{"data":{"slug":"kedro-org-kedro","name":"kedro","tagline":"Toolbox for production-ready data science","github_url":"https://github.com/kedro-org/kedro","owner":"kedro-org","repo":"kedro","owner_avatar_url":"https://avatars.githubusercontent.com/u/93382166?v=4","primary_language":"Python","stars":10941,"forks":1066,"topics":["agentic-ai","agentic-workflow","data-pipelines","hacktoberfest","kedro","machine-learning","machine-learning-engineering","mlops","python"],"archived":false,"github_pushed_at":"2026-08-01T19:06:08+00:00","maintenance_label":"Very active","url":"https://www.graphcanon.com/tools/kedro-org-kedro","markdown_url":"https://www.graphcanon.com/tools/kedro-org-kedro.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/kedro-org-kedro","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=kedro-org-kedro","description":"Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.","homepage_url":"https://kedro.org","license":"Apache-2.0","open_issues":163,"watchers":100,"ai_summary":"Kedro is designed to help create reproducible, maintainable, and modular data engineering and data science pipelines using software engineering best practices.","readme_excerpt":"## How do I install Kedro?\n\nTo install Kedro from the Python Package Index (PyPI) run:\n\n```\nuv pip install kedro\n```\n\nIt is also possible to install Kedro using `conda`:\n\n```\nconda install -c conda-forge kedro\n```\n\nOur [Get Started guide](https://docs.kedro.org/en/stable/getting-started/install/) contains full installation instructions, and includes how to set up Python virtual environments.\n\n---\n\n### Installation from source\nTo access the latest Kedro version before its official release, install it from the `main` branch.\n```\nuv pip install git+https://github.com/kedro-org/kedro@main\n```","github_created_at":"2019-04-18T10:29:56+00:00","created_at":"2026-07-11T23:23:22.818269+00:00","updated_at":"2026-08-03T00:02:08.160757+00:00","categories":[{"slug":"data-retrieval","name":"Data & Retrieval","url":"https://www.graphcanon.com/categories/data-retrieval","markdown_url":"https://www.graphcanon.com/categories/data-retrieval.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/data-retrieval"},{"slug":"developer-tools","name":"Developer Tools","url":"https://www.graphcanon.com/categories/developer-tools","markdown_url":"https://www.graphcanon.com/categories/developer-tools.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/developer-tools"}],"tags":[{"slug":"agentic-ai","name":"agentic-ai"},{"slug":"data-pipelines","name":"data-pipelines"},{"slug":"machine-learning","name":"machine-learning"},{"slug":"mlops","name":"mlops"}],"trust":{"provenance":{"is_fork":false,"github_id":182067506,"owner_type":"Organization","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-08-03T00:02:07.376Z","maintenance":{"label":"Very active","score":96,"methodology":"github_public_v1","releases_90d":2,"days_since_push":1,"last_release_at":"2026-06-29T14:53:10Z"},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T23:23:24.376Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-08-03T00:02:07.839Z"},"has_cli":{"value":true,"source":"pyproject.toml:[project.scripts]","observed_at":"2026-08-03T00:02:07.839Z"},"languages":{"value":["python"],"source":"github.language+pyproject.toml","observed_at":"2026-08-03T00:02:07.839Z"},"license_spdx":{"value":"Apache-2.0","source":"github.license","observed_at":"2026-08-03T00:02:07.839Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":{"notes":["Installable via pip or conda, ideal for Python environments."],"min_ram_gb":null,"requires_docker":false},"constraints":{"min_ram_gb":null,"requires_docker":false},"when_to_use":["When your project demands reproducibility and maintainability in pipelines, leveraging Kedro's focus on modular design can greatly enhance these attributes.","For teams seeking to integrate rigorous version control and dependency management practices into their data science workflow, Kedro provides a structured approach."],"when_not_to_use":["Avoid using Kedro if your project requirements favor minimal setup and quick experimentation over robust pipeline architecture; its structured framework might feel restrictive.","If the team is not well-versed in Python or software engineering best practices, Kedro may pose a learning curve that could delay project progress."],"source":"enrich:decision_facts","observed_at":"2026-07-15T10:28:52.581Z"},"constraint_facets":{"min_ram_gb":null,"requires_docker":false},"decision_summary":[{"label":"Requirements","value":"Installable via pip or conda, ideal for Python environments."},{"label":"Adopt for","value":"Kedro is ideal for productionizing data science projects through its emphasis on software engineering discipline within the Python environment."}]}}