Hallucination Detector is a free and open-source tool that helps you verify the accuracy of your LLM generated content instantly.

Python AI Slop Detector - Find over-engineering, hallucinations, and dead code in Python codebases

Retrieval-based hallucination detection using FAISS, FastAPI, and Gradio

AI citation hallucination detector — verify .bib files against CrossRef, Semantic Scholar, OpenAlex. Claude Code skill.

Zero-dependency Claude Code plugin that catches speculation, invented causality, and fake citations before they pollute your context. Install in one command, works offline, no API keys needed.

Small Agent Can Also Rock! Empowering Small Language Models as Hallucination Detector

Real-time LLM hallucination detector for Nav2 — 94 % unsafe-trajectory catch, <50 ms on TurtleBot3 + Orin Nano (n=103).

Hallucination Detector Gen大模型幻觉检测生成网站，是一个专为大语言模型（LLM）设计的幻觉检测工具。它结合规则过滤+向量相似度方法，帮助开发者、研究者快速识别模型输出中可能存在的不准确或虚构信息。

A Citation Hallucination Detector & Auto-Fixer

A cool lil Hallucination detector for GenAI tools ! Hope you like it <3

LLM hallucination guardrails for Python and CLI — deterministic, pluggable, JSON-first output validation.

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Physics-informed hallucination detection in accelerated MRI reconstruction. 22 detectors benchmarked against null-space ground truth on fastMRI single-coil knee data. Distributed training infrastructure with DDP, Hydra configs, and SLURM job scripts.

🔍 A real-time hallucination detection API for LLM pipelines — per-claim verdicts, web-search grounding, and confidence-weighted risk scores

AI Hallucination Detector — Experimental project combining Java (Spring Boot) and Python (FastAPI). Java orchestrates requests, while Python handles NLP-based checks (mocked for now). Modular, scalable, and designed to evolve into a fact-checking and response reliability system.

DeepSeek AI幻觉检测器 - Chrome扩展

“SHROOM-Guard: Detect hallucinations in LLM outputs with real-time predictions, probability scores, token-level explanations, multilingual support, and community feedback through reporting and leaderboards.”

Varun Chowdary Sayapaneni

Developed as part of IEM HackOsis 2.0 under Problem Statement HOGN02. Team Goal: Build a system that balances speed, accuracy, and explainability while remaining scalable and practical.

Detect hallucinations in LLM outputs. Claim extraction, evidence retrieval, NLI scoring pipeline. pip install hallucination-detector

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这是一个基于deep seek的一款幻觉检测器，

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A Hallucination Detection Tool powered by UQML, designed to identify whether outputs from Large Language Models (LLMs) are accurate or contain hallucinations.

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