8 Best Hallucination Leaderboard Alternatives in 2026 (Open Source)

Hallucination Leaderboard — Leaderboard Comparing LLM Performance at Producing Hallucinations when Summarizing Short Documents. The only continuously-updated automated hallucination benchmark using a dedicated evaluation model (HHEM) rather than human annotations, enabling scalable and repeatable factual consistency measurement across 7,700+ test documents

These 8 open-source tools do the same job. They are ordered by how closely they match Hallucination Leaderboard, with live GitHub data so you can see which projects are actively maintained.

ToolGitHub starsStars / 30dLast commit
Hallucination Leaderboard(original)3.3k+252026-09-23
DeepEval18.5k+6762026-09-29
Ragas15.9k+4432026-02-24
Auto-evaluator1.1k+522023-05-10
UQLM1.2k+122026-09-03
OpenAI Evals19.5k+2312026-04-14
LLM-eval-survey1.6k+32026-09-13
UpTrain2.4k+42024-07-29
LLM Comparator526+12024-10-18
  1. 1. DeepEval

    The LLM Evaluation Framework

    What sets it apart: Most comprehensive open-source LLM eval framework with 30+ research-backed metrics including agentic, RAG, multi-turn, MCP, and multimodal — vs Ragas (RAG-only) or custom eval scripts

    Best for: Teams needing comprehensive LLM/agent evaluation pipelines; CI/CD integration for LLM app quality gates; RAG pipeline evaluation and optimization

  2. 2. Ragas

    Supercharge Your LLM Application Evaluations 🚀

    What sets it apart: vs manual LLM evaluation: Purpose-built evaluation framework with both LLM-based and traditional metrics, automated test generation, and seamless integration with popular LLM frameworks

    Best for: Evaluating RAG pipeline quality with automated metrics; Generating comprehensive test datasets for LLM apps; Building continuous evaluation feedback loops

  3. 3. Auto-evaluator

    Evaluation tool for LLM QA chains

    What sets it apart: Lightweight QA evaluation tool that auto-generates question-answer pairs from documents and scores LLM chain configurations

    Best for: evaluating-qa-chain-configurations; comparing-retrieval-strategies; rapid-llm-evaluation-prototyping

  4. 4. UQLM

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

    What sets it apart: Academically rigorous uncertainty quantification (published in JMLR/TMLR) with the broadest scorer variety — unlike guardrails tools that use simple heuristics, UQLM applies information-theoretic methods like semantic entropy for precise hallucination detection

    Best for: Adding hallucination detection to existing LLM applications; Research teams studying LLM uncertainty and reliability

  5. 5. OpenAI Evals

    Evals is a framework for evaluating LLMs and LLM systems, and an open-source registry of benchmarks.

    Best for: Teams systematically evaluating LLM performance across model versions; Prompt engineers needing no-code YAML-based evaluation workflows; Organizations building quality assurance pipelines for LLM applications

  6. 6. LLM-eval-survey

    The official GitHub page for the survey paper "A Survey on Evaluation of Large Language Models".

    What sets it apart: Comprehensive survey and curated collection of LLM evaluation papers and resources organized by what, where, and how to evaluate

    Best for: llm-evaluation-research; finding-evaluation-benchmarks; understanding-eval-landscape

  7. 7. UpTrain

    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 ro

    What sets it apart: vs generic eval tools: 20+ preconfigured evaluations with customizable prompts, few-shot examples, and scenario descriptions — all running locally for data privacy with root cause analysis on failures

    Best for: RAG system evaluation and quality assurance; LLM application testing before production deployment; Safety and security testing for prompt injection vulnerabilities

  8. 8. LLM Comparator

    LLM Comparator is an interactive data visualization tool for evaluating and analyzing LLM responses side-by-side, developed by the PAIR team.

    What sets it apart: vs generic eval dashboards: combines visual analytics with rationale clustering and custom field analysis to identify specific behavioral differences between models — from Google PAIR team

    Best for: Comparing two LLM outputs with numerical evaluation scores; Discovering when and why one model outperforms another; Analyzing response patterns across prompt categories