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

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

Agents Used

• agents/optimizer/ - Memory and cache optimization

Tool Use

Invoke via babysitter process: methodologies/ruflo/ruflo-intelligence