vector-memory

name	vector-memory
description	HNSW vector search for pattern similarity retrieval and knowledge graph maintenance with PageRank scoring, community detection, and 3-tier memory management.
allowed-tools	Read, Write, Edit, Bash, Grep, Glob, WebFetch, WebSearch, Agent, AskUserQuestion
graph	{"domains":["domain:software-engineering"],"skillAreas":["skill-area:agentic-loops","skill-area:orchestration-loop"],"workflows":["workflow:feature-development"],"topics":["topic:developer-experience"],"roles":["role:tech-lead","role:backend-engineer"]}

• 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