{"id":119876,"date":"2026-10-07T16:35:29","date_gmt":"2026-10-07T11:05:29","guid":{"rendered":"https:\/\/www.mygreatlearning.com\/blog\/langgraph-tutorial-how-to-build-a-stateful-ai-agent\/"},"modified":"2026-10-07T16:40:40","modified_gmt":"2026-10-07T11:10:40","slug":"langgraph-tutorial-how-to-build-a-stateful-ai-agent","status":"publish","type":"post","link":"https:\/\/www.mygreatlearning.com\/blog\/langgraph-tutorial-how-to-build-a-stateful-ai-agent\/","title":{"rendered":"LangGraph Tutorial: Build a Stateful AI Agent"},"content":{"rendered":"\n

Building an AI agent<\/a> is more than connecting an LLM<\/a> to a prompt<\/a>. As soon as an application needs to plan a task, call a tool, retain intermediate results, and use those results to produce an answer, the workflow becomes stateful.<\/p>\n\n\n\n

This is where LangGraph becomes useful.<\/p>\n\n\n\n

In this LangGraph<\/strong><\/a> tutorial<\/strong>, you will build a small but complete AI agent<\/a> in Python<\/a> with three nodes:<\/p>\n\n\n\n

Plan \u2192 Tool Call \u2192 Answer<\/strong><\/p>\n\n\n\n

The workflow will accept a user request, create a structured plan, call a tool based on that plan, and use the tool result to generate the final response. <\/p>\n\n\n\n

This fixed workflow provides a simple foundation for understanding how LangGraph manages state, nodes, tools, and execution flow<\/strong>.<\/p>\n\n\n\n

You will then extend the basic workflow with persistence and conditional routing<\/strong> to show how LangGraph can move toward more agentic behavior, where the next step can depend on the current state and model output.<\/p>\n\n\n\n

The final workflow will look like this:<\/p>\n\n\n

\"\"<\/figure>\n\n\n\n

By the end, you will have a working LangGraph application that you can extend with more tools, conditional routing, memory, human approval, or additional agent steps.<\/p>\n\n\n\n

What Is LangGraph and Why Is It Used for Stateful AI Agents?<\/strong><\/h2>\n\n\n\n

LangGraph<\/strong><\/a> is a framework for building stateful AI agents and multi-step LLM workflows.<\/strong> It models an AI application as a graph of nodes and edges<\/strong>, with shared state used to store and pass information between steps. <\/p>\n\n\n\n

This allows developers to build agents that can plan tasks, call tools, make decisions, maintain context, and execute complex workflows<\/strong> in a controlled manner.<\/p>\n\n\n\n

LangGraph is particularly useful when an AI application needs explicit control over its execution flow. <\/p>\n\n\n\n

Developers can define what each step does, determine what happens next, and preserve relevant information throughout the workflow, making it well suited for tool-using agents, conditional workflows, and stateful AI applications<\/strong>.<\/p>\n\n\n\n

If you want to build practical skills with LangGraph, explore Great Learning\u2019s AI Agents in LangGraph<\/strong><\/a>. It covers LangGraph fundamentals, graph-based workflows, stateful AI agents, and multi-agent orchestration using Python.<\/p>\n\n\n\n

1. Set Up the LangGraph Project<\/strong><\/h2>\n\n\n\n

We will use Python and an OpenAI-compatible chat model through LangChain's OpenAI integration<\/a>.<\/p>\n\n\n\n

Create a project directory:<\/strong><\/p>\n\n\n\n

bash<\/span>