This article provides a complete, practical walkthrough of the Webhook Code Automate 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:
   Automating AI-Powered Resume Screening in ERPNext Using n8n
   
   Meta Description:
   Discover how to automate AI-powered resume screening for ERPNext job applicants using n8n. This no-code workflow integrates OpenAI, ERPNext, and document parsing to streamline candidate evaluation.
   
   Keywords:
   n8n workflow, ERPNext automation, resume screening, AI recruitment, OpenAI, job applicant automation, Google Gemini, resume parser, ERPNext webhook, PDF to text, AI hiring agent, HR automation
   
   Third-Party APIs Used:
   
   1. ERPNext API – for retrieving job applicant and job opening records and posting updates to the application status.
   2. Google Gemini (PaLM) API – for AI analysis of resume text against job descriptions.
   3. Microsoft Outlook API – for sending notification emails to candidates.
   4. WhatsApp Business Cloud API – for sending WhatsApp messages to inform applicants of the status.
   5. n8n Extract from File Node – for parsing resume content from PDFs and text files.
   
   Article:
   
   How to Automate AI-Based Resume Screening in ERPNext with n8n
   
   Recruiting top talent often involves sifting through piles of resumes and manually checking criteria against job descriptions. But with the power of AI and automation tools like n8n, you can streamline this process, saving time while making smarter decisions. In this article, we’ll walk through an advanced n8n workflow created by Amjid Ali that fully automates resume screening for ERPNext job applicants using artificial intelligence.
   
   Overview of the Workflow
   
   This n8n workflow begins when a job applicant submits a resume through ERPNext. Using ERPNext's webhook system, the workflow is triggered on the "Insert" event for the Job Applicant DocType. The entire process then follows a structured path that includes input validation, document parsing, AI analysis, and automated updates based on AI recommendations.
   
   Let’s dive into each component of this powerful no-code automation.
   
   Step 1: Receiving the Application via Webhook
   
   The process starts with an ERPNext webhook node, which sends applicant data whenever a new job application is created. This includes the applicant's metadata, job ID, and a link to the resume.
   
   Step 2: Data Validation and Resume Check
   
   Before any analysis happens, the workflow checks if a resume link is provided and whether the applicant applied against a specific job opening:
   
   - If no resume is found, the applicant is automatically marked as Rejected in ERPNext.
   - If a resume is present but no job opening is selected, the applicant’s status is set to Hold.
   
   These checks ensure that only valid applications proceed through the AI evaluation pipeline.
   
   Step 3: File Download and Resume Parsing
   
   Next, the workflow fetches the resume file using an HTTP node. At this point, the file’s type is determined using a Switch node. While the current implementation handles PDF files, the workflow is flexible enough to accommodate additional formats like DOCX or images (e.g., OCR via JPGs).
   
   The PDF resume is converted into text using n8n’s built-in “Extract from File” node. This converts the document into raw plain text, ready for AI processing.
   
   Step 4: Merge with Job Description
   
   At this stage, the workflow retrieves job description data from ERPNext's Job Opening record. This is crucial because the AI agent needs both the resume content and the job description to assess compatibility.
   
   A Merge node combines the two inputs — the job description and the parsed resume text — into one unified dataset.
   
   Step 5: AI-Powered Evaluation via Google Gemini
   
   This is where the magic happens.
   
   The workflow uses Google Gemini’s language model (from the PaLM API) and sends both the job description and the resume text into a prompt-engineered AI agent. The system prompt is meticulously designed to:
   
   - Compare skills, experience, education, and keywords.
   - Assign a fit level (Strong, Moderate, Weak).
   - Score the candidate from 0–100 based on alignment.
   - Generate a brief justification for the decision.
   
   The AI returns structured text with the candidate’s fit level, score, rating (out of 5), and analysis.
   
   Step 6: Parsing AI Results into ERPNext Fields
   
   A JavaScript Code node parses the AI’s response and extracts the values into fields compatible with ERPNext:
   - fit_level
   - score
   - applicant_rating
   - justification_by_ai
   
   These custom fields should be pre-created in ERPNext to accept and store this data.
   
   Step 7: Decision Making – Accept or Reject
   
   The workflow uses an If node to determine if the score is above or below the threshold (80 out of 100). Based on this:
   
   - If the score is 80 or higher, the applicant is Accepted.
   - Otherwise, the applicant is Rejected.
   
   These decisions are automatically pushed into ERPNext using HTTP PUT requests.
   
   Step 8: Candidate Notification
   
   Once the application status is updated, the candidate is notified through integrations like:
   
   - Microsoft Outlook – to send a formal email notification.
   - WhatsApp Business Cloud – to send a message via WhatsApp.
   
   This ensures seamless and instant communication with applicants.
   
   Flexibility and Extensibility
   
   The workflow is brilliantly modular and designed with multiple resume formats in mind. Future iterations could include OCR capabilities for image-based resumes or integrations with Google Drive or Dropbox for extended file handling. Similarly, additional messaging platforms (like SMS or Slack) can be plugged in to expand communication options.
   
   What's more impressive is that the AI prompt is thoughtfully engineered to act like a recruitment specialist, focusing solely on facts presented in the resume and job description. This makes the evaluation objective and eliminates unconscious bias.
   
   Conclusion
   
   This n8n workflow is a shining example of how no-code tools, paired with artificial intelligence, can revolutionize HR processes. By automating resume screening, you reduce human effort, enhance candidate matching, and ensure timely decisions — all while keeping everything centralized in ERPNext.
   
   Created meticulously by Amjid Ali, this workflow can save countless hours in manual screening while increasing your recruitment accuracy and efficiency. Whether you're managing a growing startup or a large-scale HR operation, this template is instrumental in making resume processing smarter and faster.
   
   To support the creator or access full training on ERPNext and automation workflows using AI, visit SyncBricks:
   
   🔗 https://lms.syncbricks.com  
   📧 Email: amjid@amjidali.com  
   🔗 https://linkedin.com/in/amjidali
   
   If you’re ready to transform your hiring with automation and AI, give this workflow a try!

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.