Building ReAct agents with Memory using LangGraph

Author(s): Aayushi_Sharma

Originally published on Towards AI.

“How do AI agents decide when to think, when to act, and when to stop?”

Welcome to the world of ReAct Agents — powerful AI workflows that combine reasoning and actions. If you’ve ever wondered how AI systems like ChatGPT plan, decide, and call tools dynamically, this guide is for you.

This article explores the concept of ReAct agents, highlighting their ability to merge reasoning with action using LangGraph, an open-source library. It details how to implement these agents, starting from core principles and progressing to creating simple workflows, integrating tools, and establishing memory mechanisms to enhance decision-making processes. The guide concludes with suggestions for further enhancements and real-world applications of these intelligent workflows.

Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI