agent-v3-memory-specialist

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);
    }
  }
}

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);
  }
}

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

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

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

// 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'
});

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));
  }
}

-- 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(?), ?);

// 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'
    }
  });
}

// 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()
    };
  }
}