Five Agent Memory Types in LangGraph: A Deep Code Walkthrough (Part 2)

In Part-1 [https://dev.to/sreeni5018/the-5-types-of-ai-agent-memory-every-developer-needs-to-know-part-1-52fn] we covered the five memory types, why the LLM is stateless by design, and why memory is always an infrastructure concern. This post is the how. Same five types, but now we wire each one up with LangGraph, dissect every line of code, flag the gotchas, and leave you with a single working script you can run today. Before We Write a Single Line: Two Things You Must Understand The Context Window Is the Only Reality Repeat this like a mantra and the model only knows what is in the context window at inference time. Every token your message, retrieved facts, conversation history, tool results, system instructions has to be physically present in that window at the moment of the call. If it is not there, the model does not know it exists. Your memory infrastructure's entire job is to decide what goes in, when, and in what form. Checkpointer ≠ Store This Confusion Breaks Designs LangGrap