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** ```typescript 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** ```typescript 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** ```bash # 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** ```bash # 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** ```bash # 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** ```typescript // 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** ```typescript 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** ```sql -- 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** ```typescript // 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** ```typescript // 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 { queriesPerSecond: queries.length / (endTime - startTime) * 1000, avgLatency: (endTime - startTime) / queries.length, improvement: this.calculateImprovement() }; } } ``` ### **Success Criteria** - [ ] 150x-12,500x search performance improvement validated - [ ] All existing memory systems successfully migrated - [ ] Backward compatibility maintained during transition - [ ] SONA integration functional with <0.05ms adaptation - [ ] Cross-agent memory sharing operational - [ ] 50-75% memory usage reduction achieved ## Coordination Points ### **Integration Architect (Agent #10)** - AgentDB integration with agentic-flow@alpha - SONA learning mode configuration - Performance optimization coordination ### **Core Architect (Agent #5)** - Memory service interfaces in DDD structure - Event sourcing integration for memory operations - Domain boundary definitions for memory access ### **Performance Engineer (Agent #14)** - Benchmark validation of 150x-12,500x improvements - Memory usage profiling and optimization - Performance regression testing