Overview
Research summary
Ray is a distributed runtime and collection of Python AI libraries for scaling workloads beyond one process or machine. Its core abstractions include remote tasks, stateful actors, and shared objects, while libraries cover serving, datasets, training, reinforcement learning, and tuning. Ray Serve supports programmable inference services built on that runtime. Developers can run applications on individual machines, clusters, cloud infrastructure, or Kubernetes and inspect execution through its dashboard. This entry represents the Ray project as a whole, with serving as the catalogue's primary classification.
Repository summary
- Stars
- Unavailable
- Open issues
- Unavailable
- Last push
- Unavailable
- Commits, 90 days
- Unavailable
- Repository activity
- Not scored
- Version
- Unavailable
Recorded catalogue figures. View repository data and provenance →
Classification
Pricing & services
Paid services available
The provider offers paid products or services. Free options may also be available.
Commercial offering checked 2026-10-02.
Licence scope
Implementation
Recorded implementation details and interfaces for Ray.
Implementation details
Python distributed-computing framework with native runtime components
- Languages
- Python
- Repository type
- source
Recorded interfaces and capabilities
Licence scope
Repository
Repository snapshots, release information and recorded maintenance signals.
Repository snapshot
- Stars
- Unavailable
- Open issues
- Unavailable
- Last push
- Unavailable
- Commits, 90 days
- Unavailable
- Repository activity
- Not scored
- Archived
- Not recorded
Repository activity is a snapshot, not a quality or popularity ranking. It combines recent-push freshness (50%), 90-day commits (30%) and issue pressure (20%).
Maintenance and provenance
- Catalogue snapshot
- 2026-10-06
Documentation
Recorded references and research provenance for this entry.
Recorded sources 6
- https://ray.io Project page
- https://github.com/ray-project/ray Linked repository · Research reference
- https://github.com/ray-project/ray/blob/master/README.rst Research reference
- https://api.github.com/repos/ray-project/ray Research reference
- https://www.ray.io/ Commercial offering evidence
- https://www.anyscale.com/pricing Commercial offering evidence
Research metadata
- Research date
- 2026-10-02
- Catalogue snapshot
- 2026-10-06
Community & social 4 channels
Communities
- Discoursediscuss.ray.ioOfficial
- Stack Overflowstackoverflow.com/questions/tagged/rayEndorsed
- Slackwww.ray.io/join-slackOfficialDiscussion
Social & updates
- Xx.com/raydistributedOfficial