agent-memory-systems

Name: agent-memory-systems
Availability: InStock
Author: involvex

Manage agent memory architecture and retrieval across six memory types.

Updated Mar 7, 2026

Search Tags:#vector database #retrieval #agent memory #long-term memory #chunking #ai architecture

Ready to run

npx skills add https://github.com/involvex/llms-remote --skill agent-memory-systems-involvex

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-memory-systems
Source: https://github.com/involvex/llms-remote/tree/main/.agents/skills/agent-memory-systems
Command: npx skills add https://github.com/involvex/llms-remote --skill agent-memory-systems-involvex

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill addresses the fundamental challenge of enabling AI agents to retain and effectively utilize information over time, preventing them from starting each interaction from scratch.

Core Features & Use Cases

Memory Architecture: Designs and implements various memory types including short-term, long-term, working, episodic, semantic, and procedural memory.
Retrieval Optimization: Focuses on efficient and accurate retrieval of relevant memories, recognizing that retrieval failures often manifest as intelligence failures.
Use Case: Building an AI customer support agent that can recall past interactions with a user to provide personalized and consistent service.

Quick Start

Configure the agent's memory system to prioritize recent interactions in its short-term memory.

Frequently Asked Questions about agent-memory-systems

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

FAQPage Schema

How do I build long-term memory for an AI agent?▼

To build long-term memory for an AI agent, you implement memory architectures like semantic and episodic memory, optimizing information formation and decay strategies. This allows agents to retain and utilize past interactions over time.

What is the best way to retrieve relevant memories from a vector database?▼

The best way to retrieve relevant memories from a vector database is by applying retrieval filtering and optimizing chunking strategies. Effective retrieval prevents intelligence failures by ensuring accurate information recall from the memory system.

How does chunking affect agent memory retrieval?▼

Chunking affects agent memory retrieval by dictating how information is segmented and stored in vector databases. Proper chunking strategies directly optimize memory formation and determine the accuracy of subsequent retrieval filtering.

Can I implement both short-term and long-term memory in a single agent architecture?▼

You can implement both short-term and long-term memory in a single agent architecture. This involves designing distinct memory types, including working and episodic memory, to manage recent interactions and historical data simultaneously.

Why does my AI agent fail to recall past interactions consistently?▼

AI agents fail to recall past interactions consistently due to unoptimized retrieval mechanisms in the memory system. Retrieval failures often manifest as intelligence failures, requiring improved vector store selection and memory decay strategies.

When should I configure memory decay strategies in an agent memory system?▼

You should configure memory decay strategies when managing long-term memory to prevent information overload and maintain relevance. Optimizing decay ensures the agent forgets outdated data while preserving critical semantic and episodic memories.