MemMachine vs MemMolt
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. — versus — Structured long-term memory over MCP: an enforced bucket-thread-memo hierarchy in one SQLite file, hybrid FTS5 + vector search fused with RRF, local embeddings.
Both agent memory layers over MCP: MemMachine ships episodic/profile/working memory with framework SDKs; MemMolt bets on one enforced bucket-thread-memo hierarchy in a single SQLite file.
| MemMachine | MemMolt | |
|---|---|---|
| Stars | 3.3k | 4 |
| Forks | 196 | 0 |
| Language | Python | JavaScript |
| License | Apache-2.0 | MIT |
| Last activity | yesterday | 3 months ago |
| Topics | memory | memory |
| Curated connections | 6 | 2 |
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.
MemMolt — the curator's take
The anti-sprawl memory play: a forced 3-level hierarchy the agent can't turn into a jungle, with ~10ms hybrid search from a single SQLite file and zero cloud calls. Young and tiny (4 stars) — the schema idea is worth studying even if you don't adopt it. Skip if you want auto-capture; this is deliberate, curated memory.