memory-system-v2

Persistent memory for AI agents is achieved through a file-based system that stores and retrieves learnings, decisions, and interactions across sessions. This approach prevents information loss by capturing memories with their type, importance, and content for later recall.

Fast semantic search for an AI memory store can be implemented using bash and jq for efficient, file-based operations. This approach achieves sub-20ms search times, allowing agents to quickly find relevant memories without heavy database dependencies.

Yes, you can store and retrieve AI agent learnings and decisions using only bash and jq. This Skill is built entirely on these tools for efficient, file-based operation, enabling persistent storage and fast semantic search without requiring external databases.

Yes, this persistent AI memory system features auto-consolidation and importance scoring. It automatically manages the stored learnings, insights, and events, ensuring that the memory remains efficient and relevant for the agent to recall past technical solutions or user preferences.

A file-based memory system for AI agents relies on bash and jq, meaning it operates without a dedicated database server. While it offers sub-20ms search and auto-consolidation, extreme scale or complex relational queries might exceed its file-based design limits.