The Best Kandle Alternatives in 2026

Catniff provides JavaScript developers with a Torch-like deep learning framework.

Semantic Cache Router presents a distributed semantic cache and stateful routing system.

PyToune provides a simplified framework and utilities for working with PyTorch.

The Datumbox framework is written in Java for developing machine learning and statistical applications.

The linfa project provides a machine learning framework written in Rust.

Hypernets offers a general automated machine learning framework for developing end to end AutoML toolkits in specific domains.

Build machine learning applications with Delta, an open-source framework implemented in Rust.

The fenn Python framework supports machine learning workflows and LLM agents, with trainers, templates, logging, and configuration management.

Flower provides a federated AI framework for machine learning projects.

Train and run models with OneFlow, a scalable deep learning framework for distributed machine learning workloads.

DJL is a Java framework for running deep learning models across different engines.

PaddlePaddle provides a deep learning framework for training models across devices and platforms.

FATE supports federated learning through an industrial grade framework.

The MegEngine framework supports deep learning and automatic differentiation, according to its repository description.

Rockpool supports training spiking neural networks with PyTorch and JAX and deploying them to neuromorphic hardware.

Sinabs supports fast training of spiking neural networks with PyTorch and inference on neuromorphic hardware.

PyTorch Ignite provides a high level library for flexibly training and evaluating neural networks.

Torchvision provides datasets, transforms, and models for computer vision with PyTorch.

DeepEval is an LLM evaluation framework, according to its repository description.

Lucent brings the Lucid library approach to PyTorch, according to its repository description.

Kubeflow Pipelines orchestrates and manages machine learning workflows on Kubeflow.

Sagify describes its project as a way to work with large language models and machine learning more easily.

TPOT uses genetic programming to optimize machine learning pipelines in Python.

ORMB packages machine learning models as OCI artifacts, in a workflow described as Docker for ML models.