AgentDB Persistent Memory Patterns Skill

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

Agent Skills: AgentDB Persistent Memory Patterns

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