Latenode
Google sheets
Headless browser

Automate job scraping and analysis with Bright Data and LLMs

This automation helps users identify relevant job opportunities on Indeed by scraping job listings based on user-defined criteria, appending the results to a Google Sheet, and then using a large language model (LLM) to analyze each job post to determine if the user would be a good fit.

The workflow is triggered by a form where the user inputs job location, keyword, and country code. Bright Data's scraping service is used to retrieve the job listings, which are then processed by an LLM to provide a 'YES' or 'NO' assessment of the user's fit for each role. The results are updated in the Google Sheet, providing the user with a curated list of relevant job opportunities to pursue.

Updated Apr 2, 2026Est. run: 11sEst. cost: $0.0007
How Latenode estimates time and cost

Latenode bills workflow runs in credits: 1 credit = 30 seconds of processing. Minimum charge per run depends on your plan. Plug-and-Play (PnP) AI nodes are billed separately—each PnP token is $1 USD, charged pay-as-you-go at vendor cost plus a small processing fee, with no API keys required.

Full pricing — how credits work →
Scraping & data collection

Workflow preview

What this template does

  • Scrapes job listings from Indeed based on user-defined criteria
  • Appends the job listings to a Google Sheet
  • Analyzes each job post using an LLM to determine user's fit
  • Provides a 'YES' or 'NO' assessment for each job opportunity
  • Updates the Google Sheet with the LLM-based assessments

How it works

1
Trigger

User Trigger

The user provides job search criteria, including location, keyword, and country code, through a form or other interface.

2
Logic

Bright Data Scraper

The user-provided criteria are used to scrape relevant job listings from Indeed using the Bright Data headless browser integration.

3
Action

Google Sheets

The scraped job listings are appended to a designated Google Sheet, providing a centralized repository of the job data.

4
AI

Data Transformation

A large language model (LLM) is used to analyze each job listing in the Google Sheet and determine if the user would be a good fit for the role. The assessment, either 'YES' or 'NO', is added to the spreadsheet.

Setup guide

1

Add Bright Data Credential

Add your Bright Data API key as a credential in the Latenode Credentials panel.

2

Configure Bright Data Scraper Node

In the Latenode visual builder, add a Bright Data Scraper node and configure it with the following settings: - Job Location: Map the job location input from the trigger form - Keyword: Map the keyword input from the trigger form - Country Code: Map the country code input from the trigger form - Scrape Parameters: Choose 'Last 24 hours' and any other relevant filters

3

Add Google Sheets Credential

Add your Google Sheets OAuth2 credentials in the Latenode Credentials panel.

4

Configure Google Sheets Output Node

Add a Google Sheets node to the workflow and configure it to append the job listing data to a new or existing Google Sheet.

5

Add OpenAI Credential and Configure LLM Node

Add your OpenAI API key as a credential in the Latenode Credentials panel. Then, add a JavaScript node to the workflow and use the Latenode JavaScript node style to call the OpenAI API and analyze each job listing, writing the 'YES' or 'NO' assessment to the Google Sheet.

Requirements

Create a Google Sheet to store the scraped job listings and analysis results
Obtain a Bright Data API key to use their web scraping service
Set up a Latenode workspace with the required nodes (Code, Headless Browser, Google Sheets)
Configure the Latenode workspace with the Bright Data API key, Google Sheet details, and any other necessary variables

FAQ

Common questions about this template

Each run uses credits on your Latenode plan. We charge for processing time (1 credit = 30 seconds). Your actual cost depends on your plan and how long the run takes. See pricing plans for plans and how credits work.
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