A persistent memory layer for AI agents that automates context capture and semantic retrieval across sessions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-persistent-memory
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 openclaw-persistent-memory 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 OpenClaw Persistent Memory?
OpenClaw Persistent Memory is a sophisticated system designed to give AI agents long-term retention capabilities. By integrating a persistent storage layer directly into the agent workflow, this skill ensures that critical observations, user preferences, and project details are preserved across multiple sessions. It solves the problem of context loss, making it a vital component for developers building complex automation within the Openclaw Skills ecosystem.
The system utilizes SQLite with FTS5 (Full-Text Search) to provide high-performance retrieval. Whether an agent is performing research or managing long-term coding projects, it can automatically capture relevant data points and recall them exactly when needed. This creates a more intelligent, personalized, and efficient user experience by reducing the need for repetitive instructions.
OpenClaw Persistent Memory Use Cases
- Maintaining architectural decisions and coding preferences across separate development sessions.
- Automatically building a project-specific knowledge base through continuous observation capture.
- Reducing token consumption by injecting only relevant memories via semantic search instead of full session histories.
- Synchronizing state and shared knowledge between different AI agents using the same memory backend.
- Providing agents with a searchable historical record of past user interactions and completed tasks.
How OpenClaw Persistent Memory Works
- The skill launches a background worker service that manages a local SQLite database equipped with FTS5 for fast indexing.
- As the agent interacts with the user, the auto-capture mechanism monitors responses for significant information to store as memories.
- Before each new prompt is sent to the LLM, the auto-recall feature performs a full-text search against the database to find relevant context.
- Matching memories are dynamically injected into the agent's context window, allowing the agent to 'remember' past details.
- Developers can manually trigger memory tools like memory_search or memory_store for granular control over the agent's knowledge base.
OpenClaw Persistent Memory Setup
Install the core package via npm:
npm install -g openclaw-persistent-memory
Start the persistent memory worker service:
openclaw-persistent-memory start
Register the extension by copying it to your local Openclaw Skills directory and installing dependencies:
cp -r node_modules/openclaw-persistent-memory/extension ~/.openclaw/extensions/openclaw-mem
cd ~/.openclaw/extensions/openclaw-mem && npm install
Finally, update your ~/.openclaw/openclaw.json configuration to enable the memory slot and point it to the worker URL (typically http://127.0.0.1:37778).
OpenClaw Persistent Memory Data Schema & Taxonomy
The skill manages data through a structured SQLite schema optimized for retrieval speed. The following table describes the primary data organization:
| Attribute | Type | Description |
|---|---|---|
| Memory ID | UUID/Int | Unique identifier for the captured observation |
| Content | Text | The raw text or data point captured from the agent session |
| FTS Index | Virtual Table | Indexed text for high-speed natural language search |
| Timestamp | ISO8601 | The date and time the memory was recorded for chronological sorting |
| Metadata | JSON | Additional context such as source agent or project tags |
OpenClaw Persistent Memory Advanced Features
- SQLite FTS5 integration for professional-grade full-text search across thousands of memories.
- Progressive disclosure logic to ensure only the most relevant context is retrieved, saving on LLM token costs.
- Automated lifecycle hooks for auto-capture and auto-recall that require zero manual intervention from the user.
- REST API endpoints for monitoring database health, statistics, and manual CRUD operations on observations.
- Seamless multi-agent support through a centralized memory worker service.
SKILL.md
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