GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026
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Decision brief
OME is a Kubernetes operator tailored for LLM serving and management, focusing on tasks such as GPU scheduling and model lifecycle control, compatible with SGLang, vLLM, TensorRT-LLM, and Triton.
Good fit when
- If you need robust GPU scheduling alongside LLM serving
- For enterprises that wish to streamline the deployment of models like Qwen on Kubernetes
Avoid when
- In environments where a language other than Go for the operator's implementation is preferred
- When your infrastructure does not support or utilize Kubernetes for orchestration purposes
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of Sep 20, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 20, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
go get github.com/ome-projects/omeSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
OME is designed to handle LLM serving, GPU scheduling, and model lifecycle management through Kubernetes operations.
Capability facts
- Languages
- go
Source: github.language · Sep 20, 2026
Categories
Tags
README
Installation Requires Kubernetes 1.28 or newer Install OME CRDs helm upgrade install ome crd oci://ghcr.io/moirai internal/charts/ome crd namespace ome create namespace Install OME resources helm upgrade install ome oci://ghcr.io/moirai internal/charts/ome resources namespace ome...
For agents
This page has a .md twin and JSON over the API.