Build an WhatsApp AI Agent with No-Code Automation — n8n

Modern customer support is all about speed, intelligence, and scale — and in this tutorial, I show you how to build a WhatsApp AI Customer Support Agent using n8n, OpenAI, Twilio, and PostgreSQL — without writing complex backend code.

This is more than just a chatbot — it’s a smart assistant that:

• Understands natural language queries
• Pulls real-time data from your database
• Responds instantly on WhatsApp
• Remembers users for contextual replies
• Can escalate issues when needed

🎥 Watch the Full YouTube Tutorial (with setup + demo):  👉 https://www.youtube.com/watch?v=kbyinTe6S4k

🧠 Why This Project Matters

Most businesses use WhatsApp manually for support — slow, repetitive, and hard to scale.  This project automates it all using tools that are:

• Easy to set up
• No-code (n8n)
• Powered by AI (OpenAI)
• Integrated with real business data (PostgreSQL)

Whether you’re a founder, DevOps engineer, or builder — this project gives you a working WhatsApp AI support system you can deploy fast.

🔧 Tech Stack

• n8n — No-code automation tool to link everything together
• Twilio — WhatsApp API for sending and receiving messages
• OpenAI — Understands user intent and generates replies
• PostgreSQL — Stores your product data, order status, FAQs, etc.

⚙️ How It Works (High-Level)

1. A user sends a WhatsApp message like “Where’s my order?”
2. Twilio receives the message and triggers an n8n workflow
3. OpenAI processes the message to detect the user’s intent
4. Based on that intent, PostgreSQL is queried to fetch order or product data
5. A relevant, human-like response is sent back to WhatsApp

🧠 Built-in Logic & Expressions

Here’s a glimpse of how the logic is set up inside the n8n workflow:

User Message:

{{ $json.data.body }}

Clean Phone Number (from WhatsApp format):

{{ $('Twilio Trigger').item.json.data.from.replace('whatsapp:', '') }}

Current Timestamp:

{{ $now }}

Annotation Cleaner Function:

function removeAnnotations(input) {
  return input.replace(/【[^】]*】/g, '').trim();
}

// Example usage
const output = removeAnnotations(query);
return output;

🚀 Getting Started

1. Clone the Repository  GitHub — whatsapp-n8n
2. Set Environment Variables  Add your Twilio SID, Auth Token, OpenAI API key, and PostgreSQL connection
3. Create Your Workflow in n8n

• Twilio Trigger: To receive WhatsApp messages
• Code Node: For cleaning up input annotations
• OpenAI Node: To generate intelligent replies
• PostgreSQL Node: To query order/product data
• Twilio SMS Node: To send a reply

1. Deploy n8n Workflow

• Use n8n Cloud or self-host using Docker

📌 Notes & Tips

• Make sure your Twilio account is WhatsApp-enabled
• Customize OpenAI prompts to match your tone (friendly, formal, etc.)
• Expand memory fields to support contextual conversations
• Log user queries with timestamps for tracking and analysis

💡 Use Cases

• Order tracking via WhatsApp
• Product or pricing inquiries
• Instant FAQ support
• Lead generation and support routing
• Internal business workflows or team tools

📺 Full Video Tutorial + Demo

For full setup walkthrough and live demo:  👉 https://youtu.be/kbyinTe6S4k

You’ll see exactly how the n8n flow works, how to handle inputs, connect APIs, and structure replies with real business value.

🎯 Final Thoughts

This project is a great example of how AI + automation + no-code can deliver real results — fast.  You don’t need to write a full backend or hire a team. Just plug the right tools together.

If you’re interested in DevOps, no-code tools, AI agents, or automation that scales, this is a great place to start.

👉 Subscribe to my YouTube channel for more projects like this:  https://www.youtube.com/@codewithmuh

Have questions? Want help building something similar?  Drop a comment or DM — I’d love to hear what you’d automate on WhatsApp.