agentdb-persistent-memory-patterns

# AgentDB Persistent Memory Patterns ## Overview Implement persistent memory patterns for AI agents using AgentDB - session memory, long-term storage, pattern learning, and context management for stateful agents, chat systems, and intelligent assistants. ## SOP Framework: 5-Phase Memory Implementation ### Phase 1: Design Memory Architecture (1-2 hours) - Define memory schemas (episodic, semantic, procedural) - Plan storage layers (short-term, working, long-term) - Design retrieval mechanisms - Configure persistence strategies ### Phase 2: Implement Storage Layer (2-3 hours) - Create memory stores in AgentDB - Implement session management - Build long-term memory persistence - Setup memory indexing ### Phase 3: Test Memory Operations (1-2 hours) - Validate store/retrieve operations - Test memory consolidation - Verify pattern recognition - Benchmark performance ### Phase 4: Optimize Performance (1-2 hours) - Implement caching layers - Optimize retrieval queries - Add memory compression - Performance tuning ### Phase 5: Document Patterns (1 hour) - Create usage documentation - Document memory patterns - Write integration examples - Generate API documentation ## Quick Start ```typescript import { AgentDB, MemoryManager } from 'agentdb-memory'; // Initialize memory system const memoryDB = new AgentDB({ name: 'agent-memory', dimensions: 768, memory: { sessionTTL: 3600, consolidationInterval: 300, maxSessionSize: 1000 } }); const memoryManager = new ...