MemoryLayer

What This Skill Does

MemoryLayer is a high-performance semantic memory infrastructure designed for AI agents that require long-term context retention without the prohibitive costs of standard token inflation. By utilizing vector-based similarity search, the skill enables agents to store and retrieve specific, relevant information from vast datasets with a sub-200ms latency. The core value proposition is a 95% reduction in token consumption, as it allows developers to inject only the most pertinent information into the LLM context window rather than loading entire document sets. It is architected for multi-tenancy, ensuring that agent instances remain isolated and secure.

Installation

To integrate MemoryLayer into your OpenClaw environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/khli01/memorylayer. Once installed, you must configure your authentication by setting the environment variables MEMORYLAYER_EMAIL and MEMORYLAYER_PASSWORD, or ideally, use the MEMORYLAYER_API_KEY for production-grade security. If you are integrating this into a standalone Python environment, you may also need to run pip install memorylayer to acquire the core SDK.

Use Cases

MemoryLayer is ideal for long-running autonomous agents. For instance, customer support bots can use it to maintain 'customer personality profiles' that persist across multiple sessions. In coding assistants, it can be used to index documentation snippets or internal style guides, allowing the agent to reference specific API rules without dumping the entire codebase into the context. It is also highly effective for personal task managers that need to track user preferences over time, such as meeting scheduling styles, preferred software tools, or dietary restrictions.

Example Prompts

1. "Store in my semantic memory that I prefer to finish all major project tasks by Thursday at 5 PM local time."
2. "Search my recent memory layer to see what the client mentioned about their preferred color palette for the new landing page."
3. "Summarize my context for the upcoming meeting based on the last three months of project history regarding the Q4 rollout."

Tips & Limitations

To maximize the efficiency of MemoryLayer, prioritize high-quality, concise data when calling the remember function; verbose or irrelevant information increases the noise-to-signal ratio during vector searches. While the semantic search is highly accurate, note that very abstract or ambiguous queries may return broader matches than expected, so use explicit language in your retrieval prompts. Additionally, keep an eye on your usage stats via the memory.stats() command to stay within your monthly quota of 10,000 operations. Remember that the memory is stored on remote servers; avoid uploading highly sensitive PII or raw credentials into the memory layer unless you are utilizing appropriate encryption practices.