Memory Systems

This walks you through building memory architectures for agents that need to remember things across sessions. It covers the progression from simple vector stores to knowledge graphs to temporal knowledge graphs, explaining why vector similarity search falls short when you need to reason about relationships. The benchmark data is useful: Zep's temporal KG hits 94.8% accuracy on the DMR benchmark while GraphRAG shows 20-35% gains over baseline RAG. The layered approach is practical, breaking memory into working, short-term, long-term, entity, and temporal layers. If you're building anything that needs to track entities or relationships over time rather than just retrieving similar documents, the graph-based sections will save you from reinventing this yourself.

skills.sh installs: 559
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last updated: Jul 14, 2026

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Install

$ npx -y skills add sickn33/antigravity-awesome-skills --skill memory-systems --agent claude-code

Installs into .claude/skills of the current project.

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When to Use This Skill

Design short-term, long-term, and graph-based memory architectures

Use this skill when working with design short-term, long-term, and graph-based memory architectures.

Memory System Design

Memory provides the persistence layer that allows agents to maintain continuity across sessions and reason over accumulated knowledge. Simple agents rely entirely on context for memory, losing all state when sessions end. Sophisticated agents implement layered memory architectures that balance immediate context needs with long-term knowledge retention. The evolution from vector stores to knowledge graphs to temporal knowledge graphs represents increasing investment in structured memory for improved retrieval and reasoning.