EverOS vs Memoria
One portable memory layer for every agent: conversations, files and trajectories kept as canonical Markdown, indexed locally by SQLite and LanceDB, with offline reflection that refines them. — versus — Rust memory layer for AI agents with Git-style version control — snapshot, branch, merge and rollback over MatrixOne's copy-on-write engine, plus hybrid vector + full-text retrieval.
everos keeps memory as canonical Markdown you can open and edit; memoria treats it as a versioned database â snapshot, branch, merge, rollback. Readable artifacts vs auditable history.
| EverOS | Memoria | |
|---|---|---|
| Stars | 12k | 589 |
| Forks | 904 | 77 |
| Language | Python | Rust |
| License | Apache-2.0 | Apache-2.0 |
| Last activity | today | 10 days ago |
| Topics | memory, skills | memory |
| Curated connections | 7 | 6 |
EverOS — the curator's take
Pick it if you want memory you can open in an editor: `.md` files are the source of truth, edited directly and picked up by a cascade watcher, with SQLite plus LanceDB as disposable indexes and no MongoDB, Elasticsearch or Redis in the stack. User episodes and profile sit apart from agent cases and skills as first-class tracks, retrieval scopes orthogonally by user, agent, app, project and session, and reflection consolidates episode clusters between sessions instead of only recalling. Trade-offs: it runs as a server with its own TOML config, full capability needs embedding and rerank models configured beyond the one-key start, Markdown-as-truth means large vaults get large, and `/api/v1` is already a legacy alias.
Memoria — the curator's take
Pick it when memory auditability and reversibility matter — you want to branch an agent's memory, roll back a bad write, or trace the provenance of every fact, and you're fine running MatrixOne underneath. Overkill if you just need a simple recall store (memmachine, memmolt) or fully-local files (memanto) — the versioning engine and DB dependency are the cost. Young project, Apache-2.0.