V3 Memory Unification
Skill Verified ActiveUnify 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).
To streamline and accelerate AI agent memory management by unifying disparate systems into a high-performance, searchable AgentDB.
Features
- Unify 6+ memory systems
- Implement HNSW indexing for search
- Achieve 150x-12,500x search speed improvements
- Migrate data from SQLite and Markdown
- Integrate with SONA learning patterns
Use Cases
- When needing to accelerate agent memory retrieval
- When managing multiple, incompatible memory backends
- When migrating historical agent data to a unified system
- When integrating learning patterns into agent memory
Non-Goals
- Replacing the LLM provider
- Providing a general-purpose database
- Managing agent task execution directly (focus is memory)
Workflow
- Design AgentDB unification strategy
- Configure HNSW indexing and vector search
- Migrate data from SQLite/Markdown to AgentDB
- Store and query memory entries via unified interface
- Integrate learning patterns into memory
Practices
- Memory management
- Data migration
- Performance optimization
- System integration
Installation
npx skills add ruvnet/rufloRuns the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.
Quality Score
VerifiedTrust Signals
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