This skill provides robust memory management patterns for AI agents, leveraging AgentDB's persistent storage and ReasoningBank integration to enable agents to recall conversations, learn from interactions, and maintain complex context across sessions. It features specialized modules for session history, long-term fact storage, and pattern learning with performance gains up to 12,500x over traditional solutions. By integrating this skill, developers can build stateful, intelligent assistants that improve over time through various reinforcement learning algorithms, hierarchical memory organization, and efficient vector search with HNSW indexing.
Key Features
01Automatic context synthesis and memory consolidation for optimized storage.
022 GitHub stars
03HNSW-indexed vector search supporting binary and scalar quantization for 32x memory reduction.
04Persistent session and long-term memory management with sub-millisecond retrieval.
05Advanced pattern learning using algorithms like Decision Transformer and Actor-Critic.
06Seamless MCP server integration for immediate use within Claude Code environments.
