If you've used Claude or GPT for a long coding session, you know the drill. Context fills up, it summarizes, and a few turns later it's suggesting a library you already rejected, or forgetting why you picked postgres over MySQL in the first place.
I built TokenMizer to fix that. It's a local proxy that sits between your app and whatever LLM you're using. Instead of dumping raw conversation history back at the model, it builds a small graph of what actually happened in the session, tasks, decisions with the reasoning behind them, files touched, errors hit.
Using it is a one line change:
from openai import OpenAI
client = OpenAI(api_key="your-key", base_url="http://localhost:8000/v1")
