* [RAGFlow v0.27.0](https://github.com/infiniflow/ragflow) – RAG engine combining large language models with document understanding for accurate question answering and citation.

* [Netron v9.2.0](https://github.com/lutzroeder/netron) – Viewer for neural network, deep learning, and machine learning models.

* [FiftyOne v1.21.0](https://github.com/voxel51/fiftyone) – FiftyOne is a tool for visualizing and labeling datasets, evaluating computer vision models, and improving data and model quality.

* [DeePMD-kit v3.2.0](https://github.com/deepmodeling/deepmd-kit) – Deep learning package for many-body potential energy representation and molecular dynamics.

* [Cog v0.22.0](https://github.com/replicate/cog) – Tool for packaging machine learning models in production-ready containers.

* [pg\_onnx v1.29.0](https://github.com/kibae/pg_onnx) – ONNX Runtime integration enabling machine learning inference within PostgreSQL databases.

* [VLM-AutoYOLO v1.5.14](https://github.com/Somnusochi/VLM-AutoYOLO) – End-to-end pipeline for VLM-powered image/video auto-annotation, SAM-based mask refinement, manual correction, multi-format export, and one-click YOLO training and validation.

* [Arena v0.15.5](https://github.com/kubeflow/arena) – Command-line interface for running and monitoring machine learning training jobs with GPU cluster resource management.

* [tensai v0.0.10](https://github.com/mattn/tensai) – Tiny machine-learning framework for Go, implementing forward passes, backpropagation, and optimization with optional SIMD and WebGPU acceleration.

* [go-tflite v1.0.7](https://github.com/mattn/go-tflite) – Go binding for TensorFlow Lite, providing model loading, interpreter setup, and tensor input/output access with optional Edge TPU support.

* [Born v0.9.19](https://github.com/born-ml/born) – Production-ready ML framework for Go with zero dependencies, type-safe tensors, automatic differentiation, and single-binary deployment.