langgraph
![Brian Piercy](data:image/svg+xml;utf8,<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="%230A66C2"><path d="M20.447 20.452H16.89v-5.569c0-1.328-.027-3.037-1.852-3.037-1.853 0-2.136 1.445-2.136 2.939v5.667H9.351V9h3.414v1.561h.046c.477-.9 1.637-1.85 3.37-1.85 3.601 0 4.267 2.37 4.267 5.455v6.286zM5.337 7.433a1.982 1.982 0 1 1 0-3.964 1.982 1.982 0 0 1 0 3.964zm1.782 13.019H3.555V9h3.564v11.452zM22.225 0H1.771C.792 0 0 .774 0 1.729v20.542C0 23.227.792 24 1.771 24h20.451C23.2 24 24 23.227 24 22.271V1.729C24 .774 23.2 0 22.222 0h.003z"/></svg>)
In this article, I’ll walk you through a complete guide to LangGraph from the ground up. LangGraph Explained from Scratch.
Check out this comparison of 5 AI frameworks to determine which you should choose.
In our previous tutorial, we built an AI agent capable of answering queries by surfing the web. However, when building agents for longer-running tasks, two critical concepts come into play: persistence and streaming. Persistence allows you to save the state of an agent at any given point, enabling you to resume from that state in future interactions. This is crucial for long-running applications. On the other hand, streaming lets you emit real-time signals about what the agent is doing at any moment, providing transparency and control over its actions. In this tutorial, we’ll enhance our agent by adding these powerful