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. observed · 2026-08-28
Health v2 · maintenance only
89/100
- Activity 99
- Release rhythm 90
- Longevity 67
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: 17
- age_days: 943
- days_rel: 69
- days_push: 9
- n_releases_24m: 24
Adoption not part of the score
1408 stars · 184 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
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
cli-tool · maturity active
developer-tools deployment ci-cd package-manager security container-runtime machine-learning artificial-intelligence self-hosted cli cross-platform windows python mlops model-packaging oci-artifacts modelkit modelpack model-registry supply-chain-security huggingface gguf cncf devops containers macos linux kubernetes docker
10 sources
- readme: https://github.com/kitops-ml/kitops · fetched 2026-08-28 · 863a69884ece
- homepage: https://KitOps.org · fetched 2026-08-29 · fe2ac549ded6
- site_page: https://kitops.org/docs/get-started · fetched 2026-08-29 · f8bfe490c771
- site_page: https://kitops.org/docs/overview · fetched 2026-08-29 · 69a636a92595
- site_page: https://kitops.org/docs/cli/installation · fetched 2026-08-29 · 9542fe215b6a
- site_page: https://kitops.org/docs/cli/cli-reference · fetched 2026-08-29 · f3ca71e7d703
- site_page: https://kitops.org/docs/pykitops · fetched 2026-08-29 · e47969acbba4
- site_page: https://kitops.org/docs/integrations/mlflow · fetched 2026-08-29 · 92ed86db1b5a
- site_page: https://kitops.org/docs/integrations/cicd · fetched 2026-08-29 · e5c3ec7b7014
- site_page: https://kitops.org/docs/why-kitops · fetched 2026-08-29 · 1512345b3749
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| kitops-ml/kitops | main | 89 |
For agents
markdown · JSON · MCP: product_card(name="kitops-ml/kitops")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem