AgentDB Memory Patterns

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AgentDB Memory Patterns provides a sophisticated framework for managing AI agent state through persistent storage and ReasoningBank integration. It enables developers to implement complex memory architectures—including session-based, long-term, and hierarchical memory—that allow agents to learn from interactions and maintain deep context across sessions. With sub-millisecond retrieval speeds and built-in support for multiple reinforcement learning algorithms like Decision Transformers and Q-learning, this skill is essential for building robust, stateful intelligent assistants that evolve through experience.

Características Principales

• Nine integrated learning algorithms including Decision Transformer and Actor-Critic models
• High-performance vector search with HNSW indexing for sub-millisecond retrieval
• Built-in MCP server support for seamless integration with Claude Code workflows
• Automated memory consolidation and context synthesis to optimize storage and relevance
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• Hierarchical memory management covering session, long-term, and semantic storage

Casos de Uso

• Developing high-scale agent architectures requiring low-latency memory retrieval and efficient vector quantization
• Building stateful AI assistants that maintain persistent context and user preferences across many interactions
• Creating self-optimizing agents that utilize reinforcement learning to improve decision-making over time