vector-memory

# 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`