@elliotchen100: Our open-source library EverOS recently underwent a major refactoring, transforming the developer experience from "pulling" to "solidifying". Previously, when working with AI memory, we often had to troubleshoot across databases, vector stores, index sync, and dependency environments. Where is the memory stored? Why isn't it recalled? Is the index broken? Many times it was not straight…

Our open-source library EverOS recently underwent a major refactoring, transforming the developer experience from "pulling" to "solidifying". Previously, when working with AI memory, we often had to troubleshoot across databases, vector stores, index sync, and dependency environments. Where is the memory stored? Why isn't it recalled? Is the index broken? Many times it was not intuitive. The new version of EverOS directly stores raw memory in Markdown. Developers can inspect memory just like code: open a file, search text, view Git diff, manually correct, and if necessary, directly delete and rebuild the index. The overall architecture is also clearer: Raw memory = Markdown Runtime state = SQLite Search index = LanceDB Markdown is responsible for readability, editability, and portability. SQLite manages state and queues. LanceDB handles vector, full-text, and filtered search. The advantage of this approach is that the index is no longer a single point of failure. If the index breaks, it can be rebuilt from Markdown; if retrieval is slow, it can be re-optimized. But the raw memory always remains a file that developers can open, understand, and take with them. We believe that if AI memory is to become developer infrastructure, the first step is not to make it complex, but to make it readable, editable, and recoverable.