AgentDB Memory Patterns

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Enables AI agents to remember conversations, learn from interactions, and maintain long-term context using AgentDB's persistent storage and ReasoningBank integration. It offers a suite of memory management patterns—including session, hierarchical, and long-term storage—optimized for performance with speeds up to 12,500x faster than traditional solutions. By integrating this skill with Claude Code via MCP, developers can build stateful assistants that consolidate experiences, apply learned patterns, and utilize advanced reinforcement learning algorithms for enhanced decision-making.

Características Principales

• Automated pattern learning with 9 reinforcement learning algorithms
• Session, long-term, and hierarchical memory management patterns
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• Memory consolidation and optimization to manage storage and data quality
• High-performance vector search and pattern retrieval with HNSW indexing
• Context synthesis for generating rich background information for agent prompts

Casos de Uso

• Developing autonomous agents that learn and refine strategies over time
• Building stateful chat systems or intelligent assistants that remember user preferences
• Implementing low-latency persistent vector storage for AI applications