# Fast Internshala Jobs & Internships Scraper (`mostafa-ennadi/internshala-fastapi-scraper`) Actor \[💰 $1.00 / 1K] Extract Internshala internships and fresher jobs by keyword, category, city, remote mode, and schedule. Get clean titles, companies, stipends, salaries, skills, locations, deadlines, and URLs. - **URL**: https://apify.com/mostafa-ennadi/internshala-fastapi-scraper.md - **Developed by:** [mostafa ennadi](https://apify.com/mostafa-ennadi) (community) - **Categories:** Jobs, Lead generation, Automation - **Stats:** 4 total users, 2 monthly users, 66.7% 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/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 ## Fast Internshala Jobs & Internships Scraper Search Internshala jobs and internships from Apify using structured filters, then export clean listing data from the default Dataset. This Actor is designed for users who need reliable Internshala listing data without managing browser automation, pagination, retries, or response cleanup. Each run accepts a search configuration, retrieves matching listings through the Actor owner's private retrieval service, normalizes the response, removes duplicates, and stores one Dataset item per listing. ### What This Actor Does - Finds internships or jobs by keywords, categories, cities, and work mode - Supports work-from-home, part-time, full-time, fresher, salary, experience, and stipend filters - Saves clean structured records to the Apify Dataset - Limits output with `maxListings` - Retries temporary network or service failures - Produces export-ready data in JSON, JSONL, CSV, XLSX, XML, and RSS through Apify - Keeps private connection details outside Actor input, Dataset items, and logs ### Pricing This Actor is intended to use usage-based pricing: | Charge | Price | |---|---:| | Actor start | `$0.00005` per run | | Saved listings | `$1.00` per 1,000 Dataset records | The listing charge is based on records saved to the Dataset after filtering, normalization, and deduplication. ### How To Use 1. Open the Actor in Apify Console. 2. Enter your search filters in the Input tab. 3. Click Start. 4. Open the Dataset tab after the run finishes. 5. Export the results in your preferred format. ### Input The Actor input is a JSON object. The most common fields are shown below. #### Search | Field | Type | Default | Description | |---|---|---:|---| | `listingType` | string | `internships` | Use `internships` or `jobs`. | | `keywords` | string | empty | Role, skill, or search phrase such as `Python`, `Data Analyst`, or `Marketing`. | | `categories` | string array | `[]` | Optional Internshala category names. Leave empty to search all categories. | | `cities` | string array | `[]` | Optional city names. Ignored when `workFromHome` is enabled. | | `workFromHome` | boolean | `false` | Return only remote/work-from-home listings. | #### Listing Filters | Field | Type | Default | Description | |---|---|---:|---| | `scheduleType` | string | `any` | One schedule choice: `any`, `part_time`, or `full_time`. Full-time applies only to jobs. | | `minStipend` | integer | `0` | Internships only. Minimum monthly stipend filter applied after results are fetched. | | `fresherOnly` | boolean | `false` | Jobs only. Return fresher jobs. | | `salaryLevel` | integer | `0` | Jobs only. Salary filter level from `0` to `10`; `0` means no salary filter. | | `experienceYears` | integer | `0` | Jobs only. Minimum experience from `0` to `10`; ignored when `fresherOnly` is enabled. | | `matchingPreference` | boolean | `false` | Requests preference-matched listings when supported. | #### Limits And Reliability | Field | Type | Default | Description | |---|---|---:|---| | `maxListings` | integer | `100` | Maximum number of Dataset records to save. Allowed range: `1` to `2000`. | | `requestTimeoutSecs` | integer | `180` | Request timeout. Allowed range: `10` to `600`. | | `retryCount` | integer | `3` | Number of attempts for retryable network or service failures. Allowed range: `1` to `5`. | ### Example Inputs #### Remote Python Internships ```json { "listingType": "internships", "keywords": "Python", "categories": [], "cities": ["Delhi"], "workFromHome": true, "scheduleType": "any", "minStipend": 0, "fresherOnly": false, "salaryLevel": 0, "experienceYears": 0, "matchingPreference": false, "maxListings": 25, "requestTimeoutSecs": 180, "retryCount": 3 } ```` #### Remote Data Analyst Jobs ```json { "listingType": "jobs", "keywords": "Data Analyst", "categories": [], "cities": ["Bengaluru", "Hyderabad"], "workFromHome": true, "scheduleType": "full_time", "minStipend": 0, "fresherOnly": false, "salaryLevel": 4, "experienceYears": 2, "matchingPreference": false, "maxListings": 50, "requestTimeoutSecs": 180, "retryCount": 3 } ``` ### Output The Actor stores results in the default Apify Dataset. Each Dataset item represents one job or internship listing. Important fields include: | Field | Description | |---|---| | `listingId` | Stable listing identifier from the source data. | | `listingType` | `internships` or `jobs`. | | `url` | Public Internshala listing URL. | | `title` | Listing title. | | `company` | Company name. | | `location` | Display location, or `Work From Home` for remote listings. | | `locations` | List of locations when available. | | `isRemote` | Whether the listing is remote/work-from-home. | | `isPartTime` | Whether the listing is part-time. | | `stipend`, `stipendMin`, `stipendMax` | Internship stipend data. These are usually empty for jobs. | | `salary`, `salaryMin`, `salaryMax` | Job salary data. These are empty for internship records. | | `durationMonths` | Internship duration when available. | | `experienceYears` | Job experience requirement when available. | | `skills` | Skills extracted from the listing. | | `perks` | Perks or benefits listed by the employer. | | `description` | Listing description. | | `openings` | Number of openings when available. | | `applicationDeadline` | Application deadline text. | | `postedAt` | Human-readable posting date. | | `postedAtIso` | Source-provided posting timestamp when available. | | `scrapedAt` | Timestamp when the Actor saved the record. | | `source` | Always `Internshala`. | #### Example Dataset Item ```json { "listingId": "3202603", "listingType": "internships", "url": "https://internshala.com/work-from-home-data-analytics-internship-at-example", "title": "Data Analytics - Internship (WFH)", "company": "Example Company", "location": "Work From Home", "locations": [], "workMode": "remote", "isRemote": true, "isPartTime": false, "stipend": "Rs. 10,000 - 20,000 /month", "stipendMin": 10000, "stipendMax": 20000, "salary": null, "salaryMin": null, "salaryMax": null, "durationMonths": null, "experienceYears": null, "skills": ["Python", "SQL", "Data Analytics"], "perks": ["Certificate", "Flexible work hours"], "description": "Listing description...", "openings": 4, "applicationDeadline": "4 Aug' 26", "postedAt": "5 Jul' 26", "postedAtIso": "1783189800", "activelyHiring": true, "earlyApplicant": false, "hasJobOffer": false, "womenOnly": false, "fastResponse": false, "aboutCompany": "Company summary...", "searchCategories": [], "searchCities": [], "scrapedAt": "2026-07-05T15:54:17.684250Z", "source": "Internshala" } ``` ### Output Summary At the end of each successful run, the Actor writes an `OUTPUT` record to the default key-value store. It includes: - `success` - `listingType` - `fetchedResults` - `savedResults` - `requestedMax` - `minimumStipend` - `pagesFetched` - `startedAt` - `finishedAt` - `durationSeconds` ### Behavior Notes - `workFromHome=true` overrides `cities`. - `scheduleType` is a single-choice filter, so users cannot select part-time and full-time together. - `scheduleType=full_time`, `fresherOnly`, `salaryLevel`, and `experienceYears` are job-focused filters. - `minStipend` is applied after source results are fetched. The Actor may fetch extra internship listings internally, then filter and trim to `maxListings`. - Dataset records are deduplicated before saving. - If no listings match the input, the run can still succeed with an empty Dataset. ### Security - Private connection details are not exposed in Actor input. - Private credentials are not written to Dataset items. - Error messages redact sensitive values if an upstream response includes them. - The Actor logs only high-level run information, such as listing type and result counts. # Actor input Schema ## `listingType` (type: `string`): Choose which type of listing to search. Internship-only filters are applied only to internships, and job-only filters are applied only to jobs. ## `keywords` (type: `string`): Optional role, skill, or search phrase. Examples: Python, Data Analyst, Digital Marketing. ## `categories` (type: `array`): Use Internshala category names, not URLs or IDs. Add one category per row, or separate multiple names with commas. Examples: Android App Development, Data Science, Digital Marketing, Content Writing. Leave empty to search all categories. ## `cities` (type: `array`): Use city names only, not URLs or location IDs. Add one city per row, or separate multiple names with commas. Examples: Delhi, Mumbai, Bengaluru, Hyderabad. Ignored when Work From Home / Remote Only is enabled. ## `workFromHome` (type: `boolean`): Return only remote or work-from-home listings. When enabled, city filters are ignored. ## `scheduleType` (type: `string`): Choose one schedule filter. Use Any to include all schedules. Part-Time works for jobs and internships. Full-Time applies only to jobs. ## `minStipend` (type: `integer`): For internships only. Results are filtered after listings are fetched. Set 0 for no stipend filter. ## `fresherOnly` (type: `boolean`): For jobs only. Return fresher jobs and ignore Minimum Experience. ## `salaryLevel` (type: `integer`): For jobs only. Salary filter level from 0 to 10. Use 0 for no salary filter. ## `experienceYears` (type: `integer`): For jobs only. Minimum experience in years. Use 0 for no experience filter. Ignored when Fresher Jobs Only is enabled. ## `matchingPreference` (type: `boolean`): Request preference-matched listings when supported. ## `maxListings` (type: `integer`): Maximum number of Dataset items to save after normalization, deduplication, and local stipend filtering. ## `requestTimeoutSecs` (type: `integer`): Maximum time to wait for one request. ## `retryCount` (type: `integer`): Number of attempts for network errors and retryable service responses. ## Actor input object example ```json { "listingType": "internships", "keywords": "", "categories": [], "cities": [], "workFromHome": false, "scheduleType": "any", "minStipend": 0, "fresherOnly": false, "salaryLevel": 0, "experienceYears": 0, "matchingPreference": false, "maxListings": 100, "requestTimeoutSecs": 180, "retryCount": 3 } ``` # Actor output Schema ## `results` (type: `string`): No description ## `dataset` (type: `string`): No description ## `summary` (type: `string`): No description # 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("mostafa-ennadi/internshala-fastapi-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 = {} # Run the Actor and wait for it to finish run = client.actor("mostafa-ennadi/internshala-fastapi-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 '{}' | apify call mostafa-ennadi/internshala-fastapi-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=mostafa-ennadi/internshala-fastapi-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Fast Internshala Jobs & Internships Scraper", "description": "[💰 $1.00 / 1K] Extract Internshala internships and fresher jobs by keyword, category, city, remote mode, and schedule. Get clean titles, companies, stipends, salaries, skills, locations, deadlines, and URLs.", "version": "1.0", "x-build-id": "AUydNu78M5dNZnezK" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/mostafa-ennadi~internshala-fastapi-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-mostafa-ennadi-internshala-fastapi-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/mostafa-ennadi~internshala-fastapi-scraper/runs": { "post": { "operationId": "runs-sync-mostafa-ennadi-internshala-fastapi-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/mostafa-ennadi~internshala-fastapi-scraper/run-sync": { "post": { "operationId": "run-sync-mostafa-ennadi-internshala-fastapi-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": [ "listingType", "maxListings" ], "properties": { "listingType": { "title": "Listing Type", "enum": [ "internships", "jobs" ], "type": "string", "description": "Choose which type of listing to search. Internship-only filters are applied only to internships, and job-only filters are applied only to jobs.", "default": "internships" }, "keywords": { "title": "Keywords", "type": "string", "description": "Optional role, skill, or search phrase. Examples: Python, Data Analyst, Digital Marketing.", "default": "" }, "categories": { "title": "Internshala Categories", "type": "array", "description": "Use Internshala category names, not URLs or IDs. Add one category per row, or separate multiple names with commas. Examples: Android App Development, Data Science, Digital Marketing, Content Writing. Leave empty to search all categories.", "items": { "type": "string" }, "default": [] }, "cities": { "title": "Cities / Locations", "type": "array", "description": "Use city names only, not URLs or location IDs. Add one city per row, or separate multiple names with commas. Examples: Delhi, Mumbai, Bengaluru, Hyderabad. Ignored when Work From Home / Remote Only is enabled.", "items": { "type": "string" }, "default": [] }, "workFromHome": { "title": "Work From Home / Remote Only", "type": "boolean", "description": "Return only remote or work-from-home listings. When enabled, city filters are ignored.", "default": false }, "scheduleType": { "title": "Schedule Type", "enum": [ "any", "part_time", "full_time" ], "type": "string", "description": "Choose one schedule filter. Use Any to include all schedules. Part-Time works for jobs and internships. Full-Time applies only to jobs.", "default": "any" }, "minStipend": { "title": "Minimum Stipend (₹/month)", "minimum": 0, "maximum": 1000000, "type": "integer", "description": "For internships only. Results are filtered after listings are fetched. Set 0 for no stipend filter.", "default": 0 }, "fresherOnly": { "title": "Fresher Jobs Only", "type": "boolean", "description": "For jobs only. Return fresher jobs and ignore Minimum Experience.", "default": false }, "salaryLevel": { "title": "Salary Level (0–10)", "minimum": 0, "maximum": 10, "type": "integer", "description": "For jobs only. Salary filter level from 0 to 10. Use 0 for no salary filter.", "default": 0 }, "experienceYears": { "title": "Minimum Experience (years)", "minimum": 0, "maximum": 10, "type": "integer", "description": "For jobs only. Minimum experience in years. Use 0 for no experience filter. Ignored when Fresher Jobs Only is enabled.", "default": 0 }, "matchingPreference": { "title": "Matching Preference", "type": "boolean", "description": "Request preference-matched listings when supported.", "default": false }, "maxListings": { "title": "Maximum Results", "minimum": 1, "maximum": 2000, "type": "integer", "description": "Maximum number of Dataset items to save after normalization, deduplication, and local stipend filtering.", "default": 100 }, "requestTimeoutSecs": { "title": "Request Timeout (seconds)", "minimum": 10, "maximum": 600, "type": "integer", "description": "Maximum time to wait for one request.", "default": 180 }, "retryCount": { "title": "Retry Count", "minimum": 1, "maximum": 5, "type": "integer", "description": "Number of attempts for network errors and retryable service responses.", "default": 3 } } }, "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 } } } } } } } } } } ```