#Xai
Showing 60 of 60 repositories tagged #xai, ranked by stars
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Fit interpretable models. Explain blackbox machine learning.
DeepAudit：人人拥有的 AI 黑客战队，让漏洞挖掘触手可及。国内首个开源的代码漏洞挖掘多智能体系统。小白一键部署运行，自主协作审计 + 自动化沙箱 PoC 验证。支持 Ollama 私有部署 ，一键生成报告。支持中转站。让安全不再昂贵，让审计不再复杂。
One delightful Ruby framework for every major AI provider. Build AI agents, chatbots, RAG apps, and multimodal workflows in beautiful, expressive code.
A curated list of awesome responsible machine learning resources.
An open-source coding agent for the Grok API
Algorithms for explaining machine learning models
Interpretability and explainability of data and machine learning models
Generate Diverse Counterfactual Explanations for any machine learning model.
moDel Agnostic Language for Exploration and eXplanation
BaseAI — The Web AI Framework. The easiest way to build serverless autonomous AI agents with memory. Start building local-first, agentic pipes, tools, and memory. Deploy serverless with one command.
XAI - An eXplainability toolbox for machine learning
Production-ready AI chat. Start here and make it your own. Formerly Sparka AI
稳定省钱的OpenAI、Claude、Gemini等的API接口-For企业和开发者。OpenAI的api proxy，支持ChatGPT的API调用，支持Anthropic claude的官方接口形式，支持Google gemini的官方接口形式，支持：gpt-5，sora。不需要openai Key, 不需要买openai的账号，不需要美元的银行卡，通通不用的，直接调用就行，稳定好用！！ztoken pro
All-in-one native macOS AI chat application for virtually any AI provider
Papers about explainability of GNNs
parrot.nvim 🦜 - the plugin that brings stochastic parrots to Neovim.
ComfyUI-IF_AI_tools is a set of custom nodes for ComfyUI that allows you to generate prompts using a local Large Language Model (LLM) via Ollama. This tool enables you to enhance your image generation workflow by leveraging the power of language models.
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
[JMLR 2023] Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
Unreal Engine plugin for LLM/GenAI models & MCP UE5 server. OpenAI GPT-5, Deepseek R1, Claude Opus/Sonnet, Gemini 3, Grok 4, Alibaba Qwen, Kimi, ElevenLabs TTS, Inworld, OpenRouter, Groq, GLM, Ollama, Local, Meshy, Tripo, Hunyuan3D, Rodin, fal, Dashscope, Seedream. NPC AI, agentic, chat, 3D gen, TTS, multimodal, image gen. UnrealMCP/UnrealClaude
H2O.ai Machine Learning Interpretability Resources
🎙️ Speak with AI - Run locally using Ollama, OpenAI, Anthropic or xAI - Speech uses SparkTTS, OpenAI, ElevenLabs, Kokoro, Typecast or xAI
A Python library for Interpretable Machine Learning in Text Classification using the SS3 model, with easy-to-use visualization tools for Explainable AI :octocat:
A terminal utility for intelligent shell command generation
Neural network visualization toolkit for tf.keras
📍 Interactive Studio for Explanatory Model Analysis
Visualization tool for Graph Neural Networks
🤖 Discord AI assistant with OpenAI, Gemini, Claude & DeepSeek integration, multilingual support, multimodal chat, image generation, web search, and deep thinking | 一个强大的 Discord AI 助手，整合多种顶级 AI 模型，支持多语言、多模态交流、图片生成、联网搜索和深度思考
This framework works as a form of user/machine calibration, with a focus on user-context and user-intent, deconstructing your ideas logically from A to B to Z.
Interpret text data with LLMs (sklearn compatible).
Glowby lets you build production-ready software with coding agents.
Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)
Advanced Multi-Agent AI Crude Oil Trading System with Adversarial Validation 2026
Claudeus WordPress MCP Server
Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications
SHAP Plots in R
Ask Codex, Gemini, Grok, and 400+ OpenRouter models (Qwen, Kimi, DeepSeek) for second opinions or arbiter-mediated consensus. One MCP server for Claude Code, Codex, Cursor, Kiro, OpenCode. Measures which models earn their seat.
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
[CVPR2021] "Visualizing Adapted Knowledge in Domain Transfer". Visualization for domain adaptation. #explainable-ai
Model Agnostic Counterfactual Explanations
Adaptive, interpretable wavelets across domains (NeurIPS 2021)
Repository for our NeurIPS 2022 paper "Concept Embedding Models", our NeurIPS 2023 paper "Learning to Receive Help", and our ICML 2025 paper "Avoiding Leakage Poisoning"
A lightweight, open, and extensible multi-LLM interaction studio.
Model-agnostic agent harness for Elixir
This is an official implementation for PROMPT-CAM: A Simpler Interpretable Transformer for Fine-Grained Analysis (CVPR'25). Explore fine-grained trait distinctions between different specified species.
AI framework for Ruby/Rails developers
A curated list of valuable resources from our studies at the University of Tehran (UT), School of Electrical and Computer Engineering (ECE)
Helping AI practitioners better understand their datasets and models in text classification. From ServiceNow.
Generating and validating natural-language explanations for the brain.
Real-time explainable machine learning for business optimisation
This repository is a mirror. If you want to raise an issue or contact us, we encourage you to do it on Gitlab (https://gitlab.com/agrumery/aGrUM).
two alien trained LLMs talking to each other in alien language.
Finding semantically meaningful and accurate prompts.
Interpretable text embeddings by asking LLMs yes/no questions (NeurIPS 2024)
A diff tool for language models
Attribution methods that explain image classification models, implemented in PyTorch, and support batch inputs and GPU.
A Visual Studio Code extension that integrates with the xAI API, enabling developers to query Grok about their codebase directly in the editor. Ask about your entire workspace, specific files, functions, or selected code snippets, with customizable output and model selection.
Experiments with experimental rule-based models to go along with imodels.