What is LangGraph?

LangGraph is a framework for building complex AI workflows with loops, branching, and state management. It’s like LangChain, but designed for more sophisticated agentic systems where the AI needs to make decisions about what to do next based on what just happened. It was created by the LangChain team in 2024.

Where LangChain is about chaining steps in a sequence, LangGraph lets you build workflows that can loop back, branch in different directions, and maintain state across multiple steps. Think of it like the difference between following a recipe step by step versus playing chess, where your next move depends on what your opponent just did.

LangGraph represents workflows as graphs. Each node in the graph is a step (like calling an AI model or running a tool), and the edges define how you move from one step to another. You can have conditional edges that decide where to go next based on the output of the current step. This makes it perfect for building AI agents that need to adapt their behavior on the fly.

The framework handles state management automatically, so your AI can remember what it’s done across multiple steps and use that context to make better decisions. This is crucial for building agents that need to try something, see if it worked, and adjust their approach if it didn’t.

LangGraph is becoming the go-to choice for building production-grade agentic systems. If you’re building something where the AI needs to make decisions and adapt rather than just following a fixed sequence of steps, LangGraph is what you want.

I used LangGraph on a prototype at my day job. I was trying to add end-to-end automation to a front-end product I’m working on. It was a good fit for performing various disparate tasks in a row, like a human would.