# Greenhouse (`dlandberg/greenhouse`) Actor Scrape jobs posted from companies that use Greenhouse as their applicant tracking software. Easily get content, salary ranges, location, and much more. - **URL**: https://apify.com/dlandberg/greenhouse.md - **Developed by:** [Dmitry Landberg](https://apify.com/dlandberg) (community) - **Categories:** Jobs - **Stats:** 144 total users, 0 monthly users, 100.0% runs succeeded, 7 bookmarks - **User rating**: No ratings yet ## Pricing $10.00/month + usage To use this Actor, you pay a monthly rental fee to the developer. The rent is subtracted from your prepaid usage every month after the free trial period.You also pay for the Apify platform usage, which gets cheaper the higher Apify subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#rental-actors ## 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 ## What does Greenhouse Scraper do? Greenhouse Scraper allows you to extract job data from Greenhouse's job listings: job title, content of the job description, company name, location, salary ranges, department. ### Why scrape Greenhouse You might want to scrape Job postings in order to: - Monitor hiring trends for skills and technology gaps - Create a custom database for available positions - Automate the job search to speed up and simplify the process - Salary benchmarking - Competition tracking ### How to use this Greenhouse scraper? - Create a free Apify account using your email. - Open Greenhouse Scraper. - (Optional) Add the list of companies that you want to scrape. If you don't provide the list of companies, the scraper comes with the default list of dozens of companies that use Greenhouse - (Optional) Add yout OpenAI API Key. It will be used to extract the salary ranges from the job descriptions - (Optional) Specify the max number of jobs you want to parse per one company. Default is 10. - (Optional) Specify the job titles you want to scrape. The scraper will do a regex matching on the string provided. - Click "Start" and wait for the data to be extracted. - Download your data in JSON, XML, CSV, Excel, or HTML. ### Input schema The input for Indeed Scraper should be a job position, location and number of results. For a fuller explanation of input in JSON, head over to the input tab. { "companies": [ "affirm", "okta" ], "position_titles": [ "engineer" ], "max_jobs": 1, "openai_api_key": "" } ### Output Example The results will be wrapped into a dataset which you can find in the Storage tab. This is a screenshot of the view that you can expect to see ![Greenhouse parser output schema](/assets/output.png) ### Is it legal to scrape Indeed data? Our scrapers are ethical and do not extract any private user data. They only extract what the user has chosen to share publicly, on the website. However, you should be aware that your results could contain personal data. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers. You should not scrape personal data unless you have a legitimate reason to do so. If you’re unsure whether your reason is legitimate, consult your lawyers. You can also read our blog post on the legality of web scraping and ethical scraping. # Actor input Schema ## `companies` (type: `array`): Names of companies in downcase, no spaces. ## `position_titles` (type: `array`): Keywords that you want to capture. For ex. 'engineering', 'engineering manager' ## `max_jobs` (type: `integer`): Max number of jobs you want to parse for each company ## `openai_api_key` (type: `string`): Parser uses it for extracting the salary range. It's optional ## Actor input object example ```json { "companies": [ "affirm", "okta" ], "max_jobs": 10 } ```` # 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 = { "companies": [ "affirm", "okta" ] }; // Run the Actor and wait for it to finish const run = await client.actor("dlandberg/greenhouse").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 = { "companies": [ "affirm", "okta", ] } # Run the Actor and wait for it to finish run = client.actor("dlandberg/greenhouse").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 '{ "companies": [ "affirm", "okta" ] }' | apify call dlandberg/greenhouse --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dlandberg/greenhouse", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Greenhouse", "description": "Scrape jobs posted from companies that use Greenhouse as their applicant tracking software. Easily get content, salary ranges, location, and much more.", "version": "0.2", "x-build-id": "BsFzBfz2nS8WvRn0i" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dlandberg~greenhouse/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dlandberg-greenhouse", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/dlandberg~greenhouse/runs": { "post": { "operationId": "runs-sync-dlandberg-greenhouse", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/dlandberg~greenhouse/run-sync": { "post": { "operationId": "run-sync-dlandberg-greenhouse", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "companies": { "title": "List of companies, job posting of which you want to scrape", "type": "array", "description": "Names of companies in downcase, no spaces." }, "position_titles": { "title": "Job title that you're interested in. For example 'engineering'", "type": "array", "description": "Keywords that you want to capture. For ex. 'engineering', 'engineering manager'" }, "max_jobs": { "title": "Maximum number of jobs you want to parse for each company", "type": "integer", "description": "Max number of jobs you want to parse for each company", "default": 10 }, "openai_api_key": { "title": "OpenAI API key", "type": "string", "description": "Parser uses it for extracting the salary range. It's optional" } } }, "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 } } } } } } } } } } ```