Implements high-performance persistent memory and context management for AI agents using AgentDB.
AgentDB Memory Patterns provides a comprehensive framework for managing AI agent state through persistent storage and ReasoningBank integration. It enables developers to build intelligent assistants that remember conversations, learn from interactions via reinforcement learning algorithms, and maintain multi-tier context—including immediate, short-term, and long-term memory—across sessions. With features like HNSW indexing and quantization, it offers extreme performance benefits, up to 12,500x faster than traditional solutions, while remaining fully compatible with Claude Code via MCP integration.
주요 기능
01Multi-tier memory management (Session, Long-Term, and Hierarchical)
02Advanced pattern learning with 9 built-in reinforcement learning algorithms
03Automated memory consolidation and importance-based pruning
04High-performance vector search with HNSW indexing and scalar/binary quantization
05Seamless MCP integration for stateful Claude Code workflows
