AgentDB Memory Patterns

关于

AgentDB Memory Patterns provides a sophisticated framework for managing AI agent state, session history, and long-term knowledge retrieval within Claude Code. By integrating AgentDB's high-performance vector storage and ReasoningBank, this skill enables Claude to implement hierarchical memory structures, pattern recognition, and reinforcement learning algorithms like Decision Transformers. It is an essential toolkit for developers building complex chat systems, intelligent assistants, or autonomous agents that need to maintain deep context and improve performance across multiple interactions through memory consolidation and semantic search.

主要功能

• High-performance persistent vector memory with <100µs search times
• Hierarchical memory management including session, short-term, and long-term storage
• Built-in learning algorithms including Decision Transformer, Q-Learning, and Actor-Critic
• MCP server integration for seamless interaction with Claude Code and other AI tools
• Advanced memory consolidation and quantization for 4-32x memory efficiency
• 114 GitHub stars

使用场景

• Implementing pattern-based learning to improve agent decision-making logic over time
• Building autonomous agents that remember user preferences and past interactions across sessions
• Creating stateful chat systems with efficient context window management via vector retrieval