred_llama) (community) - **Categories:** Jobs, Automation, Lead generation - **Stats:** 1 total users, 0 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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 ## 💼 Multi-Site Job Scraper A powerful multi-site job aggregation platform built with FastAPI and Python. This actor scrapes job postings from multiple platforms, cleans the data, parses salaries and experience requirements, and pushes structured results to your Apify dataset. ### ✨ Features - **Multi-Source Scraping**: Scrapes job postings from LinkedIn, Indeed, SimplyHired, Remotive, RemoteOK, Arbeitnow, and Jobicy. - **Smart Deduplication**: Automatically deduplicates cross-posted jobs by matching titles and company names. - **Intelligent Parsing**: Extracts salary ranges, experience requirements, and required skills directly from raw job descriptions. - **Advanced Filtering**: Match score calculation based on strict keyword matching, location, job type, education level, and required skills. ### 📥 Input The Actor accepts the following input parameters: | Field | Type | Description | Default | | ----- | ---- | ----------- | ------- | | `keyword` | String | The job title or keyword to search for (e.g., "Software Engineer") | *(Required)* | | `location` | String | Location for the search (e.g., "India", "Remote") | `""` | | `experience` | Integer | Max years of experience allowed (Enter 0 for Freshers) | `0` | | `maxResults` | Integer | Max jobs to scrape per selected source | `20` | | `sources` | Array | Select which job portals to scrape | `["LinkedIn", "Indeed", "SimplyHired"]` | | `postedMaxDays` | Integer | Scrape jobs posted in the last X days | `0` (Any time) | | `jobType` | String | Filter by Job Type ("Any", "Full-time", "Contract", "Remote") | `"Any"` | | `educationLevel` | String | Filter by Education ("Any", "Bachelors", "Masters") | `"Any"` | | `skills` | String | Comma-separated required skills (e.g., "Python, React") | `""` | ### 📤 Output The Actor pushes the scraped jobs to the default Apify **Dataset**. Each output record represents a matched job containing: ```json { "title": "Senior Python Developer", "company": "Tech Corp", "location": "Remote, India", "url": "https://linkedin.com/jobs/view/...", "salary": "₹15,00,000 - ₹25,00,000", "experience_range": "3 - 5 years", "skills_found": "python, django, fastapi, aws", "summary": "We are looking for a Senior Python Developer with strong backend experience...", "source": "LinkedIn", "posted_date": "2023-10-25", "match_score": 95 } ```` ### 💻 Dual-Mode Execution This codebase is unique! It can act as a standard **Apify Scraper** (which you are using right now) or it can be run as a **Web Interface Server**. If you clone the repository and run it locally with Docker, it will spin up a beautiful web dashboard UI on port 7860! ```bash docker build -t job-scraper . docker run -p 7860:7860 job-scraper ``` # Actor input Schema ## `keyword` (type: `string`): The job title or keyword to search for (e.g., 'Software Engineer'). ## `location` (type: `string`): The location to search in (e.g., 'India', 'Remote', 'Bangalore'). ## `experience` (type: `integer`): Years of experience required. Enter 0 for Freshers. ## `maxResults` (type: `integer`): Maximum number of jobs to scrape per source. ## `sources` (type: `array`): Job portals to scrape (e.g. LinkedIn, Indeed, SimplyHired) ## `postedMaxDays` (type: `integer`): Only get jobs posted in the last X days. Enter 0 for any time. ## `jobType` (type: `string`): Filter by job type. ## `educationLevel` (type: `string`): Filter by minimum education level. ## `skills` (type: `string`): Comma-separated list of skills (e.g., 'Python, React, AWS'). ## Actor input object example ```json { "experience": 0, "maxResults": 20, "sources": [ "LinkedIn", "Indeed", "SimplyHired" ], "postedMaxDays": 0, "jobType": "Any", "educationLevel": "Any" } ``` # 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("kindred_llama/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 = {} # Run the Actor and wait for it to finish run = client.actor("kindred_llama/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 '{}' | apify call kindred_llama/job-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=kindred_llama/job-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Job Scraper", "description": "🚀 Multi-Site Job Scraper fetches real-time jobs from 7+ leading job boards in one run. Search by title, location, and experience, then get clean, deduplicated, structured results—perfect for recruiters, job boards, analytics, and AI-powered job search applications.", "version": "1.0", "x-build-id": "WXSM3yotF3lM7xvdW" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/kindred_llama~job-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-kindred_llama-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/kindred_llama~job-scraper/runs": { "post": { "operationId": "runs-sync-kindred_llama-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/kindred_llama~job-scraper/run-sync": { "post": { "operationId": "run-sync-kindred_llama-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": [ "keyword" ], "properties": { "keyword": { "title": "Job Title / Keyword", "type": "string", "description": "The job title or keyword to search for (e.g., 'Software Engineer')." }, "location": { "title": "Location", "type": "string", "description": "The location to search in (e.g., 'India', 'Remote', 'Bangalore')." }, "experience": { "title": "Experience (Years)", "minimum": 0, "type": "integer", "description": "Years of experience required. Enter 0 for Freshers.", "default": 0 }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 200, "type": "integer", "description": "Maximum number of jobs to scrape per source.", "default": 20 }, "sources": { "title": "Job Sources", "type": "array", "description": "Job portals to scrape (e.g. LinkedIn, Indeed, SimplyHired)", "items": { "type": "string" }, "default": [ "LinkedIn", "Indeed", "SimplyHired" ] }, "postedMaxDays": { "title": "Posted Max Days", "minimum": 0, "type": "integer", "description": "Only get jobs posted in the last X days. Enter 0 for any time.", "default": 0 }, "jobType": { "title": "Job Type", "enum": [ "Any", "Full-time", "Part-time", "Contract", "Internship", "Remote" ], "type": "string", "description": "Filter by job type.", "default": "Any" }, "educationLevel": { "title": "Education Level", "enum": [ "Any", "Bachelors", "Masters", "PhD" ], "type": "string", "description": "Filter by minimum education level.", "default": "Any" }, "skills": { "title": "Required Skills", "type": "string", "description": "Comma-separated list of skills (e.g., 'Python, React, AWS')." } } }, "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 } } } } } } } } } } ```