In this article, you'll build an AI agent with LangGraph from scratch and learn the core concepts behind stateful, tool-enabled workflows.
In this article, you will learn how to build a complete agentic workflow in Python with LangGraph, from a single model call to a tool-using agent with persistent conversation memory.
Topics we will cover include:
How state, nodes, and edges combine to define the execution flow of a LangGraph agent.
How to register a tool and route the model’s tool calls through the graph’s reasoning loop.
How a checkpointer persists conversation history across separate graph invocations.
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