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Comparison

OpenRLHF vs ray-llm

Verdict

Pick OpenRLHF if openRLHF is a reinforcement learning framework designed for efficient distributed scheduling and large-scale model training up to 70B+ parameters, leveraging Ray for resource management and integrating vLLM, DeepSpeed, H; pick ray-llm if archived LLM deployment tool integrated into Ray; now focus on built-in APIs (`ray.serve.llm` & `ray.data.llm`).

Markdown twin · OpenRLHF alternatives · ray-llm alternatives

GraphCanon updated 1w

OpenRLHF logo

OpenRLHF

OpenRLHF/OpenRLHF

★ 9.9kpushed Jul 14, 2026
vs
ray-llm logo

ray-llm

ray-project/ray-llm

★ 1.3kpushed Mar 13, 2025

Trust & integrity

SignalOpenRLHFray-llm
Maintenance
Active (24d since push)
As of 1w · github_public_v1
Archived (507d since push)
As of 2w · github_public_v1
Provenance
Not a fork · Organization account
As of 1w · github_public_v1
Not a fork · Organization account
As of 2w · github_public_v1
OSV dependency advisories
Published findings
As of 1mo · osv@v1
No lockfile (source not queried)
As of 1mo · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1
OpenRLHF trust reportray-llm trust reportTrust methodology

Tagline

OpenRLHF
Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray
ray-llm
Archived repository; LLM serving APIs integrated into the Ray project

Stars

OpenRLHF
9.9k
ray-llm
1.3k

Forks

OpenRLHF
996
ray-llm
90

Open issues

OpenRLHF
367
ray-llm
0

Language

OpenRLHF
Python
ray-llm
-

Adopt for

OpenRLHF
OpenRLHF is a reinforcement learning framework designed for efficient distributed scheduling and large-scale model training up to 70B+ parameters, leveraging Ray for resource management and integrating vLLM, DeepSpeed, H
ray-llm
Archived LLM deployment tool integrated into Ray; now focus on built-in APIs (`ray.serve.llm` & `ray.data.llm`).

Persona

OpenRLHF
-
ray-llm
-

Runtime

OpenRLHF
-
ray-llm
-

License

OpenRLHF
Apache-2.0
ray-llm
-

Last pushed

OpenRLHF
Jul 14, 2026
ray-llm
Mar 13, 2025

Categories

OpenRLHF
Inference & Serving, Model Training
ray-llm
Inference & Serving, Model Training

Trust and health

Maintenance

OpenRLHF
Active (82%)
ray-llm
Archived (8%)

Days since push

OpenRLHF
24d
ray-llm
507d

Archived on GitHub

OpenRLHF
No
ray-llm
Yes

Open issues (now)

OpenRLHF
367
ray-llm
0

Stars delta

OpenRLHF
+132 (30d)
ray-llm
Unknown

Open issues delta

OpenRLHF
+25 (30d)
ray-llm
Unknown

OSV dependency advisories

OpenRLHF
Published findings
ray-llm
No lockfile (source not queried)

Full report

OpenRLHF
Trust report
ray-llm
Trust report

Choose OpenRLHF if…

  • Pricing: OpenRLHF is primarily available free of cost under the Apache-2.0 license, but users might incur costs based on their infrastructure usage for distributed training setups (e.g., cloud GPU instances)..
  • Tags unique to OpenRLHF: large language models, proximal-policy-optimization, raylib, reinforcement-learning.
  • When you require high-throughput sample generation with minimal idle time on limited hardware due to its hybrid engine scheduling that enables sharing of GPU resources between models and vLLM engines.

When NOT to use OpenRLHF

  • If your project does not require distributed training or large-scale model parameters (above 70B), as OpenRLHF is specifically optimized for scenarios where efficient distribution across multiple GPUs
  • When your environment cannot support Ray or vLLM, as these are crucial components of the framework for scheduling and high-performance sample generation, respectively.
  • If minimal Docker setup and hardware requirements with GPU constraints are not acceptable in your scenario.
GitHubDetails

Choose ray-llm if…

  • Tags unique to ray-llm: llm-serving, ray.
  • For deploying LLMs with new Ray-integrated APIs, ensuring direct support and updates from the Ray team.
  • Leaner open-issue backlog (0).

When NOT to use ray-llm

  • If seeking a standalone solution distinct from Ray’s main project, preferring specialized tools.
  • For needs requiring active maintenance and development in this specific repository; consider alternative up-to-date projects.
GitHubDetails

Explore

OpenRLHF trust report →ray-llm trust report →Inference & Serving category →Model Training category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: OpenRLHF 9.9k · ray-llm 1.3k (synced Aug 7, 2026).

Common questions

What is the difference between OpenRLHF and ray-llm?
OpenRLHF: Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray. ray-llm: Archived repository; LLM serving APIs integrated into the Ray project. See the comparison table for live GitHub stats and shared categories.
When should I choose OpenRLHF over ray-llm?
Choose OpenRLHF over ray-llm when Pricing: OpenRLHF is primarily available free of cost under the Apache-2.0 license, but users might incur costs based on their infrastructure usage for distributed training setups (e.g., cloud GPU instances).; Tags unique to OpenRLHF: large language models, proximal-policy-optimization, raylib, reinforcement-learning; When you require high-throughput sample generation with minimal idle time on limited hardware due to its hybrid engine scheduling that enables sharing of GPU resources between models and vLLM engines.
When should I choose ray-llm over OpenRLHF?
Choose ray-llm over OpenRLHF when Tags unique to ray-llm: llm-serving, ray; For deploying LLMs with new Ray-integrated APIs, ensuring direct support and updates from the Ray team; Leaner open-issue backlog (0).
When should I avoid OpenRLHF?
If your project does not require distributed training or large-scale model parameters (above 70B), as OpenRLHF is specifically optimized for scenarios where efficient distribution across multiple GPUs When your environment cannot support Ray or vLLM, as these are crucial components of the framework for scheduling and high-performance sample generation, respectively. If minimal Docker setup and hardware requirements with GPU constraints are not acceptable in your scenario.
When should I avoid ray-llm?
If seeking a standalone solution distinct from Ray’s main project, preferring specialized tools. For needs requiring active maintenance and development in this specific repository; consider alternative up-to-date projects.
Is OpenRLHF or ray-llm more popular on GitHub?
OpenRLHF has more GitHub stars (9,891 vs 1,261). Stars measure visibility, not whether either tool fits your constraints.
Are OpenRLHF and ray-llm open source?
Yes - both are open-source projects on GitHub.
Where can I find alternatives to OpenRLHF or ray-llm?
GraphCanon lists graph-backed alternatives at OpenRLHF alternatives and ray-llm alternatives (OpenRLHF markdown twin, ray-llm markdown twin), ranked by typed relationship edges rather than popularity votes.
Is there a machine-readable version of this comparison?
Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
Which is better maintained, OpenRLHF or ray-llm?
OpenRLHF: Active. ray-llm: Archived. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
Where are the full trust reports for OpenRLHF and ray-llm?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: OpenRLHF trust report; ray-llm trust report.

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