SKILL.md
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 || trueV3 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 interfacePhase 2: Gradual Migration
# Week 4-5: System-by-system migration
- SQLiteBackend → AgentDB (structured data)
- MarkdownBackend → AgentDB (document storage)
- MemoryManager → Unified interface
- DistributedMemorySystem → Cross-agent sharingPhase 3: Advanced Features
# Week 6: Performance optimization
- SONA integration for learning patterns
- Cross-agent memory sharing
- Performance benchmarking (150x validation)
- Backward compatibility layer cleanupPerformance 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 {
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