metaxy

name	metaxy
description	This skill should be used when the user asks to "define a feature", "create a BaseFeature class", "track feature versions", "set up metadata store", "field-level dependencies", "FieldSpec", "FeatureDep", "run metaxy CLI", "metaxy migrations", or needs guidance on metaxy feature definitions, versioning, metadata stores, CLI commands, or testing patterns.

Metaxy

Metaxy is a metadata layer for multi-modal Data and ML pipelines that manages and tracks feature versions, dependencies, and data lineage across complex computational graphs.

Core Concepts

Feature Definitions

To define a feature, create a class inheriting from mx.BaseFeature with a FeatureSpec metaclass argument:

import metaxy as mx


class MyFeature(
    mx.BaseFeature,
    spec=mx.FeatureSpec(
        key="my/feature",
        id_columns=["sample_id"],
        fields=["embedding", "score"],
    ),
):
    sample_id: str
    embedding: list[float]
    score: float

To add dependencies between features, use the deps parameter with FeatureDep. To specify field-level dependencies (for partial data dependencies processing), use FieldSpec with FieldDep or FieldsMapping.

Data Versioning

Metaxy automatically tracks sample versions and propagates changes through the dependency graph. To trigger recomputation when code changes, set on :