The Best Goptuna Alternatives in 2026

HPOBench is a collection of benchmark problems for hyperparameter optimization.

mljar-supervised provides tabular data AutoML with feature engineering, hyperparameter tuning, explanations, and documentation.

Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.

Adala automates data labeling through an autonomous agent framework for organizing labeled datasets.

Cornac provides a framework for comparing multimodal recommender systems and recommendation algorithms.

The LightGBM framework uses gradient boosting for ranking, classification, and other machine learning tasks.

pytorch tabular provides a unified framework for deep learning models on tabular data.

modAL provides a modular active learning framework for Python built on top of scikit-learn.

Spektral provides a Python framework for creating graph neural networks with Keras and TensorFlow.

19 Questions assigns attributes to objects using machine learning and Bayesian inference, according to its project description.

Giskard provides open source evaluation and testing tools for LLM agents.

oneDAL is a C++ and DPC++ library for accelerated machine learning routines on tabular data, governed by the UXL Foundation.

NALP covers Natural Adversarial Language Processing through a dedicated library.

Omnigraph provides a graph engine for lakehouse data with Git-style workflows.

neuropredict evaluates predictive performance using neuroimaging features.

Spatial AI and procedural knowledge research: a GPU-native reference implementation for concepts proposed to PM-KR, exploring shared knowledge for human and AI clients.

InterpretML fits interpretable models and explains results from black-box machine learning systems.

Open-RAG-Eval evaluates retrieval-augmented generation without requiring golden answers.

An open-source visual programming environment for battle-testing prompts to LLMs.

nilearn provides Python tools for machine learning and neuroimaging analysis.

This repository implements YOLOv3 variants in PyTorch for real time object detection, training, validation, inference, and export.

Captum explains model behavior and supports interpretability analysis for PyTorch systems.

AutoViz automatically creates visualizations for datasets of different sizes from a single line of code.

PyTorch Frame provides a library for applying deep learning models to tabular data.