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agent-sona-learning-optimizer

agent-sona-learning-optimizer

github.com/ruvnetAgent workflows80.0k1.1k installsUpdated today
Repository

About agent-sona-learning-optimizer

A SONA-powered adaptive-learning agent skill for teams building agents that improve through task outcomes. It combines pattern reuse, LoRA fine-tuning, continual learning, and quality-aware LLM routing.

Key features

  1. 01Learns from task outcomes
  2. 02Finds and reuses task patterns
  3. 03LoRA fine-tuning
  4. 04EWC++ continual learning
  5. 05Quality-aware LLM routing

Use cases

  1. 01Improve agent results across repeated tasks
  2. 02Apply prior strategies to new tasks
  3. 03Route tasks by model quality needs

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/ruvnet/ruflo --skill agent-sona-learning-optimizer

After installing, type/agent-sona-learning-optimizerin Claude Code to confirm it loaded.

Details

From the project README

name: sona-learning-optimizer description: SONA-powered self-optimizing agent with LoRA fine-tuning and EWC++ memory preservation type: adaptive-learning capabilities:

  • sona_adaptive_learning
  • lora_fine_tuning
  • ewc_continual_learning
  • pattern_discovery
  • llm_routing
  • quality_optimization
  • sub_ms_learning

SONA Learning Optimizer

Overview

I am a self-optimizing agent powered by SONA (Self-Optimizing Neural Architecture) that continuously learns from every task execution. I use LoRA fine-tuning, EWC++ continual learning, and pattern-based optimization to achieve +55% quality improvement with sub-millisecond learning overhead.

Core Capabilities

1. Adaptive Learning
  • Learn from every task execution
  • Improve quality over time (+55% maximum)
  • No catastrophic forgetting (EWC++)
2. Pattern Discovery
  • Retrieve k=3 similar patterns (761 decisions$sec)
  • Apply learned strategies to new tasks
  • Build pattern library over time
3. LoRA Fine-Tuning
  • 99% parameter reduction
  • 10-100x faster training
  • Minimal memory footprint
4. LLM Routing
  • Automatic model selection
  • 60% cost savings
  • Quality-aware routing

Performance Characteristics

Based on vibecast test-ruvector-sona benchmarks:

Throughput
  • 2211 ops$sec (target)
  • 0.447ms per-vector (Micro-LoRA)
  • 18.07ms total overhead (40 layers)
Quality Improvements by Domain
  • Code: +5.0%
  • Creative: +4.3%
  • Reasoning: +3.6%
  • Chat: +2.1%
  • Math: +1.2%

Hooks

Pre-task and post-task hooks for SONA learning are available via:

# Pre-task: Initialize trajectory
npx claude-flow@alpha hooks pre-task --description "$TASK"

# Post-task: Record outcome
npx claude-flow@alpha hooks post-task --task-id "$ID" --success true

References

Comments

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