LLM Memory Compression Lab

Large Language Models process text through fixed-size context windows, a fundamental constraint that shapes how they ‘remember’ and ‘forget.’ As conversations grow beyond this window, systems must make difficult choices: what to keep, what to compress, and what to discard entirely.

This interactive lab explores six distinct memory architectures through hands-on simulations and a decision-tree wizard. From the brutal simplicity of sliding windows to the structured reasoning of knowledge graphs, each approach represents a different philosophy of what it means for a machine to remember.

Navigate through seven modules, each presenting a different lens on the memory problem. Adjust parameters, watch simulations unfold, and develop intuition for the tradeoffs that define modern LLM systems. Each section includes a Validate Live experiment where you can test these concepts with a real LLM using your own data.