Built A FAQ-Based AI Agent in n8n Using Gemini + Google Sheets + Memory

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I experimented with n8n's AI Agent capabilities and built a simple yet powerful FAQ assistant without writing any custom code.

What impressed me most is how quickly n8n allows us to move from a simple spreadsheet to a functional AI-powered assistant using an LLM, memory, and tool integrations—all through a visual workflow. This makes AI agent development accessible even for engineers who want to focus on business logic rather than infrastructure.

The objective was straightforward:

  • Store FAQs in Google Sheets
  • Allow users to ask questions in natural language
  • Let the AI Agent find the most relevant answer
  • Maintain conversation context using memory
  • Deliver responses through n8n's built-in chat interface
Before working on the AI Agent flow design, pls make sure to have n8n community edition setup done.
I have set it up in my Windows PC and here is the link to check on the setup.













🔹 Components Used

1. Chat Trigger

  • Acts as the entry point for user conversations.
  • Opens an interactive chat window directly inside n8n.

2. AI Agent Node

  • Central orchestration component.
  • Receives user questions.
  • Determines which tool to invoke.
  • Combines retrieved data with LLM reasoning. n8n AI Agents use a tool-calling approach that allows the model to select and invoke connected tools when needed.

3. Google Gemini Chat Model

  • Provides the reasoning engine for the AI Agent.
  • Understands user intent and generates natural language responses.

4. Simple Memory

  • Maintains conversational context.
  • Enables follow-up questions without repeating previous information.
  • Creates a more human-like interaction experience. n8n supports attaching memory to AI Agents to handle ongoing conversations.

5. Google Sheets as a Knowledge Base

Google Sheets can be used as a tool for AI Agents, enabling the agent to retrieve and work with spreadsheet data dynamically.





🔹 How It Works

  1. User opens the chat window.
  2. User asks:
  3. AI Agent analyzes the request.
  4. Agent invokes the Google Sheets FAQ tool.
  5. Relevant row is retrieved from the spreadsheet.
  6. Gemini formats the response naturally.
  7. Memory stores the interaction for future context.

💡 Sample Interaction

User: refund policy

AI Agent: "Our refund policy allows returns within 30 days of purchase. Items must be unused and in their original packaging to be eligible for a refund or exchange."


🎯 Key Benefits

✅ No coding required ✅ Google Sheets acts as a lightweight knowledge base ✅ Natural language querying ✅ Conversational memory support ✅ Easily expandable with more tools and data sources ✅ Ideal for FAQs, HR assistants, IT helpdesks, customer support, and internal knowledge bots

🔮 Next Enhancements

My next experiments will include:

  • Vector databases for semantic search
  • RAG implementation
  • MCP integration
  • Multi-agent orchestration
  • Document and PDF search
  • Knowledge graph integration

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