memory-mgmt

Design layered memory architectures with short-term and long-term knowledge graphs.

7|2|Updated Jan 28, 2026

Search Tags:#knowledge graph #ai memory #data persistence #long-term memory #agent architecture #temporal data

Ready to run

npx skills add https://github.com/LuisSambrano/antigravity-config --skill memory-mgmt

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memory-mgmt
Source: https://github.com/LuisSambrano/antigravity-config/tree/main/skills/2-ai/memory-mgmt
Command: npx skills add https://github.com/LuisSambrano/antigravity-config --skill memory-mgmt

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of enabling AI agents to retain and effectively utilize information across extended interactions and sessions, moving beyond volatile context windows.

Core Features & Use Cases

Layered Memory Architectures: Design systems incorporating working, short-term, long-term, entity, and temporal knowledge graphs.
Data Persistence & Retrieval: Implement mechanisms for agents to learn, recall, and reason over accumulated knowledge.
Use Case: Building a customer support chatbot that remembers past interactions and user preferences to provide personalized and consistent service over multiple conversations.

Quick Start

Design a memory architecture for an AI agent that includes short-term and long-term storage.

Frequently Asked Questions about memory-mgmt

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

FAQPage Schema

How do I implement long-term memory for an AI agent across multiple sessions?▼

Designing long-term memory for AI agents involves building a layered memory architecture that persists data outside the context window, enabling the agent to learn, recall, and reason over accumulated knowledge across multiple sessions.

What is a layered memory architecture for AI agents?▼

A layered memory architecture is a structured system that incorporates working, short-term, long-term, entity, and temporal knowledge graphs to manage data persistence, retrieval, and reasoning over accumulated knowledge for AI agents.

How do temporal knowledge graphs help with AI memory management?▼

Temporal knowledge graphs structure accumulated knowledge chronologically, enabling AI agents to reason over time-sensitive information and maintain continuity during data retrieval across different conversational sessions.

Can I use vector stores for AI agent data persistence and retrieval?▼

Yes, you can use vector stores for AI agent data persistence and retrieval by combining them with knowledge graphs and temporal data structures to ensure enhanced agent continuity and learning.

What is the best way to design a memory architecture for a customer support chatbot?▼

The best way to design memory architecture for a customer support chatbot is implementing short-term and long-term storage layers, allowing the agent to remember past interactions and user preferences for personalized service.