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memanto

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.

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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.

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README.md

Memory that AI Agents Love!

A companion memory agent that lets your agents focus and improve while you keep ownership of everything they learn.

Persistent memory for Claude Code, Cursor, Codex, and 14+ other agents, built on the world's first information-theoretic search engine. 100% free, open source, and runs entirely on your machine - no API keys, no vector database, no backend to babysit.

What Is MEMANTO?

MEMANTO is a memory agent. It remembers, recalls, and answers — so your agents can achieve long-term goals and avoid confusion.

Most memory tools today are passive infrastructure: agents have to query them, parse the results, and figure out what to do next. MEMANTO is built differently. It's an active memory agent designed from the gaps agents themselves named when asked about their memory — three operations (remember, recall, answer) that give your agents persistent context across sessions, with state-of-the-art retrieval and zero ingestion latency.

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Continue your stack

What teams reach for next — and why each earns a place beside memanto. Ranked by curator confidence.

Alternative to See all + compare →

memsearch

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.

Why it fitsSame job — one persistent memory shared across your coding agents — opposite infrastructure bets: memsearch runs Markdown + Milvus with embedding models; Memanto runs an information-theoretic engine with no vector DB and no keys at all.

MemMolt

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.

Why it fitsBoth 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.