Mnemon (/ˈniːmɒn/), from Ancient Greek μνήμων (mnemon), formed by μνάομαι ("to remember") and the agent suffix -μων, meaning "one who remembers, a person of good memory." Homer uses "καὶ γὰρ μνήμων εἰμί" ("I remember it well") in the Odyssey to describe this quality. In the city-states of Ancient Greece, Mnemones were officials dedicated to record-keeping, serving as witnesses and archivists in property transactions and legal proceedings — institutional memory carriers during the transition from oral tradition to written records.
The word shares its root with Mnemosyne (Μνημοσύνη), the goddess of memory — from her union with Zeus the nine Muses were born, symbolizing memory as the wellspring of all knowledge and creativity.
Mnemon is a persistent memory system designed for LLM agents. It adopts the LLM-Supervised pattern: the host LLM acts as external orchestrator of a standalone memory binary through symbolic CLI interfaces, while the binary handles deterministic storage, graph indexing, and lifecycle management. Memory is organized as a four-graph knowledge structure with temporal, entity, causal, and semantic edges. Implemented as a single Go binary + SQLite, with no external API dependencies.
Why Mnemon exists — the amnesia problem in LLM agents, structural bottlenecks of traditional approaches, and a comparison with existing solutions (Mem0, MemGPT, Claude Code Memory).
The LLM-Supervised pattern, Organs vs Textbooks metaphor, Memory Gateway protocol (the MCP analogy for LLM↔DB interaction), key design insights, and theoretical foundations from RLM, MAGMA, and Graph-LLM structural analysis.
The Insight/Edge data model, database schema (SQLite WAL), system architecture (CLI layer → engine → storage), code structure, and store isolation via named stores.
MAGMA four-graph model (temporal, entity, causal, semantic), structural isomorphism between LLM attention and graph storage, the Extract→Candidate→Associate paradigm, read-write symmetry, remember/link/recall as universal algebra, the LLM↔DB protocol gap, and academic positioning.
The write pipeline (remember with built-in diff), read pipeline (Smart Recall with intent detection, RRF anchor fusion, Beam Search traversal, multi-factor re-ranking), and deduplication/conflict detection.
Effective Importance (EI) decay formula, immunity rules, auto-pruning, GC commands, and optional embedding support via Ollama (nomic-embed-text).
Lifecycle hooks (Prime, Remind, Nudge, Compact), skill file, behavioral guide, automated setup via mnemon setup, sub-agent delegation pattern, and adaptation to other LLM CLIs.
Key trade-offs (LLM-Supervised vs embedded, SQLite WAL vs graph DB, Beam Search vs BFS, soft delete), deviations from the MAGMA paper, storage-side pluggability roadmap, and the vision toward a memory gateway.