AI agents are all the rage these days. They’re like traditional chatbots, but they have the ability to utilize a plethora of tools in the background. They can also decide which tool to use and when to use it to answer your questions. In this tutorial...

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A practical guide to building AI agents with LangGraph, using a real-world financial assistant (FinanceGPT) as the reference project. Covers three core concepts: tools (Python functions decorated with @tool, including a factory pattern for dependency injection), agent state (TypedDict-based state that accumulates conversation history and tool results), and the agent graph (nodes, edges, and conditional routing that orchestrate the LLM-tool loop). Walks through assembling a complete agent step by step, then traces a full example query end-to-end to show how the agent reasons, selects tools, and produces a final answer.

How to Develop AI Agents Using LangGraph: A Practical Guide