# Ambitionbox Salary Scraper (`getdataforme/ambitionbox-salary-scraper`) Actor Extract salary insights from AmbitionBox for targeted companies. This Apify Actor provides reliable, structured JSON data on compensation trends, using residential proxies for undetected scraping.... - **URL**: https://apify.com/getdataforme/ambitionbox-salary-scraper.md - **Developed by:** [GetDataForMe](https://apify.com/getdataforme) (community) - **Categories:** Automation, Lead generation, Other - **Stats:** 21 total users, 5 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $9.00 / 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. 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 ## Ambitionbox Salary Scraper ### Introduction The Ambitionbox Salary Scraper is a powerful Apify Actor designed to extract detailed salary information from AmbitionBox, India's leading platform for company reviews and employee insights. By targeting specific companies, this tool provides valuable data on compensation trends, helping businesses, researchers, and job seekers make informed decisions. It ensures reliable, structured data extraction with minimal setup, delivering insights into salary ranges, roles, and more. ### Features - **Targeted Scraping**: Extracts salary data specifically for user-specified companies on AmbitionBox. - **High Reliability**: Uses residential proxies to avoid detection and ensure consistent data retrieval. - **Customizable Limits**: Allows setting maximum requests per crawl to control usage and costs. - **Structured Output**: Delivers clean, JSON-formatted data ready for analysis or integration. - **Fast and Efficient**: Optimized for quick crawls, handling multiple pages seamlessly. - **Error-Resilient**: Built-in handling for common scraping challenges like rate limits and page changes. - **Apify Integration**: Fully compatible with Apify's ecosystem for easy scheduling and storage. ### Input Parameters | Parameter | Type | Required | Description | Example | |-----------|------|----------|-------------|---------| | companyName | string | Yes | The name of the company to scrape salary data for, typically in slug format (e.g., "federal-bank"). | "federal-bank" | | proxyConfiguration | object | No | Configures proxy servers to hide the scraper's origin, using Apify's proxy service with residential groups by default. | {"useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"]} | | maxRequestsPerCrawl | integer | No | Limits the number of HTTP requests made during the crawl to manage resources. | 10 | ### Example Usage #### Input JSON ```json { "companyName": "federal-bank", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"] }, "maxRequestsPerCrawl": 10 } ```` #### Output JSON ```json [ { "company": "Federal Bank", "role": "Software Engineer", "salaryRange": "₹5,00,000 - ₹8,00,000", "averageSalary": "₹6,50,000", "experience": "2-5 years", "location": "Mumbai", "reviews": 45 }, { "company": "Federal Bank", "role": "Data Analyst", "salaryRange": "₹4,00,000 - ₹7,00,000", "averageSalary": "₹5,50,000", "experience": "1-3 years", "location": "Bangalore", "reviews": 32 } ] ``` ### Use Cases - **Market Research**: Analyze salary trends across industries to benchmark compensation. - **Competitive Intelligence**: Compare salaries at rival companies for strategic hiring. - **HR Planning**: Assist HR teams in setting competitive pay scales. - **Job Seekers**: Help individuals research fair salaries for negotiations. - **Academic Research**: Gather data for studies on labor markets and economic trends. - **Business Automation**: Integrate salary data into dashboards for automated reporting. ### Installation and Usage 1. Search for "Ambitionbox Salary Scraper" in the Apify Store. 2. Click "Try for free" or "Run". 3. Configure input parameters as needed. 4. Click "Start" to begin extraction. 5. Monitor progress in the log. 6. Export results in your preferred format (JSON, CSV, Excel). ### Output Format The output is an array of JSON objects, each representing a salary entry. Key fields include: - `company`: Name of the company. - `role`: Job title or position. - `salaryRange`: Minimum and maximum salary figures. - `averageSalary`: Mean salary value. - `experience`: Required years of experience. - `location`: Geographic location of the role. - `reviews`: Number of user reviews contributing to the data. This structured format allows for easy parsing and analysis in tools like Excel or databases. ### Support For custom/simplified outputs or bug reports, please contact: - Email: support@getdataforme.com - Subject line: "custom support" - Contact form: https://getdataforme.com/contact/ We're here to help you get the most out of this Actor! *** # Actor input Schema ## `companyName` (type: `string`): Company name to scrape salary data for (e.g., federal-bank) ## `proxyConfiguration` (type: `object`): Specifies proxy servers that will be used by the scraper in order to hide its origin. ## `maxRequestsPerCrawl` (type: `integer`): Maximum number of requests that can be made by this crawler. ## Actor input object example ```json { "companyName": "federal-bank", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] }, "maxRequestsPerCrawl": 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 = { "companyName": "federal-bank", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }; // Run the Actor and wait for it to finish const run = await client.actor("getdataforme/ambitionbox-salary-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 = { "companyName": "federal-bank", "proxyConfiguration": { "useApifyProxy": True, "apifyProxyGroups": ["RESIDENTIAL"], }, } # Run the Actor and wait for it to finish run = client.actor("getdataforme/ambitionbox-salary-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 '{ "companyName": "federal-bank", "proxyConfiguration": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }' | apify call getdataforme/ambitionbox-salary-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=getdataforme/ambitionbox-salary-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Ambitionbox Salary Scraper", "description": "Extract salary insights from AmbitionBox for targeted companies. This Apify Actor provides reliable, structured JSON data on compensation trends, using residential proxies for undetected scraping....", "version": "0.0", "x-build-id": "XywMEOT4mw5UQs5gF" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/getdataforme~ambitionbox-salary-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-getdataforme-ambitionbox-salary-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/getdataforme~ambitionbox-salary-scraper/runs": { "post": { "operationId": "runs-sync-getdataforme-ambitionbox-salary-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/getdataforme~ambitionbox-salary-scraper/run-sync": { "post": { "operationId": "run-sync-getdataforme-ambitionbox-salary-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "companyName": { "title": "Enter the Company Name", "type": "string", "description": "Company name to scrape salary data for (e.g., federal-bank)" }, "proxyConfiguration": { "title": "Proxy configuration", "type": "object", "description": "Specifies proxy servers that will be used by the scraper in order to hide its origin.", "default": { "useApifyProxy": true, "apifyProxyGroups": [ "RESIDENTIAL" ] } }, "maxRequestsPerCrawl": { "title": "Max Requests per Crawl", "type": "integer", "description": "Maximum number of requests that can be made by this crawler.", "default": 10 } } }, "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 } } } } } } } } } } ```