Memory Manager for Openclaw

A professional memory architecture for AI agents implementing episodic, semantic, and procedural storage to prevent context loss.

marmikcfc
v1.0.0
Jan 31, 2026
94
31.9k
0

Install & Download

1. ClawHub CLI

The fastest way to install a skill directly from the registry.

npx clawhub@latest install memory-manager

2. Manual Installation

Copy the skill folder to one of these locations

Global
~/.openclaw/skills/
Workspace
<project>/skills/

Priority: Workspace > Local > Bundled

3. Prompt Installation

Copy this prompt to OpenClaw to install it automatically.

Help me install memory-manager using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).

Prefer to download?

Get the raw skill files in a ZIP archive.

What is Memory Manager?

Memory Manager is a professional-grade architecture designed for AI agents to handle context with the same complexity as human cognitive systems. By implementing a three-tier memory pattern—episodic, semantic, and procedural—this tool ensures that agents don't just store data, but organize it for optimal retrieval. Using Openclaw Skills like this one allows developers to move beyond flat text files and implement a structured knowledge graph approach.

This system is built for agents that need to maintain long-term context without the overhead or privacy concerns of external vector databases. It provides local-first, human-readable storage that allows for 18.5% better retrieval efficiency. By monitoring memory usage and providing automated snapshots, it acts as a safeguard against the context loss typically associated with LLM token limits.

Memory Manager Use Cases

  • Preventing critical context loss during high-token agent sessions.
  • Categorizing raw agent logs into actionable procedural workflows.
  • Building a local, private knowledge base of facts extracted from agent interactions.
  • Auditing agent history through chronological episodic event logs.
  • Detecting and managing memory compression risks before data is purged.

How Memory Manager Works

  1. The system initializes a specialized directory structure to house different types of agent memories.
  2. Episodic logs capture chronological events, providing a daily history of agent actions.
  3. The detection engine monitors memory volume and issues warnings at 70% and 85% capacity thresholds.
  4. An organization script parses raw memories to identify and migrate facts into semantic storage and workflows into procedural storage.
  5. Search utilities allow the agent to query specific memory tiers (episodic, semantic, or procedural) for highly relevant context retrieval.

Memory Manager Setup

To begin managing agent context with Openclaw Skills, run the initialization script to set up the directory structure:

~/.openclaw/skills/memory-manager/init.sh

You can then check for existing memory risks or organize your current flat-file memory logs:

# Check compression risk
~/.openclaw/skills/memory-manager/detect.sh

# Organize existing memories
~/.openclaw/skills/memory-manager/organize.sh

Memory Manager Data Schema & Taxonomy

The skill organizes data into a tiered hierarchy to maximize retrieval accuracy and human readability:

Directory Type Description
memory/episodic/ Episodic Chronological event logs (e.g., YYYY-MM-DD.md).
memory/semantic/ Semantic Distilled facts, concepts, and permanent knowledge nodes.
memory/procedural/ Procedural Reusable step-by-step guides and workflow patterns.
memory/snapshots/ Backups Compressed snapshots taken before memory purging.
memory/legacy/ Archive Original files preserved after migration to the tiered structure.

Memory Manager Advanced Features

  • Multi-tier memory architecture including episodic, semantic, and procedural patterns.
  • Automated compression risk detection with tiered warning thresholds (Safe, Warning, Critical).
  • Intelligent organization engine that extracts facts and workflows from flat logs.
  • Type-specific search capabilities to narrow down context retrieval by memory category.
  • Usage statistics and growth rate monitoring to track agent learning over time.
  • Local-first privacy, ensuring that agent identity and memory stay entirely on your hardware.

SKILL.md


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