We talk about memory as if it were a folder of files. In practice, both human recall and useful agent memory work by linking new information to what you already know. Here is how those connections form, why isolated facts fall short, and what that means when you design systems that remember.
A useful agent does not remember everything. It remembers the right things in the right way. Here is how working memory, episodic recall, and semantic facts fit together, and why confusing them is the most common reason agents feel forgetful.
TurboMem asks you to make one real decision when you set it up i.e storage. Where your vectors and facts live depends almost entirely on where your code runs, and the adapters are built around that difference.
Storing every chat message and hoping the model finds the right ones does not scale. Here is what actually happens inside a memory pipeline, from fact extraction to scoped vector search, and how to debug it when recall goes sideways.
