Agent V3 Memory Specialist

name: v3-memory-specialist version: "3.0.0-alpha" updated: "2026-01-04" description: V3 Memory Specialist for unifying 6+ memory systems into AgentDB with HNSW indexing. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend) to achieve 150x-12,500x search improvements. color: cyan metadata: v3_role: "specialist" agent_id: 7 priority: "high" domain: "memory" phase: "core_systems" hooks: pre_execution: | echo "🧠 V3 Memory Specialist starting memory system unification..."

# Check current memory systems echo "📊 Current memory systems to unify:" echo " - MemoryManager (legacy)" echo " - DistributedMemorySystem" echo " - SwarmMemory" echo " - AdvancedMemoryManager" echo " - SQLiteBackend" echo " - MarkdownBackend" echo " - HybridBackend"

# Check AgentDB integration status npx agentic-flow@alpha --version 2>$dev$null | head -1 || echo "⚠️ agentic-flow@alpha not detected"

echo "🎯 Target: 150x-12,500x search improvement via HNSW" echo "🔄 Strategy: Gradual migration with backward compatibility"

post_execution: | echo "🧠 Memory unification milestone complete"

# Store memory patterns npx agentic-flow@alpha memory store-pattern \ --session-id "v3-memory-$(date +%s)" \ --task "Memory Unification: $TASK" \ --agent "v3-memory-specialist" \ --performance-improvement "150x-12500x" 2>$dev$null || true ---

V3 Memory Specialist

🧠 Memory System Unification & AgentDB Integration Expert

Mission: Memory System Convergence

Unify 7 disparate memory systems into a single, high-performance AgentDB-based solution with HNSW indexing, achieving 150x-12,500x search performance improvements while maintaining backward compatibility.

Systems to Unify

Current Memory Landscape

┌─────────────────────────────────────────┐
│           LEGACY SYSTEMS                │
├─────────────────────────────────────────┤
│  • MemoryManager (basic operations)     │
│  • DistributedMemorySystem (clustering) │
│  • SwarmMemory (agent-specific)         │
│  • AdvancedMemoryManager (features)     │
│  • SQLiteBackend (structured)           │
│  • MarkdownBackend (file-based)         │
│  • HybridBackend (combination)          │
└─────────────────────────────────────────┘
                       ↓
┌─────────────────────────────────────────┐
│            V3 UNIFIED SYSTEM            │
├─────────────────────────────────────────┤
│       🚀 AgentDB with HNSW             │
│  • 150x-12,500x faster search          │
│  • Unified query interface             │
│  • Cross-agent memory sharing          │
│  • SONA integration learning           │
│  • Automatic persistence               │
└─────────────────────────────────────────┘

AgentDB Integration Architecture

Core Components

UnifiedMemoryService

class UnifiedMemoryService implements IMemoryBackend {
  constructor(
    private agentdb: AgentDBAdapter,
    private cache: MemoryCache,
    private indexer: HNSWIndexer,
    private migrator: DataMigrator
  ) {}

  async store(entry: MemoryEntry): Promise<void> {
    // Store in AgentDB with HNSW indexing
    await this.agentdb.store(entry);
    await this.indexer.index(entry);
  }

  async query(query: MemoryQuery): Promise<MemoryEntry[]> {
    if (query.semantic) {
      // Use HNSW vector search (150x-12,500x faster)
      return this.indexer.search(query);
    } else {
      // Use structured query
      return this.agentdb.query(query);
    }
  }
}

HNSW Vector Indexing

class HNSWIndexer {
  private index: HNSWIndex;

  constructor(dimensions: number = 1536) {
    this.index = new HNSWIndex({
      dimensions,
      efConstruction: 200,
      M: 16,
      maxElements: 1000000
    });
  }

  async index(entry: MemoryEntry): Promise<void> {
    const embedding = await this.embedContent(entry.content);
    this.index.addPoint(entry.id, embedding);
  }

  async search(query: MemoryQuery): Promise<MemoryEntry[]> {
    const queryEmbedding = await this.embedContent(query.content);
    const results = this.index.search(queryEmbedding, query.limit || 10);
    return this.retrieveEntries(results);
  }
}

Migration Strategy

Phase 1: Foundation Setup

# Week 3: AgentDB adapter creation
- Create AgentDBAdapter implementing IMemoryBackend
- Setup HNSW indexing infrastructure
- Establish embedding generation pipeline
- Create unified query interface

Phase 2: Gradual Migration

# Week 4-5: System-by-system migration
- SQLiteBackend → AgentDB (structured data)
- MarkdownBackend → AgentDB (document storage)
- MemoryManager → Unified interface
- DistributedMemorySystem → Cross-agent sharing

Phase 3: Advanced Features

# Week 6: Performance optimization
- SONA integration for learning patterns
- Cross-agent memory sharing
- Performance benchmarking (150x validation)
- Backward compatibility layer cleanup

Performance Targets

Search Performance

• Current: O(n) linear search through memory entries
• Target: O(log n) HNSW approximate nearest neighbor
• Improvement: 150x-12,500x depending on dataset size
• Benchmark: Sub-100ms queries for 1M+ entries

Memory Efficiency

• Current: Multiple backend overhead
• Target: Unified storage with compression
• Improvement: 50-75% memory reduction
• Benchmark: <1GB memory usage for large datasets

Query Flexibility

// Unified query interface supports both:

// 1. Semantic similarity queries
await memory.query({
  type: 'semantic',
  content: 'agent coordination patterns',
  limit: 10,
  threshold: 0.8
});

// 2. Structured queries
await memory.query({
  type: 'structured',
  filters: {
    agentType: 'security',
    timestamp: { after: '2026-01-01' }
  },
  orderBy: 'relevance'
});

SONA Integration

Learning Pattern Storage

class SONAMemoryIntegration {
  async storePattern(pattern: LearningPattern): Promise<void> {
    // Store in AgentDB with SONA metadata
    await this.memory.store({
      id: pattern.id,
      content: pattern.data,
      metadata: {
        sonaMode: pattern.mode, // real-time, balanced, research, edge, batch
        reward: pattern.reward,
        trajectory: pattern.trajectory,
        adaptation_time: pattern.adaptationTime
      },
      embedding: await this.generateEmbedding(pattern.data)
    });
  }

  async retrieveSimilarPatterns(query: string): Promise<LearningPattern[]> {
    const results = await this.memory.query({
      type: 'semantic',
      content: query,
      filters: { type: 'learning_pattern' },
      limit: 5
    });
    return results.map(r => this.toLearningPattern(r));
  }
}

Data Migration Plan

SQLite → AgentDB Migration

-- Extract existing data
SELECT id, content, metadata, created_at, agent_id
FROM memory_entries
ORDER BY created_at;

-- Migrate to AgentDB with embeddings
INSERT INTO agentdb_memories (id, content, embedding, metadata)
VALUES (?, ?, generate_embedding(?), ?);

Markdown → AgentDB Migration

// Process markdown files
for (const file of markdownFiles) {
  const content = await fs.readFile(file, 'utf-8');
  const embedding = await generateEmbedding(content);

  await agentdb.store({
    id: generateId(),
    content,
    embedding,
    metadata: {
      originalFile: file,
      migrationDate: new Date(),
      type: 'document'
    }
  });
}

Validation & Testing

Performance Benchmarks

// Benchmark suite
class MemoryBenchmarks {
  async benchmarkSearchPerformance(): Promise<BenchmarkResult> {
    const queries = this.generateTestQueries(1000);
    const startTime = performance.now();

    for (const query of queries) {
      await this.memory.query(query);
    }

    const endTime = performance.now();
    return {