memory-system

AI agent session continuity is maintained by loading active context at the start of a new session. This memory system uses a 3-tier hierarchical structure to automatically retrieve past interactions, ensuring the agent picks up exactly where it left off.

A hierarchical memory system organizes information into three tiers: L1 active context, L2 weekly decisions, and L3 long-term archive. This structure enables efficient resource management and semantic search for memory retrieval.

AI agent self-improvement is achieved through built-in mistake tracking and automated heartbeat maintenance. The system reviews past errors from its memory tiers to prevent recurrence and promote continuous learning.

Automated memory compression and promotion are needed when managing a growing AI knowledge base. The system triggers auto-compression to optimize storage and uses heartbeats to promote active context to long-term archive tiers.

Yes, this memory management approach supports long-term knowledge archiving in AI through its L3 tier. It permanently stores critical information and utilizes semantic search to retrieve archived knowledge during future sessions.

This AI memory system requires no external dependencies or tools to function. It operates independently using internal scripts and references to manage automated session checklists and context tiers.