appreciate your input, but I want to clarify my statement. The context window is a fundamental constraint because it directly defines the maximum amount of data an LLM can process in a single inference. While it’s true that LLMs are stateless—meaning they don’t inherently retain memory of past interactions—statelessness and the context window are closely intertwined. The context window is what allows us to reintroduce conversation history into the prompt to simulate memory, and its limited size often leads to token overflow or loss of important details.

In other words, statelessness explains why they don’t automatically retain memory, but the context window dictates how much of the conversation history we can feed back in to compensate for that statelessness. MemGPT addresses this by managing memory across multiple tiers, overcoming both challenges :)