dsh-mnemon

<h1 align="center">Mnemon</h1> <p align="center"><strong>English</strong> · <a href="docs/zh/README.md">中文</a></p> <p align="center"><strong>LLM-supervised persistent memory for AI agents.</strong></p>

---

LLM agents forget everything between sessions. Context compaction drops critical decisions, cross-session knowledge vanishes, and long conversations push early information out of the window.

Mnemon gives your agent persistent, cross-session memory — a four-graph knowledge store with intent-aware recall, importance decay, and automatic deduplication. The mnemon memory path remains one local binary with zero API keys and one setup command.

Mnemon ships one executable with two separate surfaces. Memory stays at the mnemon root; Agency Preview lives at mnemon agency ... and adds durable, project-local responsibility and effect admission to an existing Pi agent. Agency does not replace Memory or the Agent Runtime.

Claude Max / Pro subscriber? Mnemon works entirely through your existing subscription — no separate API key required. Your LLM subscription is the intelligence layer. Two commands and you're done.

Why Mnemon?

Most memory tools embed their own LLM inside the pipeline. Mnemon takes a different approach: your host LLM is the supervisor. The binary handles deterministic computation (storage, graph indexing, search, decay); the LLM makes judgment calls (what to remember, how to link, when to forget). No middleman, no extra inference cost.

Pattern	LLM Role	Representative
LLM-Embedded	Executor inside the pipeline	Mem0, Letta
File Injection	None — reads file at session start	Claude Code Memory
MCP Server	Tool provider via MCP protocol	claude-mem
LLM-Supervised	External supervisor of a standalone binary	Mnemon

Mnemon also addresses a gap in the protocol stack. MCP standardizes how LLMs discover and invoke tools. ODBC/JDBC standardizes how applications access databases. But how LLMs interact with databases using memory semantics — this layer has no protocol. Mnemon's three primitives — remember, link, recall — form an intent-native protocol: command names map to the LLM's cognitive vocabulary (remember not INSERT, recall not SELECT), and output is structured JSON with signal transparency rather than raw database rows.

<br /> _{The LLM-Supervised pattern: hooks drive the lifecycle, the host LLM makes judgment calls, the binary handles deterministic computation.}

Memory has a compound interest effect — the longer it accumulates, the greater its value. LLM engines iterate constantly, skill files cost nearly nothing to write, but memory is a private asset that grows with the user. It is the only component in the agent ecosystem worth deep investment.

<br /> _{A real knowledge graph built by Mnemon — 87 insights, 2150 edges across four graph types.}

See Design & Architecture for details.

Web knowledge workspace

Browse the knowledge accumulated by your agents in a standalone local website:

mnemon web
# Open http://127.0.0.1:8484

Explore a searchable memory library, readable memory pages, a relationship graph, entities, and recorded activity. Select a memory store in the sidebar while agents continue writing through the existing CLI. Browsing preserves memory content, access counts, retention weights, and the CLI's active-store selection.

…