A sophisticated memory architecture for AI agents that synchronizes short-term windows, medium-term summaries, and long-term vector retrieval.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install three-tier-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 three-tier-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 Three-Tier Memory Manager?
The Three-Tier Memory Manager is a powerful architecture designed to solve the persistence and context limitations of modern AI agents. By leveraging Openclaw Skills, this system provides a structured approach to memory that mimics human cognitive functions. It manages a sliding window for immediate interactions, generates automated summaries for historical continuity, and utilizes vector storage for semantic retrieval of long-term knowledge.
This framework ensures that your AI agents remain contextually aware without exhausting token limits. It is specifically built to integrate seamlessly with modular workflows, allowing developers to build more intelligent, personalized, and reliable agentic systems through the Openclaw Skills ecosystem.
Three-Tier Memory Manager Use Cases
- Managing long-duration conversation histories while staying within LLM token limits.
- Building personalized AI assistants that remember user preferences and past interactions.
- Creating persistent knowledge bases that agents can query semantically over time.
- Automating the summarization of complex tasks to maintain high-level project context.
How Three-Tier Memory Manager Works
- The system captures real-time interactions and stores them in a FIFO sliding window within a local JSON file.
- When the short-term window reaches its capacity, it triggers an LLM-based summarization process to move data into medium-term storage.
- Important data or explicit long-term memory additions are indexed into a vector store for semantic search capability.
- Openclaw Skills hooks automatically trigger memory saves and summarization at the end of each session or upon reaching specific token thresholds.
Three-Tier Memory Manager Setup
To get started with this memory management system, follow these steps:
# Initialize the memory management environment
python3 scripts/memory_manager.py init
# Install optional dependencies for long-term vector retrieval
pip install chromadb
# Enable the automatic session hook within the OpenClaw environment
openclaw hooks enable memory-manager-hook
Three-Tier Memory Manager Data Schema & Taxonomy
The system organizes data across three distinct layers to optimize retrieval speed and context relevance:
| Memory Tier | Storage Backend | Trigger Mechanism | Capacity |
|---|---|---|---|
| Short-Term | sliding-window.json |
Real-time / FIFO | 10 interactions |
| Medium-Term | summaries/ |
Token threshold | Unlimited |
| Long-Term | vector-store/ |
Semantic RAG | Unlimited |
Configuration settings like window size and summary thresholds are managed via memory/config.json.
Three-Tier Memory Manager Advanced Features
- Native integration with Openclaw Skills hooks for automated
session:endstate persistence. - Semantic search functionality allowing agents to perform RAG-based lookups on historical data.
- Automated medium-term summarization to prevent context window overflow while preserving core information.
- Configurable memory parameters including window sizes and RAG top-k results via centralized JSON configuration.
SKILL.md
Loading
Related Openclaw Skills
Memory Manager
A sophisticated three-tier memory architecture for AI agents that manages short-term context, medium-term summaries, and long-term vector-based retrieval.
OpenClaw Auto-Doctor
An intelligent maintenance assistant that monitors logs to automatically diagnose errors, search for solutions, and apply fixes or create pull requests.
Memory Manager
A sophisticated three-tier memory management system for AI agents that handles short-term context, mid-term summaries, and long-term vector storage.
Memory Manager
A sophisticated three-tier memory architecture providing sliding windows, automated summarization, and vector-based long-term storage for AI agents.
Viking Memory
Viking Memory provides AI agents with persistent, long-term semantic memory for recall of user preferences and conversation history.
X-Deep-Miner
An automated scanning and archiving tool that transforms high-engagement X (Twitter) threads into structured Obsidian notes with expert translation.
Featured*
