Memory Management
Skill Verified ActiveAgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous tasks, sharing knowledge between agents, building knowledge base. Skip when: no learning needed, ephemeral one-off tasks, external data sources available, read-only exploration.
To offer a persistent and semantically searchable memory system for AI agents, enabling learning, knowledge management, and faster pattern retrieval through HNSW vector search.
Features
- Persistent agent memory storage
- HNSW vector search for faster retrieval
- Semantic search capabilities
- Commands for CRUD operations on memory entries
- Memory statistics and export functionality
Use Cases
- Storing successful agent patterns
- Searching for similar solutions in memory
- Semantic lookup of past work
- Learning from previous agent tasks
- Building a knowledge base for agents
Non-Goals
- No learning needed
- Ephemeral one-off tasks
- External data sources
- Read-only exploration
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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