Kirby (by Kadoa)
The autonomous AI web agent for reliable, structured data extraction at scale.

The open-source Python framework for reproducible, maintainable, and modular data science code.
Kedro is an open-source Python framework designed to help data scientists and engineers create production-ready data pipelines. Originally developed by McKinsey's QuantumBlack and now a part of the LF AI & Data Foundation, Kedro addresses the 'notebook-to-production' gap by enforcing software engineering best practices—such as modularity, separation of concerns, and versioning—within the data science workflow. Its architecture centers around a 'Data Catalog' which abstracts data access, and a 'Pipeline' structure composed of 'Nodes' (pure Python functions). This decoupling allows teams to swap data sources or execution environments without rewriting core logic. In the 2026 market, Kedro remains the gold standard for enterprise-grade data orchestration where governance, auditability, and team collaboration are paramount. It integrates seamlessly with modern stack components like MLflow, Great Expectations, and Airflow, providing a standardized project structure (based on Cookiecutter) that enables rapid onboarding and scale-out capabilities across distributed computing environments like Apache Spark or Dask.
Separates the code logic from data storage details using a YAML-based registry for datasets.
The autonomous AI web agent for reliable, structured data extraction at scale.
The premier community-driven cloud environment for high-performance data science and machine learning.
The open-source gold standard for programmatic workflow orchestration and complex data pipelines.
Efficient bulk data transfer between Apache Hadoop and structured datastores.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
An interactive tool for visualizing the data pipeline, showing the relationship between nodes and datasets.
The ability to save and load the same dataset in multiple formats or using different libraries automatically.
An extensible system allowing users to inject custom behavior into the Kedro lifecycle (e.g., after a node runs).
Namespace-isolated pipelines that can be reused across different projects or within different parts of the same project.
Capability to package specific pipelines as standalone Python packages for distribution.
Templates for deploying Kedro pipelines to Airflow, Kubeflow, or AWS Batch.
Moving messy, non-linear Jupyter notebooks into a robust, repeatable production environment.
Registry Updated:2/7/2026
Run 'kedro run' to execute and validate the full flow.
Operating pipelines that need to transition data from on-premise SQL servers to cloud-native storage like Azure Data Lake.
Providing full data lineage and audit trails for credit risk models.