A local-first tiered memory framework that gives AI agents long-term persistence and session recovery capabilities.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install agent-memory-system-guide
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 agent-memory-system-guide 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?
This skill provides a comprehensive guide and toolkit for establishing a robust, local-first memory architecture for AI agents. By utilizing a tiered approach—spanning from immediate session states to long-term distilled facts—it ensures that agents maintain context across restarts. This framework is essential for power users of Openclaw Skills who want to avoid the amnesia effect in LLM sessions and build a reliable, auditable knowledge base.
The system integrates seamlessly with Obsidian and leverages the native memory_search capabilities of OpenClaw. It emphasizes a capture first, distill later philosophy, allowing developers to maintain high-velocity workflows while systematically building an agent's long-term intelligence through a local recovery layer that remains portable and auditable.
Memory System for AI Agents Use Cases
- Initializing a persistent memory environment for new AI agents.
- Recovering agent context after session interruptions or crashes.
- Distilling daily activity logs into long-term architectural decisions and user preferences.
- Synchronizing agent knowledge bases across different devices using local-first protocols.
How Memory System for AI Agents Works
- The agent reads the SESSION-STATE.md file to recover the immediate context and pending items of the current task.
- It scans the most recent daily notes (1-3 days) to understand the latest project developments and raw logs.
- It executes a memory_search query within the Openclaw Skills environment to retrieve relevant historical facts from the long-term MEMORY.md file.
- During the active session, the agent captures new decisions and temporary data into the working-buffer.md.
- At the end of the task or day, the agent distills important insights and pitfall records into the long-term memory layer or archives them to Obsidian.
Memory System for AI Agents Setup
To get started with this memory system for Openclaw Skills, follow these steps:
- Initialize your workspace structure using the provided bootstrap script:
python3 scripts/memory_capture.py bootstrap --workspace /path/to/workspace
Create your core MEMORY.md file manually to define high-level user preferences and project mirrors.
Configure your agent system prompt to prioritize reading SESSION-STATE.md and MEMORY.md at the start of every session.
(Optional) Set up an Obsidian vault pointing to the workspace for visual management of daily notes and attachments.
Memory System for AI Agents Data Schema & Taxonomy
The system organizes data into five logical layers to optimize context window usage:
| Layer | File/Path | Content Responsibility |
|---|---|---|
| Recovery | SESSION-STATE.md | Current task name, completed items, blockers, and next steps. |
| Buffer | working-buffer.md | Temporary decisions, raw logs, and items pending distillation. |
| Long-term | MEMORY.md | Distilled facts, recurring pitfalls, naming conventions, and user preferences. |
| Daily Logs | memory/YYYY-MM-DD.md | Raw chronological activity records and unprocessed daily learnings. |
| Assets | attachments/ | Local media, images, and referenced documentation files. |
Memory System for AI Agents Advanced Features
- Cross-device migration support via automated export and import commands to transfer agent states.
- Automated workspace health reporting to identify missing memory files or permission anomalies.
- Optional OpenViking integration for enhanced semantic retrieval and vector-based memory expansion.
- Obsidian-native metadata support using YAML frontmatter, Dataview queries, and wikilink connectivity.
- A strict distillation protocol designed for Openclaw Skills that prevents memory bloat by keeping core files under 200 lines.
SKILL.md
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