# Memory Systems

Should be used for persistent semantic memory in agent systems: cross-session knowledge...

- Repository: https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering (18272 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-10-12
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add muratcankoylan/Agent-Skills-for-Context-Engineering --skill memory-systems -g -a claude-code -y`
Codex: `npx skills add muratcankoylan/Agent-Skills-for-Context-Engineering --skill memory-systems -g -a codex -y`
Cursor: `npx skills add muratcankoylan/Agent-Skills-for-Context-Engineering --skill memory-systems -g -a cursor -y`
Gemini CLI: `npx skills add muratcankoylan/Agent-Skills-for-Context-Engineering --skill memory-systems -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add muratcankoylan/Agent-Skills-for-Context-Engineering`

## Description

This skill should be used for persistent semantic memory in agent systems: cross-session knowledge retention, entity tracking, temporal validity, graph or vector retrieval, memory consolidation, and memory benchmark selection. Route file-backed scratchpads to filesystem-context, handoff summaries to context-compression, and token-efficiency tactics to context-optimization.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Memory System Design

Memory provides the persistence layer that allows agents to maintain continuity across sessions and reason over accumulated knowledge. Simple agents rely entirely on context for memory, losing all state when sessions end. Sophisticated agents implement layered memory architectures that balance immediate context needs with long-term knowledge retention. The evolution from vector stores to knowledge graphs to temporal knowledge graphs represents increasing investment in structured memory for improved retrieval and reasoning.

## When to Activate

Activate this skill when:
- Building agents that must persist knowledge across sessions
- Choosing between memory frameworks (Mem0, Zep/Graphiti, Letta, LangMem, Cognee)
- Needing to maintain entity consistency across conversations
- Implementing reasoning over accumulated knowledge
- Designing memory architectures that scale in production
- Evaluating memory systems against benchmarks (LoCoMo, LongMemEval, DMR)
- Building dynamic memory with automatic entity/relationship extraction and self-improving memory (Cognee)

Do not activate this skill for adjacent work owned by other skills:
- File-backed scratchpads, run logs, and tool-output offloading: `filesystem-context`.
- Conversation compaction or human-readable handoff summaries: `context-compression`.
- Masking, prefix caching, token budgets, or retrieval scoping inside one trajectory: `context-optimization`.
- Formal belief/desire/intention models over RDF state: `bdi-mental-states`.

## Core Concepts

The rest: https://github.com/muratcankoylan/Agent-Skills-for-Context-Engineering/blob/HEAD/skills/memory-systems/SKILL.md