In this tutorial, we explore NadirClaw as an intelligent routing layer that classifies prompts into simple and complex tiers before sending them to the most suitable model. We start by installing the required packages, setting up an optional Gemini API key, and testing the local classifier through the NadirClaw CLI without making any live LLM calls. We then inspect the centroid vectors that power the routing decision, embed our own prompts, visualize how similarity scores separate simple and complex tasks, and experiment with confidence thresholds. After understanding the local routing logic, we move into live routing by launching the NadirClaw proxy server, sending OpenAI-compatible requests through it, comparing routed model behavior, and estimating cost savings against an always-Pro baseline.
import subprocess, sys
def _pip(*pkgs):
subprocess.run([sys.executable, "-m", "pip", "install", "-q", *pkgs], check=True)
_pip("nadirclaw", "openai", "sentence-transformers", "matplotlib",
