[P] neuropt: LLM-guided hyperparameter optimization that reads your training curves

The problem: You're tuning hyperparameters. Each run takes multiple hours. You have a budget of maybe 15–20 trials before you run out of time or compute. Bayesian optimization picks your next config based entirely on the final validation score, it has no idea your model overfit at epoch 3, or that val loss was flat for 20 epochs before diverging.

What neuropt does differently: After each trial, it sends the full per-epoch train/val curves (and any other information you want) to an LLM and asks it to reason about what's happening, then suggest the next config.

It also auto-detects tunable parameters and layers in PyTorch models, so you don't have to manually define a search space if you don't want to.

Works with: PyTorch, XGBoost, scikit-learn

Results vs. Optuna + random search (same 15-eval budget) shown below:

neuropt (red) vs. Optuna TPE and random search: 15 evaluations each on FashionMNIST (CNN, 14 params) and Covertype (XGBoost, 9 params).

The idea has academic backing (AgentHPO, CPAL 2025), but there wasn't a clean, usable open-source package. This is my attempt at that.

pip install "neuropt[llm]"

[GitHub link] | [Docs link]

Happy to answer questions! I'm curious what architectures/datasets you'd want to see benchmarked next (: