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. It integrates AgentDB's persistent storage with ReasoningBank to offer session memory, long-term storage, and pattern learning capabilities. With performance benchmarks up to 12,500x faster than traditional solutions, this skill enables Claude Code to manage complex agent architectures using features like vector-based pattern matching, hierarchical memory organization, and advanced reinforcement learning plugins.

主要功能

• High-performance vector storage with HNSW indexing for sub-100µs searches.
• Seamless MCP integration to connect AgentDB directly with Claude Code.
• Data quantization support for up to 32x reduction in memory footprint.
• Advanced reinforcement learning plugins including Decision Transformers and Q-Learning.
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• Hierarchical memory organization for managing immediate, short-term, and long-term context.

使用场景

• Developing self-improving agents that learn optimal strategies through reinforcement learning patterns.
• Building stateful chat systems that remember user preferences and past interactions across sessions.
• Managing large-scale knowledge bases for agents requiring fast, semantic context retrieval.