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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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/ruvnet/ruflo --skill v3-memory-unification- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads v3 memory unification automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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
# 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
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
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
// AgentDB adapter setup
const agentdb = new AgentDBAdapter({
dimensions: 1536,
indexType: 'HNSW',
speedupTarget: '150x-12500x'
});
Phase 2: Data Migration
// 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
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
FAQ
What does the v3 memory unification skill do?
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).
How do I install v3 memory unification?
Run npx skills add https://github.com/ruvnet/ruflo --skill v3-memory-unification in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/v3-memory-unification/ (personal) or .claude/skills/v3-memory-unification/ (project).
Which AI agents can use v3 memory unification?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is v3 memory unification safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains v3 memory unification?
It is published by ruvnet in ruvnet/ruflo. It has 1,123 recorded installs.
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