A sophisticated memory management framework for AI agents featuring importance scoring, automated time-decay cleanup, and hybrid semantic retrieval.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install memory-management-lite
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install memory-management-lite 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 Management System?
The Memory Management skill is a comprehensive architectural framework designed to transform how AI agents handle persistent information within the Openclaw Skills ecosystem. It moves beyond simple logging by implementing a tiered storage strategy that distinguishes between core long-term principles and transient daily interactions. By utilizing this skill, developers can ensure their agents remain contextually aware without suffering from the performance degradation and 'noise' associated with unmanaged memory bloat.
At its core, the system utilizes a structured workspace layout and a set of logical triggers to govern when and where information is stored. It balances immediate utility with long-term relevance through a daily maintenance workflow that migrates critical insights to permanent storage while archiving or deleting expired logs. This ensures that the most important user preferences, project milestones, and decision-making patterns are always at the agent's fingertips.
Memory Management System Use Cases
- Ensuring agents remember user communication styles and technical preferences across disconnected sessions.
- Maintaining a 'Source of Truth' for project milestones and architectural decisions to prevent repetitive questioning.
- Preventing memory bloat in long-running agents by automatically archiving chat logs older than 30 days.
- Implementing a high-reliability recall system where the agent checks historical data before fabricating answers.
How Memory Management System Works
- The system evaluates incoming information based on an Importance Score (1-5) to determine the storage destination.
- High-importance data (Scores 4-5) is written to MEMORY.md for long-term persistence.
- Mid-to-low importance data (Scores 2-3) is logged in date-stamped daily files within the memory/ directory.
- During user interaction, the agent employs hybrid retrieval (vector semantics + keyword search) to recall relevant past contexts.
- A daily maintenance cron job runs to scan logs, apply time-decay weights, and migrate 'worth-keeping' content to the long-term knowledge base.
- Automated cleanup processes delete or archive logs older than 30 days to keep the workspace efficient.
Memory Management System Setup
To deploy this skill within your workspace, ensure your directory structure matches the recommended layout and configure your memorySearch provider.
# Create the required directory structure
mkdir -p ~/.openclaw/workspace/memory/
touch ~/.openclaw/workspace/MEMORY.md
# Search for existing memories to verify setup
openclaw memory search "user preferences"
Add the Memory Management rules to your AGENTS.md to instruct your agent on the new scoring and retrieval protocols.
Memory Management System Data Schema & Taxonomy
The skill organizes data into a tiered hierarchy based on retrieval frequency and importance:
| File / Directory | Purpose | Retention |
|---|---|---|
MEMORY.md |
Core principles, key decisions, and identity | Permanent |
memory/preferences.md |
User-specific communication and tool settings | Indefinite |
memory/patterns.md |
Best practices and learned workflow patterns | Indefinite |
memory/YYYY-MM-DD.md |
Daily session logs and short-term context | 30 Days (then archived) |
memory/decisions.md |
Log of project-specific architectural choices | Indefinite |
Memory Management System Advanced Features
- Hybrid Retrieval Engine: Combines vector semantics (e.g., Voyage AI) with Full-Text Search (FTS) for maximum accuracy.
- Automated Cron Maintenance: Scheduled triggers for daily log rotation, backups, and summarization reports.
- Importance-Weighted Decay: Silently reduces the retrieval weight of older, non-essential logs while keeping core memories 'hot'.
- Manual Override Triggers: Specific natural language phrases like 'permanently save' force immediate high-priority indexing.
- Multi-File Taxonomy: Specialized files for contacts, feedback, and projects to prevent cross-topic interference.
SKILL.md
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