Naukri Scraper

What does Naukri Scraper do?

Naukri Scraper extracts structured job data from naukri.com — including salary data, contact details, company metadata, full descriptions, and skill tags. It supports keyword search, location filters, and controllable result limits, so you can run the same query consistently over time. The actor also offers detail enrichment (full descriptions and company metadata) where the source provides them, incremental monitoring that only returns new or changed results on recurring runs, and a compact output mode for AI-agent and MCP workflows.

What data can you extract from naukri.com?

Each result includes Core listing fields (jobId, groupId, title, experienceText, minimumExperience, maximumExperience, salary, and salaryMin, and more), detail fields when enrichment is enabled (description, roleCategory, jobRole, and vacancy), contact and apply information (applyCount), and company metadata (companyName, companyId, companyWebsite, and companyDescription). In standard mode, all fields are always present — unavailable data points are returned as null, never omitted. In compact mode, only core fields are returned.

Enable detail enrichment in the input to get richer fields such as full descriptions and company metadata where the source provides them.

Input

The main inputs are a search keyword, an optional location filter, and a result limit. Additional filters and options are available in the input schema.

Key parameters:

• keyword — Job search keyword (e.g. 'python developer', 'data analyst'). Not required if jobIds is provided.
• jobIds — Fetch specific jobs by their Naukri job IDs (skips keyword search). Each ID is fetched via the detail API.
• location — Filter by location (e.g. 'Bangalore', 'Mumbai', 'Delhi')
• maxResults — Maximum number of jobs to return (0 = unlimited) (default: 100)
• fetchDetails — Fetch full job descriptions and additional fields via detail API (slower but richer data) (default: false)
• incremental — Only return new or changed jobs since last run (state scoped per keyword+location) (default: false)
• compact — Return only core fields (jobId, title, company, location, salary, experience, skills, createdDate, portalUrl, description). Ideal for AI agents and MCP workflows. (default: false)
• descriptionMaxLength — Truncate job descriptions to this many characters (0 or empty = no truncation)
• experienceMin — Minimum years of experience filter
• experienceMax — Maximum years of experience filter
• salary — Salary range filter in lakhs per annum
• sortBy — Sort results by relevance or posting date
• ...and 2 more parameters

Input example

{
  "keyword": "python developer",
  "maxResults": 100,
  "fetchDetails": false,
  "incremental": false,
  "compact": false,
  "maxConcurrency": 3,
  "maxRequestRetries": 3
}

Output

Example job record

Each run produces a dataset of structured job records. Results can be downloaded as JSON, CSV, or Excel from the Dataset tab in Apify Console.

In compact mode, output is reduced to core fields: jobId, title, companyName, location, salary, experienceText, skills, createdDate, and portalUrl.

How to scrape naukri.com

1. Go to Naukri Scraper in Apify Console.
2. Enter a search keyword and optional location filter.
3. Set maxResults to control how many results you need.
4. Enable includeDetails if you need full descriptions, contact info, or company data.
5. Click Start and wait for the run to finish.
6. Export the dataset as JSON, CSV, or Excel.

Use cases

• Extract job data from naukri.com for market research and competitive analysis.
• Track salary trends across regions and categories over time.
• Monitor new and changed listings on scheduled runs without processing the full dataset every time.
• Build outreach lists using contact details and apply URLs from listings.
• Research company hiring patterns, employer profiles, and industry distribution.
• Feed structured data into AI agents, MCP tools, and automated pipelines using compact mode.
• Export clean, structured data to dashboards, spreadsheets, or data warehouses.
• Analyze skill demand across listings using structured skill tags.

How much does it cost to scrape naukri.com?

Naukri Scraper uses pay-per-event pricing. You pay a small fee when the run starts and then for each result that is actually produced.

• Run start: $0.01 per run
• Per result: $0.002 per job record

Example costs:

• 10 results: $0.03
• 100 results: $0.21
• 500 results: $1.01

Incremental runs only charge for new or changed results, making recurring monitoring cost-efficient.

FAQ

How many results can I get from naukri.com?

The number of results depends on the search query and available listings on naukri.com. Use the maxResults parameter to control how many results are returned per run.

Does Naukri Scraper support recurring monitoring?

Yes. Enable incremental mode to only receive new or changed listings on subsequent runs. This is ideal for scheduled monitoring where you want to track changes over time without re-processing the full dataset.

Can I integrate Naukri Scraper with other apps?

Yes. Naukri Scraper works with Apify's integrations to connect with tools like Zapier, Make, Google Sheets, Slack, and more. You can also use webhooks to trigger actions when a run completes.

Can I use Naukri Scraper with the Apify API?

Yes. You can start runs, manage inputs, and retrieve results programmatically through the Apify API. Client libraries are available for JavaScript, Python, and other languages.

Can I use Naukri Scraper through an MCP Server?

Yes. Apify provides an MCP Server that lets AI assistants and agents call this actor directly. Use compact mode and descriptionMaxLength to keep payloads manageable for LLM context windows.

Is it legal to scrape naukri.com?

This actor extracts publicly available data from naukri.com. Web scraping of public information is generally considered legal, but you should always review the target site's terms of service and ensure your use case complies with applicable laws and regulations, including GDPR where relevant.

Your feedback

If you have questions, need a feature, or found a bug, please open an issue on the actor's page in Apify Console. Your feedback helps us improve.

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