# claimed-framework/claimed

The goal of CLAIMED is to enable low-code/no-code rapid prototyping style programming to seamlessly CI/CD into production.

Repository: https://github.com/claimed-framework/claimed
Canonical: https://ross.abutalabs.com/products/claimed
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: data-science, machine-learning
Last push: 2026-07-07T18:28:50+00:00

## Health v2 (maintenance only)
Score: 88/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 91, release rhythm 78, longevity 100
- inputs: {"age_days": 3026, "days_push": 57, "days_rel": 64, "gap_med": 38.5, "n_releases_24m": 5}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2305, forks 3912 (observed 2026-08-28T04:06:35.752123+00:00)

## What it is
CLAIMED (C3) is a component compiler that turns Jupyter notebooks, Python scripts, and R scripts into portable containerized AI components. It automatically builds container images, installs dependencies, and generates Kubeflow Pipeline components and Kubernetes job configs for CI/CD-driven production deployment.

## Use cases
- turn a jupyter notebook into a kubeflow pipeline component
- containerize a python script for kubernetes execution
- run data science code in ci/cd pipelines
- parallelize ai workloads across a compute grid
- deploy notebooks to production without rewriting them
- generate kubernetes job configs from scripts

## When to choose
- you prototype in notebooks or scripts and want low-code paths to production
- you need automatic container image building with dependency installation
- you target Kubeflow Pipelines or Kubernetes/OpenShift execution
- you want grid compute parallelization of AI workloads

## When to avoid
- you need a general-purpose workflow engine rather than component compilation
- your code doesn't follow C3's operator script requirements
- you don't use containers or Kubernetes-based execution

## Facets
- artifact type: framework
- maturity: active
- function: ci-cd, deployment, container-runtime, machine-learning, workflow-automation, cli
- domain: data-science, machine-learning
- platform: python, cli, cloud
- tags: jupyter-notebooks, kubeflow, component-compiler, low-code, grid-computing, mlops, devops, containers, automation, kubernetes, docker

## Member repositories
- claimed-framework/claimed (main) score 88

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:35.752123+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 2026-08-30T02:39:42.122841+00:00, confidence not recorded.
  - readme: https://github.com/claimed-framework/claimed (fetched 2026-08-28T04:06:35.752123+00:00, sha cdf24b19c106)
  - registry_pypi: https://pypi.org/pypi/claimed/json (fetched 2026-08-29T10:20:01.167082+00:00, sha 32610c1e3ad6)
- Data as of 2026-08-30T08:39:29.467469+00:00.