# India Internships & Fresher Jobs Scraper (`dp862/india-internships-fresher-jobs`) Actor - **URL**: https://apify.com/dp862/india-internships-fresher-jobs.md - **Developed by:** [Deepthi](https://apify.com/dp862) (community) - **Categories:** Jobs, Lead generation, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $0.01 / 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 ## India Internships & Fresher Jobs — Internshala + Freshersworld Scraper Scrape **internships and fresher / entry-level jobs** from **Internshala** and **Freshersworld** in a single run, merged into one clean schema with automatic deduplication. The only actor that unifies India's two biggest early-career job sources. ### Why this actor India's early-career hiring market is huge and fragmented. Internshala and Freshersworld are the two largest sources for internships and fresher jobs — but no single tool pulls both into one consistent dataset. This does: - ✅ **2 sources, 1 schema** — `title`, `company`, `locations`, `salaryRaw` (stipend), `experienceMin/Max`, `employmentType`, `jobUrl`. - ✅ **Internships + fresher jobs + entry-level** in one pass. - ✅ **Cross-source dedupe** (toggle off for raw). - ✅ **Pay-per-event** — pay only for the listings you get. ### Input | Field | Type | Description | |---|---|---| | `keyword` | string | Role/profile, e.g. `web development`, `marketing`. Blank = all | | `location` | string | City (Internshala), e.g. `bangalore`. Blank = all India | | `sources` | array | `internshala-internship`, `internshala-job`, `freshersworld` | | `maxItemsPerSource` | integer | Cap per source (default 100) | | `dedupe` | boolean | Remove duplicates across sources (default true) | #### Example ```json { "keyword": "web development", "location": "bangalore", "maxItemsPerSource": 100, "dedupe": true } ```` ### Output ```json { "source": "internshala-internship", "title": "Web Development", "company": "Acme Labs", "locations": ["Bangalore"], "salaryRaw": "₹ 10,000 - 15,000 /month", "employmentType": "Internship", "jobUrl": "https://internshala.com/internship/detail/...", "scrapedAt": "2026-06-09T22:00:00.000Z" } ``` ### Use cases - **Recruiters & placement firms** sourcing interns / freshers at scale. - **Ed-tech & upskilling companies** matching students to openings. - **Job aggregators** building an India early-career index. - **AI job-search agents** — callable as an MCP tool. - **Researchers** tracking stipends, hiring demand and in-demand profiles. ### Notes Scrapes publicly visible listing data only. Pure HTTP (no browser) = fast and low cost. ### Pricing Pay-per-event: small flat fee per run + per-listing fee. See the **Pricing** tab. # Actor input Schema ## `keyword` (type: `string`): Role / profile to search, e.g. "web development", "marketing", "data science". Leave blank for all. ## `location` (type: `string`): City, e.g. "bangalore", "delhi". Applies to Internshala. Leave blank for all-India. ## `sources` (type: `array`): Which surfaces to scrape and merge. ## `maxItemsPerSource` (type: `integer`): Maximum listings to collect from each source before merging and dedupe. ## `dedupe` (type: `boolean`): Drop the same listing (title + company) when it appears in more than one source. ## Actor input object example ```json { "keyword": "", "location": "", "sources": [ "internshala-internship", "internshala-job", "freshersworld" ], "maxItemsPerSource": 100, "dedupe": true } ``` # 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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("dp862/india-internships-fresher-jobs").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 = {} # Run the Actor and wait for it to finish run = client.actor("dp862/india-internships-fresher-jobs").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 '{}' | apify call dp862/india-internships-fresher-jobs --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=dp862/india-internships-fresher-jobs", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "India Internships & Fresher Jobs Scraper", "version": "0.1", "x-build-id": "YESJ0aQzKq2NBzuVB" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/dp862~india-internships-fresher-jobs/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-dp862-india-internships-fresher-jobs", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/dp862~india-internships-fresher-jobs/runs": { "post": { "operationId": "runs-sync-dp862-india-internships-fresher-jobs", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/dp862~india-internships-fresher-jobs/run-sync": { "post": { "operationId": "run-sync-dp862-india-internships-fresher-jobs", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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 (optional)", "type": "string", "description": "Role / profile to search, e.g. \"web development\", \"marketing\", \"data science\". Leave blank for all.", "default": "" }, "location": { "title": "Location (optional)", "type": "string", "description": "City, e.g. \"bangalore\", \"delhi\". Applies to Internshala. Leave blank for all-India.", "default": "" }, "sources": { "title": "Sources", "type": "array", "description": "Which surfaces to scrape and merge.", "items": { "type": "string", "enum": [ "internshala-internship", "internshala-job", "freshersworld" ], "enumTitles": [ "Internshala Internships", "Internshala Fresher Jobs", "Freshersworld" ] }, "default": [ "internshala-internship", "internshala-job", "freshersworld" ] }, "maxItemsPerSource": { "title": "Max results per source", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum listings to collect from each source before merging and dedupe.", "default": 100 }, "dedupe": { "title": "Remove duplicates across sources", "type": "boolean", "description": "Drop the same listing (title + company) when it appears in more than one source.", "default": true } } }, "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 } } } } } } } } } } ```