Build Your First AI Agent with LangGraph — Step-by-Step Python Tutorial (2026)

Build Your First AI Agent with LangGraph — Step-by-Step Python Tutorial (2026) AI agents are not chatbots. A chatbot answers questions. An agent decides what to do, uses tools, evaluates results, and loops until the job is done. If you have tried building agents with raw API calls and prompt chains, you know the pain: managing state across steps, handling tool failures, deciding when to loop and when to stop. It gets messy fast. LangGraph solves this. It lets you model your agent as a graph — nodes are actions, edges are decisions — and it handles state, persistence, and control flow for you. It reached v1.0 in late 2025, and as of Q1 2026, it is the most widely adopted Python framework for building production AI agents. Companies like Klarna, Uber, Replit, and Elastic run LangGraph agents in production. In this tutorial, you will build a working research agent from scratch. Not a toy demo — a real agent that takes a research question, searches the web, reads results, decides if it has