A sophisticated memory architecture for AI agents featuring hierarchical storage, semantic retrieval, and automatic importance-based consolidation.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-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 openclaw-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 specialized framework designed to solve the context window limitations of modern LLMs. By providing a structured, persistent memory layer, it allows AI agents to retain information across sessions, recognize repeating patterns, and prioritize critical user data. This Openclaw Skills implementation bridges the gap between fleeting conversation context and permanent knowledge storage.
Built on a layered architecture, the system manages working memory for immediate tasks, short-term session data, and long-term storage via SQLite and vector databases. This ensures that agents remain context-aware without overflowing their token budgets, utilizing semantic search and keyword indexing to retrieve only the most relevant memories for any given prompt.
Agent Memory System Use Cases
- Creating personalized AI assistants that remember user preferences and past interactions over months or years.
- Developing complex coding agents that maintain context across large-scale project architectures without losing track of previous decisions.
- Automating long-term research tasks where the agent must consolidate findings from multiple sessions into a coherent knowledge base.
- Optimizing token usage in high-frequency chat applications by offloading historical data to a managed memory layer.
How Agent Memory System Works
- Data Ingestion: The system captures input from the current session and classifies it as episodic, semantic, or procedural memory.
- Importance Scoring: An evaluation algorithm assigns a score (0-10) based on keywords and user intent to determine storage priority.
- Multi-Layer Storage: High-importance data is vectorized for RAG retrieval, while all data is logged in a persistent SQLite database.
- Context Synthesis: When a query is made, the system performs a hybrid search (semantic + keyword) to fetch the top relevant memories.
- Memory Consolidation: Periodic routines compress short-term session data into long-term summaries to save space and maintain relevance.
- Automated Forgetting: Low-value memories that haven't been accessed frequently are eventually purged to maintain system efficiency.
Agent Memory System Setup
To integrate this memory system into your Openclaw Skills project, follow these steps:
# Install required dependencies
npm install sqlite3 chromadb openai
# Initialize the memory system in your agent code
# Ensure you have your OPENAI_API_KEY exported in your environment
export OPENAI_API_KEY='your-api-key-here'
Configure your database path and session ID within your agent initialization script to begin persisting memory logs immediately.
Agent Memory System Data Schema & Taxonomy
The system utilizes a structured MemoryEntry schema to ensure consistency across different storage layers:
| Field | Type | Description |
|---|---|---|
id |
UUID | Unique identifier for the memory entry |
type |
String | Classification: episodic, semantic, or procedural |
content |
Text | The actual stored information or dialogue snippet |
importance |
Integer | Priority score from 0 to 10 |
accessCount |
Integer | Tracks how many times this memory has been retrieved |
tags |
Array | Metadata tags for filtered search |
timestamp |
BigInt | Epoch time of memory creation |
Agent Memory System Advanced Features
- Hybrid Retrieval Engine: Combines ChromaDB vector search for meaning with SQLite keyword search for exact matches.
- Smart Consolidation: Automatically merges similar memories to prevent redundancy and keep the context window clean.
- Importance-Based Vectorization: Only embeds high-value information to save on API costs and optimize search performance.
- TTL Management: Configurable expiration for session-based short-term memory while maintaining permanent records in long-term storage.
- Privacy Encryption: Support for sensitive information handling through encrypted metadata storage.
SKILL.md
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