This n8n workflow is designed to automate the process of scraping job listings from the web, analyzing their relevance, and organizing the results in a Google Sheet. The workflow starts with a form trigger where users input specific job search parameters such as location, keywords, and country. It then sends a request to Bright Data’s API to initiate a web scraping dataset based on these filters. The workflow periodically polls for the dataset’s completion status before retrieving the scraped job data.

Once the data is collected, the workflow splits the information into individual job posts for further analysis. It uses OpenAI’s GPT-4 model to evaluate whether each job post is a good match for the user’s profile, based on the company name, job title, and description text. The results, including the relevance assessment, are then stored in a Google Sheet for easy review and further processing.

Practical use cases include job seekers wanting to automate their job searching process, recruiting agencies monitoring specific roles, or businesses tracking competitors’ hiring signals. This workflow streamlines data collection, relevance filtering, and organization, saving time and effort in manual job research or competitive analysis.

Node Count	11 – 20 Nodes
Nodes Used	@n8n/n8n-nodes-langchain.chainLlm, @n8n/n8n-nodes-langchain.lmChatOpenAi, formTrigger, googleSheets, httpRequest, if, splitOut, stickyNote, wait