
Agent V3 Memory Specialist
Unify fragmented agent memory backends into AgentDB with HNSW search when you run ruflo/agentic-flow and need faster recall across sessions.
Overview
agent-v3-memory-specialist is an agent skill most often used in Build (also Operate, Ship) that unifies six or more ruflo memory backends into AgentDB with HNSW indexing for dramatically faster agent search.
Install
npx skills add https://github.com/ruvnet/ruflo --skill agent-v3-memory-specialistWhat is this skill?
- Targets unification of 6+ legacy memory systems (MemoryManager, SwarmMemory, SQLite/Markdown/Hybrid backends) per SKILL.
- Implements ADR-006 Unified Memory Service and ADR-009 Hybrid Memory Backend
- Advertises 150x–12,500x search improvement via HNSW indexing on AgentDB
- Gradual migration with backward compatibility and pre/post execution hooks via agentic-flow@alpha
- Stores completion patterns with memory store-pattern CLI after milestones
- Unifies 6+ memory systems listed in pre-execution hooks
- Targets 150x–12,500x search improvement via HNSW
- Implements ADR-006 and ADR-009
Adoption & trust: 642 installs on skills.sh; 58.5k GitHub stars; 1/3 security scanners passed (skills.sh audits).
What problem does it solve?
Your swarm agents read and write memory through half a dozen incompatible backends, so recall is slow and patterns do not transfer across sessions.
Who is it for?
Indie builders on ruflo/agentic-flow who need one searchable memory layer before scaling multi-agent tasks.
Skip if: Greenfield apps with a single SQLite log or teams not using agentic-flow memory hooks—skip until you have multiple memory systems to merge.
When should I use this skill?
V3 memory unification, AgentDB rollout, or HNSW search upgrade for ruflo/agentic-flow stacks.
What do I get? / Deliverables
After running the specialist workflow, memory routes through AgentDB with HNSW indexing and migration notes while legacy paths stay compatible until you cut over.
- Migration milestone status from specialist hooks
- Stored memory patterns via memory store-pattern
- Backward-compatible routing toward AgentDB
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Memory unification is foundational agent infrastructure you implement while building or hardening the product stack, not a launch or growth tactic. agent-tooling is the canonical shelf for specialist skills that wire memory, indexing, and AgentDB into multi-agent workflows.
Where it fits
Map six legacy memory classes and plan AgentDB cutover before spawning production specialists.
Benchmark HNSW recall against MarkdownBackend before release.
Tune hybrid backend routing when session volume grows post-launch.
How it compares
Architecture migration skill for in-repo agent memory, not a generic vector-DB SaaS integration.
Common Questions / FAQ
Who is agent-v3-memory-specialist for?
Solo and indie developers operating ruflo V3 swarms who maintain several memory backends and want AgentDB plus HNSW without a big-bang rewrite.
When should I use agent-v3-memory-specialist?
Use during build when wiring agent-tooling, again in ship when validating search latency, and in operate when tuning recall—especially before heavy swarm workloads or after adding HybridBackend.
Is agent-v3-memory-specialist safe to install?
It runs shell hooks and npx agentic-flow@alpha against your repo memory; review the Security Audits panel on this page and test migration on a branch before production cutover.
SKILL.md
READMESKILL.md - Agent V3 Memory Specialist
--- 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 || true --- # V3 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** ```typescript 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** ```typescript class HNSWIndexer { private index: HNSWIndex; constructor(dimensions: number = 1536) { this.index = new HNSWIndex({ dimensions, efConstruction: 200, M: 16, maxElements: 1000000 }); } async index(entry: Mem