A local-first, three-tier memory architecture that provides AI agents with real-time capture, vector search, and long-term curated recall.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-advanced-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-advanced-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 Advanced Memory?
OpenClaw Advanced Memory is a sophisticated infrastructure layer designed to give AI agents persistent memory that survives across separate sessions. By implementing a tiered approach—Hot, Warm, and Cold—it ensures that every conversation turn is captured and processed without the latency or privacy concerns of cloud-based APIs. This skill is essential for developers building complex Openclaw Skills that require deep context retention and the ability to recall specific technical decisions made days or weeks prior.
The system operates entirely on your own hardware using local LLMs and vector databases. It differentiates between transient conversation noise and high-value "gems" like architectural decisions or project milestones, ensuring your agent becomes smarter and more context-aware over time.
OpenClaw Advanced Memory Use Cases
- Maintaining long-term project context across multiple coding sessions.
- Retrieving specific technical decisions or lessons learned from past debugging marathons.
- Creating a searchable knowledge base of agent-curated milestones and people information.
- Reducing context window bloat by querying only relevant memory segments instead of entire histories.
How OpenClaw Advanced Memory Works
- Real-time capture (HOT tier) watches active transcripts and buffers conversation turns into Redis every 30 seconds.
- Periodic synchronization (WARM tier) chunks and embeds buffered data into a Qdrant vector store for semantic search with a 7-day retention.
- Nightly curation (COLD tier) uses a local LLM to analyze the day's logs and extract structured insights into permanent storage.
- Semantic retrieval allows the agent or user to query these tiers using a specialized recall CLI for instant context recovery.
OpenClaw Advanced Memory Setup
To integrate this into your environment for Openclaw Skills, follow these steps:
# 1. Start the backend infrastructure using Docker
docker compose up -d
# 2. Download the required local models via Ollama
ollama pull snowflake-arctic-embed2
ollama pull qwen2.5:7b
# 3. Execute the installation script to configure services and cron jobs
bash scripts/install.sh
OpenClaw Advanced Memory Data Schema & Taxonomy
The system organizes data across three distinct layers to optimize for speed and relevance:
| Tier | Storage | Retention | Purpose |
|---|---|---|---|
| HOT | Redis | < 1 Hour | Real-time buffer for active conversation turns |
| WARM | Qdrant | 7 Days | Semantic vector search for recent context chunks |
| COLD | Qdrant | Forever | LLM-extracted 'gems' including decisions and lessons |
Curated 'gems' are stored with metadata including project tags, categories (e.g., technical, decision), and importance levels.
OpenClaw Advanced Memory Advanced Features
- Local-first architecture ensures 100% data privacy with no external API dependencies.
- Automated LLM-driven curation using qwen2.5 to filter out banter and retain only critical information.
- Systemd and Cron integration for hands-off, background memory management.
- Specialized recall CLI with support for project-based filtering and tier-specific searching.
SKILL.md
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