Chat with a research assistant using Google Gemini, Wikipedia, and Sheets
Ready for 3.0Ready for 3.0. This template uses no nodes that n8n 3.0 removes. No removed nodes, versions or helpers found. Static check, last run 2026-10-19.
Nodes used (8)
- Chat Trigger
@n8n/n8n-nodes-langchain.chatTrigger - AI Agent
@n8n/n8n-nodes-langchain.agent - Google Gemini Chat Model
@n8n/n8n-nodes-langchain.lmChatGoogleGemini - Simple Memory
@n8n/n8n-nodes-langchain.memoryBufferWindow - Wikipedia
@n8n/n8n-nodes-langchain.toolWikipedia - Calculator
@n8n/n8n-nodes-langchain.toolCalculator - Basic LLM Chain
@n8n/n8n-nodes-langchain.chainLlm - Google Sheets
n8n-nodes-base.googleSheets
What it does
Quick Overview This workflow provides a chat-based research assistant powered by Google Gemini that can look up facts on Wikipedia, do calculations, and remember context, while also tagging each user question and logging it to a Google Sheets spreadsheet. How it works 1. Receives a user message through the built-in n8n chat webhook trigger. 2. Sends the message to a Google Gemini agent that can consult Wikipedia for factual lookups and use a Calculator tool for arithmetic. 3. Uses a buffer memory window to keep recent conversation context so follow-up questions stay coherent. 4. Streams the agent’s plain-language, cited response back to the chat. 5. In parallel, uses Google Gemini to generate a short 1–3 word topic label for the question. 6. Appends the original question and its topic label to a Google Sheets tab for tracking what users ask. Setup 1. Add Google Gemini (PaLM) API credentials for the agent model and the topic-tagging model. 2. Add a Google Sheets OAuth2 connection and select the target spreadsheet and the “Questions” sheet in the logging step. 3. Open the chat URL for the workflow’s public chat trigger and share it with users who should be able to ask questions.