# When Implementing Persistent Memory Use Agentdb Memory > When Implementing Persistent Memory Use Agentdb Memory - Skill: `dnyoussef/when-implementing-persistent-memory-use-agentdb-memory` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add dnyoussef/when-implementing-persistent-memory-use-agentdb-memory` - Raw SKILL.md: https://api.skillmd.com/api/skills/dnyoussef/when-implementing-persistent-memory-use-agentdb-memory/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: DNYoussef (https://skillmd.com/u/dnyoussef) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/dnyoussef/when-implementing-persistent-memory-use-agentdb-memory --- /*============================================================================*/ /* AGENTDB PERSISTENT MEMORY PATTERNS SKILL :: VERILINGUA x VERIX EDITION */ /*============================================================================*/ --- name: AgentDB Persistent Memory Patterns version: 1.0.0 description: | [assert|neutral] AgentDB Persistent Memory Patterns skill for agentdb workflows [ground:given] [conf:0.95] [state:confirmed] category: agentdb tags: - general author: system cognitive_frame: primary: evidential goal_analysis: first_order: "Execute AgentDB Persistent Memory Patterns workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic agentdb processes" --- /*----------------------------------------------------------------------------*/ /* S0 META-IDENTITY */ /*----------------------------------------------------------------------------*/ [define|neutral] SKILL := { name: "AgentDB Persistent Memory Patterns", category: "agentdb", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S1 COGNITIVE FRAME */ /*----------------------------------------------------------------------------*/ [define|neutral] COGNITIVE_FRAME := { frame: "Evidential", source: "Turkish", force: "How do you know?" } [ground:cognitive-science] [conf:0.92] [state:confirmed] ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. /*----------------------------------------------------------------------------*/ /* S2 TRIGGER CONDITIONS */ /*----------------------------------------------------------------------------*/ [define|neutral] TRIGGER_POSITIVE := { keywords: ["AgentDB Persistent Memory Patterns", "agentdb", "workflow"], context: "user needs AgentDB Persistent Memory Patterns capability" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S3 CORE CONTENT */ /*----------------------------------------------------------------------------*/ # AgentDB Persistent Memory Patterns ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. ## 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 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 ```typescript 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 ```typescript await memoryManager.consolidate({ from: 'working-memory', to: 'long-term-memory', strategy: 'importance-based' }); ``` ### Pattern Learning ```typescript const patterns = await memoryManager.learnPatterns({ memory: 'episodic', algorithm: 'clustering', minSupport: 0.1 }); ``` ## Success Metrics - [assert|neutral] Memory persists across agent restarts [ground:acceptance-criteria] [conf:0.90] [state:provisional] - [assert|neutral] Retrieval latency < 50ms (p95) [ground:acceptance-criteria] [conf:0.90] [state:provisional] - [assert|neutral] Pattern recognition accuracy > 85% [ground:acceptance-criteria] [conf:0.90] [state:provisional] - [assert|neutral] Context maintained with 95% accuracy [ground:acceptance-criteria] [conf:0.90] [state:provisional] - [assert|neutral] Memory consolidation working [ground:acceptance-criteria] [conf:0.90] [state:provisional] ## MCP Requirements This skill operates using AgentDB's npm package and API only. No additional MCP servers required. All AgentDB memory operations are performed through: - npm CLI: `npx agentdb@latest` - TypeScript/JavaScript API: `import { AgentDB, MemoryManager } from 'agentdb-memory'` ## Additional Resources - Full documentation: SKILL.md - Process guide: PROCESS.md - AgentDB Memory Docs: https://agentdb.dev/docs/memory ## Core Principles AgentDB Persistent Memory Patterns operates on 3 fundamental principles: ### Principle 1: Memory Layering - Separate Short-Term, Working, and Long-Term Storage Memory systems mirror human cognition by organizing information across distinct temporal layers. Short-term memory handles immediate context (current conversation), working memory maintains active task state, and long-term memory co /*----------------------------------------------------------------------------*/ /* S4 SUCCESS CRITERIA */ /*----------------------------------------------------------------------------*/ [define|neutral] SUCCESS_CRITERIA := { primary: "Skill execution completes successfully", quality: "Output meets quality thresholds", verification: "Results validated against requirements" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S5 MCP INTEGRATION */ /*----------------------------------------------------------------------------*/ [define|neutral] MCP_INTEGRATION := { memory_mcp: "Store execution results and patterns", tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"] } [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S6 MEMORY NAMESPACE */ /*----------------------------------------------------------------------------*/ [define|neutral] MEMORY_NAMESPACE := { pattern: "skills/agentdb/AgentDB Persistent Memory Patterns/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed] [define|neutral] MEMORY_TAGGING := { WHO: "AgentDB Persistent Memory Patterns-{session_id}", WHEN: "ISO8601_timestamp", PROJECT: "{project_name}", WHY: "skill-execution" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S7 SKILL COMPLETION VERIFICATION */ /*----------------------------------------------------------------------------*/ [direct|emphatic] COMPLETION_CHECKLIST := { agent_spawning: "Spawn agents via Task()", registry_validation: "Use registry agents only", todowrite_called: "Track progress with TodoWrite", work_delegation: "Delegate to specialized agents" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S8 ABSOLUTE RULES */ /*----------------------------------------------------------------------------*/ [direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* PROMISE */ /*----------------------------------------------------------------------------*/ [commit|confident] AGENTDB PERSISTENT MEMORY PATTERNS_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]