# Jobvector.de Scraper (`unfenced-group/jobvector-scraper`) Actor Scrape jobvector.de for structured German STEM job data: titles, companies, salaries, skills & locations. 2,900+ listings for engineers, IT professionals, scientists & medical staff. Deduplication & repost detection included. - **URL**: https://apify.com/unfenced-group/jobvector-scraper.md - **Developed by:** [Unfenced Group](https://apify.com/unfenced-group) (community) - **Categories:** Jobs, Developer tools, Automation - **Stats:** 10 total users, 5 monthly users, 61.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing from $1.20 / 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. 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 ## Jobvector.de Scraper ![Jobvector.de Scraper](https://api.apify.com/v2/key-value-stores/lJlifu6C8YQfZMPKE/records/jobvector-scraper) IyBKb2J2ZWN0b3IuZGUgU2NyYXBlcgoKRXh0cmFjdCBzdHJ1Y3R1cmVkIGpvYiBsaXN0aW5ncyBmcm9tIGpvYnZlY3Rvci4gTm8gQVBJIGtleSByZXF1aXJlZC4KCi0tLQoKIyMgRmVhdHVyZXMKCi0gRmlsdGVyIGJ5IGtleXdvcmQsIGxvY2F0aW9uLCBqb2IgdHlwZSBhbmQgbW9yZQotIFN0cnVjdHVyZWQgb3V0cHV0IHJlYWR5IGZvciBhbmFseXNpcyBhbmQgaW50ZWdyYXRpb24KLSBDb25maWd1cmFibGUgcmVzdWx0IGxpbWl0cyB0byBjb250cm9sIGNvc3RzCi0gUnVucyBlbnRpcmVseSBpbiB0aGUgY2xvdWQg4oCUIG5vIGxvY2FsIHNldHVwIG5lZWRlZAoKLS0tCgojIyBJbnB1dCBwYXJhbWV0ZXJzCgp8IFBhcmFtZXRlciB8IFR5cGUgfCBEZWZhdWx0IHwgRGVzY3JpcHRpb24gfAp8LS0tLS0tLS0tLS18LS0tLS0tfC0tLS0tLS0tLXwtLS0tLS0tLS0tLS0tfAp8IGBrZXl3b3JkYCB8IHN0cmluZyB8IOKAlCB8IFNlYXJjaCB0ZXJtIG9yIGpvYiB0aXRsZSB0byBmaWx0ZXIgcmVzdWx0cy4gTGVhdmUgZW1wdHkgdG8gcmV0cmlldmUgYWxsIGF2YWlsYWJsZSBsaXN0aW5ncy4gfAp8IGBsb2NhdGlvbmAgfCBzdHJpbmcgfCDigJQgfCBMb2NhdGlvbiB0byBmaWx0ZXIgYnksIGUuZy4gY2l0eSBvciByZWdpb24uIExlYXZlIGVtcHR5IGZvciBhbGwgbG9jYXRpb25zLiB8CnwgYG1heFJlc3VsdHNgIHwgaW50ZWdlciB8IDEwMCB8IE1heGltdW0gbnVtYmVyIG9mIGxpc3RpbmdzIHRvIHJldHVybi4gU2V0IHRvIGAwYCBmb3IgdW5saW1pdGVkLiB8CgotLS0KCiMjIE91dHB1dAoKRWFjaCByZXN1bHQgY29udGFpbnMgc3RydWN0dXJlZCBmaWVsZHMgaW5jbHVkaW5nIHRpdGxlLCBjb21wYW55LCBsb2NhdGlvbiwgVVJMLCBwdWJsaWNhdGlvbiBkYXRlLCBhbmQgbW9yZS4gUmVzdWx0cyBhcmUgZGVsaXZlcmVkIGFzIGEgZGF0YXNldCByZWFkeSBmb3IgZXhwb3J0IHRvIEpTT04sIENTViwgb3IgRXhjZWwuCgotLS0KCiMjIPCfkrAgUHJpY2luZwoKKiokMS41IHBlciAxLDAwMCByZXN1bHRzKiog4oCUIHlvdSBvbmx5IHBheSBmb3Igc3VjY2Vzc2Z1bGx5IHJldHJpZXZlZCBsaXN0aW5ncy4KRmFpbGVkIHJldHJpZXMgYW5kIGZpbHRlcmVkIHJlc3VsdHMgYXJlIG5ldmVyIGNoYXJnZWQuCgp8IFJlc3VsdHMgfCBDb3N0IHwKfC0tLS0tLS0tLXwtLS0tLS18CnwgMTAwIHwgfiQxNTAgfAp8IDEsMDAwIHwgfiQxLjUgfAp8IDEwLDAwMCB8IH4kMTUwMDAgfAp8IDEwMCwwMDAgfCB+JDE1MDAwMCB8Cgo+IEZsYXQtcmF0ZSBhbHRlcm5hdGl2ZXMgdHlwaWNhbGx5IGNoYXJnZSAkMjnigJMkNDkvbW9udGggcmVnYXJkbGVzcyBvZiB1c2FnZS4KClVzZSB0aGUgKipNYXggcmVzdWx0cyoqIGNhcCBpbiB0aGUgaW5wdXQgdG8gY29udHJvbCB5b3VyIHNwZW5kIGV4YWN0bHkuCgotLS0KCiMjIEFkZGl0aW9uYWwgc2VydmljZXMKCk5lZWQgY3VzdG9tIGRhdGEgcGlwZWxpbmVzLCBzY2hlZHVsZWQgZXhwb3J0cywgb3IgaW50ZWdyYXRpb24gc3VwcG9ydD8KQ29udGFjdCBbVW5mZW5jZWQgR3JvdXBdKGh0dHBzOi8vYXBpZnkuY29tL3VuZmVuY2VkLWdyb3VwKSBmb3IgdGFpbG9yZWQgc29sdXRpb25zLgoKLS0tCgoqUGFydCBvZiB0aGUgW1VuZmVuY2VkIEdyb3VwXShodHRwczovL2FwaWZ5LmNvbS91bmZlbmNlZC1ncm91cCkgc2NyYXBlciBwb3J0Zm9saW8gwrcgSXNzdWVzPyBPcGVuIGEgdGlja2V0IG9yIHNlbmQgYSBtZXNzYWdlLio= ### Related scrapers Other scrapers in our **Jobs โ€” Germany, Austria & Switzerland** collection: - [Arbeitsagentur.de Scraper](https://apify.com/unfenced-group/arbeitsagentur-de-scraper) - [GermanTechJobs Scraper](https://apify.com/unfenced-group/germantechjobs-scraper) - [Get-in-IT Scraper](https://apify.com/unfenced-group/get-in-it-scraper) - [Stepstone.de Scraper](https://apify.com/unfenced-group/stepstone-de-scraper) - [Xing.com Job Scraper](https://apify.com/unfenced-group/xing-scraper) # Actor input Schema ## `keyword` (type: `string`): Job title or skill to search for (e.g. 'Softwareentwickler', 'Ingenieur', 'Arzt'). Leave empty to scrape all listings. ## `location` (type: `string`): City or region to filter by (e.g. 'Mรผnchen', 'Berlin', 'Hamburg'). Leave empty to search all of Germany. ## `radius` (type: `integer`): Radius around the specified location in kilometres. Options: 25, 50, 75, 100, 150. Default is 50 km. ## `maxResults` (type: `integer`): Maximum number of job listings to return. Use 0 for unlimited (up to the platform's total). ## `daysOld` (type: `integer`): Only return listings published within the last N days. Leave empty to return all listings regardless of age. ## `skipReposts` (type: `boolean`): When enabled, listings that have already been scraped in a previous run are excluded from the output. ## `startUrls` (type: `array`): Provide specific jobvector.de search result URLs to scrape instead of using the keyword/location inputs. Example: https://www.jobvector.de/jobs/ingenieur/ ## `fetchDetails` (type: `boolean`): When enabled, the scraper fetches each job detail page to extract the full HTML job description. Increases run time proportionally (approx. 1โ€“2s per job). Recommended memory: 256 MB (HTTP-only, no browser required). ## Actor input object example ```json { "keyword": "Softwareentwickler", "radius": 50, "maxResults": 500, "daysOld": 30, "skipReposts": false, "startUrls": [], "fetchDetails": false } ```` # Actor output Schema ## `results` (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 = { "keyword": "Softwareentwickler", "location": "", "radius": 50, "maxResults": 500, "daysOld": 30, "skipReposts": false, "startUrls": [], "fetchDetails": false }; // Run the Actor and wait for it to finish const run = await client.actor("unfenced-group/jobvector-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`๐Ÿ’พ 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); }); // ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ 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 = { "keyword": "Softwareentwickler", "location": "", "radius": 50, "maxResults": 500, "daysOld": 30, "skipReposts": False, "startUrls": [], "fetchDetails": False, } # Run the Actor and wait for it to finish run = client.actor("unfenced-group/jobvector-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("๐Ÿ’พ Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # ๐Ÿ“š Want to learn more ๐Ÿ“–? Go to โ†’ https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "keyword": "Softwareentwickler", "location": "", "radius": 50, "maxResults": 500, "daysOld": 30, "skipReposts": false, "startUrls": [], "fetchDetails": false }' | apify call unfenced-group/jobvector-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=unfenced-group/jobvector-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Jobvector.de Scraper", "description": "Scrape jobvector.de for structured German STEM job data: titles, companies, salaries, skills & locations. 2,900+ listings for engineers, IT professionals, scientists & medical staff. Deduplication & repost detection included.", "version": "1.0", "x-build-id": "wL5oBeOY0KlN1N4wk" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/unfenced-group~jobvector-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-unfenced-group-jobvector-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/unfenced-group~jobvector-scraper/runs": { "post": { "operationId": "runs-sync-unfenced-group-jobvector-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/unfenced-group~jobvector-scraper/run-sync": { "post": { "operationId": "run-sync-unfenced-group-jobvector-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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", "properties": { "keyword": { "title": "Search keyword", "type": "string", "description": "Job title or skill to search for (e.g. 'Softwareentwickler', 'Ingenieur', 'Arzt'). Leave empty to scrape all listings." }, "location": { "title": "Location", "type": "string", "description": "City or region to filter by (e.g. 'Mรผnchen', 'Berlin', 'Hamburg'). Leave empty to search all of Germany." }, "radius": { "title": "Search radius (km)", "minimum": 0, "maximum": 200, "type": "integer", "description": "Radius around the specified location in kilometres. Options: 25, 50, 75, 100, 150. Default is 50 km." }, "maxResults": { "title": "Maximum results", "minimum": 1, "type": "integer", "description": "Maximum number of job listings to return. Use 0 for unlimited (up to the platform's total)." }, "daysOld": { "title": "Maximum listing age (days)", "minimum": 1, "type": "integer", "description": "Only return listings published within the last N days. Leave empty to return all listings regardless of age." }, "skipReposts": { "title": "Skip reposted listings", "type": "boolean", "description": "When enabled, listings that have already been scraped in a previous run are excluded from the output.", "default": false }, "startUrls": { "title": "Start URLs (optional override)", "type": "array", "description": "Provide specific jobvector.de search result URLs to scrape instead of using the keyword/location inputs. Example: https://www.jobvector.de/jobs/ingenieur/", "items": { "type": "object", "required": [ "url" ], "properties": { "url": { "type": "string", "title": "URL of a web page", "format": "uri" } } } }, "fetchDetails": { "title": "Fetch full job description", "type": "boolean", "description": "When enabled, the scraper fetches each job detail page to extract the full HTML job description. Increases run time proportionally (approx. 1โ€“2s per job). Recommended memory: 256 MB (HTTP-only, no browser required).", "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 } } } } } } } } } } ```