GitStar
google-ai-edgeOrganization

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

89%

Top three share

Shows whether the organization is driven by one breakout repo or several visible projects.

Breadth

14 repos

Visible snapshot

13 repositories updated in the last 90 days.

Leading language

C++

Portfolio mix

C++ (3), Jupyter Notebook (3), Python (3)

Average size

5.1K

Stars per repository

Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.

Updated: 2026-05-22(4d ago)GitHub API fallback14 repositories

Portfolio Shape

89%

of the visible star count comes from this organization's top three repositories.

Average Repository Size

5.1K

stars per repository in this same snapshot.

Current Mix

C++

is the most common language here, with 13 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 89%

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 89% 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 C++ (3), Jupyter Notebook (3), Python (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.

Source: GitHub API fallback. This is the same cache-first snapshot used by the organization ranking list, so the summary view and the detail view should stay aligned.

Top Repositories

#RepositoryLanguage⭐ Stars
1google-ai-edge/mediapipe

Cross-platform, customizable ML solutions for live and streaming media.

C++35.4K
2google-ai-edge/gallery

A gallery that showcases on-device ML/GenAI use cases and allows people to try and use models locally.

Kotlin23.3K
3google-ai-edge/LiteRT-LMC++5.2K
4google-ai-edge/mediapipe-samplesJupyter Notebook2.7K
5google-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++2.5K
6google-ai-edge/model-explorer

A modern model graph visualizer and debugger

JavaScript1.5K
7google-ai-edge/litert-torch

Support PyTorch model conversion with LiteRT.

Jupyter Notebook1K
8google-ai-edge/litert-samplesKotlin320
9google-ai-edge/ai-edge-quantizer

AI Edge Quantizer: flexible post training quantization for LiteRT models.

Python138
10google-ai-edge/mediapipe-samples-web

A collection of examples for the MediaPipe Task APIs that can run fully inside your browser.

TypeScript34
11google-ai-edge/models-samplesJupyter Notebook24
12google-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).

Python17
13google-ai-edge/google-ai-edge.github.io

A curated list of resources to use Google AI Edge software.

5
14google-ai-edge/evalPython3

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.