🚀 Key Differentiators

What makes Ruflo different from other agent frameworks? These 10 capabilities work together to create a system that learns from experience, runs efficiently on any hardware, and keeps working even when things go wrong.

	Feature	What It Does	Technical Details
🧠	SONA	Learns which agents perform best for each task type and routes work accordingly	Self-Optimizing Pattern Learning
🔒	EWC++	Preserves learned patterns when training on new ones — no forgetting	Elastic Weight Consolidation prevents catastrophic forgetting
🎯	MoE	Routes tasks through 8 specialized expert networks based on task type	Mixture of 8 Experts with dynamic gating
⚡	Flash Attention	Accelerates attention computation for faster agent responses	Optimized attention via @ruvector/attention
🌐	Hyperbolic Embeddings	Represents hierarchical code relationships in compact vector space	Poincare ball model for hierarchical data
📦	LoRA	Lightweight model adaptation so agents fit in limited memory	Low-Rank Adaptation via @ruvector/sona
🗜️	Int8 Quantization	Converts 32-bit weights to 8-bit with minimal accuracy loss	~4x memory reduction with calibrated integers
🤝	Claims System	Manages task ownership between humans and agents with handoff support	Work ownership with claim/release/handoff protocols
🛡️	Byzantine Consensus	Coordinates agents even when some fail or return bad results	Fault-tolerant, handles up to 1/3 failing agents
🐘	RuVector PostgreSQL	Enterprise-grade vector database with 77+ SQL functions for AI operations	Fast vector search with GNN/attention in SQL