agentdb-persistent-memory-patterns

skill_id when-implementing-persistent-memory-use-agentdb-memory name agentdb-persistent-memory-patterns description 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 version 1.0.0 category agentdb subcategory memory-management trigger_pattern when-implementing-persistent-memory agents ["memory-coordinator","swarm-memory-manager","backend-dev"] complexity intermediate estimated_duration 6-8 hours prerequisites ["AgentDB basics","Memory management concepts","Database schema design"] outputs ["Persistent memory architecture","Session and long-term storage","Pattern learning system","Context management APIs"] validation_criteria ["Memory persists across sessions","Fast retrieval (< 50ms)","Pattern recognition working","Context maintained accurately"] evidence_based_techniques ["Self-consistency validation","Chain-of-verification","Multi-agent consensus"] metadata {"author":"claude-flow","created":"2025-10-30T00:00:00.000Z","tags":["agentdb","memory","persistence","context-management"]} 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 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 MemoryManager ({ database : memoryDB, layers : [ 'episodic' , 'semantic' , 'procedural' ] }); // Store memory await memoryManager. store ({ type : 'episodic' , content : 'User preferred dark theme' , context : { userId : '123' , timestamp : Date . now () } }); // Retrieve memory const memories = await memoryManager. retrieve ({ query : 'user preferences' , type : 'episodic' , limit : 10 }); Memory Patterns Session Memory const session = await memoryManager. createSession ( 'user-123' ); await session. store ( 'conversation' , messageHistory); await session. store ( 'preferences' , userPrefs); const context = await session. getContext (); Long-Term Storage await memoryManager. consolidate ({ from : 'working-memory' , to : 'long-term-memory' , strategy : 'importance-based' }); Pattern Learning const patterns = await memoryManager. learnPatterns ({ memory : 'episodic' , algorithm : 'clustering' , minSupport : 0.1 }); Success Metrics Memory persists across agent restarts Retrieval latency < 50ms (p95) Pattern recognition accuracy > 85% Context maintained with 95% accuracy Memory consolidation working Additional Resources Full documentation: SKILL.md Process guide: PROCESS.md AgentDB Memory Docs: https://agentdb.dev/docs/memory