Agent Memory is a runtime that gives AI agents local, file-based persistence by mapping conversations to a directory of Markdown files indexed by SQLite. It functions as a local cache that agents can query via shell commands, allowing them to recall context across sessions without relying on external databases or cloud APIs. The system treats the filesystem as the source of truth, meaning all data remains readable by standard tools like grep even if the index is deleted.
It is worth your time if you prefer local, transparent, and git-managed data over opaque vector database abstractions. However, it requires you to manage a directory-based workflow and configure individual agent schemas, which adds overhead if you just want a plug-and-play solution. It is not for users who prefer managed services or have no interest in maintaining their own data structure.
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