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