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conversation-memory

Manage persistent short-term, long-term, and entity-based memory for LLM conversations.

Updated Mar 7, 2026
Search Tags:#llm#retrieval#memory#persistence#conversation#entity
One-click install
npx skills add https://github.com/involvex/llms-remote --skill conversation-memory
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: conversation-memory
Source: https://github.com/involvex/llms-remote/tree/main/.agents/skills/conversation-memory
Command: npx skills add https://github.com/involvex/llms-remote --skill conversation-memory

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the challenge of AI conversations lacking persistent memory, leading to repetitive interactions and a poor user experience.

Core Features & Use Cases

  • Persistent Memory: Implements short-term, long-term, and entity-based memory systems for LLMs.
  • Intelligent Retrieval: Ensures relevant memories are surfaced for contextually appropriate responses.
  • Use Case: An AI assistant can remember a user's preferences and past interactions over months, providing a personalized and continuous experience without the user needing to repeat information.

Quick Start

Use the conversation-memory skill to store a new fact about the user's favorite color.

Frequently Asked Questions about conversation-memory

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

FAQPage Schema
How do I add persistent memory to LLM conversations so they remember past interactions?▼

Persistent memory for LLM conversations is implemented by storing and retrieving short-term, long-term, and entity-based recall data, allowing AI assistants to remember user preferences and past interactions over extended periods.

What is the best way to manage unbounded memory growth in AI chat systems?▼

Unbounded memory growth in AI chat systems is managed through lifecycle management and intelligent retrieval mechanisms that consolidate stored memories, ensuring only relevant context is surfaced during retrieval.

How does entity-based memory retrieval work for personalized AI dialogue?▼

Entity-based memory retrieval works by storing specific facts related to distinct entities, allowing the LLM to recall personalized context and maintain continuous dialogue without requiring users to repeat information.

Can I use conversation-memory to store and retrieve user preferences over long periods?▼

Yes, conversation-memory can store and retrieve user preferences over long periods using long-term and entity-based recall systems, providing a personalized continuous experience without the user needing to repeat information.

Why does my AI assistant retrieve irrelevant memories during contextual dialogue?▼

Irrelevant memory retrieval during contextual dialogue occurs when intelligent retrieval mechanisms are lacking; implementing proper memory consolidation and lifecycle management surfaces only relevant memories for appropriate responses.

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