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DammianMiller
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memory-management

Manage a 4-layer memory system using SQLite and Qdrant for AI coding agents.

1|Updated Dec 26, 2025
Search Tags:#context management#knowledge graph#vector database#ai memory#agentic ai#persistent learning
One-click install
npx skills add https://github.com/DammianMiller/universal-agent-memory --skill memory-management-dammianmiller
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: memory-management
Source: https://github.com/DammianMiller/universal-agent-memory/tree/main/skills/memory-management
Command: npx skills add https://github.com/DammianMiller/universal-agent-memory --skill memory-management-dammianmiller

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill addresses the challenge of AI agents forgetting information between conversations, leading to repeated mistakes and inefficient workflows.

Core Features & Use Cases

  • 4-Layer Memory Architecture: Utilizes distinct layers (Working, Session, Semantic, Knowledge) for efficient data recall.
  • Persistent Storage: Stores lessons, decisions, and context across sessions using SQLite and vector databases (Qdrant).
  • Use Case: An AI coding assistant can remember a specific bug fix from weeks ago, preventing it from being reintroduced, or recall architectural decisions made during a previous project phase.

Quick Start

Use the memory management skill to store the lesson "Always validate user input before processing."

Frequently Asked Questions about memory-management

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

FAQPage Schema
How do I enable persistent learning for an AI coding agent across sessions?▼

Persistent learning for an AI coding agent is enabled by storing lessons, decisions, and context in a 4-layer memory system using SQLite and Qdrant vector databases for cross-session recall.

What is a 4-layer memory architecture for AI agents?▼

A 4-layer memory architecture organizes AI agent context into Working, Session, Semantic, and Knowledge layers to efficiently store and retrieve specific information like past bug fixes and architectural decisions.

Do I need a vector database like Qdrant for AI memory management?▼

Yes, a vector database like Qdrant is required to perform semantic, cross-session vector search, which supplements SQLite short-term storage and the entity-relationship knowledge graph for deep context retrieval.

How does semantic vector search retrieve context from previous AI coding sessions?▼

Semantic vector search retrieves context by using Qdrant to match query embeddings with stored vector representations of past lessons and decisions, allowing the AI to recall relevant facts across different sessions.

Can this memory management skill store entity-relationship graphs for knowledge retrieval?▼

Yes, the memory management skill supplements its vector database and SQLite storage with an entity-relationship graph to map and retrieve complex knowledge connections for agentic AI workflows.

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