How to Build Effective Agentic Systems with LangGraph

Author(s): Eivind Kjosbakken

Originally published on Towards AI.

Create AI workflows with agentic frameworks

With the rise of powerful AI models, such as GPT-5 and Gemini 2.5 Pro, we also see an increase in agentic frameworks to utilize these models. These frameworks make working with AI models simpler by abstracting away a lot of challenges, such as tool-calling, agentic state handling, and human-in-the-loop setups.

This article explores LangGraph, an agentic AI framework that simplifies the development of AI workflows by addressing challenges associated with tool usage, state management, and user interaction. It discusses the basics of LangGraph, its advantages, including ease of setup and open-source availability, as well as some disadvantages, such as remaining boilerplate code and specific errors encountered during implementation. Ultimately, it underscores the balance between abstraction and control in creating efficient agentic systems.

Read the full blog for free on Medium.

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Published via Towards AI