zylos-memory

Name: zylos-memory
Availability: InStock
Author: Zylos-AI

Manage persistent AI agent memory using tiered markdown files.

1.1k|123|Updated Feb 2, 2026

Search Tags:#state management #knowledge base #ai memory #data persistence #agentic ai #llm persistence

Ready to run

npx skills add https://github.com/zylos-ai/zylos-core --skill zylos-memory

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: zylos-memory
Source: https://github.com/zylos-ai/zylos-core/tree/main/skills/zylos-memory
Command: npx skills add https://github.com/zylos-ai/zylos-core --skill zylos-memory

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a robust system for maintaining an AI's memory across sessions, ensuring continuity and preventing amnesia, which is a fundamental limitation of current LLMs.

Core Features & Use Cases

Persistent Memory: Stores conversations and key information in tiered markdown files, surviving restarts.
Memory Sync: Automatically processes conversations into structured memory, consolidating information.
Context Management: Manages session rotation and state saving to keep relevant information accessible.
Use Case: An AI assistant that remembers your preferences, past projects, and decisions from previous interactions, allowing for more personalized and efficient assistance over time.

Quick Start

Ensure the zylos-memory skill is running as a background subagent via the Task tool.

Frequently Asked Questions about zylos-memory

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

FAQPage Schema

How do I maintain persistent memory for an AI agent across different sessions?▼

Persistent AI memory is maintained by processing conversations into structured tiered markdown files, surviving restarts and preventing LLM amnesia. This system consolidates information into identity, state, references, and session logs for continuity.

How does AI memory synchronization work when managing context for agentic workflows?▼

Memory synchronization automatically processes conversations into structured memory formats, consolidating key information. It operates as a background subagent that manages session rotation and context-aware state saving to keep relevant data accessible.

Do I need to run the Task tool to use this state management skill?▼

Yes, the state management skill operates as a background subagent launched via the Task tool. This allows it to process conversations and handle memory synchronization without interrupting your main AI workflow.

What format is used for data persistence in LLM memory systems?▼

Data persistence in this LLM memory system uses a tiered markdown file system. It structures saved context into specific formats like identity, state, references, user profiles, and session logs.

What is the Inside Out model for AI state management?▼

The Inside Out model for AI state management is an approach that structures persistent memory into distinct categories like identity, state, and references. It handles memory synchronization and session rotation to maintain context.