# Naukri Jobs Scraper (`agentx/naukri-jobs-scraper`) Actor Scrape Naukri job listings for India by keyword and location. Returns job, company, salary, experience, skills, requirements, questionnaire, remote, and application metadata. - **URL**: https://apify.com/agentx/naukri-jobs-scraper.md - **Developed by:** [AgentX](https://apify.com/agentx) (community) - **Categories:** Jobs, Lead generation, Agents - **Stats:** 4 total users, 3 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $3.20 / 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 Jobs Scraper - India Job Listings API for Skills, Salary, and Hiring Data **Naukri Jobs Scraper is an India-focused job listings API that extracts structured Naukri.com jobs by keyword and location for hiring intelligence, recruitment analytics, skills research, and salary benchmarking. It returns normalized records with job title, company, location, salary fields, experience range, skills, must-have skill signals, requirements, education requirements, questionnaire flags, application URL, and company metadata when available.** This actor is scoped to India because Naukri is an India job board. The country input is restricted to India, while the optional location input lets you search cities and regions such as Bengaluru, Mumbai, Delhi NCR, Pune, Hyderabad, Chennai, and remote-related location text. Up to 10,000 job records can be requested per run, with pay-per-result pricing and no monthly minimum. [![Naukri](https://img.shields.io/badge/Naukri-India_Jobs_API-2563eb?style=for-the-badge)](https://apify.com/agentx/naukri-jobs-scraper?fpr=aiagentapi) [![Hiring Intelligence](https://img.shields.io/badge/Hiring_Intelligence-0f766e?style=for-the-badge)](https://apify.com/agentx/naukri-jobs-scraper?fpr=aiagentapi) [![Pay Per Result](https://img.shields.io/badge/Pay_Per_Result-$3.20%2F1K-7c3aed?style=for-the-badge)](https://apify.com/agentx/naukri-jobs-scraper?fpr=aiagentapi) --- ### Why Choose This API #### Naukri India Hiring Data With Skills And Requirements **Structured job records** Get one normalized dataset item per job posting with consistent fields for title, company, location, salary, job description, posting date, and source URL. This makes the output ready for dashboards, databases, CRMs, and AI pipelines. **Platform-specific extraction** This actor focuses on Naukri only. Inputs and documentation are scoped to what the platform coverage supports, avoiding unsupported filters. **Salary and compensation signals** When the source exposes salary data, the actor maps salary minimum, maximum, currency, and interval into structured fields. The `currency` input can normalize salary values for easier cross-market comparison. **Location and remote intelligence** Use country and location inputs to target the right market. Output includes the platform location text, and remote signals are mapped when the source data exposes or implies remote work. **Scalable Apify workflow** Results are stored in the default dataset and can be exported as JSON, CSV, Excel, XML, RSS, or consumed with the Apify REST API and client SDKs. **Transparent pricing** The actor uses a pay-per-result model. You pay a small start event plus a flat result event for each job saved to the dataset. --- ### Quick Start Guide #### How to Extract Naukri Job Data in 3 Steps ##### Step 1: Define Your Search Parameters Enter a keyword such as `Java Developer`, `Data Engineer`, `Sales Manager`, or a company name. Keep `country` as `India`, optionally add an Indian city or region, and set `max_results`. ##### Step 2: Configure and Run the Actor [Open Actor Input](https://console.apify.com/actors/agentx/naukri-jobs-scraper/input?fpr=aiagentapi) Set the parameters, then click **Start**. For broad market scans, use a general keyword and a country-level location. For precise sourcing, use a role title plus a city or metro area. ##### Step 3: Download Your Data Run the actor and export the structured Naukri dataset for analytics, matching, or reporting. Each result is available in the default dataset and can be pulled by API for downstream automation. --- ### Input Parameters #### Required Configuration Fields | Parameter | Type | Required | Description | Example | | --- | --- | --- | --- | --- | | `keyword` | string | Yes | Job title, skill, company, or search keyword | "Java Developer" | | `country` | select | Yes | Target country, fixed to India | "India" | | `max_results` | integer | Yes | Maximum job records to extract, from 10 to 10,000 | 100 | | `location` | string | No | Indian city, state, region, or remote-related text | "Bengaluru" | | `currency` | select | No | Target currency for salary normalization | "INR" | #### Example Input Configuration ```json { "keyword": "Java Developer", "country": "India", "location": "Bengaluru", "max_results": 100, "currency": "INR" } ```` #### Date Format Options When `posted_since` is available for this actor, it accepts relative values such as `1 day`, `7 days`, `2 weeks`, `3 months`, or an absolute date such as `2026-05-01`. If the parameter is not present in the input schema, the platform does not consistently expose a reliable date filter and the actor relies on source ordering or source-provided posted-date fields. #### Location Formatting Best Practices - Use city and region names when you need local results, such as `New York`, `Bengaluru`, or `Toronto`. - Use broader country-level searches when you want maximum coverage for a keyword. - For remote searches, use the dedicated `remote_only` parameter only when the actor exposes it. Otherwise, remote status is returned as an output signal when available. - Keep keywords specific enough to avoid unrelated aggregator matches, but broad enough to return enough results for analysis. #### Supported Countries for Job Scraping This actor is scoped to India because Naukri is an India job board. The country input is restricted to India, while the optional location input lets you search cities and regions such as Bengaluru, Mumbai, Delhi NCR, Pune, Hyderabad, Chennai, and remote-related location text. #### Country-Aware Platform Routing This is a single-platform actor. It does not route requests to other job boards. If you need automatic routing across multiple sources, use [All Jobs Scraper](https://apify.com/agentx/all-jobs-scraper?fpr=aiagentapi). *** ### Output Data Schema #### Complete Job Listing Data Structure Each extracted Naukri job is stored as one dataset item. Field availability depends on what the source listing exposes, but the actor maps source data into a consistent job schema. #### Job Metadata Fields | Field | Description | | --- | --- | | `platform` | Source platform label, usually Naukri | | `platform_url` | Naukri job URL | | `title` | Job position title | | `company_name` | Hiring company name | | `company_url` | Naukri company or static URL when available | | `company_logo` | Company logo URL | | `location` | Job location text | | `location_details` | Structured country/locality object when available | | `posted_date` | Naukri posted-date label | | `description` | Job description content | | `requirements` | Parsed requirement text from the job description | | `education_requirements` | Education requirement text when available | | `skills` | Skills parsed from Naukri tags | | `must_have_skills` | Explicit must-have skills extracted from description text | | `experience` | Experience text | | `experience_range` | Required experience range | | `screening_questions` | Questionnaire signal when present | | `salary_minimum` | Minimum salary when available | | `salary_maximum` | Maximum salary when available | | `salary_currency` | Salary currency | | `easy_apply` | Whether the posting appears to support easier application | #### Employment Details Employment fields can include `job_type`, `is_remote`, `experience`, `experience_range`, `job_level`, `job_function`, `requirements`, `skills`, and `screening_questions` when the platform exposes those values. Empty fields usually mean the source listing did not publish that value. #### Salary Information Salary fields include `salary_minimum`, `salary_maximum`, `salary_currency`, and `salary_period`. Salary extraction is source-dependent. Many job boards do not require employers to publish salary ranges, so salary fields can be empty for otherwise valid jobs. #### Company Data Company fields can include `company_name`, `company_url`, `company_logo`, `company_website`, `company_industry`, `company_rating`, `review_count`, and related metadata when the platform returns those values. The actor avoids fabricating unavailable company data. #### Example JSON Output ```json { "platform": "Naukri", "platform_url": "https://www.naukri.com/job-listings-example", "title": "Java Developer", "company_name": "Example Technologies", "location": "Bengaluru", "experience_range": "3-6 Yrs", "skills": ["Java", "Spring Boot", "SQL"], "description": "Build and maintain backend services.", "salary_currency": "INR", "is_remote": false } ``` #### Export Formats - **JSON** - Full structured records with nested objects and arrays preserved. - **CSV** - Spreadsheet-compatible exports for BI and reporting workflows. - **Excel** - Download-ready tabular files for analysts and recruiters. - **XML / RSS** - Feed-style exports where supported by Apify datasets. - **API Access** - Pull items programmatically via the Apify REST API or official SDKs. *** ### Integration Examples #### Actor ID for Platform Integration ```text agentx/naukri-jobs-scraper ``` #### Make.com Setup 1. Open Make and add the Apify **Run an Actor** module. 2. Set Actor to `agentx/naukri-jobs-scraper`. 3. Paste your input JSON into the module. 4. Enable synchronous run when you want the workflow to wait for completion. 5. Add **Get dataset items** and use the returned `defaultDatasetId`. #### N8N.io Setup 1. Add the Apify node. 2. Choose **Run an Actor and get dataset**. 3. Use Actor ID `agentx/naukri-jobs-scraper`. 4. Map your input fields from previous workflow steps. 5. Send the resulting dataset items to your CRM, database, spreadsheet, or AI pipeline. #### Python Integration Example ```python from apify_client import ApifyClient client = ApifyClient("YOUR_API_TOKEN") run_input = { "keyword": "Java Developer", "country": "India", "location": "Bengaluru", "max_results": 100, "currency": "INR" } run = client.actor("agentx/naukri-jobs-scraper").call(run_input=run_input) items = list(client.dataset(run["defaultDatasetId"]).iterate_items()) print(f"Fetched {len(items)} jobs") ``` #### JavaScript/Node.js Integration ```javascript import { ApifyClient } from 'apify-client'; const client = new ApifyClient({ token: 'YOUR_API_TOKEN' }); const input = { "keyword": "Java Developer", "country": "India", "location": "Bengaluru", "max_results": 100, "currency": "INR" }; const run = await client.actor('agentx/naukri-jobs-scraper').call(input); const { items } = await client.dataset(run.defaultDatasetId).listItems(); console.log(`Fetched ${items.length} jobs`); ``` #### JSON-LD Metadata ```json { "@context": "https://schema.org", "@graph": [ { "@type": "SoftwareApplication", "name": "Naukri Jobs Scraper", "applicationCategory": "BusinessApplication", "operatingSystem": "Cloud API", "url": "https://apify.com/agentx/naukri-jobs-scraper?fpr=aiagentapi", "description": "Extract structured Naukri job listings for recruiting analytics, market research, salary benchmarking, and job board automation.", "offers": { "@type": "Offer", "price": "0.0032", "priceCurrency": "USD", "description": "Pay per job result saved to the Apify dataset." }, "featureList": [ "Keyword-based job search", "Country and location targeting", "Structured job listing output", "Salary and company metadata when available", "JSON, CSV, Excel, XML, and API exports" ], "provider": { "@type": "Organization", "name": "AgentX", "url": "https://apify.com/agentx?fpr=aiagentapi" } }, { "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "name": "How do I scrape Naukri jobs?", "acceptedAnswer": { "@type": "Answer", "text": "Configure keyword, country, max_results, and optional supported filters, then run the actor. Results are saved to the Apify dataset and can be exported or pulled by API." } }, { "@type": "Question", "name": "What data fields does this actor return?", "acceptedAnswer": { "@type": "Answer", "text": "The actor returns structured job records including title, company, location, job URL, description, salary fields, posting date, and platform-specific metadata when available." } }, { "@type": "Question", "name": "How much does this actor cost?", "acceptedAnswer": { "@type": "Answer", "text": "The actor charges $0.01 per run plus $0.0032 per saved job result. 1,000 jobs cost about $3.21 including the start event." } } ] } ] } ``` *** ### Pricing & Cost Calculator #### Transparent Pay-Per-Use Model | Event Type | Price | Description | | --- | --- | --- | | **Actor Start** | `$0.01` | One-time charge per Actor run | | **result** | `$0.0032` | Charged for each Naukri job posting saved to the default dataset | #### Cost Examples **Small Scale** (100 jobs): - Job Data: 100 × `$0.0032` = `$0.32` - Actor Start: `$0.01` - **Total**: `$0.33` **Medium Scale** (1,000 jobs): - Job Data: 1,000 × `$0.0032` = `$3.20` - Actor Start: `$0.01` - **Total**: `$3.21` **Large Scale** (10,000 jobs): - Job Data: 10,000 × `$0.0032` = `$32.00` - Actor Start: `$0.01` - **Total**: `$32.01` #### Competitive Pricing Comparison | Feature | Naukri Jobs Scraper | Manual Collection | Generic Browser Scraper | Enterprise Data Vendor | | --- | --- | --- | --- | --- | | Cost model | Pay per result | Labor hours | Compute + maintenance | Contract pricing | | API access | Yes | No | Custom work required | Usually yes | | Output schema | Structured job data | Inconsistent | Custom parsing required | Varies | | Maintenance | Managed actor | Manual | User-owned | Vendor-owned | | Best for | Fast job data extraction | Small one-off checks | Custom internal crawlers | Large procurement workflows | *** ### Use Cases & Applications #### Recruitment & Talent Acquisition **Hiring pipeline monitoring** Track new jobs by title, company, location, and posting date to understand employer demand and recruitment velocity. **Candidate matching workflows** Feed structured job descriptions, requirements, skills, and locations into candidate matching systems or internal search tools. **Competitor hiring analysis** Monitor which companies are expanding, what roles they hire for, and how job requirements shift across locations. #### Market Research & Business Intelligence **Labor market dashboards** Build dashboards that show role demand, location density, remote-work trends, salary availability, and company-level hiring patterns. **Salary benchmarking** Analyze published salary ranges where available and normalize values into the selected currency. **Regional market entry analysis** Compare job volume and role mix across countries, cities, or metro areas before hiring or expansion decisions. #### AI & Machine Learning Applications **Training data for job matching** Use job descriptions and requirements as training or evaluation data for semantic search, matching, classification, and recommendation systems. **Skill extraction and taxonomy mapping** Map job descriptions into standardized skills, roles, departments, and seniority levels for workforce intelligence. **Automated alerts** Trigger notifications when new jobs matching a keyword, company, salary, or location appear in the dataset. #### Workforce Planning & HR Analytics **Skills gap analysis** Identify commonly requested skills and qualifications in job postings to guide training plans and workforce planning. **Hiring trend reports** Build weekly or monthly reports that summarize job volume, salary ranges, and demand by role or location. **Talent market benchmarking** Compare hiring signals across companies and geographies for recruiting strategy and compensation planning. #### Career Services & Education **Career guidance** Use live job market data to inform career advice, resume coaching, and learning path recommendations. **Curriculum alignment** Analyze job requirements and skills to keep courses aligned with employer demand. **Student job feeds** Power filtered job feeds for students, alumni, or workforce development programs. *** ### FAQ #### How do I scrape Naukri jobs with this actor? Open the actor input page, set `keyword`, `country`, and `max_results`, add optional supported filters, and run the actor. Results are saved in the default Apify dataset. #### What is included in each Naukri job record? Typical records include title, company, location, platform URL, description, posting date, salary fields, remote signal, job type, and platform-specific metadata when available. #### How many jobs can I extract per run? The input schema allows up to 10,000 requested results. Actual output depends on source availability, keyword breadth, selected location, available result volume, and source availability. #### Which countries are supported? This actor is scoped to India because Naukri is an India job board. The country input is restricted to India, while the optional location input lets you search cities and regions such as Bengaluru, Mumbai, Delhi NCR, Pune, Hyderabad, Chennai, and remote-related location text. #### How fresh is the data? The actor fetches live source results at run time. When a date filter is supported, use `posted_since` to target recent listings. Otherwise, use source-provided posted-date output fields for freshness analysis. #### Why are some salary fields empty? Salary fields are populated only when the source listing publishes salary information. Many employers do not include salary ranges in every job post. #### Why are some company fields empty? Company metadata depends on what the source returns for a listing. The actor maps available values and leaves unavailable fields empty instead of fabricating data. #### Can I integrate this with Make, N8N, Zapier, or my own backend? Yes. Use the Apify API, official Python and JavaScript clients, Make's Apify modules, N8N's Apify node, or dataset export URLs. #### What if no jobs are found? Try a broader keyword, remove narrow location filters, increase `max_results`, or use a country-level search. Some platforms may have limited inventory for niche keywords. #### Is this compatible with AI frameworks? Yes. The JSON output can be used with LangChain, vector databases, custom ranking systems, enrichment workflows, and internal recruitment agents. *** ### SEO Keywords & Search Terms #### Primary Keywords Naukri scraper API, Naukri jobs scraper, Naukri job listings API, India job data API, Apify Naukri scraper #### Long-Tail Keywords how to scrape Naukri jobs, extract Naukri skills, Naukri salary data scraper, India hiring intelligence API, Naukri recruitment analytics dataset #### Industry Terms job scraping API, job board scraper, job data extraction, hiring intelligence API, recruitment analytics, labor market data, salary benchmarking, job listing dataset, workforce analytics, talent intelligence. #### Location-Based Keywords country job data API, city job scraper, remote jobs dataset, regional hiring trends, local job market intelligence, international job listings API. #### Use Case Keywords competitor hiring monitoring, job board enrichment, talent market research, salary range extraction, skills demand tracking, recruitment automation, AI job matching dataset. *** ### Trust & Certifications - **Public data workflow** - Designed for publicly available job listing data. - **Transparent billing** - Pay-per-result pricing with clear start and result events. - **API-first delivery** - Works with Apify datasets, REST API, Python, JavaScript, Make, and N8N. - **Structured output** - Normalized fields reduce cleanup work for analysts and engineers. - **Maintenance-oriented design** - Platform-specific parsing can be updated as source pages evolve. *** ### Legal & Compliance #### Data Rights & Usage This actor extracts publicly available job listing data. Users are responsible for ensuring their use of output data complies with applicable laws, contractual requirements, and internal policies. #### Privacy Compliance The actor is intended for job listing and employer-side market intelligence workflows, not for collecting private candidate data. Users should apply GDPR, CCPA, and other privacy requirements to downstream storage and processing. #### Platform Terms of Service Users must review and comply with Naukri terms and any applicable policies before using extracted data in production workflows. #### Enterprise Support For enterprise usage, compliance questions, private deployments, or custom data workflows, contact [AgentX support](https://t.me/AiAgentApi). *** ### Related Tools #### Jobs & Hiring - **[All Jobs Scraper](https://apify.com/agentx/all-jobs-scraper?fpr=aiagentapi)** - Multi-Platform Job Listings Intelligence API - **[Glassdoor Hiring Scraper](https://apify.com/agentx/glassdoor-hiring-scraper?fpr=aiagentapi)** - Glassdoor Job Listings Intelligence API - **[Glints Employer Data Extractor](https://apify.com/agentx/glints-employer-data-extractor?fpr=aiagentapi)** - Glints Employer Intelligence API - **[Glints Jobs Data Extractor](https://apify.com/agentx/glints-jobs-data-extractor?fpr=aiagentapi)** - Glints Job Listings Intelligence API - 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Zomato Restaurant Search Intelligence API #### Other - **[Global Weather Scraper](https://apify.com/agentx/global-weather-scraper?fpr=aiagentapi)** - Global Weather Intelligence API *** ### Support & Community - **Community**: [@Apify\_Actor](https://t.me/Apify_Actor) - **Contact Team**: [@AiAgentApi](https://t.me/AiAgentApi) - **All AgentX APIs**: *** **Last Updated:** May 25, 2026 # Actor input Schema ## `keyword` (type: `string`): Job title, skill, or company name to search for ## `country` (type: `string`): Country where the target job listings are located ## `max_results` (type: `integer`): Maximum number of jobs to return ## `location` (type: `string`): City or region to focus the job search results on ## `currency` (type: `string`): Target currency for converting salary figures ## Actor input object example ```json { "keyword": "Java Developer", "country": "India", "max_results": 10, "currency": "USD" } ``` # Actor output Schema ## `results` (type: `string`): All job listings found across platforms with full details including salary, company info, and contact details # 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 = { "keyword": "Java Developer", "country": "India", "max_results": 10, "currency": "USD" }; // Run the Actor and wait for it to finish const run = await client.actor("agentx/naukri-jobs-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 = { "keyword": "Java Developer", "country": "India", "max_results": 10, "currency": "USD", } # Run the Actor and wait for it to finish run = client.actor("agentx/naukri-jobs-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 '{ "keyword": "Java Developer", "country": "India", "max_results": 10, "currency": "USD" }' | apify call agentx/naukri-jobs-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=agentx/naukri-jobs-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Naukri Jobs Scraper", "description": "Scrape Naukri job listings for India by keyword and location. Returns job, company, salary, experience, skills, requirements, questionnaire, remote, and application metadata.", "version": "1.0", "x-build-id": "JDOz9tE4nN9vEQj5e" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/agentx~naukri-jobs-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-agentx-naukri-jobs-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/agentx~naukri-jobs-scraper/runs": { "post": { "operationId": "runs-sync-agentx-naukri-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/agentx~naukri-jobs-scraper/run-sync": { "post": { "operationId": "run-sync-agentx-naukri-jobs-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": [ "keyword", "country", "max_results" ], "properties": { "keyword": { "title": "🔍 Keyword", "type": "string", "description": "Job title, skill, or company name to search for" }, "country": { "title": "🌍 Country", "enum": [ "India" ], "type": "string", "description": "Country where the target job listings are located" }, "max_results": { "title": "📊 Max Results", "minimum": 10, "maximum": 10000, "type": "integer", "description": "Maximum number of jobs to return" }, "location": { "title": "📍 Location", "type": "string", "description": "City or region to focus the job search results on" }, "currency": { "title": "💱 Currency", "enum": [ "AED", "AFN", "ALL", "AMD", "ANG", "AOA", "ARS", "AUD", "AWG", "AZN", "BAM", "BBD", "BDT", "BGN", "BHD", "BIF", "BMD", "BND", "BOB", "BRL", "BSD", "BTN", "BWP", "BYN", "BZD", "CAD", "CDF", "CHF", "CLF", "CLP", "CNH", "CNY", "COP", "CRC", "CUP", "CVE", "CZK", "DJF", "DKK", "DOP", "DZD", "EGP", "ERN", "ETB", "EUR", "FJD", "FKP", "FOK", "GBP", "GEL", "GGP", "GHS", "GIP", "GMD", "GNF", "GTQ", "GYD", "HKD", "HNL", "HRK", "HTG", "HUF", "IDR", "ILS", "IMP", "INR", "IQD", "IRR", "ISK", "JEP", "JMD", "JOD", "JPY", "KES", "KGS", "KHR", "KID", "KMF", "KRW", "KWD", "KYD", "KZT", "LAK", "LBP", "LKR", "LRD", "LSL", "LYD", "MAD", "MDL", "MGA", "MKD", "MMK", "MNT", "MOP", "MRU", "MUR", "MVR", "MWK", "MXN", "MYR", "MZN", "NAD", "NGN", "NIO", "NOK", "NPR", "NZD", "OMR", "PAB", "PEN", "PGK", "PHP", "PKR", "PLN", "PYG", "QAR", "RON", "RSD", "RUB", "RWF", "SAR", "SBD", "SCR", "SDG", "SEK", "SGD", "SHP", "SLE", "SLL", "SOS", "SRD", "SSP", "STN", "SYP", "SZL", "THB", "TJS", "TMT", "TND", "TOP", "TRY", "TTD", "TVD", "TWD", "TZS", "UAH", "UGX", "USD", "UYU", "UZS", "VES", "VND", "VUV", "WST", "XAF", "XCD", "XCG", "XDR", "XOF", "XPF", "YER", "ZAR", "ZMW", "ZWG", "ZWL" ], "type": "string", "description": "Target currency for converting salary figures" } } }, "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 } } } } } } } } } } ```