| 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 lineage", "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 multimodal 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 Video(
mx.BaseFeature,
spec=mx.FeatureSpec(
key="media/video",
id_columns=["video_id"],
fields=["audio", "frames"],
),
):
video_id: str
path: str
duration: float
height: int
width: int
Important distinction: fields in FeatureSpec are logical field specs that describe the data contents for versioning and lineage tracking. Class attributes are metadata columns stored in the metadata store. They serve different purposes and should not overlap.
To add dependencies between features, use the deps parameter with FeatureDep. To specify field-level lineage (for partial data dependencies), 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 code_version on FieldSpec:
fields = [
mx.FieldSpec(key="embedding", code_version="2"),
]
Metadata Stores
To configure a metadata store, create a metaxy.toml file or use programmatic configuration:
config = mx.MetaxyConfig(
stores={"dev": mx.StoreConfig(
type="metaxy.ext.metadata_stores.delta.DeltaMetadataStore",
config={"root_path": "/tmp/metaxy"},
)}
)
with config.use() as cfg:
store = cfg.get_store("dev")
Supported backends: DuckDB, ClickHouse, BigQuery, LanceDB, Delta Lake.
Feature Graph
To visualize and manage the feature dependency graph, use the CLI:
mx graph render
mx push --store dev
CLI
Metaxy provides a CLI (metaxy or mx alias) for managing features, metadata, and migrations:
mx list features --verbose
mx graph render
mx metadata status --all-features
mx mcp
Testing
To test features in isolation, use context managers to avoid polluting the global registry:
import pytest
import metaxy as mx
@pytest.fixture
def metaxy_env(tmp_path):
with mx.FeatureGraph().use():
with mx.MetaxyConfig(
stores={"test": mx.StoreConfig(
type="metaxy.ext.metadata_stores.delta.DeltaMetadataStore",
config={"root_path": str(tmp_path / "delta_test")},
)}
).use() as config:
yield config
Examples
For complete code examples, see:
examples/feature-definitions.md - Feature classes with dependencies and field-level deps
examples/configuration.md - TOML and programmatic configuration
examples/metadata-stores.md - Store operations
examples/testing.md - Test isolation patterns
examples/cli.md - CLI command reference
Documentation
For comprehensive documentation: https://docs.metaxy.io/latest/
Key pages: