Implements persistent, high-performance memory and learning patterns for AI agents using AgentDB vector storage.
AgentDB Memory Patterns provides a comprehensive framework for managing stateful AI agents within the Claude Code environment. By leveraging AgentDB's high-performance vector storage and ReasoningBank integration, this skill enables agents to maintain long-term memory, learn from past interactions via reinforcement learning plugins, and consolidate context across multiple sessions. It is specifically designed for developers building sophisticated chat systems, autonomous agents, or any agentic workflow requiring ultra-fast retrieval and hierarchical memory organization.
주요 기능
010 GitHub stars
02Hierarchical memory management including session, short-term, long-term, and semantic tiers.
03Scalar and binary quantization for up to 32x reduction in memory footprint.
04Nine built-in reinforcement learning algorithms including Decision Transformer and Q-Learning.
05Advanced memory consolidation and pruning to maintain efficiency and relevance.
06High-performance vector search with HNSW indexing and sub-100µs retrieval times.
사용 사례
01Managing massive context windows for complex RAG systems through intelligent context synthesis.
02Building stateful chatbots that maintain user preferences and history across multiple sessions.
03Developing self-improving agents that learn optimal strategies from successful task completions.
