Xai Python Packages

interpret-core

Fit interpretable models. Explain blackbox machine learning.

aplr

APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering smoother and interpretable predictions.

interpret

Fit interpretable models. Explain blackbox machine learning.

explainerdashboard

Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

grad-cam

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

dalex

moDel Agnostic Language for Exploration and eXplanation

llmshim

A pure-Rust LLM API translation layer — one OpenAI-shaped request, routed and translated to OpenAI, Anthropic, Gemini, and xAI.

dice-ml

Generate Diverse Counterfactual Explanations for any machine learning model.

agentmake

AgentMake AI: a kit for developing agentic AI applications that support 24 AI backends and and work with 7 agentic components, such as tools and agents. (Developer: Eliran Wong) Supported backends: anthropic, azure, azure_any, cohere, custom, deepseek, genai, github, github_any, googleai, groq, llamacpp, mistral, ollama, openai, vertexai, xai

chibi-bot

Your Digital Companion. Self-hosted Telegram bot orchestrating multiple AI providers (OpenAI, Anthropic, Google, xAI, DeepSeek, Mistral, Alibaba, MiniMax) with autonomous agent capabilities, MCP integrations, and async task execution. Not a tool. A partner.

hugiml-core

High-performance interpretable ML classifier using High Utility Gain patterns (IEEE Access 2024). C++ accelerated, scikit-learn compatible, with EBM-style explanations, adaptive binning, pattern pruning, and deployment tooling for regulated domains.

pyxai

PyXAI (Python eXplainable AI) is a Python library (version 3.6 or later) allowing to bring formal explanations suited to (regression or classification) tree-based ML models (Decision Trees, Random Forests, Boosted Trees, ...).

alibi

Algorithms for explaining machine learning models

effector

Effector - a Python package for global and regional effect methods

xaikit-py

Unofficial Python kit for the xAI (Grok) API — typed client, model catalog, usage metering, image/video, and realtime voice

calibrated-explanations

Calibrated Explanations is an explanation method for uncertainty-aware explanations of classification and regression models.

bnnr

XAI-driven augmentation & diagnostics for PyTorch vision - find model failures, fix with saliency-guided augmentation (ICD/AICD), prove with auditable reports.

aix360

Interpretability and explainability of data and machine learning models

zennit

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

interpreto

🪄 Interpreto is an interpretability toolbox for LLMs

shelloracle

A terminal utility for intelligent shell command generation

tf-keras-vis

Neural network visualization toolkit for tf.keras

treecf

Constrained, threshold-aware counterfactual explanations for tree ensembles (XGBoost, LightGBM, CatBoost, sklearn) on a bundled Rust engine

nlp-shap

mllm-shap: Shapley-value explainability for conversational and multimodal language models.