# Vector Memory

> HNSW vector search for pattern similarity retrieval and knowledge graph maintenance with PageRank scoring, community detection, and 3-tier memory management.

- Skill: `majiayu000/vector-memory` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add majiayu000/vector-memory`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/vector-memory/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/vector-memory

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# Vector Memory

## Overview

High-performance vector search using HNSW (Hierarchical Navigable Small World) graphs for pattern storage and retrieval, combined with a knowledge graph for relational reasoning.

## When to Use

- Retrieving similar patterns from execution history
- 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`