Infrastructure / Core ML frameworks & libraries

Syft / PySyft

PySyft is OpenMined's open source library for privacy-preserving and remote data science, letting data scientists run computations on private data they cannot see or copy via secure 'Datasite' servers. It supports techniques associated with federated learning and differential privacy and offers a NumPy-like remote analysis interface. It is one of the most established open frameworks for collaborative analysis on sensitive data.

Verified live 2026-06-22 via primary sources. Apache-2.0 licensed with full public source code.

Openness

5 high confidence

5.0

license: Apache-2.0(OSI permissive)
source: public

Apache-2.0 licensed with full public source code.

https://github.com/OpenMined/PySyft recorded 2026-06-22
Apache-2.0 license, public source, 9.9k stars
https://pypi.org/project/syft/ recorded 2026-06-22
PyPI metadata lists License Apache-2.0, source OpenMined/PySyft

Adoption

3 medium confidence

3.0

9.9k GitHub stars (capped at 3 per stars-fallback rule); ~10.8k monthly PyPI downloads and 1M+ all-time.

https://github.com/OpenMined/PySyft recorded 2026-06-22
9.9k GitHub stars
https://pepy.tech/project/syft recorded 2026-06-22
1,043,989 total downloads; 10,846 last 30 days

Capability

4 high confidence

4.0

Mature, feature-rich platform for remote data science on private data.

https://pypi.org/project/syft/ recorded 2026-06-22
remote data science, Datasite servers, NumPy-like analysis

Unchanged since 2026-06-22 (last edited, not re-checked)