# Internshala Scraper: India Internships & Fresher Jobs (`precious_bathmat/internshala-scraper`) Actor

Scrape internships and fresher jobs in India from Internshala: title, company, city or work from home, stipend or salary, duration, skills, posted date, part-time and job-offer flags, with optional full details (openings, applicants, perks, deadline). Plus a report per search. No login.

- **URL**: https://apify.com/precious_bathmat/internshala-scraper.md
- **Developed by:** [Mariam Ahmed](https://apify.com/precious_bathmat) (community)
- **Categories:** Jobs, Education
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $1.00 / 1,000 listings

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/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## Internshala Scraper: India Internships & Fresher Jobs

Scrape **internships and fresher jobs in India from Internshala** (internshala.com), India's biggest internship site, with no login and no API key, and get a **report for every search**:

- **Every listing**: title, company, **city or work from home**, **stipend per month** (internships) or **salary per year** (jobs), duration, experience asked for, **skills**, posted date, part-time, **job offer after internship**, fresher job and actively-hiring flags, link
- **Listing details** (optional): full description, who can apply, **perks**, **number of openings**, **number of applicants**, start date, **apply-by date**, exact posting date, CTC breakup, industry, company website, and the employer's track record (hiring since, opportunities posted, candidates hired)
- **Filters**: keywords, cities, work from home, minimum monthly stipend, minimum yearly salary (LPA)
- **Report per search**: **stipend or salary ranges**, unpaid share, work-from-home and part-time share, durations, **top skills**, top companies and cities, freshness; with details also applicants, openings, perks and industries

### What does this Internshala scraper do?

```
Internshala · "python" internships · 277 listings, all read
 Stipend median ₹12,000 a month (₹7,500 – 18,500) · 17% unpaid
 62% work from home · 41% part time · 16% offer a job after the internship
 Skills asked with Python: Machine Learning 30% · HTML 22% · SQL 21%
```

1. Choose **internships** or **jobs** and enter **keywords** (one search each) and optional **cities**.
2. Add filters: **work from home**, **minimum stipend** or **minimum salary**.
3. Turn on **listing details** for openings, applicants, perks and deadlines.
4. You get **one row per listing** and **one report per search**.

```mermaid
flowchart LR
    A[Keywords,<br/>cities,<br/>filters] --> B[Internshala<br/>listing pages<br/>40-60 per page]
    B --> C[Listings<br/>stipend, city,<br/>skills, flags]
    C --> D[Listing pages<br/>openings, applicants,<br/>perks, deadline<br/>optional]
    C --> E[Report<br/>pay ranges,<br/>top skills,<br/>companies, cities]
    D --> E
```

### Who is it for?

- **Colleges, placement cells and career coaches**: which internships are open for a field, what they pay, and which skills students need
- **Ed-tech and training companies**: the skills employers ask for most, by role, to plan courses
- **Recruiters and staffing agencies**: companies hiring freshers and interns now, by city
- **Sales teams selling to startups and SMEs**: employers actively hiring on Internshala, with their website and how much they hire
- **Students and job seekers**: every open internship for a role in one sheet, with stipend, deadline and how many people already applied
- **Analysts and journalists**: stipends, unpaid internships and remote work across India's entry-level market

### What data do you get?

**Listings** (dataset, one row per internship or job)

| Field | What it tells you |
|---|---|
| `id`, `type`, `title`, `url`, `company`, `activelyHiring` | The listing and the employer |
| `locations`, `workFromHome` | Cities, or work from home |
| `payText`, `payMin`, `payMax`, `payPeriod`, `unpaid`, `incentives`, `performanceBased` | Stipend (internships, mostly per month) or salary (jobs, per year) in rupees |
| `durationText`, `durationMonths` | Internship length |
| `experienceText`, `experienceMinYears`, `experienceMaxYears` | Experience a job asks for |
| `partTime`, `jobOfferAfterInternship`, `fresherJob`, `international`, `earlyApplicant` | Internshala's own labels |
| `skills`, `snippet` | Skills on the card and the first line of the ad |
| `postedAgoText`, `approxPostedAt` | When it was posted |
| `description`, `whoCanApply`, `perks`, `otherRequirements`, `additionalInformation`, `ctcBreakup` | With details: the full ad |
| `openings`, `applicants`, `applicantsAtLeast`, `startDate`, `applyBy`, `datePosted` | With details: openings, applicants (`1000+` is `1000` with `applicantsAtLeast`), dates |
| `aboutCompany`, `companyWebsite`, `industry`, `category`, `employerHiringSince`, `employerOpportunitiesPosted`, `employerCandidatesHired` | With details: the employer |

**Report** (`REPORTS`, one per search; `REPORTS_CSV` as a flat sheet)

| Field | What it tells you |
|---|---|
| `matchingListings`, `allMatchesRead`, `listingsRead` | How many listings match, and whether all were read |
| `pay` | Monthly stipend (internships) or yearly salary (jobs): 25th percentile, median, 75th percentile, highest |
| `unpaidPercent`, `workFromHomePercent`, `partTimePercent`, `jobOfferAfterInternshipPercent`, `fresherJobPercent` | The shape of the market |
| `medianDurationMonths`, `durations`, `experience` | How long internships run, what jobs ask for |
| `topSkills`, `topCompanies`, `topCities` | Who asks for what, where |
| `freshness` | Share posted in the last 24 hours and 7 days |
| `applicants`, `medianOpenings`, `topPerks`, `topIndustries` | With details: competition, openings, perks, industries |

### Example: 24 October 2026

Five internship searches, up to 1,000 listings each (3,023 listings in 7 minutes):

| Search | Listings | Median stipend / month | Unpaid | Work from home | Top skill besides the keyword |
|---|---|---|---|---|---|
| python | 277 (all) | ₹12,000 | 17% | 62% | Machine Learning 30% |
| data science | 373 (all) | ₹10,900 | 12% | 57% | MS-Excel 26% |
| marketing | 3,508 | ₹11,000 | 1% | 5% | Social Media Marketing 31% |
| content writing | 1,292 | ₹9,500 | 5% | 31% | Social Media Marketing 57% |
| graphic design | 373 (all) | ₹10,000 | 3% | 28% | Adobe Photoshop 57% |

Three job searches with details on (170 jobs, all detail pages read, about 2 minutes):

| Search | Jobs | Median salary / year | For freshers | Median applicants | Top perk |
|---|---|---|---|---|---|
| sales | 2,191 (100 read) | ₹8,00,000 | 61% | 110 | Health insurance 30% |
| data analyst | 51 (all) | ₹5,30,000 | 98% | 370 | – |
| software developer | 19 (all) | ₹3,70,000 | 58% | 1,000+ | 5 days a week 58% |

### Pricing

Pay per event:

| Event | Price |
|---|---|
| Listing saved | $0.001 |
| Listing details read (only with details on) | $0.001 |
| Search report | $0.02 |

1,000 internships with a report cost **$1.02**; with details **$2.02**. Platform usage is small: the 1,000-listing marketing search used under $0.001 of compute.

### Good to know

- **Cities**: Internshala has one page per city name and treats spellings as different cities (Bangalore had 2,900 internships, Bengaluru 650, and the Bengaluru ones checked all appear under Bangalore too). Bengaluru, New Delhi, Gurugram, Bombay, Madras and Calcutta are mapped to Internshala's main page. A city Internshala has no page for is reported in the run summary.
- **Work from home with a city** returns work-from-home listings **plus** listings in that city, as on Internshala.
- **Minimum stipend** is Internshala's own filter: it keeps a listing when the **top** of its range reaches the amount, so "₹1,000 - 30,000" passes ₹8,000. It accepts ₹2,000 to ₹10,000 in steps of 2,000; minimum salary accepts 2 to 10 LPA.
- **Counts**: `matchingListings` is exact when every page was read (`allMatchesRead`), otherwise it is the number in Internshala's page heading.
- **Pay**: "Unpaid", "Performance based" and "Competitive salary" carry no number. Lump-sum and weekly stipends keep their own `payPeriod` and are left out of the monthly median.
- **Dates**: listing pages say "3 days ago"; `approxPostedAt` is worked out from that. With details it is the exact posting date when Internshala publishes it.
- **No personal data**: recruiters' names are not collected.

### Input

| Field | Default | What it does |
|---|---|---|
| `listingType` | `internships` | Internships or jobs |
| `keywords` | `["python"]` | One search per keyword; empty = every listing |
| `locations` | – | One search per city (combined with each keyword) |
| `workFromHome` | `false` | Remote listings only |
| `minStipend` | any | Internships: ₹2,000 to ₹10,000 a month |
| `minSalaryLpa` | any | Jobs: 2 to 10 lakh a year |
| `maxResultsPerSearch` | `200` | Most listings per search, newest first (up to 5,000) |
| `includeDetails` | `false` | Open each listing for openings, applicants, perks and dates |
| `includeReports` | `true` | One report per search |

```json
{
  "listingType": "internships",
  "keywords": ["python", "data science"],
  "locations": ["Bangalore"],
  "workFromHome": false,
  "minStipend": "6000",
  "maxResultsPerSearch": 500,
  "includeDetails": true
}
```

### Integrations

Run it on a schedule to track new internships every morning, send the dataset to **Google Sheets**, **Airtable** or **Excel**, or call it from **Make**, **Zapier**, **n8n** or the **Apify API**. The `REPORTS_CSV` record opens directly in any spreadsheet.

# Actor input Schema

## `listingType` (type: `string`):

Internships or jobs.

## `keywords` (type: `array`):

Roles, skills or fields, one search each, e.g. "python", "data science", "marketing", "content writing". Empty = every listing (with the filters below).

## `locations` (type: `array`):

Optional cities, one search each (combined with every keyword), e.g. "Bangalore", "Mumbai", "Delhi", "Pune", "Hyderabad". Empty = all of India.

## `workFromHome` (type: `boolean`):

Only remote listings. With cities as well, Internshala shows remote listings plus listings in that city.

## `minStipend` (type: `string`):

Internships only: Internshala's own stipend filter.

## `minSalaryLpa` (type: `string`):

Jobs only: Internshala's own salary filter, in lakh rupees a year.

## `maxResultsPerSearch` (type: `integer`):

Most listings to read per search (1 to 5,000), 40 to 60 per page, newest first.

## `includeDetails` (type: `boolean`):

Open each listing for the full description, who can apply, perks, number of openings, number of applicants, exact posting date, apply-by date, industry and company website. One extra page per listing, charged per listing.

## `includeReports` (type: `boolean`):

One report per search: stipend or salary ranges, unpaid, work-from-home, part-time and job-offer shares, durations, top skills, companies and cities, freshness; with details also applicants, openings, perks and industries.

## Actor input object example

```json
{
  "listingType": "internships",
  "keywords": [
    "python"
  ],
  "workFromHome": false,
  "minStipend": "any",
  "minSalaryLpa": "any",
  "maxResultsPerSearch": 200,
  "includeDetails": false,
  "includeReports": true
}
```

# Actor output Schema

## `listings` (type: `string`):

One row per internship or job: title, company, cities or work from home, stipend or salary, duration or experience, skills, flags, posted date, link; with details also description, who can apply, perks, openings, applicants, dates, industry and website.

## `reports` (type: `string`):

Per search: pay ranges, unpaid, work-from-home, part-time and job-offer shares, durations, top skills, companies and cities, freshness.

## `reportsCsv` (type: `string`):

One flat row per search.

## `summary` (type: `string`):

Requests, problems and the limits of the data.

# 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 '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "keywords": [
        "python"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("precious_bathmat/internshala-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 '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "keywords": ["python"] }

# Run the Actor and wait for it to finish
run = client.actor("precious_bathmat/internshala-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "keywords": [
    "python"
  ]
}' |
apify call precious_bathmat/internshala-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,precious_bathmat/internshala-scraper"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/3cJjYRG36gcbwk1ek/builds/FH4nshRfWJdybjbor/openapi.json