Fast Unified Memory

What This Skill Does

The Fast Unified Memory skill by broedkrummen revolutionizes how OpenClaw interacts with your stored knowledge. By creating a hybrid storage layer, it bridges the gap between traditional file-based keyword searching and modern, AI-driven semantic understanding. It utilizes the Ollama runtime and the high-efficiency 'nomic-embed-text' model to process data locally, ensuring that your memory retrieval remains private and free of charge. The system performs a dual-lookup: it scans your OpenClaw local files for exact keyword matches while simultaneously generating vector embeddings to find contextually relevant information. This combination produces a ranked, unified result set that significantly improves the quality of context available to the agent, enabling it to recall facts or preferences with high accuracy and low latency.

Installation

To get started, ensure you have Ollama installed on your system. Run curl -fsSL https://ollama.ai/install.sh | sh to install the service. Once installed, download the necessary embedding model by executing ollama pull nomic-embed-text in your terminal. Ensure the Ollama service is active by running ollama serve. Finally, install the OpenClaw skill directly via the command line: clawhub install openclaw/skills/skills/broedkrummen/fast-unified-memory. The configuration will automatically default to local memory paths; verify that your system user has read and write permissions to the defined storage directories (~/.mem0 and ~/.openclaw).

Use Cases

This skill is ideal for power users who maintain extensive personal notes, code snippets, or project documentation within OpenClaw. It is perfect for developers who need to quickly recall specific API configurations or project-wide patterns that aren't easily indexed by simple search strings. Additionally, it serves as a robust personal research assistant, effectively 'reminding' the AI of previous conversations or research findings that would otherwise be lost in the context window.

Example Prompts

1. "Search my memory for the password policy I defined for the staging environment last week."
2. "Add a memory: The client prefers architectural documentation to be formatted in Markdown tables."
3. "List all memories related to the current Q4 budget analysis project to ensure I haven't missed any constraints."

Tips & Limitations

For optimal performance, keep your memories organized and descriptive. Since the system relies on local embedding, longer, more nuanced memory entries will result in better search relevance. A key limitation is the dependency on local hardware; search speeds may vary depending on your CPU/GPU performance. Ensure you have sufficient disk space for the vector database if you intend to store thousands of memories. Finally, if you change your Ollama port from 11434, be sure to update the internal skill constant to prevent connection timeouts.