EverOS vs MemMachine
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 — Long-term memory layer for AI agents — episodic (graph), profile (SQL) and working memory behind Python/TS SDKs, REST and MCP; ships LangChain, LangGraph, CrewAI and LlamaIndex integrations.
Same job, opposite substrate: MemMachine puts episodic memory in a graph and profiles in SQL behind SDKs and REST; EverOS puts everything in Markdown you can edit by hand. Choose by whether memory should be a service or a folder.
| EverOS | MemMachine | |
|---|---|---|
| Stars | 12k | 3.2k |
| Forks | 901 | 205 |
| Language | Python | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last activity | 6 days ago | 4 days ago |
| Topics | memory, skills | memory |
| Curated connections | 4 | 12 |
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.
MemMachine — the curator's take
Pick it when memory is a product requirement, not a cache: separating episodic (graph) from profile (SQL) from working memory maps to how assistants actually personalize, and the documented LangGraph/CrewAI/LlamaIndex integrations mean you don't write the glue. NOT worth the footprint for a single-user tool — it wants a server plus Neo4j and SQL; a vector store or a JSON file gets a prototype further. Watch the open-core boundary: the managed platform is the business model.