# Naukri Job Scraper (`leadstrategus/naukri-job-scraper`) Actor Extract and normalize job listings from Naukri.com using search inputs like keyword, location, and job count. Returns structured job data including roles, company info, experience, skills, and descriptions, ready in dataset/CSV format for analysis or automation - **URL**: https://apify.com/leadstrategus/naukri-job-scraper.md - **Developed by:** [Komala Maran](https://apify.com/leadstrategus) (community) - **Categories:** Agents, Jobs, Automation - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $12.00 / 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 ## Naukri Job Scraper It is designed for scrapping Naukri job you provide search input (keyword, location, max jobs, etc.), the actor runs the underlying scraper, and pushes filtered job records to the dataset. ### What this actor does 1. Initializes the Apify Actor runtime. 2. Reads input from `Actor.getInput()`. 3. Builds scraper input with defaults. 4. Reads the run dataset items. 5. Maps each item to a compact output schema. 6. Pushes normalized results to this actor's dataset. ### Project structure - `main.js` - Actor logic (input mapping, scraper call, output transformation). - `package.json` - Node package metadata and runtime dependencies. - `Dockerfile` - Container image definition used to run the actor. ### Runtime and dependencies - **Node.js** via `apify/actor-node:20` Docker base image. - **Main dependency:** `apify` (JavaScript SDK). - **Start command:** `npm start` (runs `node main.js`). ### Input The actor accepts the following input fields: | Field | Type | Default | Description | |---|---|---|---| | `searchQuery` | string | `"developer"` | Keyword/job title query. | | `location` | string | `"india"` | Target location for jobs. | | `maximumJobs` | number | `20` | Maximum jobs requested from underlying scraper. | | `platform` | string | `"naukri"` | Platform passed to underlying scraper. | | `startUrls` | array | `[]` | Optional start URLs for scraping. | | `includeAmbitionBoxDetails` | boolean | `false` | Whether to include AmbitionBox-related details from source actor. | | `proxy` | object | `{ useApifyProxy: true, apifyProxyGroups: ["RESIDENTIAL"] }` | Proxy setup passed to source actor. | #### Example input ```json { "searchQuery": "data scientist", "location": "Bengaluru", "maximumJobs": 50, "platform": "naukri", "startUrls": [], "includeAmbitionBoxDetails": false, "proxy": { "useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"] } } ```` ### Output dataset schema Each output item is normalized to the fields below: - `companyName` - `applyCount` - `roleCategory` - `jobRole` - `companyDetail` - `functionalArea` - `description` - `industry` - `url` - `title` - `walkIn` - `maximumExperience` - `minimumExperience` - `locations` - `keySkills` - `shortDescription` #### Field mapping notes - `companyName` prefers `item.companyDetail.name`, then `item.staticCompanyName`. - `description` prefers `item.jobDescription`, then `item.description`. - `url` prefers `item.staticUrl`, then `item.jdURL`, then `item.applyUrl`. - `locations` is taken from `item.locations` or fallback `item.location`. - `keySkills` is taken from `item.tagsAndSkills` or fallback `item.keySkills`. ### Error handling behavior - If the called scraper run is not `SUCCEEDED`, the actor fails with run ID details. - Any runtime exception is caught and reported using `Actor.fail`. ### Important limitations - Output fields are transformed and may omit source fields not included in the mapping. - Current fetch limit for reading items from the source dataset is `99999`. ### Script entrypoint - Main entrypoint: `main.js` - NPM script: `npm start` # Actor input Schema ## `searchQuery` (type: `string`): Keywords like 'Frontend Developer' or 'Data Scientist' ## `location` (type: `string`): City or Country ## `maximumJobs` (type: `integer`): Number of jobs to scrape ## `platform` (type: `string`): Select the target website ## `startUrls` (type: `array`): Paste direct search result URLs from Naukri here. ## Actor input object example ```json { "searchQuery": "developer", "location": "india", "maximumJobs": 20, "platform": "naukri" } ``` # 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("leadstrategus/naukri-job-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("leadstrategus/naukri-job-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 leadstrategus/naukri-job-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=leadstrategus/naukri-job-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Naukri Job Scraper", "description": "Extract and normalize job listings from Naukri.com using search inputs like keyword, location, and job count. Returns structured job data including roles, company info, experience, skills, and descriptions, ready in dataset/CSV format for analysis or automation", "version": "0.0", "x-build-id": "cCXbTe7DpTP8uw7G9" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/leadstrategus~naukri-job-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-leadstrategus-naukri-job-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/leadstrategus~naukri-job-scraper/runs": { "post": { "operationId": "runs-sync-leadstrategus-naukri-job-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/leadstrategus~naukri-job-scraper/run-sync": { "post": { "operationId": "run-sync-leadstrategus-naukri-job-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "searchQuery": { "title": "Search Query", "type": "string", "description": "Keywords like 'Frontend Developer' or 'Data Scientist'", "default": "developer" }, "location": { "title": "Location", "type": "string", "description": "City or Country", "default": "india" }, "maximumJobs": { "title": "Maximum Jobs", "type": "integer", "description": "Number of jobs to scrape", "default": 20 }, "platform": { "title": "Platform", "enum": [ "naukri", "naukrigulf" ], "type": "string", "description": "Select the target website", "default": "naukri" }, "startUrls": { "title": "Direct Search URLs (Optional)", "type": "array", "description": "Paste direct search result URLs from Naukri here.", "items": { "type": "string" } } } }, "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 } } } } } } } } } } ```