A multi-layered memory framework for AI agents to manage context through short-term, long-term, and semantic storage tiers.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install skylv-agent-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 skylv-agent-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 Agent Memory System?
The Agent Memory System is a robust architectural framework designed to provide AI agents with persistent, tiered memory capabilities. It addresses the limitations of standard context windows by organizing data into working, short-term, long-term, semantic, and procedural layers. By implementing this system within Openclaw Skills, developers can ensure their agents retain user preferences, historical interactions, and specific procedural knowledge across different sessions and environments.
This system leverages a combination of SQLite for structured long-term storage and vector databases like Chroma for semantic retrieval. It enables agents to act more intelligently by retrieving relevant context based on importance, recency, and semantic similarity, transforming a simple chatbot into a sophisticated autonomous agent with a continuous persona.
Agent Memory System Use Cases
- Creating personalized AI assistants that remember user history and preferences across multiple sessions.
- Building complex RAG (Retrieval-Augmented Generation) agents that autonomously manage their own knowledge base.
- Developing task-oriented agents that require procedural memory to use specific tools and workflows.
- Reducing token consumption in high-traffic applications by compressing session history into long-term summaries.
How Agent Memory System Works
- The system initializes a dual-storage backend consisting of a relational database for metadata and a vector store for semantic embeddings.
- Every interaction is converted into a MemoryEntry, which includes content, a timestamp, and an importance score.
- High-importance entries are automatically vectorized and stored in the semantic layer for future RAG-style retrieval.
- During a dialogue, the system queries both the vector store and the relational database to build a comprehensive context window for the LLM.
- A consolidation process periodically merges similar memories and prunes low-value data to maintain system efficiency and manage storage overhead.
Agent Memory System Setup
To integrate this memory architecture into your project, ensure you have the necessary storage dependencies installed:
npm install chromadb sqlite3 openai
Configure your environment variables for embedding generation:
export OPENAI_API_KEY='your-api-key'
Initialize the AgentMemory class with a unique session ID and a local path for the SQLite database to begin managing your agent context through Openclaw Skills.
Agent Memory System Data Schema & Taxonomy
The memory system organizes data using a structured schema to balance retrieval speed and depth:
| Attribute | Type | Description |
|---|---|---|
| id | UUID | Unique identifier for each memory fragment |
| type | String | Categorized as episodic, semantic, or procedural |
| importance | Integer | 0-10 score used to prioritize retrieval and vectorization |
| accessCount | Integer | Tracks how often a memory is retrieved for forgetting logic |
| metadata | JSON | Stores additional context like source, tags, or session IDs |
Agent Memory System Advanced Features
- Hybrid retrieval logic combining semantic vector search with keyword-based SQL queries.
- Automated importance evaluation that scores memories based on specific keywords and user requirements.
- Forgetting mechanism that deletes low-impact and low-access data to prevent database bloat.
- Memory consolidation hooks that periodically compress short-term session data into permanent long-term summaries.
- Privacy-conscious storage options allowing for encryption of sensitive information within the memory tiers.
SKILL.md
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