memory-systems

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Design short-term, long-term, and graph-based memory architectures. Use when building agents that must persist across sessions, needing to maintain entity consistency across conversations, or implementing reasoning over accumulated knowledge.

AI & Automation 39,227 stars 6374 forks Updated today MIT

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Skill Content

## 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. ## When to Use Activate this skill when: - Building agents that must persist across sessions - Needing to maintain entity consistency across conversations - Implementing reasoning over accumulated knowledge - Designing systems that learn from past interactions - Creating knowledge bases that grow over time - Building temporal-aware systems that track state changes ## Core Concepts Memory exists on a spectrum from immediate context to permanent storage. At one extreme, working memory in the context window provides zero-latency access but vanishes when sessions end. At the other extreme, permanent storage persists indefinitely but requires retrieval to enter context. Simple vector stores lack relationship and temporal structure. Knowledge ...

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Author
sickn33
Repository
sickn33/antigravity-awesome-skills
Created
4 months ago
Last Updated
today
Language
Python
License
MIT

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