MLRun

license

Apache-2.0(OSI)

source

public(mlrun/mlrun is the runnable platform, self-host included)

core-gated

ungated(a 2,580-entry recursive tree carries one root LICENSE and no path matching enterprise, ee, commercial or proprietary)

Apache-2.0 with the platform itself in the open tree. Iguazio sells a managed offering alongside, which the ladder does not read as gating: the 2,580-entry recursive tree carries one governing LICENSE and no enterprise, ee or commercial path, and MLRun's own project site describes the open framework as the product. This is the langchain shape rather than the langgraph one.

• https://github.com/mlrun/mlrun recorded 2026-09-15

  Repository page for mlrun/mlrun: Apache-2.0 license, public and unarchived, described as an open source MLOps platform for building and managing continuous ML applications. Establishes the license and that the source is public.

• https://api.github.com/repos/mlrun/mlrun/git/trees/development?recursive=1 recorded 2026-09-15

  Full untruncated recursive tree of the default branch, 2,580 entries. One root LICENSE. No path matches enterprise, ee, commercial or proprietary. A tree lists paths; it is cited for that and for nothing about the vendor's offerings.

• https://www.mlrun.org/ recorded 2026-09-16

  MLRun's own project site: 'MLRun is an open-source AI orchestration framework for managing ML and generative AI applications across their lifecycle.' The vendor describes the open framework as the product rather than as a reduced edition of a paid one.

The PyPI download API reports 21,372 downloads of mlrun in the trailing 30 days, inside the 10K-100K band for level 2. PyPI is the product's declared distribution channel, so the level can be recomputed from that feed and challenged.

• https://pypistats.org/api/packages/mlrun/recent recorded 2026-09-15

  Trailing-window download counts for the declared PyPI artifact: 21,372 downloads in the trailing month, inside the 10K-100K band.

Admitted on the ML-job-model limb: runs, models and artifacts are first-class types it schedules and tracks. Band 3 because MLRun decides when a function runs and on which cluster, and the Kubernetes scheduler chooses the machine.

• https://github.com/mlrun/mlrun recorded 2026-09-15

  Repository page and README for mlrun/mlrun describing functions executed on Kubernetes, pipelines, the feature store and model tracking.