OrganizationGoogle Research
@google-research • Open source projects from google-research. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.
Portfolio concentration
90%
Top three share
Shows whether the organization is driven by one breakout repo or several visible projects.
Breadth
30 repos
Visible snapshot
22 repositories updated in the last 90 days.
Leading language
Python
Portfolio mix
Python (26), Jupyter Notebook (2), C++ (1)
Average size
645
Stars per repository
Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.
Updated: 2026-10-16(3d ago)GitHub API fallback30 repositories
90%
of the visible star count comes from this organization's top three repositories.
645
stars per repository in this same snapshot.
Python
is the most common language here, with 22 repositories updated in the last 90 days.
Why this rank
This organization stands out because one flagship repo drives 67% of its visible star count.
Flagship share 67%Breakout repo: harness2
Organization pages work best when you separate portfolio breadth from flagship concentration. In Google Research's case, the visible top three repositories account for about 90% 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 Python (26), Jupyter Notebook (2), C++ (1). 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 | google-research/harness2 | Python | 13K |
| 2 | google-research/tabfm TabFM (Tabular Foundation Model) is a pretrained tabular foundation model developed by Google Research for tabular data regression and classification. | Python | 2.8K |
| 3 | google-research/rrsi | Python | 1.6K |
| 4 | google-research/envharness | Python | 653 |
| 5 | google-research/reasoning-bank | Python | 618 |
| 6 | google-research/pointdit [ICML'26] PointDiT: Pixel-Space Diffusion for Monocular Geometry Estimation | Python | 235 |
| 7 | google-research/paper-orchestra PaperOrchestra is a multi-agent framework for automated AI research paper writing. | Python | 161 |
| 8 | google-research/veriharness | Python | 75 |
| 9 | google-research/agentic-visualization CoDA is a multi-agent framework that turns natural language queries into publication-quality visualizations. | Python | 41 |
| 10 | google-research/m2svid This is the official code release for “M2SVid: End-to-End Inpainting and Refinement for Monocular-to-Stereo Video Conversion”. 3DV 2026 | Python | 39 |
| 11 | google-research/diffusion-score-smoothing | Jupyter Notebook | 36 |
| 12 | google-research/remote-sensing A code repository for Google Earth AI Remote Sensing models | Python | 30 |
| 13 | google-research/mapl | Python | 20 |
| 14 | google-research/budget-aware-agent Budget-Aware Tool-Use Enables Effective Agent Scaling @ COLM 2026 | Python | 20 |
| 15 | google-research/opengrm Formal grammars and related representations | C++ | 15 |
| 16 | google-research/geospatial-reasoning-benchmarks | Python | 12 |
| 17 | google-research/t2j-bench T2J-Bench: A benchmark testing whether coding agents can migrate real PyTorch training and inference repositories to native JAX/Flax. | Python | 12 |
| 18 | google-research/CLASHarness | Python | 8 |
| 19 | google-research/codegen-classroom | Python | 7 |
| 20 | google-research/dream-rsi | Python | 7 |
| 21 | google-research/nodesyn | Python | 7 |
| 22 | google-research/ds-star | Python | 6 |
| 23 | google-research/tpu-hpc | Jupyter Notebook | 5 |
| 24 | google-research/roga | Rust | 3 |
| 25 | google-research/gemma4-mtp-drafter-domain-adaptation | Python | 2 |
| 26 | google-research/ljer | Python | 2 |
| 27 | google-research/census-bluedown | Python | 2 |
| 28 | google-research/ple | Python | 1 |
| 29 | google-research/gowu | Python | 1 |
| 30 | google-research/symmetric-chain-decomposition | 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.