Author(s): Aayushi_Sharma

Originally published on Towards AI.

💡 “Ever wondered how an AI assistant can decide what to do next, like a human?”

Behind the scenes, most LLM apps follow a linear flow — input in, output out. But real-world tasks aren’t always linear. They involve loops, decisions, retries, and memory.

This article provides an introduction to LangGraph, a stateful orchestration framework built on LangChain that enables building dynamic AI workflows. It covers the fundamental components of LangGraph, including nodes and edges, and outlines how to create a question-answering agent using the framework. Key concepts such as using tools, decision nodes, and maintaining a shared state for memory enhance the agent’s capability, leading to real-world applications like autonomous report generators and multi-agent systems.

Read the full blog for free on Medium.

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Published via Towards AI