Different jobs need different notebooks.
A shopping list, a diary, and an engineering notebook all hold information. Their purposes differ. The same is true of systems called AI memory: the name alone does not tell you what they capture or how they use it.
Source book by 递归客 (diguike) · Full attribution
Begin with the job
A preference memory helps an assistant recall something about a person. A document retrieval system finds passages from a reference collection. A work memory records discoveries and decisions made while doing a task. These categories can overlap, but they pose different questions.
Do you need the assistant to remember your preferred writing style, find a policy in a manual, or avoid repeating an investigation from yesterday? Answering that question is more useful than starting with a database brand.
Choose the job before choosing the shelves
A preference notebook records a stable preference about you, with a way to revise or remove it. It should not infer a permanent preference from one casual request.
Fictional example. This experiment runs locally and does not call an AI or access your memory.
Retrieval adds material to the desk
RAG stands for retrieval-augmented generation. In plain language, the system fetches useful material before or during an answer so the model can use it. That material might be a document excerpt or a work observation.
Claude-Mem combines capture, structured observations, and layered recall. The book surveys several other designs, including framework-managed state, fact-oriented memory, time-aware memory, and model-managed external memory. That survey is a snapshot of its writing period, not a current ranking of products.
Compare the plumbing and the promise
Ask what gets captured, how records are updated, what a query returns, and what happens when a record is wrong. Also ask who can access the notebook and where its data goes.
No single shelf arrangement guarantees good recall. A useful evaluation uses your own tasks and checks whether the retrieved information actually helps. More stored notes or a larger window can look impressive while failing the simple test: did the agent remember the right thing?
Behind this lesson
Related chapters in 递归客’s original book:
The source examines Claude-Mem v12.6.2. Links are pinned to the book revision used for this course; current implementation details may differ.