Agent Memory Framework

Overview

The agent-memory skill provides a standardized architectural approach to building intelligent memory systems for agents within the KILO-KIT ecosystem. It bridges the gap between ephemeral context windows and durable, long-term storage, enabling agents to maintain user preferences, project-specific conventions, and debugging history across multiple sessions.

When To Use

• When designing systems that must persist state across independent interaction sessions.
• When an agent needs to manage large volumes of user-specific facts that exceed the context window.
• When implementing self-managed memory tools (MemGPT/Letta patterns) to allow agents to control their own knowledge base.
• When building systems requiring automatic entity updates (Mem0 pattern) to resolve conflicting or stale information.

Core Concepts

• Memory Hierarchy: Differentiating between transient working context and persistent knowledge.
• Fact Extraction: Identifying core entities, preferences, and relationships from conversational flow.
• Supersession: Automatically replacing outdated facts with new information to maintain "ground truth."
• Temporal Validity: Tracking the lifespan and relevance of memory entries over time.
• Persistence: Ensuring data survives agent resets or session termination.