GitHub topic

explainable-ai GitHub repositories

Explore 12 tracked GitHub repositories tagged explainable-ai. Compare their recorded star activity and implementation languages, then follow related topics to find other projects.

Snapshot: 2026-10-28. The collection gained 33 stars since the previous daily snapshot.

Tracked repositories: 12
Total stars: 39,898
Stars gained · 7 days: +309
Stars gained · 30 days: +1,679

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Star activity for explainable-ai repositories on this page
Repository	Total stars	Today	7 days	30 days
semantica-agi/semantica	14.3K	+16	+114	+776
astroautomata/PySR	4.2K	+12	+142	+429
alexgreensh/attention-span	1.5K	+4	+26	+364
obss/sahi	5.6K	+1	+11	+53
interpretml/interpret	7.0K	+0	+6	+20

Explore repositories

Showing 1–12 of 12 tracked repositories.

semantica-agi/semantica

Graph-Native Infrastructure for Context and Accountable AI Systems

astroautomata/PySR

Symbolic Learning in Python and Julia

★ 4.2K+12 todayPython

machine-learning data-science

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alexgreensh/attention-span

Make your agents talk human. ADHD-friendly output styles for Claude Code, Codex, and others. So you can pay attention, not tokens.

★ 1.5K+4 todayPython

claude-code skills

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obss/sahi

Framework agnostic sliced/tiled inference + interactive ui + error analysis plots

interpretml/interpret

Fit interpretable models. Explain blackbox machine learning.

astroautomata/SymbolicRegression.jl

Distributed High-Performance Symbolic Regression in Julia

★ 812+0 todayJulia

machine-learning data-science

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fraunhoferportugal/tsfel

Time Series Feature Extraction Library (TSFEL): 60+ statistical, temporal, spectral and fractal features for time series classification, regression and signal processing in Python

understandable-machine-intelligence-lab/Quantus

[JMLR 2023] Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations

★ 678+0 todayJupyter Notebook

machine-learning deep-learning

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microsoft/responsible-ai-toolbox

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.