Memory Management
Skill Verifiziert AktivAgentDB 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.
Funktionen
- 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
Anwendungsfälle
- 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
Nicht-Ziele
- No learning needed
- Ephemeral one-off tasks
- External data sources
- Read-only exploration
Installation
npx skills add ruvnet/rufloFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
VerifiziertVertrauenssignale
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