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

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

• [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]