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MemMolt vs memsearch

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. — versus — Zilliz's unified memory for coding agents: one Markdown + Milvus store shared across Claude Code, Codex, OpenCode and OpenClaw — hybrid search, plus repeated workflows distilled into skills.

The curated verdict

Both are structured long-term memory with hybrid search for agents: MemMolt enforces a bucket-thread-memo hierarchy in one SQLite file over MCP; memsearch bets on free-form Markdown + Milvus and per-agent plugins.

MemMoltmemsearch
Stars42.3k
Forks0206
LanguageJavaScriptPython
LicenseMITMIT
Last activity3 months agoyesterday
Topicsmemorymemory, coding
Curated connections33

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.

memsearch — the curator's take

The cross-platform play is the point: a conversation in Claude Code becomes searchable context in Codex, OpenCode and OpenClaw — one memory, four plugins, zero per-agent setup. Memories live in readable Markdown (greppable, versionable) with Milvus doing hybrid search, and the standout feature is procedural: it watches for workflows you repeat and distills them into installable skills, maintained in the background. NOT for single-agent loyalists — if you only run Claude Code, opencode-mem-style plugins are lighter — and it's a Zilliz project: the Milvus dependency is also the funnel; check what 'backed by Milvus' costs you operationally before teams adopt.