Why Traditional Chains Fall Short?

Imagine you’re at a dinner party. Everyone’s chatting, and new topics pop up constantly. Now, consider two scenarios:

In the first scenario, everyone’s memory is extremely short—like goldfish. Only the last thing anyone said can be remembered. If a guest mentions chocolate, after a few more comments, that topic is completely forgotten unless someone brings it up again. The conversation can’t really "look back" at prior statements. In the second scenario, there’s a huge whiteboard on the wall. Every comment—about chocolate, apples, or travel plans—is written down. Anyone can glance at the board to recall what was said five minutes ago, an hour ago, or even at the start of the party.

LangGraph is like the second scenario. While traditional LangChain workflows give you a simple linear pipeline—step to step, forward only—LangGraph gives you a whiteboard. Every node (think of it as a party guest) can see the entire conversation history stored in a shared state. This means that when you say, "As I mentioned earlier..." the AI doesn’t have to guess. It can check the whiteboard and know exactly what you said earlier, no guesswork needed.

Traditional LangChain implementations, often work like the first scenerio. They process each message in isolation or with limited context, leading to several challenges such as important details mentioned earlier are forgotten, users needing to restate information multiple times and each step operating independently, unaware of the broader conversation. As a result developers spend time writing code to pass information between steps. However, LangGraph solves these problems by providing built-in ...