---

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: 2025-10-30 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

Copyimport { 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

Copyconst session = await memoryManager.createSession('user-123');
await session.store('conversation', messageHistory);
await session.store('preferences', userPrefs);
const context = await session.getContext();

Long-Term Storage

Copyawait memoryManager.consolidate({
  from: 'working-memory',
  to: 'long-term-memory',
  strategy: 'importance-based'
});

Pattern Learning

Copyconst 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