A high-performance persistence layer that enables AI agents to retain context and project knowledge across multiple sessions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install memo-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 memo-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 specialized system designed to solve the problem of ephemeral context in AI agent interactions. By integrating this into your Openclaw Skills library, you provide your agents with a long-term brain capable of storing observations, technical decisions, and project milestones in a local SQLite database.
This system leverages the FTS5 extension to enable rapid full-text search, ensuring that the agent can retrieve the most relevant information without overwhelming the token window. It serves as a bridge between separate coding sessions, allowing the AI to maintain a continuous understanding of complex codebases and evolving requirements.
OpenClaw Persistent Memory Use Cases
- Maintaining architectural consistency across weeks of development by recalling previous design decisions.
- Automating the documentation of project progress through background observation capture.
- Reducing manual re-prompting by allowing the agent to self-retrieve context from earlier interactions.
- Building a local, searchable knowledge base of all AI-driven development activities.
How OpenClaw Persistent Memory Works
- The system monitors agent interactions and automatically identifies important observations to save.
- Observations are indexed into a local SQLite database using FTS5 for efficient querying.
- Upon starting a new session or prompt, the auto-recall mechanism searches the memory database for relevant keywords.
- Relevant historical data is injected into the current context window, providing the agent with "memory" of past events.
- Developers can also manually manage memories using provided tools like memory_store and memory_search.
OpenClaw Persistent Memory Setup
To integrate this into your workflow, follow these steps to install the necessary Openclaw Skills components:
- Install the global npm package:
npm install -g openclaw-persistent-memory
- Initialize the background worker service:
openclaw-persistent-memory start
- Deploy the extension to your local configuration:
cp -r node_modules/openclaw-persistent-memory/extension ~/.openclaw/extensions/openclaw-mem
cd ~/.openclaw/extensions/openclaw-mem && npm install
- Update your
~/.openclaw/openclaw.jsonto enable the plugin withautoCaptureandautoRecallset to true.
OpenClaw Persistent Memory Data Schema & Taxonomy
The skill organizes its memory via a structured SQLite schema optimized for retrieval:
| Property | Type | Description |
|---|---|---|
| Memory ID | UUID | Unique identifier for each stored observation |
| Content | Text | The actual context or information captured by the agent |
| FTS Index | Virtual Table | Full-text search index for semantic-like natural language querying |
| Timestamp | DateTime | Record of when the memory was captured for chronological sorting |
OpenClaw Persistent Memory Advanced Features
- Progressive disclosure logic to ensure token-efficient retrieval of only the most relevant snippets.
- Full REST API support for interacting with the memory database from external scripts.
- Manual toolset including memory_search, memory_get, and memory_delete for precise context control.
- Multi-session synchronization via a centralized worker service running on a local port.
SKILL.md
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