heady-episodic-memory

To enable durable episodic memory for AI agents, you need a multi-tier memory system that stores timestamped events with capacity limits and applies vector-based retrieval for context-aware reasoning across sessions.

Phi-decay forgetting in AI memory management is a technique that gradually retires irrelevant memories while preserving critical ones, keeping the memory graph scalable and preventing capacity-limited stores from overflowing.

Vector-based retrieval for multi-tier agent memory works by using a 384D embedding space to calculate similarity, allowing the system to hot cache top items in working memory and retrieve relevant context efficiently.

Yes, you can share semantic facts across multiple AI agents by using a memory system that supports cross-agent knowledge sharing with configurable privacy boundaries to secure the memory graph.

The best way to consolidate extracted facts in a semantic knowledge graph is to apply nightly consolidation routines that process persistent semantic memories to maintain an organized and scalable knowledge base.

You should not rely on a single capacity-limited episodic store when your agent requires long-term fact retention, as procedural rules and semantic facts need separate persistent tiers to avoid premature memory decay.