HELM

Overview

HELM is an open source Python framework from Stanford CRFM for holistic, reproducible and transparent evaluation of foundation models, including LLMs and multimodal models. It provides standardized benchmarks and metrics to compare model performance across multiple dimensions.

Best for

Best for
Researchers and developers who need rigorous, multi-dimensional evaluation of foundation models

Use cases

• Running standardized evaluations to compare LLM capabilities across tasks
• Generating reproducible benchmark results for research papers or model releases
• Analyzing model strengths and weaknesses on diverse scenarios like reasoning, fairness, and robustness

Notes

HELM is an open source Python framework from Stanford CRFM for holistic, reproducible and transparent evaluation of foundation models, including LLMs and multimodal models. It provides standardized benchmarks and metrics to compare model performance across multiple dimensions.

2,811 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.

Use cases

• Running standardized evaluations to compare LLM capabilities across tasks
• Generating reproducible benchmark results for research papers or model releases
• Analyzing model strengths and weaknesses on diverse scenarios like reasoning, fairness, and robustness

Pros

• Covers a wide range of evaluation scenarios for holistic assessment
• Emphasizes reproducibility and transparency of results
• Backed by academic research and community contributions

Cons

• Requires Python expertise and familiarity with command-line tools
• May have a learning curve for configuring custom evaluations
• Limited to models accessible via APIs or local inference; no built-in model hosting

Indexed from awesome-llm and enriched against its public facts.