This workflow enables dynamic management and application of rules for an AI-powered chatbot integrated with n8n. It combines chat triggers, language models, database operations, and Google Sheets to create a flexible system where rules can be retrieved, modified, and stored seamlessly. The primary goal is to enhance the chatbot’s behavior by allowing runtime updates to its rules, making it adaptable to user feedback and evolving requirements.

The process begins with the ‘When chat message received’ trigger detecting incoming messages. The message is processed by the ‘Ultimate Assistant,’ an AI agent that uses the LangChain framework to determine the appropriate response or action based on rules. The workflow fetches existing rules from either a PostgreSQL database or Google Sheets, depending on user preferences, and integrates these rules into the AI context.

The fetched rules are aggregated and merged, and the AI agent utilizes this consolidated information to craft intelligent responses. Users can add, update, or delete rules directly within Google Sheets or via database queries, enabling real-time customization of the chatbot’s behavior. The system stores new or modified rules back into the database or sheet for future use.

This flexible setup is ideal for managing complex chatbot behaviors, creating adaptive user interactions, and maintaining a centralized rule repository that can be easily edited without altering core code. It is especially useful for customer support bots, interactive assistants, or any AI-driven service that benefits from dynamic rule adjustments.

Node Count	11 – 20 Nodes
Nodes Used	@n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.chatTrigger, @n8n/n8n-nodes-langchain.lmChatAnthropic, @n8n/n8n-nodes-langchain.memoryBufferWindow, aggregate, googleSheets, googleSheetsTool, merge, postgres, postgresTool, stickyNote