v3-memory-unification
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).
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- ruvnet/agentic-flow
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- 13 février 2026 à 16:43
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SKILL.md
Instructions source · Aperçu en lecture seule- name
- V3 Memory Unification
- description
- 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).
# 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 unification
Task("Memory architecture", "Design AgentDB unification strategy", "v3-memory-specialist")
# AgentDB integration
Task("AgentDB setup", "Configure HNSW indexing and vector search", "v3-memory-specialist")
# Data migration
Task("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 │
└─────────────────────────────────────────┘
```
## 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);
}
}
```
### 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);
}
}
```
## Migration Strategy
### Phase 1: Foundation
```typescript
// AgentDB adapter setup
const agentdb = new AgentDBAdapter({
dimensions: 1536,
indexType: "HNSW",
speedupTarget: "150x-12500x",
});
```
### Phase 2: Data Migration
```typescript
// SQLite → AgentDB
const migrateFromSQLite = async () => {
const entries = await sqlite.getAll();
for (const entry of entries) {
const embedding = await generateEmbedding(entry.content);
await agentdb.store({ ...entry, embedding });
}
};
// Markdown → AgentDB
const 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" },
});
}
}
```
## 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
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