HELM vs Ragas
HELM
crfm.stanford.edu
Ragas
ragas.ioSide by side
| Figure | HELM | Ragas |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | — |
| Linux | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · LLM Evaluation Tools | 8.3 (#7) | 7.2 (#23) |
| Spec score · AI LLM Evaluation Tools | 8.4 (#12) | 7.2 (#26) |
| Evaluation coverage | The project says it maintains leaderboards across domains such as medicine and finance, and aspects such as multilinguality and world knowledge. | Documented metrics cover RAG, agent and tool use, factual correctness, SQL, and other tasks. |
| Installation | The project instructs users to install HELM from PyPI with `pip install crfm-helm`. | Ragas can be installed with pip or from its GitHub main branch. |
| Metrics | Its metrics cover aspects beyond accuracy, including efficiency, bias, and toxicity. | Users can use available evaluation metrics or create custom metrics. |
| Model providers | Models from providers including OpenAI, Anthropic, and Google are accessible through a unified interface. | The quick start documents use with OpenAI, Anthropic, Google Gemini, Ollama, and OpenAI-compatible providers. |
| Purpose | HELM is an open-source Python framework for holistic, reproducible, and transparent evaluation of foundation models, including language and multimodal models. | Ragas is a library for systematically evaluating AI and large language model applications. |
| Support | Users are directed to open GitHub issues for questions, which maintainers address when bandwidth is available. | The Ragas site directs users with questions to its Discord questions chatroom and gives an email for enterprise features and collaborations. |
Plans
In their own words
HELM
HELM is an open-source Python framework for evaluating foundation models, including large language and multimodal systems. It supplies standardized datasets and benchmarks such as MMLU-Pro, GPQA, IFEval, and WildBench, with a unified interface for models from providers including OpenAI, Anthropic, and Google. Evaluations can examine accuracy, efficiency, bias, and toxicity, and can include custom metrics, LLM-as-a-judge checks, safety evaluations, and human review workflows. Users can configure private checkpoints or model servers, run inference locally, or send requests to an API. A web interface displays individual prompts and responses, while leaderboards compare models and benchmarks across areas such as medicine, finance, multilinguality, and world knowledge. HELM can reproduce published evaluation results and documents commands for running benchmarks, summarizing results, and starting a local server. It is free, Apache-2.0 licensed, and entered maintenance mode on June 1, 2026.