Individual skill
agent-memory
Use when implementing or managing persistent, hierarchical memory systems for AI agents. Covers cross-session state, fact supersession, and self-managed memory tools to enable long-term recall and adaptive agent behavior.
skills/agent-frameworks/agent-memory/SKILL.md
Source commit 1c04afc2a760ed58cea1308851e7626f76c4ff44
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- Yes
- Observed behavior
- Not checked yet
- Last checked
- Nov 11, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 27
- Repository last updated
- Oct 24, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- claude-code Repository topic
- agent-skills Repository topic
- agentic-workflow Repository topic
- skill-md Repository topic
- ai-agent Repository topic
- ai-workflows Repository topic
- codex Repository topic
- coding-agent Repository topic
- developer-tools Repository topic
- gemini-cli Repository topic
- engineering Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 4,774 bytes
Read excerpt
# Agent Memory Framework
## Overview
The `agent-memory` skill provides a standardized architectural approach to building intelligent memory systems for agents within the KILO-KIT ecosystem. It bridges the gap between ephemeral context windows and durable, long-term storage, enabling agents to maintain user preferences, project-specific conventions, and debugging history across multiple sessions.
## When To Use
- When designing systems that must persist state across independent interaction sessions.
- When an agent needs to manage large volumes of user-specific facts that exceed the context window.
- When implementing self-managed memory tools (MemGPT/Letta patterns) to allow agents to control their own knowledge base.
- When building systems requiring automatic entity updates (Mem0 pattern) to resolve conflicting or stale information.
## Core Concepts
- **Memory Hierarchy**: Differentiating between transient working context and persistent knowledge.
- **Fact Extraction**: Identifying core entities, preferences, and relationships from conversational flow.