V3 Memory Unification

ruvnet/ruflo/v3/@claude-flow/cli/.claude/skills/v3-memory-unification

作者 ruvnet58e0ae7e14e68aab45a4127d6f42f567bbcfb328无许可证74K 个星标收录于 2026年10月9日更新于 2026年10月9日仓库昨天更新

Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend).

仅含说明AI & Agents

AI 生成的概览

将多个遗留记忆系统整合为带 HNSW 向量检索的统一 AgentDB 后端。

功能: 该技能描述了一套方案，把七个遗留记忆系统（MemoryManager、DistributedMemorySystem、SwarmMemory、AdvancedMemoryManager、SQLiteBackend、MarkdownBackend、HybridBackend）统一到单一 AgentDB 后端，并使用 HNSW 向量索引。它给出 UnifiedMemoryService 接口、用于语义检索的 HNSWIndexer，以及把 SQLite 与 Markdown 数据连同生成的嵌入迁移到 AgentDB 的迁移流程。内容还涉及 SONA 学习模式存储，并列出了检索更快、内存占用更低、跨代理记忆共享等性能目标。
适用场景: 适用于规划或记录将多个分散的代理记忆存储整合为单一向量检索后端的场景。适合围绕 AgentDB 采用、HNSW 索引以及迁移现有 SQLite 或 Markdown 记忆数据的架构工作。
运行要求: 仅为说明性指令，不附带脚本。文中引用 TypeScript 组件（AgentDBAdapter、HNSWIndexer、DataMigrator）、嵌入生成器以及 AgentDB/HNSW 运行时，这些需另行提供。

V3 Memory Unification

What This Skill Does

Consolidates disparate memory systems into unified AgentDB backend with HNSW vector search, achieving 150x-12,500x search performance improvements while maintaining backward compatibility.

Quick Start

bash

# Initialize memory unificationTask("Memory architecture", "Design AgentDB unification strategy", "v3-memory-specialist")
# AgentDB integrationTask("AgentDB setup", "Configure HNSW indexing and vector search", "v3-memory-specialist")
# Data migrationTask("Memory migration", "Migrate SQLite/Markdown to AgentDB", "v3-memory-specialist")

# Initialize memory unificationTask("Memory architecture", "Design AgentDB unification strategy", "v3-memory-specialist")
# AgentDB integrationTask("AgentDB setup", "Configure HNSW indexing and vector search", "v3-memory-specialist")
# Data migrationTask("Memory migration", "Migrate SQLite/Markdown to AgentDB", "v3-memory-specialist")

Systems to Unify

Legacy Systems → AgentDB

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

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

Implementation Architecture

Unified Memory Service

typescript

class UnifiedMemoryService implements IMemoryBackend {  constructor(    private agentdb: AgentDBAdapter,    private indexer: HNSWIndexer,    private migrator: DataMigrator  ) {}
  async store(entry: MemoryEntry): Promise<void> {    await this.agentdb.store(entry);    await this.indexer.index(entry);  }
  async query(query: MemoryQuery): Promise<MemoryEntry[]> {    if (query.semantic) {      return this.indexer.search(query); // 150x-12,500x faster    }    return this.agentdb.query(query);  }}

class UnifiedMemoryService implements IMemoryBackend {  constructor(    private agentdb: AgentDBAdapter,    private indexer: HNSWIndexer,    private migrator: DataMigrator  ) {}
  async store(entry: MemoryEntry): Promise<void> {    await this.agentdb.store(entry);    await this.indexer.index(entry);  }
  async query(query: MemoryQuery): Promise<MemoryEntry[]> {    if (query.semantic) {      return this.indexer.search(query); // 150x-12,500x faster    }    return this.agentdb.query(query);  }}

HNSW Vector Search

typescript

class HNSWIndexer {  constructor(dimensions: number = 1536) {    this.index = new HNSWIndex({      dimensions,      efConstruction: 200,      M: 16,      speedupTarget: '150x-12500x'    });  }
  async search(query: MemoryQuery): Promise<MemoryEntry[]> {    const embedding = await this.embedContent(query.content);    const results = this.index.search(embedding, query.limit || 10);    return this.retrieveEntries(results);  }}

class HNSWIndexer {  constructor(dimensions: number = 1536) {    this.index = new HNSWIndex({      dimensions,      efConstruction: 200,      M: 16,      speedupTarget: '150x-12500x'    });  }
  async search(query: MemoryQuery): Promise<MemoryEntry[]> {    const embedding = await this.embedContent(query.content);    const results = this.index.search(embedding, query.limit || 10);    return this.retrieveEntries(results);  }}

Migration Strategy

Phase 1: Foundation

typescript

// AgentDB adapter setupconst agentdb = new AgentDBAdapter({  dimensions: 1536,  indexType: 'HNSW',  speedupTarget: '150x-12500x'});

// AgentDB adapter setupconst agentdb = new AgentDBAdapter({  dimensions: 1536,  indexType: 'HNSW',  speedupTarget: '150x-12500x'});

Phase 2: Data Migration

typescript

// SQLite → AgentDBconst migrateFromSQLite = async () => {  const entries = await sqlite.getAll();  for (const entry of entries) {    const embedding = await generateEmbedding(entry.content);    await agentdb.store({ ...entry, embedding });  }};
// Markdown → AgentDBconst migrateFromMarkdown = async () => {  const files = await glob('**/*.md');  for (const file of files) {    const content = await fs.readFile(file, 'utf-8');    await agentdb.store({      id: generateId(),      content,      embedding: await generateEmbedding(content),      metadata: { originalFile: file }    });  }};

// SQLite → AgentDBconst migrateFromSQLite = async () => {  const entries = await sqlite.getAll();  for (const entry of entries) {    const embedding = await generateEmbedding(entry.content);    await agentdb.store({ ...entry, embedding });  }};
// Markdown → AgentDBconst migrateFromMarkdown = async () => {  const files = await glob('**/*.md');  for (const file of files) {    const content = await fs.readFile(file, 'utf-8');    await agentdb.store({      id: generateId(),      content,      embedding: await generateEmbedding(content),      metadata: { originalFile: file }    });  }};

SONA Integration

Learning Pattern Storage

typescript

class SONAMemoryIntegration {  async storePattern(pattern: LearningPattern): Promise<void> {    await this.memory.store({      id: pattern.id,      content: pattern.data,      metadata: {        sonaMode: pattern.mode,        reward: pattern.reward,        adaptationTime: pattern.adaptationTime      },      embedding: await this.generateEmbedding(pattern.data)    });  }
  async retrieveSimilarPatterns(query: string): Promise<LearningPattern[]> {    return this.memory.query({      type: 'semantic',      content: query,      filters: { type: 'learning_pattern' }    });  }}

class SONAMemoryIntegration {  async storePattern(pattern: LearningPattern): Promise<void> {    await this.memory.store({      id: pattern.id,      content: pattern.data,      metadata: {        sonaMode: pattern.mode,        reward: pattern.reward,        adaptationTime: pattern.adaptationTime      },      embedding: await this.generateEmbedding(pattern.data)    });  }
  async retrieveSimilarPatterns(query: string): Promise<LearningPattern[]> {    return this.memory.query({      type: 'semantic',      content: query,      filters: { type: 'learning_pattern' }    });  }}

Performance Targets

Search Speed: 150x-12,500x improvement via HNSW
Memory Usage: 50-75% reduction through optimization
Query Latency: <100ms for 1M+ entries
Cross-Agent Sharing: Real-time memory synchronization
SONA Integration: <0.05ms adaptation time

Success Metrics

All 7 legacy memory systems migrated to AgentDB
150x-12,500x search performance validated
50-75% memory usage reduction achieved
Backward compatibility maintained
SONA learning patterns integrated
Cross-agent memory sharing operational

来源与署名

来源：ruvnet/ruflo位于v3/@claude-flow/cli/.claude/skills/v3-memory-unification提交58e0ae7

许可证：无许可证

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V3 Memory Unification

V3 Memory Unification

What This Skill Does

Quick Start

Systems to Unify

Legacy Systems → AgentDB

Implementation Architecture

Unified Memory Service

HNSW Vector Search

Migration Strategy

Phase 1: Foundation

Phase 2: Data Migration

SONA Integration

Learning Pattern Storage

Performance Targets

Success Metrics

来源与署名

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