# Internshala Scrapper (`bareezh_codes/internshala-scrapper`) Actor This Apify actor scrapes jobs and internship listings from Internshala.com based on custom filters like job category, location, work-from-home preferences, and more. - **URL**: https://apify.com/bareezh\_codes/internshala-scrapper.md - **Developed by:** [Salman PSGCAS](https://apify.com/bareezh_codes) (community) - **Categories:** Jobs - **Stats:** 523 total users, 20 monthly users, 100.0% runs succeeded, 1 bookmarks - **User rating**: No ratings yet ## Pricing $5.00 / 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 ## Internshala Scraper ### 🤖 Overview This Apify actor scrapes jobs and internship listings from Internshala.com based on custom filters like job category, location, work-from-home preferences, and more. With this actor, you can easily collect detailed information about internships and use the data for research, job hunting, aggregation services, or any other purpose. ### ✅ Features - **Customizable Search**: Filter internships by job category, location, work-from-home status, part-time availability, and minimum stipend - **Detailed Data**: Collects comprehensive information about each internship, including job title, company name, location, stipend, duration, and more - **Duplicate Prevention**: Built-in logic to avoid collecting duplicate internship listings - **Robust Parsing**: Advanced parsing algorithms to handle website changes and ensure reliable data extraction - **Pagination Support**: Automatically navigates through multiple pages to collect all relevant listings - **Rate Limiting**: Implements respectful scraping practices to avoid overloading the target website ### 📋 Input Parameters | Parameter | Type | Description | |-----------|------|-------------| | `max_results` | Integer | Maximum number of internships to collect (default: 30) | | `job_category` | String | Category of internships to search for (e.g., "Software Development", "Marketing") | | `work_from_home` | Boolean | Whether to search for work-from-home internships | | `location` | String | Location for internships (used if work_from_home is false) | | `part_time` | Boolean | Whether to search for part-time internships | | `stipend` | String | Minimum stipend requirement (leave empty if not applicable) | | `pages_to_scrape` | Integer | Maximum number of pages to scrape (default: 20) | ### 📊 Output Data Structure Each internship object contains the following fields (when available): ```json { "title": "Software Development Intern", "company": "Tech Solutions Ltd", "location": "Delhi", "duration": "3 months", "stipend": "₹10,000 /month", "actively_hiring": true, "early_applicant": false, "type": "Internship", "posted": "Posted 2 days ago", "apply_link": "https://internshala.com/internship/detail/...", "logo_url": "https://internshala.com/uploads/logo/..." } ```` ### 💰 Usecase of This Actor 1. **Job Aggregation Websites**: Create a niche job portal focused on internships and early-career opportunities 2. **Subscription Services**: Offer premium internship alerts or reports to students and job seekers 3. **Data Analysis**: Sell insights on internship trends, popular skills, and compensation patterns 4. **College & University Services**: Partner with educational institutions to provide customized internship feeds 5. **Integration Services**: Build APIs or plugins that integrate this data with career portals, student management systems, or HR software 6. **Custom Search Tools**: Create specialized search engines for students looking for internships with specific criteria 7. **Content Marketing**: Use the data to generate articles about internship opportunities, trends, and advice 8. **Lead Generation**: Sell qualified leads to companies looking to hire interns in specific fields # Actor input Schema ## `max_results` (type: `integer`): Maximum number of internships to collect ## `job_category` (type: `string`): Category of internships to search for (e.g., Software Development, Marketing) ## `work_from_home` (type: `boolean`): Search for work from home internships ## `location` (type: `string`): Location for internships (used if work from home is false) ## `part_time` (type: `boolean`): Search for part-time internships ## `stipend` (type: `string`): Minimum stipend requirement (leave empty if not applicable) ## `pages_to_scrape` (type: `integer`): Maximum number of pages to scrape ## Actor input object example ```json { "max_results": 30, "job_category": "Software Development", "work_from_home": true, "location": "Delhi", "part_time": false, "stipend": "", "pages_to_scrape": 20 } ``` # 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 = { "max_results": 30, "job_category": "Software Development", "location": "Delhi", "pages_to_scrape": 20 }; // Run the Actor and wait for it to finish const run = await client.actor("bareezh_codes/internshala-scrapper").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 = { "max_results": 30, "job_category": "Software Development", "location": "Delhi", "pages_to_scrape": 20, } # Run the Actor and wait for it to finish run = client.actor("bareezh_codes/internshala-scrapper").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 '{ "max_results": 30, "job_category": "Software Development", "location": "Delhi", "pages_to_scrape": 20 }' | apify call bareezh_codes/internshala-scrapper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=bareezh_codes/internshala-scrapper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Internshala Scrapper", "description": "This Apify actor scrapes jobs and internship listings from Internshala.com based on custom filters like job category, location, work-from-home preferences, and more.", "version": "0.0", "x-build-id": "sMhSa2vGRDRI1WH5u" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/bareezh_codes~internshala-scrapper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-bareezh_codes-internshala-scrapper", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/bareezh_codes~internshala-scrapper/runs": { "post": { "operationId": "runs-sync-bareezh_codes-internshala-scrapper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/bareezh_codes~internshala-scrapper/run-sync": { "post": { "operationId": "run-sync-bareezh_codes-internshala-scrapper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "max_results": { "title": "Maximum Results", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of internships to collect", "default": 30 }, "job_category": { "title": "Job Category", "type": "string", "description": "Category of internships to search for (e.g., Software Development, Marketing)", "default": "Software Development" }, "work_from_home": { "title": "Work From Home", "type": "boolean", "description": "Search for work from home internships", "default": true }, "location": { "title": "Location", "type": "string", "description": "Location for internships (used if work from home is false)", "default": "Delhi" }, "part_time": { "title": "Part Time", "type": "boolean", "description": "Search for part-time internships", "default": false }, "stipend": { "title": "Minimum Stipend", "type": "string", "description": "Minimum stipend requirement (leave empty if not applicable)", "default": "" }, "pages_to_scrape": { "title": "Pages to Scrape", "minimum": 1, "maximum": 100, "type": "integer", "description": "Maximum number of pages to scrape", "default": 20 } } }, "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 } } } } } } } } } } ```