Design multi-layer memory architectures with vector stores and knowledge graphs.
npx skills add https://github.com/mhismail3/dotfiles --skill memory-systems
Please help me install this Agent Skill. Skill: memory-systems Source: https://github.com/mhismail3/dotfiles/tree/main/claude/skills/memory-systems Command: npx skills add https://github.com/mhismail3/dotfiles --skill memory-systems
Memory systems enable agents to persist state across sessions, maintain entity consistency, and reason over accumulated knowledge.
Outline a memory stack with a working memory, a long-term store, and a simple temporal fact graph for a sample domain.
High-intent search queries and answers about installing and using this skill.
A memory architecture layers working memory, short-term memory, long-term memory, and permanent storage to maintain agent state across sessions. This Skill guides you through building these layers, integrating vector stores and knowledge graphs, and implementing entity and time-based indexing so your agent retrieves relevant facts efficiently.
Entity consistency requires a temporal knowledge graph that tracks facts about entities over time. This Skill shows how to structure multi-layer memory to resolve the same entity across conversations, handle temporal validity, and update entity state as new information arrives.
Yes. A knowledge graph organizes facts so agents can reason across relationships and infer new conclusions. This Skill covers temporal knowledge graph design for facts with time validity, entity-based indexing for fast retrieval, and integration with vector stores for semantic search.
Efficient retrieval requires dual indexing: organize facts by entity identity and temporal validity. This Skill demonstrates scalable indexing strategies within a layered memory stack so agents retrieve relevant historical context without full-scan overhead.
Working memory holds immediate context for the current task; long-term memory stores facts for later retrieval across sessions. This Skill shows how to separate these layers, when to promote facts from working to long-term storage, and how to integrate permanent storage for historical reference.
A vector store enables semantic search across agent memory, but it's optional depending on your retrieval needs. This Skill covers integration patterns with vector stores and knowledge graphs so you can choose architectures suited to your agent's reasoning requirements.
LuisSambranoDesign layered memory architectures with short-term and long-term knowledge graphs.
Ikram-AlamDesign layered memory architectures for persistent agent state across sessions.
FVossebeldStore and retrieve structured memories with vector and graph search.
hongmaple0820Design layered memory systems for AI agents using vector stores and temporal graphs.
