A sophisticated three-tier memory recovery system that prevents AI agents from losing context after session restarts by organizing data into permanent, daily, and session layers.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install memory-system
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-system 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 System for AI Agents?
The Memory System is a specialized architectural framework designed to solve the common issue of context loss in AI interactions. When an AI session restarts, agents often lose all previous progress—a phenomenon described as waking up and forgetting everything. This skill provides a structured solution by implementing a robust three-layer storage hierarchy that ensures continuity and long-term intelligence.
By utilizing this Openclaw Skills implementation, developers can grant their agents a persistent identity and a technical memory that spans months or years. Unlike cloud-dependent vector databases, this system is entirely local, private, and auditable, allowing for high-frequency retrieval of project decisions, technical stacks, and user preferences without incurring API costs or latency.
Memory System for AI Agents Use Cases
- Restoring full project context immediately after an IDE or session restart.
- Maintaining a permanent log of key technical decisions and lessons learned to avoid repeating mistakes.
- Tracking evolving user preferences and identity traits for a personalized agent experience.
- Managing long-running development tasks that require context from multiple previous sessions.
- Archiving high-density session data into compressed, searchable daily summaries.
How Memory System for AI Agents Works
- When a new session begins, the system executes a recovery flow that reads the current daily log and global long-term memory files.
- The agent parses classified permanent records such as identity profiles and technical stack specifications to establish current constraints.
- The memory_search utility is employed to locate and inject relevant historical context into the active prompt.
- During the interaction, critical decisions and new discoveries are automatically flagged for the key-decisions log.
- Upon session termination, an automated hook triggers a summary process that compresses the session context and archives it into the daily memory layer.
Memory System for AI Agents Setup
To activate the memory recovery and ensure your agent retains its state, run the provided recovery script in your environment.
bash /data/workspace/scripts/memory-recovery.sh
Ensure your workspace contains a memory/ directory with a permanent/ subdirectory to store your foundational identity and technical stack markdown files. This Openclaw Skills setup requires these files to be present for optimal context restoration.
Memory System for AI Agents Data Schema & Taxonomy
The system organizes data using a hierarchical markdown-based schema to ensure human readability and machine parsability.
| Layer | Component | Description |
|---|---|---|
| L1: Permanent | identity.md |
Stores user identity, long-term preferences, and relationships. |
| L1: Permanent | technical-stack.md |
Maintains records of tools, frameworks, and coding standards. |
| L1: Permanent | key-decisions.md |
A chronological log of critical project pivots and architectural choices. |
| L2: Daily | YYYY-MM-DD.md |
A daily aggregation of all session summaries and important events. |
| L3: Session | session-N.md |
Temporary storage for active task contexts before they are compressed. |
Memory System for AI Agents Advanced Features
- Session End Hooks that automatically trigger context persistence without user intervention.
- Context Compression Alerts that provide a 2-minute warning to highlight essential information before data is summarized.
- Local-first architecture that functions entirely without external API dependencies or vector database overhead.
- Multi-tier retrieval logic that prioritizes permanent project constraints over daily logs to optimize token usage.
- Periodic optimization cycles that merge duplicate information and update technical stack records for maximum efficiency.
SKILL.md
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