Topics / #explainable-ai

Explainable AI projects on GitHub

Techniques that explain why machine learning models make predictions.

As of , the most-starred Explainable AI projects on GitHub are shap (25.8K stars), parlant (18.3K stars) and leedl-tutorial (16.8K stars). GitHub.Locker tracks 10 Explainable AI repositories with 114.9K stars combined. The fastest-growing on October 17 was Unsupervised-Log-Anomaly-Detection-Microservices, with +52 stars in a day.

10Projects
114.9KStars in total
+86Stars in a day
Browse in the catalog →github.com/topics/explainable-ai ↗

Hot right now

Most stars gained in a day · Oct 17

+52

Unsupervised-Log-Anomaly-Detection-Microservices

chuansirrr/Unsupervised-Log-Anomaly-Detection-Microservices · ★ 1K

It is a research-grade, unsupervised anomaly detection framework for microservice logs that combines Transformer, LSTM, and statistical features with graph-based modeling and ensemble scoring to flag unusual log behavior without labeled data.

+20

semantica

semantica-agi/semantica · ★ 14.1K

Semantica is an open-source, graph-native infrastructure layer that ingests enterprise data and builds a structured Context Graph and knowledge graph with deterministic reasoning, ontology governance, and full decision provenance, so AI systems can be explained and audited instead of treated as black boxes.

+5

dsh-memory

FuRongJun-1999/dsh-memory · ★ 414

dsh-memory is a memory plugin for DeepSeek Harness (DSH) that gives AI agents persistent, cross-session memory built on a mostly non-LLM 'white-box' reasoning pipeline, a spatiotemporal knowledge graph, and auditable guardrails. It targets AGI research into self-cognition, continual learning, world models and self-improvement rather than everyday chat memory.

+4

shap

shap/shap · ★ 25.8K

SHAP is a Python library that explains the output of any machine learning model using Shapley values from game theory — it tells you how much each feature contributed to a given prediction.

The all-time top

10 projects · by stars

  1. #1 · shap/shap

    shap

    SHAP is a Python library that explains the output of any machine learning model using Shapley values from game theory — it tells you how much each feature contributed to a given prediction.

    25.8K
  2. #2 · emcie-co/parlant

    parlant

    An open-source interaction control harness for building customer-facing AI agents that behave consistently, compliantly, and traceably — support, sales, onboarding — positioned as an open-source alternative to Ada, Decagon, or Sierra.

    18.3K
  3. #3 · datawhalechina/leedl-tutorial

    leedl-tutorial

    A free Chinese-language deep learning tutorial (nicknamed the 'Apple Book') based on Prof. Li Hongyi's (Hung-yi Lee) 'Machine Learning' course, covering most major deep learning topics in detail.

    16.8K
  1. 4

    semantica

    14.1Ksemantica-agi/semantica · Python

    Semantica is an open-source, graph-native infrastructure layer that ingests enterprise data and builds a structured Context Graph and knowledge graph with deterministic reasoning, ontology governance, and full decision provenance, so AI systems can be explained and audited instead of treated as black boxes.

  2. 5

    pytorch-grad-cam

    13Kjacobgil/pytorch-grad-cam · Python

    A Python package that implements many state-of-the-art Explainable AI (pixel attribution/Class Activation Map) methods for computer vision models, letting you visualize which parts of an image a model focuses on.

  3. 6

    deep-learning-drizzle

    13Kkmario23/deep-learning-drizzle · HTML

    A curated index that collects links to full video lectures and course materials for Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision and NLP courses taught at top universities worldwide.

  4. 7

    lime

    12.2Kmarcotcr/lime · JavaScript

    LIME (Local Interpretable Model-agnostic Explanations) is a Python package that explains individual predictions of any machine learning classifier, whether it works on text, tabular data, or images.

  5. 8

    Unsupervised-Log-Anomaly-Detection-Microservices

    1Kchuansirrr/Unsupervised-Log-Anomaly-Detection-Microservices · Python

    It is a research-grade, unsupervised anomaly detection framework for microservice logs that combines Transformer, LSTM, and statistical features with graph-based modeling and ensemble scoring to flag unusual log behavior without labeled data.

  6. 9

    dsh-memory

    414FuRongJun-1999/dsh-memory · Python

    dsh-memory is a memory plugin for DeepSeek Harness (DSH) that gives AI agents persistent, cross-session memory built on a mostly non-LLM 'white-box' reasoning pipeline, a spatiotemporal knowledge graph, and auditable guardrails. It targets AGI research into self-cognition, continual learning, world models and self-improvement rather than everyday chat memory.

  7. 10

    From-Calibrated-Confidence-to-Calibrated-Reliance

    304OliverDOU776/From-Calibrated-Confidence-to-Calibrated-Reliance · Python

    Companion code for an accepted ACM TSC paper: it implements the Reliance Calibration Error (RCE) metric, the g0-g3 confidence-display policies, and a reproducible CPU-only analysis of how well human-AI teams calibrate on displayed confidence.

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