# EURES Jobs Scraper - Low-costπŸ’²πŸ”₯πŸ‡ͺπŸ‡ΊπŸ’Ό (`delectable_incubator/eures-jobs-scraper-low-cost`) Actor Scrape EURES jobs across Europe πŸ‡ͺπŸ‡ΊπŸ’Ό with a powerful job scraper. Extract job titles, companies, salaries, locations, dates, descriptions, and application details by keyword or region. Ideal for recruitment intelligence, labor market research, hiring trends, and structured employment datasets πŸ“ŠπŸš€ - **URL**: https://apify.com/delectable\_incubator/eures-jobs-scraper-low-cost.md - **Developed by:** [Prime Scrape](https://apify.com/delectable_incubator) (community) - **Categories:** Jobs, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $0.00005 / actor start This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. 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

EURES Job Scraper

--- ## EURES Job Scraper πŸš€πŸ’ΌπŸ‡ͺπŸ‡Ί The EURES Job Scraper is a powerful and scalable Apify Actor designed to extract structured job listings directly from the European Employment Services (EURES) portal. It enables European job market research, recruitment intelligence, labor market analysis, job monitoring, lead generation, talent sourcing, employment trend tracking, and structured dataset generation from one of Europe's largest official job platforms. --- ### 🎯 What This Scraper Does Simply provide one or more keywords and the scraper handles everything automatically. βœ… Extracts structured job listings from EURES βœ… Supports bulk keyword scraping βœ… Searches jobs across European countries βœ… Handles result pagination automatically βœ… Applies maxItems limits βœ… Extracts employer and company information βœ… Captures job descriptions and contract details βœ… Collects salary and language requirements when available βœ… Generates clean and structured datasets βœ… Ready for analytics and recruitment workflows βœ… Export-ready output format --- ### πŸ“Š Data Extracted #### πŸ’Ό Job Information | Field | Description | | ----------------------- | ------------------------- | | πŸ†” id | Unique job identifier | | πŸ“„ title | Job title | | πŸ”— urlToJob | Direct URL to job posting | | πŸ“… publicationDate | Job publication date | | πŸ“ location | Job location | | 🏒 company | Employer name | | 🌐 companyWebsite | Employer website | | πŸ“ description | Full job description | | πŸ’Ό employmentType | Employment type | | πŸ•’ contractStartDate | Contract start date | | πŸš€ applicationLink | Job application URL | | πŸ“ž contactInformation | Contact information | | πŸ’Ά salary | Salary information | | 🌍 languageRequirements | Required languages | | 🏷️ escoCategories | ESCO job classification | | πŸ” keyword | Search keyword used | --- ### πŸ›  How to Use #### 1️⃣ Configure Input Provide one or multiple keywords: ```` { "keywords": \[ "Software Engineer", "Data Scientist", "Marketing Manager" ], "sortBy": "MOST\_RECENT", "maxItems": 100 } ``` #### 2️⃣ Run the Actor β€’ Searches EURES job listings β€’ Processes results automatically β€’ Extracts structured job information β€’ Collects employer and contract details β€’ Applies maxItems limits β€’ Stops automatically when limits are reached #### 3️⃣ Export the Dataset Download your results in multiple formats: βœ… JSON βœ… CSV βœ… Excel βœ… XML βœ… HTML --- ### βš™οΈ Input Configuration #### πŸ“₯ Input Example ``` { "keywords": \[ "Software Engineer", "Data Scientist", "Marketing Manager" ], "sortBy": "MOST\_RECENT", "maxItems": 100 } ``` #### Input Fields | Field | Type | Description | | -------- | ------- | --------------------------------- | | keywords | array | List of job keywords to search | | sortBy | string | BEST_MATCH or MOST_RECENT | | maxItems | integer | Maximum number of jobs to collect | --- ### πŸ“€ Output Example ``` { "id": "MTg5MzUtMTcwNjUyNi1TIDE", "title": "Data Scientist (m/w/d)", "urlToJob": "https://europa.eu/eures/portal/jv-se/jv-details/MTg5MzUtMTcwNjUyNi1TIDE", "publicationDate": "2025-06-10T08:45:00Z", "location": "Germany", "company": "CANCOM SE", "companyWebsite": "https://www.cancom.com", "description": "Full job description...", "employmentType": "Full-time", "salary": null, "languageRequirements": \["German", "English"], "keyword": "Data Scientist" } ```` --- ### πŸ“Š Output Explanation | Use Case | Description | | -------------------------- | ----------------------------------------- | | πŸ’Ό Recruitment | Build candidate sourcing pipelines | | πŸ“Š Labor Market Research | Analyze hiring trends across Europe | | 🌍 Cross-Border Employment | Monitor opportunities across EU countries | | πŸ“ˆ Talent Intelligence | Track employer hiring activity | | πŸ€– Automation Workflows | Feed job data into recruitment systems | --- ### 🌍 Why Use This Scraper? πŸ’Ό Collect European job listings at scale 🌍 Search multiple keywords simultaneously πŸ“ˆ Monitor hiring trends across Europe 🏒 Extract employer and company information πŸ“Š Build recruitment intelligence datasets πŸ€– Automate job market monitoring πŸ“¦ Bulk keyword scraping support ⚑ Fast and scalable extraction 🧠 Ideal for recruiters, staffing agencies, researchers, analysts, and job boards πŸš€ Enterprise-ready architecture --- ### ❓ FAQ #### How does this scraper work? The scraper searches EURES using your keywords and extracts structured job information including job titles, employers, locations, descriptions, contract details, salaries, and application links. #### Can I scrape multiple keywords in one run? Yes. You can provide multiple keywords inside the keywords array. #### Does the scraper extract company information? Yes. Employer names and company websites are extracted whenever available. #### Can I monitor new job postings over time? Yes. You can schedule recurring Apify runs to continuously monitor new opportunities. #### Is the data collected live? Yes. Data is extracted directly from EURES during every run. #### What export formats are supported? JSON, CSV, Excel, XML, and HTML. #### Can I use the extracted data commercially? Yes. The extracted data can be used for recruitment, analytics, research, automation, monitoring, and commercial applications. #### What happens if the scraper fails? The Actor includes retry mechanisms and automated error handling to improve reliability. #### How long does a run take? Most extractions complete within minutes depending on the number of keywords and requested results. --- ### πŸš€ How to Use 1️⃣ Sign up β€” Create a free Apify account 2️⃣ Find the tool β€” Search for "EURES Job Scraper" in the Apify Store 3️⃣ Configure keywords β€” Add one or multiple job keywords 4️⃣ Run it β€” Start the Actor and wait for extraction 5️⃣ Export data β€” Download results in JSON, CSV, Excel, XML, or HTML --- ### ⚠️ Disclaimer This tool is an independent solution and is not affiliated with, endorsed by, or sponsored by EURES, the European Commission, or any European Union institution. --- ### πŸ’Έ Pricing This scraper runs on a **pay per events subscription model**. You only pay for **successful runs**. πŸ’³ **Price:** $2.98 / 1000 results --- ### Related Actors If you're interested in other Jobs, Recruitment, HR, Lead Generation, Company Intelligence, Real Estate, Marketplace, or Research scraping solutions, explore more tools: *(Coming soon)* --- ### πŸ“¬ Support ⭐⭐⭐⭐⭐ Leave a 5-star rating if you like this tool. --- ### 🌍 PrimeScrape Built for scalable web data extraction & automation. Contact us for custom scraping solutions or enterprise requests via Apify or by email. # Actor input Schema ## `keywords` (type: `array`): List of keywords or fields to search for job listings on EURES Job. The scraper will run a separate search for each keyword. Examples: β€’ software β€’ data engineer β€’ product manager β€’ marketing β€’ sales ## `sort_by` (type: `string`): Choose how you want to sort the job listings. ## `maxItemsPerKeyword` (type: `integer`): Set the maximum number of job listings to scrape for EACH keyword. ## Actor input object example ```json { "keywords": [ "software", "data" ], "sort_by": "BEST_MATCH", "maxItemsPerKeyword": 100 } ```` # Actor output Schema ## `overview` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "keywords": [ "software", "data" ], "sort_by": "BEST_MATCH", "maxItemsPerKeyword": 100 }; // Run the Actor and wait for it to finish const run = await client.actor("delectable_incubator/eures-jobs-scraper-low-cost").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // πŸ“š Want to learn more πŸ“–? Go to β†’ 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", "data", ], "sort_by": "BEST_MATCH", "maxItemsPerKeyword": 100, } # Run the Actor and wait for it to finish run = client.actor("delectable_incubator/eures-jobs-scraper-low-cost").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("πŸ’Ύ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # πŸ“š Want to learn more πŸ“–? Go to β†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "keywords": [ "software", "data" ], "sort_by": "BEST_MATCH", "maxItemsPerKeyword": 100 }' | apify call delectable_incubator/eures-jobs-scraper-low-cost --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=delectable_incubator/eures-jobs-scraper-low-cost", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "EURES Jobs Scraper - Low-costπŸ’²πŸ”₯πŸ‡ͺπŸ‡ΊπŸ’Ό", "description": "Scrape EURES jobs across Europe πŸ‡ͺπŸ‡ΊπŸ’Ό with a powerful job scraper. Extract job titles, companies, salaries, locations, dates, descriptions, and application details by keyword or region. Ideal for recruitment intelligence, labor market research, hiring trends, and structured employment datasets πŸ“ŠπŸš€", "version": "0.0", "x-build-id": "9ZLB5Fd5QgfSJf7zW" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/delectable_incubator~eures-jobs-scraper-low-cost/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-delectable_incubator-eures-jobs-scraper-low-cost", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/delectable_incubator~eures-jobs-scraper-low-cost/runs": { "post": { "operationId": "runs-sync-delectable_incubator-eures-jobs-scraper-low-cost", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/delectable_incubator~eures-jobs-scraper-low-cost/run-sync": { "post": { "operationId": "run-sync-delectable_incubator-eures-jobs-scraper-low-cost", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keywords", "maxItemsPerKeyword" ], "properties": { "keywords": { "title": "Keywords/StichwΓΆrter to search for on EURES Job πŸ”πŸ’Ό", "type": "array", "description": "List of keywords or fields to search for job listings on EURES Job. The scraper will run a separate search for each keyword.\n\nExamples:\nβ€’ software\nβ€’ data engineer\nβ€’ product manager\nβ€’ marketing\nβ€’ sales", "default": [ "software", "data" ], "items": { "type": "string" } }, "sort_by": { "title": "Select a sorting option/Sortieroption πŸ“…", "enum": [ "BEST_MATCH", "MOST_RECENT" ], "type": "string", "description": "Choose how you want to sort the job listings.", "default": "BEST_MATCH" }, "maxItemsPerKeyword": { "title": "Maximum number of jobs per keyword πŸ“Š", "type": "integer", "description": "Set the maximum number of job listings to scrape for EACH keyword.", "default": 100 } } }, "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 } } } } } } } } } } ```