Openclaw Advanced Memory

What This Skill Does

OpenClaw Advanced Memory is a robust, three-tiered persistence framework designed to give your OpenClaw AI agent a long-term brain. Unlike default agents that lose context once a session ends, this skill enables a structured hierarchy of memory. The HOT tier utilizes a Redis buffer for real-time conversation capture, ensuring nothing is lost during active sessions. The WARM tier leverages a Qdrant vector database, allowing the agent to perform semantic searches across the last seven days of activity. Finally, the COLD tier uses a local LLM (qwen2.5:7b) to curate nightly insights, summarizing important decisions, project milestones, and critical lessons into a permanent, high-value knowledge base.

Installation

To get started, ensure you have Docker and Ollama installed on your system. Begin by launching the necessary infrastructure via docker compose up -d to spin up your Redis and Qdrant instances. Next, pull the required local models by running ollama pull snowflake-arctic-embed2 for embeddings and ollama pull qwen2.5:7b for the curation engine. Finally, execute bash scripts/install.sh to configure the systemd services and cron jobs that manage the data lifecycle. If you are running components on non-standard ports or remote hosts, verify the connection strings in the configuration scripts before finalizing your setup.

Use Cases

This skill is indispensable for long-term project management and iterative technical development. Use it to track complex architecture decisions across weeks, recall specific implementation details from previous brainstorming sessions, or maintain an ongoing log of project milestones. It serves as an automated personal assistant that remembers the context of your previous work without requiring you to manually summarize findings or feed chat histories back into the model.

Example Prompts

1. "Look through the cold memory storage and summarize the key technical decisions we made for the project architecture last week."
2. "What was the specific solution we implemented when we encountered that deployment error in the 'guardian' project?"
3. "Run a search for all notes tagged as 'decision' regarding the API integration and provide a summary of the current status."

Tips & Limitations

Keep in mind that this system relies on local hardware resources. Since the curation process runs nightly at 2 AM, ensure your machine is powered on or scheduled to wake during that window. Because the system runs entirely offline, you have complete data sovereignty; however, ensure you allocate sufficient disk space for the Qdrant vector store as it accumulates memory over time. To optimize accuracy, avoid overwhelming the system with trivial chat logs; the curation LLM is specifically tuned to extract high-value insights, so keep your primary workspace focused on professional outputs for the best results.