agentdb-persistent-memory-patterns

AgentDB Persistent Memory Patterns

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LIBRARY-FIRST PROTOCOL (MANDATORY)

Before writing ANY code, you MUST check:

Step 1: Library Catalog

• Location: .claude/library/catalog.json
• If match >70%: REUSE or ADAPT

Step 2: Patterns Guide

• Location: .claude/docs/inventories/LIBRARY-PATTERNS-GUIDE.md
• If pattern exists: FOLLOW documented approach

Step 3: Existing Projects

• Location: D:\Projects\*
• If found: EXTRACT and adapt

Decision Matrix

Match	Action
Library >90%	REUSE directly
Library 70-90%	ADAPT minimally
Pattern exists	FOLLOW pattern
In project	EXTRACT
No match	BUILD (add to library after)

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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
});