Build a SQL Agent with LangChain + LangGraph | Connect PostgreSQL & Generate Reports with AI

About this lesson

In this tutorial, I’ll show you how to build a powerful SQL Agent using LangChain and LangGraph that can automatically connect to a PostgreSQL database, inspect tables, understand schemas, generate SQL queries, validate them, and execute them — all without writing SQL manually. We’ll walk through how AI agents handle the same workflow a developer normally follows: ✅ Connect to the database ✅ Explore tables and schemas ✅ Understand table relationships ✅ Generate SQL queries automatically ✅ Validate queries before execution ✅ Execute queries and format responses In this video, you will learn: How to connect LangChain to PostgreSQL How SQLDatabaseToolkit works How to use built-in SQL tools in LangChain How to build agent workflows using LangGraph How tool calling works in AI agents How to trace agent execution using LangSmith How to optimize SQL agent latency for production use This is perfect for developers working on AI agents, database automation, text-to-SQL systems, and enterprise AI applications. If you’re learning LangChain, LangGraph, SQL Agents, or AI-powered database querying, this tutorial will help you build production-ready workflows. 🔥 Topics Covered: LangChain SQL Agent LangGraph Agent Workflow PostgreSQL Integration SQLDatabaseToolkit Tool Calling Agents AI Database Querying LangSmith Debugging Text-to-SQL Subscribe for more tutorials on AI Agents, LangChain, LangGraph, RAG, LLM Applications, and Production AI Systems.

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