---
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

```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

- 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