add-mnemon

Persistent agent memory uses a graph-based system injected into Docker configurations via binary dependencies and runtime hooks. It automatically surfaces relevant past conversation history before the agent responds to maintain continuity.

Cross-session context recall works by mapping graph-based memory storage to host-level Docker volumes. Runtime hooks store new user insights after each interaction turn, automatically surfacing relevant historical data before future responses.

Yes, this memory system targets Claude Code-based agent environments by injecting binary dependencies and runtime hooks directly into Docker configurations. It requires no additional component dependencies to function.

Retaining technical decisions in long-term projects is best handled by a persistent graph-based memory layer. It stores new user insights after each turn, ensuring agents remember specific preferences and context across days or weeks.

AI agents lose context between Docker sessions because containerized environments typically lack persistent state management. Without a graph-based memory layer mapped to host-level volumes, past interactions and insights are not retained.