ONNX
LF AI & Data (Linux Foundation)ONNX (Open Neural Network Exchange) is an open standard format and specification for representing machine learning models, defining an extensible computation graph model plus built-in operators and standard data types. It enables interoperability so models can move between frameworks, tools, runtimes and compilers. It is governed under the Linux Foundation as part of LF AI & Data.
Openness
5 high confidence- license
- Apache-2.0(OSI)
- source
- public
- core features withheld
- no
The LICENSE file contains the standard, unmodified Apache 2.0 text. The repository is public and the README describes the whole library with no paid, enterprise or hosted tier beside it, so the source is public and the core is ungated.
- https://cdn.jsdelivr.net/gh/onnx/onnx@main/LICENSE recorded 2026-09-29
LICENSE file is the Apache License text: "TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION" under "Version 2.0, January 2004".
- https://cdn.jsdelivr.net/gh/onnx/onnx@main/README.md recorded 2026-09-29
README states "ONNX provides an open source format for AI models, both deep learning and traditional ML" and closes with "Apache License v2.0". Searched the body for paid, enterprise, commercial, hosted and license key: none.
- https://cdn.jsdelivr.net/gh/onnx/onnx@main/README.md recorded 2026-09-29
README invites outside participation: "We encourage you to open" issues, and describes ONNX as "a community project"; the repository is public. Searched the body for paid, enterprise, commercial and hosted tiers: none.
- https://github.com/onnx/onnx/blob/main/LICENSE recorded 2026-08-13
LICENSE file is the verbatim Apache License Version 2.0 text
Adoption
5 high confidenceAdoption is measured by PyPI downloads of the onnx package. ONNX is the cross-framework model-exchange standard; onnxruntime is a separate product with its own package, and its downloads are not counted here.
- https://pepy.tech/projects/onnx recorded 2026-08-13
19,619,971 downloads in the last 30 days for onnx
Capability
5 high confidenceONNX defines the extensible computation graph, the built-in operators and the standard data types that frameworks, tools, runtimes and compilers use to exchange models, with a focus on inference.
- https://cdn.jsdelivr.net/gh/onnx/onnx@main/README.md recorded 2026-09-29
README: ONNX "defines an extensible computation graph model, as well as definitions of built-in operators and standard" data types, with a focus on "the capabilities needed for inferencing (scoring)".
- https://github.com/onnx/onnx recorded 2026-08-13
README still describes an open format with an extensible computation graph and built-in operators as a cross-framework interchange standard
- https://onnx.ai/ recorded 2026-09-29
Project homepage calls ONNX "The open standard for machine learning interoperability" and says ONNX defines "a common set of operators - the building blocks of machine learning and deep learning models - and a common file format".
Verified 2026-09-24