SPL Framework

About

The SPL-FRAMEWORK implements Subsumption Pattern Learning, an innovative hierarchical decision-making architecture inspired by robotics. It significantly reduces the operational costs and latency of LLM agents by intelligently suppressing unnecessary and expensive LLM calls. The system features a three-layer structure—Reactive (validation), Tactical (pattern matching), and Deliberative (full LLM reasoning)—where lower, cheaper layers can preempt and suppress higher, more costly ones, leading to cost reductions of 10-50x and massive speed improvements. This framework also supports multi-agent networks through shared pattern learning, accelerating performance gains and maximizing cost savings.

Key Features

• Three-layer hierarchical architecture for intelligent decision-making.
• 3 GitHub stars
• Achieves 10-50x cost reduction and 25x speed improvement for LLM agents.
• Configurable confidence thresholds and integration of custom domain-specific patterns.
• Enables multi-agent pattern sharing for accelerated learning and network cost savings.
• LLM-agnostic, supporting various models including Claude, GPT, Llama, and Mixtral.

Use Cases

• Email Categorization and routing
• Support Ticket Routing based on category and urgency
• Automated Content Moderation and approval workflows