AgentDB Memory Patterns

关于

AgentDB Memory Patterns provides a robust framework for building stateful AI agents that can remember conversations, learn from interactions, and maintain context across sessions. By integrating AgentDB's high-performance vector storage with ReasoningBank, it enables sophisticated features like hierarchical memory organization, pattern matching, and automated memory consolidation. This skill is ideal for developers creating intelligent assistants or complex agentic workflows that require microsecond-level search performance and efficient long-term data management.

主要功能

• High-performance vector search with HNSW indexing and quantization
• Persistent session and long-term memory storage
• Hierarchical memory organization and consolidation strategies
• 0 GitHub stars
• Seamless MCP server integration for Claude Code
• Advanced pattern learning using reinforcement learning algorithms

使用场景

• Developing intelligent chat systems with long-term context retention
• Implementing self-improving agents that learn from successful task execution patterns
• Building stateful AI assistants that track user preferences over time