# Indeed Job Search Scraper (`reverberant_equality/indeed-job-scraper`) Actor Scrape job listings from Indeed.com by keyword and location. Extract job title, company, location, salary, posting date, and job URLs. Uses Cheerio for fast, low-cost scraping. Ideal for job market research, salary analysis, and recruitment lead generation. - **URL**: https://apify.com/reverberant\_equality/indeed-job-scraper.md - **Developed by:** [Jordan C](https://apify.com/reverberant_equality) (community) - **Categories:** AI - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 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 ## Indeed Job Search Scraper A fast, CheerioCrawler-based Apify Actor that scrapes job listings from [Indeed.com](https://www.indeed.com) search results. ### Features - **CheerioCrawler** — HTTP-based, 10x faster than browser-based scrapers - **No JavaScript rendering needed** — Indeed search results are static HTML - **Pagination support** — automatically pages through results - **Structured output** — clean JSON with job title, company, location, salary, description, and URL - **PPE (Pay Per Event)** — transparent usage-based pricing ### Input Parameters | Field | Type | Required | Default | Description | |-------|------|----------|---------|-------------| | `keyword` | string | ✅ | — | Job search term (e.g., 'software engineer') | | `location` | string | ❌ | `""` | City, state, zip, or 'remote' | | `maxResults` | integer | ❌ | `50` | Max job listings to scrape (1–1000) | #### Example Input ```json { "keyword": "data scientist", "location": "New York, NY", "maxResults": 100 } ```` ### Output Dataset Fields | Field | Type | Description | |-------|------|-------------| | `jobTitle` | string | Job listing title | | `company` | string | Employer name | | `location` | string | Job location | | `salary` | string | Salary range or rate (if available) | | `description` | string | Short description snippet | | `postingDate` | string | Relative posting date (if available) | | `jobUrl` | string | Full URL to the job listing | | `source` | string | Always `"Indeed"` | | `scrapedAt` | string | ISO 8601 scrape timestamp | ### How It Works 1. Constructs search URLs like `https://www.indeed.com/jobs?q={keyword}&l={location}` 2. Uses CheerioCrawler to fetch and parse each search results page 3. Extracts job data using CSS selectors targeting Indeed's data-testid attributes 4. Follows pagination via the `&start=` parameter (0, 10, 20, …) 5. Stops when `maxResults` is reached or no more pages exist 6. Pushes structured data to the Apify dataset ### Usage ```bash ## Install dependencies npm install ## Run locally apify run ## Validate input schema apify validate-schema ## Build for production npm run build ``` ### PPE Charges | Event | Charge | |-------|--------| | `search-start` | $0.005 per search query | | `job-result` | $0.002 per job found | ### Resources - [Crawlee CheerioCrawler](https://crawlee.dev/api/cheerio-crawler) - [Apify Actor Documentation](https://docs.apify.com/platform/actors) - [Indeed](https://www.indeed.com) # Actor input Schema ## `keyword` (type: `string`): Job search term (e.g., 'software engineer', 'data scientist', 'nurse'). ## `location` (type: `string`): City, state, zip code, or 'remote' to filter by location. Leave empty for nationwide results. ## `maxResults` (type: `integer`): Maximum number of job listings to scrape. Each page returns ~15 results, capped at 1000. ## Actor input object example ```json { "keyword": "software engineer", "location": "remote", "maxResults": 50 } ``` # Actor output Schema ## `results` (type: `string`): Link to the default dataset containing scraped job listings # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "keyword": "software engineer", "location": "remote" }; // Run the Actor and wait for it to finish const run = await client.actor("reverberant_equality/indeed-job-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${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 = { "keyword": "software engineer", "location": "remote", } # Run the Actor and wait for it to finish run = client.actor("reverberant_equality/indeed-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 '{ "keyword": "software engineer", "location": "remote" }' | apify call reverberant_equality/indeed-job-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=reverberant_equality/indeed-job-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Indeed Job Search Scraper", "description": "Scrape job listings from Indeed.com by keyword and location. Extract job title, company, location, salary, posting date, and job URLs. Uses Cheerio for fast, low-cost scraping. Ideal for job market research, salary analysis, and recruitment lead generation.", "version": "0.0", "x-build-id": "qkzQMUg23Np6zMx0J" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/reverberant_equality~indeed-job-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-reverberant_equality-indeed-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/reverberant_equality~indeed-job-scraper/runs": { "post": { "operationId": "runs-sync-reverberant_equality-indeed-job-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/reverberant_equality~indeed-job-scraper/run-sync": { "post": { "operationId": "run-sync-reverberant_equality-indeed-job-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Search Keyword", "type": "string", "description": "Job search term (e.g., 'software engineer', 'data scientist', 'nurse')." }, "location": { "title": "Location", "type": "string", "description": "City, state, zip code, or 'remote' to filter by location. Leave empty for nationwide results." }, "maxResults": { "title": "Max Results", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of job listings to scrape. Each page returns ~15 results, capped at 1000.", "default": 50 } } }, "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 } } } } } } } } } } ```