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Razaib-khan
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memory-systems

Design layered memory architectures with temporal validity for AI agents.

Updated Jan 15, 2026
Search Tags:#context management#knowledge graph#agents#memory#vector store#temporal reasoning
One-click install
npx skills add https://github.com/Razaib-khan/Hackathon-2-phases-2-3-4-5- --skill memory-systems-razaib-khan
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memory-systems
Source: https://github.com/Razaib-khan/Hackathon-2-phases-2-3-4-5-/tree/main/.claude/skills/memory-systems
Command: npx skills add https://github.com/Razaib-khan/Hackathon-2-phases-2-3-4-5- --skill memory-systems-razaib-khan

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps you design memory systems for agents that need to retain state, preserve entity consistency, and reason over accumulated knowledge across sessions instead of losing everything in a single context window.

Core Features & Use Cases

  • Layered Memory Design: Plan working, short-term, long-term, entity, and temporal memory so agents can balance fast access with durable persistence.
  • Structured Retrieval: Use vector search, metadata filtering, and graph traversal to retrieve relevant memories by meaning, entity, or time.
  • Temporal Reasoning: Model facts with validity periods so agents can answer time-specific questions and avoid conflicts with outdated information.
  • Use Case: An assistant that tracks customer preferences, remembers prior decisions, and reconstructs what was true at a specific point in time can keep interactions coherent and reliable.

Quick Start

Ask for a memory architecture for an agent that must remember user entities and past facts across sessions.

Frequently Asked Questions about memory-systems

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build AI agents that remember past sessions and retain entity consistency?▼

Layered memory architectures split agent state into working, short-term, long-term, entity, and temporal memory layers. This balances fast context access with durable knowledge persistence across multiple sessions.

What is temporal reasoning in AI memory and how do validity periods work?▼

Temporal reasoning models facts with validity periods so agents answer time-specific questions accurately. This mechanism prevents conflicts with outdated information by tracking exactly when facts were true.

How do I retrieve relevant memories in an agent using vector search and knowledge graphs?▼

Structured retrieval combines vector search, metadata filtering, and knowledge graph traversal to fetch relevant memories. This retrieves context accurately by semantic meaning, entity relationships, or time.

Do I need numpy to implement a knowledge graph memory system for agents?▼

Numpy is a required dependency for implementing this memory system. It provides the numerical operations needed to support vector stores and structured retrieval pipelines within the agent architecture.

What's the best way to structure long-term memory for long-running assistants?▼

A layered memory design separating working, short-term, long-term, and entity memory is optimal. It ensures reliable consolidation and accurate context injection for long-running assistants.

When should I not use a single context window for agent memory management?▼

A single context window fails when agents need persistent state, entity consistency, and temporal reasoning across sessions. Structured memory architectures prevent losing accumulated knowledge over time.

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