AgentDB Memory Patterns

关于

This skill equips AI agents with advanced memory management capabilities, allowing them to maintain context across sessions and learn from historical interactions. By leveraging AgentDB's persistent storage and ReasoningBank integration, it provides structured patterns for session memory, long-term fact storage, and pattern recognition. It is particularly useful for developers building complex assistants or autonomous agents within Claude Code that require high-speed vector search, hierarchical memory organization, and reinforcement learning-based optimization to improve performance over time.

主要功能

• Pattern learning with 9 built-in reinforcement learning algorithms
• Persistent session and long-term memory management
• 0 GitHub stars
• Memory consolidation and hierarchical organization strategies
• High-performance vector search with HNSW indexing and caching
• Native MCP server integration for seamless Claude Code connectivity

使用场景

• Developing autonomous agents that optimize behavior through interaction patterns
• Implementing context-aware coding assistants for large-scale software projects
• Building stateful chat systems that retain user preferences and history