# kitops-ml/kitops

An open source DevOps tool from the CNCF for packaging and versioning AI/ML models, datasets, code, and configuration into an OCI Artifact.

Repository: https://github.com/kitops-ml/kitops
Canonical: https://ross.abutalabs.com/products/kitops
Homepage: https://KitOps.org
Language: Go
License: Apache-2.0
License Family: permissive
Topics: ai, code, datasets, devops, devops-tools, ml, mlops, models, mlops-tools, gguf, kubernetes, kubernetes-deployment, pytorch, sklearn, tensorflow, model-interpretability, model-serving, opensource, hacktoberfest, platform-engineering
Last push: 2026-08-24T20:25:27+00:00

## Health v2 (maintenance only)
Score: 89/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 99, release rhythm 90, longevity 67
- inputs: {"age_days": 943, "days_push": 9, "days_rel": 69, "gap_med": 17, "n_releases_24m": 24}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1408, forks 184 (observed 2026-08-28T04:04:38.595043+00:00)

## What it is
KitOps is a CNCF open-source DevOps tool (Kit CLI, written in Go) that packages AI/ML models, datasets, code, prompts, agent skills, and MCP configurations into versioned, signed OCI artifacts (ModelKits/ModelPack) stored in existing container registries. It brings container-style packaging, versioning, and supply-chain security to AI projects and integrates with CI/CD pipelines and MLOps tools like MLflow.

## Use cases
- package a model with its datasets and configs into a versioned artifact
- version and share AI projects through our existing container registry
- import models from HuggingFace into a portable package
- reproduce the exact model, prompt, and config combination running in production
- sign and secure our AI supply chain with SBOMs and Cosign
- automate ModelKit creation in CI/CD pipelines from MLflow runs
- self-host models in air-gapped or security-conscious environments

## When to choose
- you need reproducible, versioned packaging of models, datasets, prompts, and agent configs together
- you want to store AI artifacts in an existing OCI registry (Docker Hub, Harbor, GitLab) without lock-in
- security, signing, and compliance matter for your AI supply chain
- you deploy self-hosted models or agentic AI stacks on Kubernetes or air-gapped infrastructure

## When to avoid
- you only need a lightweight model hub without OCI/registry infrastructure
- your workflow is purely notebook-based experimentation with no deployment needs
- you need full model serving or monitoring rather than packaging and distribution

## Facets
- artifact type: cli-tool
- maturity: active
- function: developer-tools, deployment, ci-cd, package-manager, security, container-runtime
- domain: machine-learning, artificial-intelligence, self-hosted
- platform: cli, cross-platform, windows, python
- tags: mlops, model-packaging, oci-artifacts, modelkit, modelpack, model-registry, supply-chain-security, huggingface, gguf, cncf, devops, containers, macos, linux, kubernetes, docker

## Member repositories
- kitops-ml/kitops (main) score 89

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:38.595043+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:38:35.660381+00:00, confidence not recorded.
  - readme: https://github.com/kitops-ml/kitops (fetched 2026-08-28T04:04:38.595043+00:00, sha 863a69884ece)
  - homepage: https://KitOps.org (fetched 2026-08-29T11:52:14.646129+00:00, sha fe2ac549ded6)
  - site_page: https://kitops.org/docs/get-started (fetched 2026-08-29T11:52:14.655748+00:00, sha f8bfe490c771)
  - site_page: https://kitops.org/docs/overview (fetched 2026-08-29T11:52:14.657737+00:00, sha 69a636a92595)
  - site_page: https://kitops.org/docs/cli/installation (fetched 2026-08-29T11:52:14.660062+00:00, sha 9542fe215b6a)
  - site_page: https://kitops.org/docs/cli/cli-reference (fetched 2026-08-29T11:52:14.661905+00:00, sha f3ca71e7d703)
  - site_page: https://kitops.org/docs/pykitops (fetched 2026-08-29T11:52:14.665096+00:00, sha e47969acbba4)
  - site_page: https://kitops.org/docs/integrations/mlflow (fetched 2026-08-29T11:52:14.666832+00:00, sha 92ed86db1b5a)
  - site_page: https://kitops.org/docs/integrations/cicd (fetched 2026-08-29T11:52:14.668468+00:00, sha e5c3ec7b7014)
  - site_page: https://kitops.org/docs/why-kitops (fetched 2026-08-29T11:52:14.670028+00:00, sha 1512345b3749)
- Data as of 2026-08-30T08:39:29.467469+00:00.