agentdb-memory-patternslisted

# AgentDB Memory Patterns ## What This Skill Does Provides memory management patterns for AI agents using AgentDB's persistent storage and ReasoningBank integration. Enables agents to remember conversations, learn from interactions, and maintain context across sessions. **Performance**: 150x-12,500x faster than traditional solutions with 100% backward compatibility. ## Prerequisites - Node.js 18+ - AgentDB v1.0.7+ (via agentic-flow or standalone) - Understanding of agent architectures ## Quick Start with CLI ### Initialize AgentDB ```bash # Initialize vector database npx agentdb@latest init ./agents.db # Or with custom dimensions npx agentdb@latest init ./agents.db --dimension 768 # Use preset configurations npx agentdb@latest init ./agents.db --preset large # In-memory database for testing npx agentdb@latest init ./memory.db --in-memory ``` ### Start MCP Server for Claude Code ```bash # Start MCP server (integrates with Claude Code) npx agentdb@latest mcp # Add to Claude Code (one-time setup) claude mcp add agentdb npx agentdb@latest mcp ``` ### Create Learning Plugin ```bash # Interactive plugin wizard npx agentdb@latest create-plugin # Use template directly npx agentdb@latest create-plugin -t decision-transformer -n my-agent # Available templates: # - decision-transformer (sequence modeling RL) # - q-learning (value-based learning) # - sarsa (on-policy TD learning) # - actor-critic (policy gradient) # - curiosity-driven (exploration-based) ``` ## Quick S