This article provides a complete, practical walkthrough of the Gmail Googlesheets Create Triggered n8n agent. It connects HTTP Request, Webhook across approximately 1 node(s). Expect a Intermediate setup in 15-45 minutes. One‑time purchase: €29.

What This Agent Does

This agent orchestrates a reliable automation between HTTP Request, Webhook, handling triggers, data enrichment, and delivery with guardrails for errors and rate limits.

It streamlines multi‑step processes that would otherwise require manual exports, spreadsheet cleanup, and repeated API requests. By centralizing logic in n8n, it reduces context switching, lowers error rates, and ensures consistent results across teams.

Typical outcomes include faster lead handoffs, automated notifications, accurate data synchronization, and better visibility via execution logs and optional Slack/Email alerts.

How It Works

The workflow uses standard n8n building blocks like Webhook or Schedule triggers, HTTP Request for API calls, and control nodes (IF, Merge, Set) to validate inputs, branch on conditions, and format outputs. Retries and timeouts improve resilience, while credentials keep secrets safe.

Third‑Party Integrations

• HTTP Request
• Webhook

Import and Use in n8n

1. Open n8n and create a new workflow or collection.
2. Choose Import from File or Paste JSON.
3. Paste the JSON below, then click Import.
4. Show n8n JSON

   Title:  
   Automate Resume Screening with AI: A Step-by-Step Guide Using n8n, OpenAI, and Google Sheets
   
   Meta Description:  
   Learn how to build a fully automated resume screener using n8n’s open-source automation platform, OpenAI’s GPT-4o-mini, and Google Sheets. Turn PDF resumes from Gmail into structured applicant profiles, complete with scoring and data storage.
   
   Keywords:  
   resume screening automation, AI hiring tools, n8n workflow, OpenAI GPT-4o-mini, parse resumes to Google Sheets, Gmail resume parser, job applicant scoring, Google Sheets automation, LangChain, intelligent document processing, human resources automation
   
   Third-Party APIs Used:
   
   1. Gmail API (via Gmail Trigger)
   2. OpenAI API (GPT-4o-mini)
   3. Google Sheets API
   4. Google Drive API
   5. LangChain (used via n8n LangChain nodes)
   
   Article:
   
   Automate Your Resume Screening with AI and n8n: From Gmail to Google Sheets
   
   In today’s competitive hiring landscape, speed, accuracy, and efficiency are crucial in screening job applicants. Recruiters often sift through hundreds of resumes to identify the perfect candidate, a time-intensive process prone to oversight. Luckily, with open-source automation tools and powerful AI models, you can now automate much of this process.
   
   In this article, we walk you through a fully automated resume screening system built in n8n, the open-source workflow automation platform. This no-code/low-code solution uses GPT-4o-mini from OpenAI to evaluate resumes, extract essential applicant data such as name, email, and LinkedIn profile, and log all results into Google Sheets—all triggered automatically when a new resume hits your Gmail inbox.
   
   Let’s dive into how it works.
   
   🔧 Key Components of the Workflow
   
   This n8n workflow handles everything sequentially—from detecting a new CV in your Gmail to analyzing it and neatly organizing the results into a Google Sheet. Here’s what each part does:
   
   1. 📥 Trigger on New Gmail with Attachment  
   The workflow begins with a Gmail Trigger node. It monitors a specific inbox (e.g., hire@yourcompany.com) and looks for new, unread emails that contain attachments—presumably resumes. The trigger polls at the top of every hour, ensuring frequent checks.
   
   2. 📄 Extract Resume Text from PDF  
   Once a new email is detected, the Extract Text from PDF File node parses the attached resume. This returns unstructured text from the PDF, which is then ready to be processed by the AI.
   
   3. 🤖 Analyze Resume with GPT-4o-mini  
   The extracted text is sent to an AI Agent node powered by OpenAI’s GPT-4o-mini via n8n’s LangChain integration. The AI’s system prompt instructs it to:
      - Analyze the resume content
      - Extract key applicant details: name, email, LinkedIn URL
      - Assign a score to evaluate the resume quality (based on match to criteria, clarity, experience, etc.)
   
   4. 🧠 Output Parsing  
   An Output Parser node structures the AI's response into a JSON schema with four fields: name, email, LinkedIn, and score. This ensures the data is clean, validated, and can be easily mapped to a spreadsheet.
   
   5. 📊 Store Evaluated Data in Google Sheets  
   The parsed and scored information is sent to a pre-defined Google Sheet. Each resume submission is saved as a new row containing:
      - Applicant's name
      - Email address
      - LinkedIn profile link
      - AI-assigned resume score
      - Full resume text (for future reference)
   
   🎯 What You Get
   
   With this workflow, you gain:
   
   - An always-on resume screener that never misses an email
   - Structured applicant data that’s easy to review and filter
   - Objective initial scoring from a powerful AI model
   - Direct integration into tools used daily (i.e., Gmail and Google Sheets)
   
   This setup brings significant time savings and consistency to your hiring pipeline, especially at times when application volume is high.
   
   📝 Prerequisites
   
   Before deploying this automation, ensure you have the following:
   
   - n8n account or self-hosted instance  
   - OpenAI API key for GPT-4o-mini  
   - Google Workspace account with Gmail, Sheets, and Drive enabled  
   - Google Sheets and Drive APIs activated in Google Cloud Console  
   - OAuth 2.0 credentials configured in Google API Console
   
   🧩 How the System Fits Together
   
   Here’s a simplified overview of how the nodes interact in the n8n workflow:
   
   - Gmail receives email → Trigger fires
   - Resume (PDF) is extracted → Sent to AI
   - AI scores and summarizes data → Structured via Output Parser
   - Results logged in a Google Sheet
   
   Each stage is modular, meaning you can expand or modify the system. Want to add a notification to Slack or send an auto-reply to applicants? Just drag and drop a new node.
   
   🚀 Final Thoughts
   
   This workflow exemplifies how easy and powerful automating administrative tasks has become—especially with tools like n8n and OpenAI. Whether you’re a startup trying to streamline hiring or an HR professional buried in resumes, this solution can significantly improve your operations.
   
   By combining cutting-edge AI with intelligent automation, you get the best of both worlds: consistency and scalability with a touch of digital intelligence.
   
   Start automating your hiring process today—and let the bots handle the paperwork, so you can focus on the people.
   
   —
   
   Need help setting this up or tailoring it to your hiring criteria? n8n’s active community and templates are a great place to start.

5. Set credentials for each API node (keys, OAuth) in Credentials.
6. Run a test via Execute Workflow. Inspect Run Data, then adjust parameters.
7. Enable the workflow to run on schedule, webhook, or triggers as configured.

Tips: keep secrets in credentials, add retries and timeouts on HTTP nodes, implement error notifications, and paginate large API fetches.

Validation: use IF/Code nodes to sanitize inputs and guard against empty payloads.

Why Automate This with AI Agents

AI‑assisted automations offload repetitive, error‑prone tasks to a predictable workflow. Instead of manual copy‑paste and ad‑hoc scripts, your team gets a governed pipeline with versioned state, auditability, and observable runs.

n8n’s node graph makes data flow transparent while AI‑powered enrichment (classification, extraction, summarization) boosts throughput and consistency. Teams reclaim time, reduce operational costs, and standardize best practices without sacrificing flexibility.

Compared to one‑off integrations, an AI agent is easier to extend: swap APIs, add filters, or bolt on notifications without rewriting everything. You get reliability, control, and a faster path from idea to production.

Best Practices

• Credentials: restrict scopes and rotate tokens regularly.
• Resilience: configure retries, timeouts, and backoff for API nodes.
• Data Quality: validate inputs; normalize fields early to reduce downstream branching.
• Performance: batch records and paginate for large datasets.
• Observability: add failure alerts (Email/Slack) and persistent logs for auditing.
• Security: avoid sensitive data in logs; use environment variables and n8n credentials.

FAQs

Can I swap integrations later? Yes. Replace or add nodes and re‑map fields without rebuilding the whole flow.

How do I monitor failures? Use Execution logs and add notifications on the Error Trigger path.

Does it scale? Use queues, batching, and sub‑workflows to split responsibilities and control load.

Is my data safe? Keep secrets in Credentials, restrict token scopes, and review access logs.