UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured checks (covering language, code, embedding use-cases), perform root cause analysis on failure cases and give insights on how to resolve them.

국가법령정보MCP | 법제처 41개 API → 16개 MCP 도구. 법령·판례·조례·조약을 AI로 검색·조회·분석 + LLM 환각 방지 인용 검증 | 41 Korean legal APIs → 16 MCP tools with AI citation hallucination guard

UQLM: Uncertainty Quantification for Language Models, is a Python package for UQ-based LLM hallucination detection

Dingo: A Comprehensive AI Data, Model and Application Quality Evaluation Tool

Lightweight hallucination detection framework for RAG applications

up-to-date curated list of state-of-the-art Large vision language models hallucinations research work, papers & resources

[ACL 2024] User-friendly evaluation framework: Eval Suite & Benchmarks: UHGEval, HaluEval, HalluQA, etc.

Code for the EMNLP 2024 paper "Detecting and Mitigating Contextual Hallucinations in Large Language Models Using Only Attention Maps"

[ICLR 2025] Code and Data Repo for Paper "Latent Space Chain-of-Embedding Enables Output-free LLM Self-Evaluation"

Security scanner MCP server for AI coding agents. Prompt injection firewall, package hallucination detection (4.3M+ packages), 1000+ vulnerability rules with AST & taint analysis, auto-fix.

iFixAi. The open-source diagnostic for AI misalignment. 32 tests across fabrication, manipulation, deception, unpredictability, and opacity. Provider-agnostic. Runs against OpenAI, Anthropic, Bedrock, Azure, Gemini, and more. Letter grade in under 5 minutes, content-addressed manifest for bit-identical replay. Powered by iMe.

VerifAI initiative to build open-source easy-to-deploy generative question-answering engine that can reference and verify answers for correctness (using posteriori model)

An Easy-to-use Hallucination Detection Framework for LLMs.

[ACL 2024] ANAH & [NeurIPS 2024] ANAH-v2 & [ICLR 2025] Mask-DPO

Score any document. Prove every claim.

AISecOps (AI Security Operations) framework for deterministic verification of AI systems. QWED verifies LLM outputs using math, logic, and symbolic execution — creating an auditable trust boundary for agentic AI systems. Not generation. Verification.

🔎Official code for our paper: "VL-Uncertainty: Detecting Hallucination in Large Vision-Language Model via Uncertainty Estimation".

Official repo for the paper PHUDGE: Phi-3 as Scalable Judge. Evaluate your LLMs with or without custom rubric, reference answer, absolute, relative and much more. It contains a list of all the available tool, methods, repo, code etc to detect hallucination, LLM evaluation, grading and much more.

detect hallucinated citations in academic papers.

VideoHallucer, The first comprehensive benchmark for hallucination detection in large video-language models (LVLMs)