How I Built a Smart Two-Way Email Agent in 10 Minutes Using n8n

With ZERO lines of code

Email automation is evolving. No longer just rule-based filters or boring canned responses - today, you can build intelligent, conversational agents that understand, respond, and track context with ease.

This week, I built a two-way LLM-powered email agent using n8n, and it took me less than 10 minutes. I also recorded a detailed step-by-step video to help anyone replicate or expand on this.

Let me walk you through what I built, why it matters, and how you can start experimenting with smart email workflows - without writing a single line of code.

The stack I used

The goal was to create a lightweight yet powerful agent that can read emails, generate intelligent responses, and keep track of conversations.

Here’s what I used:

• 🔁 n8n – the orchestration engine to wire it all together

• 🧠 OpenAI’s GPT model – for generating smart, context-aware replies

• 📬 Gmail – to fetch incoming messages and send replies

• 📊 Google Sheets – to log email threads and manage state

This stack is low-code, scalable, and flexible enough for real-world automation use cases.

How the workflow works

The beauty of n8n lies in its modularity. Here’s a breakdown of the core logic behind the intelligent email agent:

1. Trigger
   A manual button starts the workflow (you can easily switch this to a schedule or webhook trigger).

2. Fetch emails
   It uses Gmail’s getAll: message node to pull recent email threads—this could be filtered by labels, sender, or time range.

3. Generate reply
   Each email is fed to OpenAI’s message model. It generates a contextual response based on the thread's content.

4. Log the conversation
   The original email and GPT-generated response are logged into a Google Sheet. This serves as a tracking database and memory for the conversation.

5. Send response
   The AI reply is sent back via Gmail using sendAndWait, keeping the thread intact.

6. Monitor for new replies
   The system watches for updates by comparing Gmail threads with the logs in Sheets-creating a memory-like behavior.

7. Send follow-ups
   If a reply is detected, a contextual follow-up is sent using reply: message.

It is a closed-loop system-automated, intelligent, and persistent.

Here is the n8n workflow diagram

Real-World Applications

This prototype can easily be adapted to solve many practical problems:

• 🎧 Automated customer support
  Provide quick and consistent responses to customer queries.

• 🎯 Lead engagement
  Follow up with leads intelligently, based on their messages.

• 🤖 Smart autoresponders
  Go beyond “Thank you for your email” with personalized, AI-written messages.

• 🗂 Inbox triage assistants
  Categorize, respond, and escalate emails based on intent and urgency.

If you are drowning in email or running a team that handles hundreds of inbound messages, this is a game-changer.

Want to Learn How to Build AI Agents?

If this sparked your curiosity, you might be interested in something even bigger.

Dr. Raj Abhijit Dandekar (MIT PhD) is running a 10-day hands-on bootcamp on AI Agents, where you will learn how to build production-grade intelligent systems using tools like n8n, LangChain, and OpenAI.

YouTube Video

Final remarks

This is just the beginning. As AI agents mature and no-code tools become more powerful, we are heading toward a future where anyone - not just developers - can build intelligent systems that think and act.

If you want to see the agent in action, stay tuned for my video breakdown. Drop your email to get notified when it goes live.

Let us automate smartly.
Let us build agents, not just workflows.