This article provides a complete, practical walkthrough of the Webhook Code Update Webhook 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:
   Automated AI-Driven Resume Screening with ERPNext and n8n: An End-to-End Workflow
   
   Meta Description:
   Discover how to build a fully automated talent acquisition workflow using n8n, ERPNext, and Google’s AI. This guide walks through a real-world use case of intelligent resume screening and applicant status updates.
   
   Keywords:
   n8n workflow, ERPNext automation, AI resume screening, Google Gemini AI, recruitment automation, applicant tracking system, PDF to text conversion workflow, HR automation, resume filtering AI, job applicant processing, webhook automation
   
   List of Third-Party APIs Used:
   
   1. ERPNext API
      - Used to fetch and update Applicant and Job Opening records
   2. Google Gemini / PaLM API (via LangChain)
      - Used as the AI agent to assess candidate fit for a job
   3. Microsoft Outlook API
      - Sends email notifications to applicants
   4. WhatsApp Business Cloud API
      - Sends WhatsApp notifications to applicants
   
   —
   
   Article:
   
   Automating Resume Screening with n8n, ERPNext, and AI: A Smarter Recruitment Pipeline
   
   Recruitment teams are constantly challenged to evaluate hundreds of resumes against job descriptions to find the right candidate. Manual screening is time-consuming, subjective, and prone to bias. What if an intelligent system could handle this task on autopilot, saving hours of manual review?
   
   In this article, we explore a powerful low-code solution built using the automation platform n8n, the enterprise resource planning system ERPNext, and Google Gemini AI. This workflow automatically parses resumes, evaluates candidates using AI, and updates their job application status in ERPNext. It integrates multiple technologies into a single, streamlined pipeline that recruiters can rely on.
   
   Let’s break down this revolutionary workflow.
   
   Step 1: Webhook Integration with ERPNext
   
   The recruitment journey begins when a job applicant submits their information through ERPNext. A custom webhook captures this data and sends it to n8n for processing. As per the design, ERPNext needs to trigger the workflow on the 'Job Applicant' DocType using ‘Insert’ as the event.
   
   Step 2: Validating the Application
   
   The workflow validates two key conditions:
   - Whether the applicant attached a résumé (resume_link must start with “http”)
   - Whether the job application is against a specific job opening (not “None”)
   
   Failure to meet these criteria results in:
   - Rejection if the resume is missing
   - The applicant is moved to "Hold" status if there's no associated job opening
   
   Step 3: Resume File Download and Text Extraction
   
   Once the resume link is validated, the workflow downloads the resume (PDF format in this template). Using n8n's built-in "Extract Text from File" node, the PDF is converted into plain text. Note: This works for text-based PDFs, not scanned image-based PDFs. The workflow also allows for future extensions for DOCX or JPEG formats using switch cases for different file types.
   
   Step 4: Fetching Job Opening Description
   
   If the résumé is successfully extracted, the system fetches the relevant job description from ERPNext based on the job opening ID. This is essential for comparing the resume details to the actual requirements of the role.
   
   Step 5: AI-Powered Resume Assessment
   
   Here’s where the magic happens.
   
   An AI agent powered by Google Gemini (via LangChain) receives two inputs:
   - The candidate’s résumé in text format
   - The job description for the relevant role
   
   The AI agent evaluates the resume based on required skills, education, certifications, and relevant experience. Then, it outputs:
   - Fit Level (Strong, Moderate, Weak)
   - Score (0–100)
   - Rating (0–5 stars)
   - Justification (why or why not the candidate fits the role)
   
   This intelligent analysis accelerates applicant screening in a consistent, unbiased, and scalable manner.
   
   Step 6: Field Mapping and ERPNext Update
   
   Another code node parses this AI output using Regular Expressions and maps it to specific ERPNext fields. The resume analysis (Fit Level, Score, Rating, and Justification) is stored in custom fields within the 'Job Applicant' record.
   
   A conditional step evaluates the score:
   - If Score ≥ 80 → Status changes to “Accepted”
   - If Score < 80 → Status changes to “Rejected”
   
   Step 7: Applicant Notifications
   
   Upon final decision, the workflow triggers:
   - A Microsoft Outlook email (in case of rejection)
   - A WhatsApp notification (in case of acceptance)
   
   This ensures timely communication with applicants and enhances the candidate experience.
   
   Benefits of This Workflow
   
   1. ‍💻 Reduced Manual Work:
      By integrating AI, recruiters avoid reading through hundreds of resumes manually.
   
   2. 🤖 Objective Candidate Evaluation:
      Every applicant is evaluated based on identical criteria using a consistent logic model.
   
   3. ⚡ Instant Status Updates:
      ERPNext is updated in real-time to reflect applicant status, scores, and feedback.
   
   4. 🔁 Scalable and Extensible:
      The workflow is modular. Additional features like OCR for scanned resumes or support for other document types (DOCX, PNG) can be added easily using n8n’s visual editor.
   
   5. 📲 Multi-Channel Notifications:
      Candidates are kept in the loop via email or WhatsApp, improving transparency and trust.
   
   Conclusion
   
   This n8n automation effectively transforms your ERPNext job applicant tracking system into a smart, AI-powered recruitment pipeline.
   
   Developed by Amjid Ali from Syncbricks, this workflow not only showcases the power of automation platforms and third-party AI tools but also lowers the barrier for HR teams of any size to adopt intelligent hiring practices.
   
   Ready to streamline your hiring process?
   
   You can view more detailed tutorials from the developer or contribute to improving the project via:
   - LMS: http://lms.syncbricks.com
   - PayPal support: http://paypal.me/pmptraining
   - Email: amjid@amjidali.com
   
   Grab your copy of the workflow and start hiring smarter today.
   
   — End of Article —

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.