Vector Memory

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

a5c-ai Updated 1.7k repo stars

File contents

  • 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

a5c-ai/babysitter/tree/main/library/methodologies/ruflo/skills/vector-memory commit e7ec14ef16

Frequently asked questions

npx skillmds@latest add a5c-ai/vector-memory