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:

Everyone’s memory is extremely short in the first scenario, like a goldfish’s. 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 look at the board to remember what was said five minutes ago, an hour ago, or even at the beginning of the party.

LangGraph is like the second scenario.

While traditional LangChain workflows provide a straightforward, linear pipeline from step to step, LangGraph offers a flexible whiteboard experience. 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 scenario.

They process each message in isolation or with limited context, causing challenges such as forgetting important details, users repeating information, and each step operating independently without awareness of the broader conversation.

As a result, developers spend extra time writing code to pass information between steps. ...