The Best Knowledge3D (K3D) Alternatives in 2026

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

VeritasGraph combines knowledge graphs and GraphRAG for multi hop reasoning and attributed retrieval over user data.

GPyTorch provides an efficient implementation of Gaussian processes using PyTorch.

This TabGAN repository reviews research on applying GANs to generate tabular data.

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.

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

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

FastDatasets helps create training datasets for large language models.

NALP covers Natural Adversarial Language Processing through a dedicated library.

neuropredict evaluates predictive performance using neuroimaging features.

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.

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

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

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

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

nilearn provides Python tools for machine learning and neuroimaging analysis.

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

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.

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

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