OpenClaw Memory Master

Problem

AI agents often lose critical context across long collaborations, while stuffing every detail into memory creates noisy retrieval, duplicate records, and hard-to-audit state. Pure vector search can miss entity relationships, and long-term memory increases token cost and privacy risk. This skill targets agent workflows that need reusable memory with layered retention, hybrid retrieval, content governance, and runtime monitoring.

How It Works

OpenClaw Memory Master organizes memory into L0 Hot, L1 Warm, L2 Cold, and L3 Archive layers, then combines vector, graph, and keyword signals through GraphRAG Fusion. Key steps include:

• Smart curation: auto classification, tagging, semantic deduplication, importance scoring, and relation discovery.
• Emotion intelligence: primary and secondary emotion detection, intensity scoring, trend tracking, trigger patterns, and wellness-oriented insights.
• Monitoring and compression: latency, cache hits, memory usage, alerts, reports, health scoring, plus AAAK compression, async batching, predictive preloading, and selective compression.
• Plugin architecture: extension points for Analyzer, Filter, Export, Integration, and Visualization plugins, useful for integrating into existing agent services.

Limits To Watch

The skill is marked as In Development, and several v4.3.0 enhancements may still change. For production use, validate deduplication quality, graph accuracy, information loss after compression, permission boundaries, and long-term memory safety. It is better treated as a memory layer for agents than as a replacement for a business database or identity system.