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Holistic Evaluation of Language Models (HELM) is an open source Python framework created by the Center for Research on Foundation Models (CRFM) at Stanford for holistic, reproducible and transparent evaluation of foundation models, including large language models (LLMs) and multimodal models.
The main features of stanford-crfm/helm are: Evaluation Frameworks, Model Evaluation and Benchmarking.
Projects with overlapping indexed features include: openai/simple-evals — This project is a language model evaluation framework and benchmarking tool designed to measure the accuracy and… eleutherai/lm-evaluation-harness — This project is a standardized framework for benchmarking large language models across a wide range of academic and… confident-ai/deepeval — Deepeval is a framework for testing and evaluating large language model applications. It provides a suite of tools for… evalplus/evalplus — Rigourous evaluation of LLM-synthesized code - NeurIPS 2023 & COLM 2024. comet-ml/opik — Opik is an observability and evaluation platform designed for generative AI applications and agentic workflows. It… explodinggradients/ragas — Ragas is an evaluation framework and performance benchmark designed to quantify the quality of retrieval augmented…
This project is a language model evaluation framework and benchmarking tool designed to measure the accuracy and performance of models across diverse datasets. It provides a system for implementing model-based graders, running standardized tests for mathematical reasoning, coding, and factuality, and calculating quantified performance metrics such as precision, recall, F1 scores, and pass-at-k. The framework utilizes model-based grading and rubrics to validate response quality against expert-defined criteria. It includes a multi-model benchmarking loop and a model-agnostic API interface to co
This project is a standardized framework for benchmarking large language models across a wide range of academic and reasoning datasets. It provides a platform for executing automated evaluation tasks to measure model accuracy and performance, ensuring consistent assessment through a structured configuration schema. The framework distinguishes itself by incorporating a dedicated utility for data decontamination, which identifies and removes overlapping training samples from evaluation sets to prevent data leakage. It also features a flexible task builder that allows users to define custom benc
Opik is an observability and evaluation platform designed for generative AI applications and agentic workflows. It provides a centralized environment for tracing execution flows, managing prompt templates, and monitoring production performance, allowing teams to gain visibility into complex model interactions and tool usage without requiring manual application code changes. The platform distinguishes itself through its integrated approach to the AI development lifecycle, combining distributed trace instrumentation with automated evaluation frameworks. It supports model-as-a-judge scoring, syn
Deepeval is a framework for testing and evaluating large language model applications. It provides a suite of tools for executing automated regression tests, validating model output quality against defined standards, and tracing the execution of complex agent workflows. By integrating these capabilities into development pipelines, the platform ensures consistent performance and reliability throughout the software lifecycle. The platform distinguishes itself through its focus on programmatic validation and observability. It utilizes secondary language models to score output quality and employs