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

Automated feature engineering for temporal and relational datasets through Deep Feature Synthesis.
Featuretools is an open-source Python library designed to automate the process of feature engineering, which is traditionally the most time-consuming part of the machine learning pipeline. It utilizes a framework called Deep Feature Synthesis (DFS) to automatically construct features from multiple related data tables. By defining 'EntitySets' and 'Relationships' between them, Featuretools can aggregate and transform data across relational structures, capturing complex temporal patterns that manual engineering often misses. As we head into 2026, Featuretools remains a foundational component of the Alteryx AI ecosystem, serving as a critical bridge between raw database architectures and high-performance ML models. The library is highly optimized for scalability, offering native integration with Dask and Spark for distributed computing environments. Its technical architecture focuses on preventing data leakage through rigorous 'cutoff time' logic, ensuring that features are calculated only using data available at a specific point in history. This makes it particularly powerful for time-series forecasting and predictive maintenance where temporal accuracy is paramount.
An algorithm that stacks primitives (mean, max, count, etc.) to create complex features across multiple related tables.
The autonomous AI web agent for reliable, structured data extraction at scale.
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Uses cutoff times to calculate features relative to a specific timestamp for every row in the output.
Allows users to define domain-specific mathematical transformations or aggregations.
Uses a semantic typing system to automatically identify logical data types (Categorical, Ordinal, LatLong).
Native scaling via Dask and Spark for datasets that exceed single-machine memory.
Maintains a lineage of how every feature was mathematically derived.
Works with the Compose library to automatically define prediction problems and generate labels.
Manually creating behavioral features from transaction history is slow and error-prone.
Registry Updated:2/7/2026
Detecting anomalies requires identifying complex patterns across merchants, cards, and locations.
Sales are influenced by historical trends and external factors across multiple store locations.