vector-memory

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

509 stars

bya5c-ai

View on GitHub Installation ↓

Best use case

vector-memory is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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

Teams using vector-memory should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/vector-memory/SKILL.md --create-dirs "https://raw.githubusercontent.com/a5c-ai/babysitter/main/library/methodologies/ruflo/skills/vector-memory/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/vector-memory/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How vector-memory Compares

Feature / Agent	vector-memory	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

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

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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`

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