memanto vs MemMolt
Fully-local persistent memory for 14+ coding agents, built on an information-theoretic search engine — no vector DB, no API keys, no backend. pip install and your agents remember. — 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 are local, no-cloud agent memory stores: MemMolt enforces a strict bucket-thread-memo hierarchy over MCP; Memanto bets on companion-agent ergonomics and keyless information-theoretic search.
| memanto | MemMolt | |
|---|---|---|
| Stars | 1.7k | 4 |
| Forks | 539 | 0 |
| Language | Python | JavaScript |
| License | MIT | MIT |
| Last activity | yesterday | 3 months ago |
| Topics | memory, local | memory |
| Curated connections | 2 | 4 |
memanto — the curator's take
The zero-infrastructure entry in the agent-memory category: no embedding provider, no vector database, no server — the information-theoretic search bet means everything runs on-device from a pip install, and 14+ agent integrations cover the usual suspects. If 'no API keys, nothing leaves the machine' is your constraint, this is the shortest path to cross-agent memory. NOT battle-hardened at team scale: single-machine by design, and the novel search engine is the differentiator AND the risk — benchmark recall on YOUR corpus before trusting it over boring embeddings; memanto.ai signals a company forming behind it.
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.