google-ai-edge
@google-ai-edge • Open source projects from google-ai-edge. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.
Portfolio concentration
88%
Top three share
Shows whether the organization is driven by one breakout repo or several visible projects.
Breadth
14 repos
Visible snapshot
12 repositories updated in the last 90 days.
Leading language
Python
Portfolio mix
Python (4), C++ (3), Jupyter Notebook (3)
Average size
5.4K
Stars per repository
Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.
Updated: 2026-07-15(19d ago)GitHub API fallback14 repositories
88%
of the visible star count comes from this organization's top three repositories.
5.4K
stars per repository in this same snapshot.
Python
is the most common language here, with 12 repositories updated in the last 90 days.
Why this rank
This organization stands out because its public portfolio is relatively balanced across 14 repositories.
Balanced portfolio across 14 reposTop 3 share 88%
Organization pages work best when you separate portfolio breadth from flagship concentration. In google-ai-edge's case, the visible top three repositories account for about 88% 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 (4), C++ (3), Jupyter Notebook (3). 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-ai-edge/mediapipe Cross-platform, customizable ML solutions for live and streaming media. | C++ | 36.5K |
| 2 | google-ai-edge/gallery A gallery that showcases on-device ML/GenAI use cases and allows people to try and use models locally. | Kotlin | 24.3K |
| 3 | google-ai-edge/LiteRT-LM LiteRT-LM is Google's production-ready, high-performance, open-source inference framework for deploying Large Language Models on edge devices. | C++ | 6.1K |
| 4 | google-ai-edge/LiteRT LiteRT, successor to TensorFlow Lite. is Google's On-device framework for high-performance ML & GenAI deployment on edge platforms, via efficient conversion, runtime, and optimization | C++ | 3.2K |
| 5 | google-ai-edge/mediapipe-samples | Jupyter Notebook | 2.8K |
| 6 | google-ai-edge/model-explorer A modern model graph visualizer and debugger | JavaScript | 1.5K |
| 7 | google-ai-edge/litert-torch Support PyTorch model conversion with LiteRT. | Jupyter Notebook | 1.1K |
| 8 | google-ai-edge/litert-samples LiteRT and LiteRT-LM sample apps, model recipes, agent skills and utilities. | Python | 383 |
| 9 | google-ai-edge/ai-edge-quantizer AI Edge Quantizer: flexible post training quantization for LiteRT models. | Python | 186 |
| 10 | google-ai-edge/mediapipe-samples-web A collection of examples for the MediaPipe Task APIs that can run fully inside your browser. | TypeScript | 61 |
| 11 | google-ai-edge/LiteRT-CLI A convenient CLI to streamline LiteRT related development workflows, including converting, quantizing, compiling, managing, running, benchmarking and visualizing LiteRT (TFLite) models on various hardwares (CPU / GPU / NPU) across platforms (desktop, mobile or cloud). | Python | 34 |
| 12 | google-ai-edge/models-samples | Jupyter Notebook | 25 |
| 13 | google-ai-edge/eval | Python | 9 |
| 14 | google-ai-edge/google-ai-edge.github.io A curated list of resources to use Google AI Edge software. | 6 |
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.