memory-systems

Name: memory-systems
Availability: InStock
Author: samvanme

Persist agent memory across sessions with layered memory and temporal knowledge graphs.

Updated Jan 9, 2026

Search Tags:#agents #retrieval #memory #persistence #temporal #cross-session #knowledge-graph

Ready to run

npx skills add https://github.com/samvanme/SBSitev0 --skill memory-systems-samvanme

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memory-systems
Source: https://github.com/samvanme/SBSitev0/tree/main/claudeskills/Agent-Skills-for-Context-Engineering-main/skills/memory-systems
Command: npx skills add https://github.com/samvanme/SBSitev0 --skill memory-systems-samvanme

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Enable agents to persist memory across sessions, maintaining continuity and enabling reasoning over past interactions.

Core Features & Use Cases

Layered memory architecture: working memory, short-term memory, long-term memory, entity memory, and temporal knowledge graphs.
Memory retrieval and consolidation: semantic search, entity-based queries, temporal validity, and consolidation patterns.
Cross-context integration: linking memories to entities and loading relevant memories into context when needed.
Use Case: A customer-support agent remembers user preferences across chats and tailors responses.
Use Case: Temporal knowledge graphs enable reasoning about how facts and relationships evolve over time.

Quick Start

Instruct the agent to start a memory session, store a memory for an entity with a timestamp, then retrieve memories for that entity.

Frequently Asked Questions about memory-systems

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

FAQPage Schema

How do I persist agent memory across sessions?▼

To persist agent memory across sessions, you need a memory architecture that supports working, short-term, long-term, and knowledge-graph layers. This enables agents to maintain continuity and reason over past cross-session interactions.

What is a temporal knowledge graph for agent entity tracking?▼

A temporal knowledge graph for agent entity tracking is a memory layer that records how facts and relationships evolve over time. It enables temporal reasoning by applying validity timestamps to stored entity memories.

How do I retrieve and consolidate memories by entity in a multi-turn conversation?▼

To retrieve and consolidate memories by entity, you query the memory architecture using semantic search and entity-based filters. Consolidation patterns then merge relevant working, short-term, and long-term memories into the active context.

Do I need numpy to implement a cross-session memory architecture?▼

Yes, numpy is required as a dependency to implement this cross-session memory architecture. It provides the numerical processing foundation needed for semantic search retrieval and memory consolidation operations.

What is the best way to structure agent memory for customer support chat history?▼

The best way to structure agent memory for customer support is a layered architecture combining working, short-term, long-term, and entity memory. This allows the agent to remember user preferences across chats and tailor responses effectively.

When should I use a knowledge graph layer instead of simple long-term memory?▼

You should use a knowledge graph layer instead of simple long-term memory when your agent requires temporal reasoning about how facts and relationships evolve over time, rather than just retrieving static historical context.