AgentDB Memory Patterns

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AgentDB Memory Patterns provides a sophisticated framework for building intelligent agents that learn and remember across interactions. By integrating AgentDB's high-performance vector storage with ReasoningBank, it enables developers to implement complex memory architectures including session history, long-term factual storage, and reinforcement learning patterns. This skill is essential for creating autonomous workflows and conversational systems that require context persistence, rapid pattern matching, and efficient memory consolidation, all while offering 100% backward compatibility with legacy reasoning systems and sub-millisecond retrieval speeds.

Características Principales

• Advanced vector search with HNSW indexing for sub-100µs pattern retrieval
• Pre-configured reinforcement learning templates including Decision Transformers and Actor-Critic
• 10,769 GitHub stars
• Native MCP protocol support for seamless integration with Claude Code
• Persistent storage patterns for session, long-term, hierarchical, and semantic memory
• Memory optimization through scalar/binary quantization and automated consolidation

Casos de Uso

• Building stateful conversational AI systems that remember user preferences and history
• Developing autonomous agent swarms that require shared knowledge and experience curation
• Implementing pattern-based learning in agents to improve decision-making over time