memanto vs MemMolt
Companion memory agent for 20+ coding agents, built on Moorcheh — its own information-theoretic engine, no third-party vector DB to manage. Runs local (Docker + Ollama, keyless) or on their cloud. — 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 put persistent memory under your coding agents: MemMolt is strictly local, a bucket-thread-memo hierarchy over MCP; Memanto is a companion memory agent on its own Moorcheh engine, deployable fully local or on their cloud.
| memanto | MemMolt | |
|---|---|---|
| Stars | 1.9k | 4 |
| Forks | 647 | 0 |
| Language | Python | JavaScript |
| License | MIT | MIT |
| Last activity | 4 days ago | 4 months ago |
| Topics | memory, local | memory |
| Curated connections | 5 | 5 |
memanto — the curator's take
The no-third-party-infrastructure entry in the agent-memory category: retrieval runs on Moorcheh, the team's own information-theoretic engine, so there is no separate vector DB, embedding pipeline or reranker to manage — one CLI wires up 20+ agent integrations. Two deployments: fully local (Docker + Ollama, keyless, nothing leaves the machine) or their managed cloud (free tier, Moorcheh API key), which is also how shared team memory scales. The novel engine is the differentiator AND the risk: benchmark recall on YOUR corpus before trusting it over boring embeddings, and betting on Moorcheh is betting on one vendor's engine — memanto.ai signals the company 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.