This workflow enables users to ask natural language questions and receive data-driven answers from a MySQL database through an AI-powered interface. It integrates chat-based input, AI processing, and database querying, making data access more intuitive and conversational.

The process begins with a webhook trigger (‘When chat message received’) that captures user queries. These queries are then sent to an AI agent (‘AI Agent’) which is configured with a detailed prompt instructing it to answer questions with the help of database schema details and SQL queries. The AI agent determines if database access is needed and constructs the SQL query accordingly.

A ‘Simple Memory’ node helps the AI retain recent conversation context, ensuring more coherent interactions. The ‘SQL DB – List Tables and Schema’ node fetches the database structure initially, allowing the AI to understand available tables and fields to generate relevant queries.

Once the AI agent creates an SQL query, it is executed using the ‘Execute a SQL query in MySQL’ node, retrieving the required data. The results can then be presented back to the user, completing the conversational data access cycle.

This workflow is practical in scenarios where non-technical users need quick, conversational access to complex database information, such as in customer support, internal data analysis, or knowledge management systems.

Node Count	6 – 10 Nodes
Nodes Used	@n8n/n8n-nodes-langchain.agent, @n8n/n8n-nodes-langchain.chatTrigger, @n8n/n8n-nodes-langchain.lmChatOpenAi, @n8n/n8n-nodes-langchain.memoryBufferWindow, mySqlTool, stickyNote