Mnemon

LLM-supervised persistent memory for AI agents. 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. Single binary, zero API keys, one setup command. 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. 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.