SQL agents with LangGraph 🦜🕸️
Creating accurate SQL queries with LLMs becomes challenging as query complexity increases. Simple prompts suffice for basic SQL, but complex joins and logic require detailed prompts, iterative feedback, and error handling. This post explores building an agentic SQL generation workflow using LangGraph , a framework in the LangChain ecosystem designed for creating stateful, multi-node graphs. It explains how to set up the graph with nodes, edges, and state management, integrate error propagation without breaking flow, and optimize prompt engineering to improve SQL generation accuracy. Experiments using the Sakila database show how richer prompts—adding schema details and few-shot examples—significantly improve query quality. For consistently correct SQL, especially with complex joins, introducing SQL views is recommended. SQL query generation creating SQL commands can be greatly affected by the details that can be given in the prompt. normal LLM calls are enough to generate simpl...
