nordlys 0.2.1

Intelligent LLM Infrastructure with Smart Model Selection

Nordlys

Smart LLM model router. Picks the best model for each prompt based on cost and quality.

Install

uv pip install -e .

Quick Start

from nordlys import Nordlys, ModelConfig
import pandas as pd

# 1. Define your models
models = [
    ModelConfig(id="openai/gpt-4", cost_input=30.0, cost_output=60.0),
    ModelConfig(id="openai/gpt-3.5-turbo", cost_input=0.5, cost_output=1.5),
]

# 2. Training data: questions + accuracy scores per model
df = pd.DataFrame({
    "questions": ["Write code", "What is 2+2?", "Explain quantum physics"],
    "openai/gpt-4": [0.95, 0.99, 0.92],
    "openai/gpt-3.5-turbo": [0.70, 0.99, 0.60],
})

# 3. Fit and route
router = Nordlys(models=models)
router.fit(df)

result = router.route("Write a sorting algorithm", cost_bias=0.5)
print(result.model_id)  # Best model for this prompt

How It Works

1. Clusters similar prompts together
2. Learns which model performs best per cluster
3. Routes new prompts to the optimal model

Cost Bias

# cost_bias=0.0 → Always cheapest
router.route("prompt", cost_bias=0.0)

# cost_bias=1.0 → Always best quality
router.route("prompt", cost_bias=1.0)

# cost_bias=0.5 → Balanced
router.route("prompt", cost_bias=0.5)

Save & Load

router.save("router.json")
loaded = Nordlys.load("router.json")

Links

• Docs
• Issues

Citation

This project is inspired by the Universal Router approach:

@article{universalrouter2025,
  title={Universal Router: Foundation Model Routing for Arbitrary Tasks},
  author={},
  journal={arXiv preprint arXiv:2502.08773},
  year={2025},
  url={https://arxiv.org/pdf/2502.08773}
}

Paper: Universal Router: Foundation Model Routing for Arbitrary Tasks