Open source
Production feature infrastructure, built in the open.
Keep feature logic in your own Python project, inspect the execution engine, and run ClearFeature in infrastructure you control.
What is open
ClearFeature is published under the Apache-2.0 license by ClearFeature Labs. The engine you would run in production — API, workers, compute core, storage integrations — is the code in the public repository, together with the documentation, the quickstart, and the engineering demo.
license
Apache-2.0
repository
One public codebase
docs
Documentation in the repo
Your Feature Project stays yours
The boundary is deliberate: ClearFeature Core is the engine; your Feature Project is a separate Python package in your repository, under your review process and your CI. Adopting the platform never means handing your feature logic to a vendor format you cannot leave.
Feature Project — your repository
snapshot-ratio/
├── feature_project.yaml
└── snapshot_ratio/
├── features.py
├── registry/features_v1.yaml
└── tests/golden.yaml- features.py — feature UDFs in plain Python
- registry — sources, features, versions, dependencies, groups
- golden.yaml — expected values, run as tests
Feature Runtime
ClearFeature Core loads your Feature Project and executes the same registry-defined DAG for historical materialization, point-in-time training datasets, and live request-time computation.
Your code stays your code — Core is the engine, not the owner.
Inspectable execution
For production ML systems, 'trust us' is not an execution model. Because the compute core is open, your engineers can read exactly how source values become feature values: how dependencies resolve, how availability is enforced in training datasets, how materialization stays idempotent. Every claim on this website is checkable against code.
Build and test features like software
Feature UDFs are plain Python functions. The registry is reviewable YAML. Golden tests pin expected values for every feature and run through the same compute core that production uses — so a feature is tested before it is ever deployed, not observed after it misbehaves.
cases:
- name: ratio
feature: payment_to_income_ratio
deps: {active_monthly_payment: 750.0, monthly_income: 3000.0}
expected: {value: 0.25}A golden case from the public quickstart: expected values as data, executed as tests.
Run it yourself
ClearFeature is designed for self-hosted deployment on components your team can operate: PostgreSQL, Valkey, S3-compatible object storage, and a Kafka-compatible broker. No required data SaaS sits between your source reports and your features.
Community path, commercial help
The open-source platform is self-service: clone it, run the quickstart, deploy it, file issues. Nothing about evaluation requires talking to us. Organizations that want direct help with production adoption — architecture review, deployment planning, migration — can start that conversation separately.
Start with the Quickstart.
Scaffold a Feature Project, define three features across two source reports, and run golden tests — in about five minutes.