Solving Agent Amnesia: A Local-First Memory Architecture for LLMs

LLM agents lose context between sessions, forcing users to repeatedly re-explain their stack and decisions. Mnemostroma addresses this fundamental limitation with an automatic, transparent memory layer that captures conversation context and surfaces it when relevant. This represents a practical solution to improving agent usability and reducing context re-entry friction in multi-session workflows.