vector-memory
SkillSearchLets your agent store and search its notes by meaning, so it can recall similar past patterns and knowledge.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the vector-memory skill
About this capability
HNSW vector search for pattern similarity retrieval and knowledge graph maintenance with PageRank scoring, community detection, and 3-tier memory management.
What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/methodologies/ruflo/skills/vector-memory/SKILL.md and read by ahel’s review.
- Building and querying knowledge graphs for project context
- Managing cross-session memory across project/local/user scopes
- Fast similarity search for routing decisions
HNSW Performance
- Search latency: ~61 microseconds
- Query throughput: ~16,400 QPS
- Configurable embedding dimensions (default: 128)
Knowledge Graph
- PageRank: Importance scoring for knowledge nodes
- Community Detection: Cluster related patterns
- LRU Cache: Fast access to frequently used patterns
- SQLite Backing: Persistent cross-session storage
3-Tier Memory
| Scope | Persistence | Content |
|---|---|---|
| Project | Codebase-level | Patterns, architecture decisions, dependencies |
| Local | Session-level | Context, adaptations, temporary patterns |
| User | Cross-project | Preferences, learned behaviors, global patterns |
Agents Used
agents/optimizer/- Memory and cache optimization
Tool Use
Invoke via babysitter process: methodologies/ruflo/ruflo-intelligence
Signals
- GitHub stars
- 2k
- Forks
- 106
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
vector-memory- Source
- github.com/a5c-ai/babysitter