ray-project
@ray-project • Open source projects from ray-project. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.
Portfolio concentration
87%
Top three share
Shows whether the organization is driven by one breakout repo or several visible projects.
Breadth
30 repos
Visible snapshot
5 repositories updated in the last 90 days.
Leading language
Jupyter Notebook
Portfolio mix
Jupyter Notebook (9), Unknown (9), Python (8)
Average size
163
Stars per repository
Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.
Updated: 2026-08-24(1d ago)GitHub API fallback30 repositories
87%
of the visible star count comes from this organization's top three repositories.
163
stars per repository in this same snapshot.
Jupyter Notebook
is the most common language here, with 5 repositories updated in the last 90 days.
Why this rank
This organization stands out because its public portfolio is relatively balanced across 30 repositories.
Balanced portfolio across 30 reposTop 3 share 87%
Organization pages work best when you separate portfolio breadth from flagship concentration. In ray-project's case, the visible top three repositories account for about 87% of total stars in this snapshot, which helps explain whether the organization is known for one breakout project or for a broader repeatable portfolio.
The dominant language mix here is Jupyter Notebook (9), Unknown (9), Python (8). That makes this page useful not just for popularity checks, but also for seeing what technical shape an organization's public ecosystem actually has.
Top Repositories
| # | Repository | Language | Stars |
|---|---|---|---|
| 1 | ray-project/llm-applications A comprehensive guide to building RAG-based LLM applications for production. | Jupyter Notebook | 1.9K |
| 2 | ray-project/ray-llm RayLLM - LLMs on Ray (Archived). Read README for more info. | 1.3K | |
| 3 | ray-project/llmperf LLMPerf is a library for validating and benchmarking LLMs | Python | 1.1K |
| 4 | ray-project/llmperf-leaderboard | 474 | |
| 5 | ray-project/Ray-Forward Some resources about Ray Forward Meetup | 36 | |
| 6 | ray-project/multimodal-training | Python | 31 |
| 7 | ray-project/llms-in-prod-workshop-2023 Deploy and Scale LLM-based applications | Jupyter Notebook | 26 |
| 8 | ray-project/Ray-Connect Material for Ray Connect 2024 Conference | 12 | |
| 9 | ray-project/anyscale-berkeley-ai-hackathon Ray and Anyscale for UC Berkeley AI Hackathon! | Jupyter Notebook | 11 |
| 10 | ray-project/docu-mentor | Python | 10 |
| 11 | ray-project/llm-application | Jupyter Notebook | 8 |
| 12 | ray-project/ray-open-ports-checker Tool to quickly check and see if your Ray cluster is exposed to untrusted clients | Python | 6 |
| 13 | ray-project/odsc-west-workshop-2023 | Jupyter Notebook | 5 |
| 14 | ray-project/distributed-zkml Distributed Proofs with ZMKL and Ray | Rust | 3 |
| 15 | ray-project/qcon-workshop-2023 | Jupyter Notebook | 3 |
| 16 | ray-project/scipy-ray-scalable-ml-tutorial-2023 | Jupyter Notebook | 3 |
| 17 | ray-project/ray-haproxy HAProxy for bundling with Ray | Shell | 2 |
| 18 | ray-project/llmval-legacy | Jupyter Notebook | 2 |
| 19 | ray-project/raydepsets raydepsets build and release repo | Shell | 1 |
| 20 | ray-project/scipy SciPy library main repository | 1 | |
| 21 | ray-project/oneflow | Python | 1 |
| 22 | ray-project/gpu-deduplication Deduplication workload on cuDF/RapidsMPF | Python | 0 |
| 23 | ray-project/haproxy-release | 0 | |
| 24 | ray-project/pandas Pandas object mirror | 0 | |
| 25 | ray-project/redis For developers, who are building real-time data-driven applications, Redis is the preferred, fastest, and most feature-rich cache, data structure server, and document and vector query engine. | 0 | |
| 26 | ray-project/wrk Modern HTTP benchmarking tool | 0 | |
| 27 | ray-project/enablement-content | HTML | 0 |
| 28 | ray-project/ray-data-user-testing The repo to study Ray Data CUJ | Jupyter Notebook | 0 |
| 29 | ray-project/test_runtime_env Used for testing. | Python | 0 |
| 30 | ray-project/ray-train-user-testing | Python | 0 |
Next step after the organization read
Open a flagship repository, compare a couple of portfolio leaders, or return to the organization map when you want a broader concentration read.
Learn and methodology
Keep trust-building context reachable, but behind the first data read instead of ahead of it.
How to read this organization snapshot
Total stars are useful as a discovery signal, but they do not tell you whether a team maintains every repository equally. Pair this page with release cadence, maintainer activity, and the flagship concentration shown above before making adoption decisions.
For broader background on GitStar's ranking logic and editorial guidance, see Methodology & Editorial Standards.