HELM: Stanford's Evaluation Framework Now in Maintenance Mode
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.
At a glance
- What is it?
- HELM standardizes benchmark datasets, model providers, and metrics behind one Python CLI, and its maintainers put the project into maintenance mode on June 1, 2026. It stays a reasonable choice for reproducing published leaderboards, less so for teams that need new benchmarks or active feature work.
- Who is it for?
- Adopt HELM if you need to reproduce a published HELM leaderboard or evaluate a model against a fixed set of standardized benchmarks with a unified provider interface, and if you accept that the project entered maintenance mode on June 1, 2026, so new features and new benchmark integrations should not be expected. Do not adopt it if your roadmap depends on benchmarks that are not already in the framework, or if you need an actively evolving evaluation tool.
- Can I use it commercially?
- Yes. Apache-2.0 is a permissive licence: you can use, modify and sell software built on it, as long as you keep its copyright and licence notices.
- Is it still maintained?
- Yes. The repository last received commits 15 days ago.
- What is it written in?
- Mainly Python, according to GitHub's language statistics.
Answers come from the project's GitHub data, last synced on September 15, 2026, and from our analysis. They are not legal advice.
DEEP OPEN-SOURCE ANALYSIS
The problem HELM solves: benchmark results that cannot be compared
Evaluation results published by model providers are rarely comparable. One report uses a five-shot prompt, another zero-shot; one reports accuracy on a filtered subset, another on the full set; the harness code is often missing. HELM's answer is to put datasets, model access, and metrics into one standardized format so that the same benchmark definition can be run against any supported model and the outputs can be placed side by side. The README lists the concrete pieces: datasets and benchmarks such as MMLU-Pro, GPQA, IFEval and WildBench; models from providers including OpenAI, Anthropic and Google Gemini behind a unified interface; metrics that go beyond accuracy to cover efficiency, bias and toxicity; and a web UI for inspecting individual prompts and responses. The intended audience is whoever has to defend a model comparison: research groups reproducing a published leaderboard, evaluation teams inside an organization, and anyone who needs per-prompt evidence rather than a single aggregate number. HELM is not a training library and not an inference server. It is the measurement layer, and the fact that it ships a leaderboard frontend alongside the Python package shows that the maintainers treat presentation of results as part of the product.
How a run is structured: run entries, suites, and the three-command pipeline
The unit of work in HELM is the run entry, written as a colon-separated specification. The README's quick start uses mmlu:subject=philosophy,model=openai/gpt2, which names a benchmark (mmlu), narrows it with a benchmark-specific argument (subject=philosophy), and binds it to a model path (openai/gpt2). Run entries are grouped into a suite, which is the name you give the collection: my-suite in the example. That suite name is the handle the rest of the pipeline uses. helm-run executes the benchmark and writes results; helm-summarize reads those results and produces the aggregated summary; helm-server serves a local web view of the summary on port 8000. This separation matters because the expensive step (querying models) is distinct from the cheap steps (summarizing and viewing), so a suite can be re-summarized or re-displayed without re-running inference. The model identifier format provider/model-name is what makes the unified interface work: the same benchmark definition can point at an OpenAI model, an Anthropic model, or a local checkpoint by changing that one field, provided the corresponding provider integration exists. Benchmark-specific arguments like subject are how a single benchmark family exposes its subsets, which is why the run entry syntax is extensible rather than a fixed schema. The web UI is described as being for inspecting individual prompts and responses, which is the part that makes debugging a suspicious score possible: you can look at what was actually sent and what came back, not just the final metric.
Getting it running: install, three commands, one port
Installation is a single pip command: pip install crfm-helm. The package name on PyPI is crfm-helm, which is worth noting because the repository is named helm and the import path and CLI names do not match the install name. The README's quick start then gives three shell commands in sequence. First, helm-run --run-entries mmlu:subject=philosophy,model=openai/gpt2 --suite my-suite --max-eval-instances 10, where --run-entries takes the run entry list, --suite names the output collection, and --max-eval-instances caps how many instances are evaluated, which is what makes a smoke test cheap. Second, helm-summarize --suite my-suite. Third, helm-server --suite my-suite, after which the README says to open http://localhost:8000/ in a browser. The --max-eval-instances flag is the one to lean on during setup: it lets you confirm that credentials, provider access and the benchmark loader all work before committing to a full evaluation. The documentation on Read the Docs is the authoritative source for installation details beyond this quick start, and the README points there rather than duplicating it. What the quick start does not cover is credential configuration for the hosted providers, so budget time for reading the provider-specific documentation before assuming the openai/gpt2 example generalizes to a paid API model.
Maintenance mode is the constraint that should drive the decision
The README carries a note at the top: HELM entered maintenance mode on June 1, 2026, with a link to a Maintenance Mode Policy in the documentation. That single line changes how the project should be evaluated. Releases continued after that date in the supplied release list, with v0.5.16 on 2026-04-30, v0.5.15 on 2026-04-23 and v0.5.14 on 2026-03-27, but those dates precede the June 1 announcement, so the material here does not show what a post-maintenance-mode release looks like. Treat the policy document as required reading rather than the README note as sufficient. The practical consequence is that benchmark coverage is frozen in practice: if a benchmark you care about is not already in the framework, you are waiting on a project that has announced it is not prioritizing new work. The same applies to provider integrations. A new model API that changes its request format will eventually need a fix, and maintenance mode is a statement about how quickly such fixes arrive. None of this makes existing results invalid. It makes the framework a fixed instrument rather than a moving one, and for reproducibility that is arguably the right posture. The failure mode to plan for is not a broken install today; it is a benchmark or provider that stops working and stays that way.
Where HELM is the wrong tool
HELM is a poor fit when the evaluation you need does not exist yet. Writing a new benchmark means conforming to the framework's standardized dataset format and its run entry conventions, and doing that work against a project in maintenance mode means you own the integration indefinitely. It is also a poor fit for latency and cost benchmarking in a production sense. The README lists efficiency among the metrics, but HELM's design centers on running benchmark instances against provider APIs, and that is not the same as measuring your own serving stack under your own traffic patterns. If your question is how many tokens per second your vLLM deployment sustains at a given concurrency, HELM is measuring a different thing. A third mismatch is scale of iteration. The three-command pipeline with a suite name is built for a deliberate evaluation run, not for a tight loop where a model is retrained hourly and re-scored each time. The --max-eval-instances flag helps for smoke tests, but the framework's orientation toward reproducible suites means the overhead is real and intentional. Finally, anyone who needs a single headline number should be aware that HELM's stated goal is holistic evaluation across multiple aspects, so the output is a set of metrics across benchmarks, not a score to put on a slide without explanation.
The alternative: EleutherAI's lm-evaluation-harness
The most direct alternative in this space is EleutherAI's lm-evaluation-harness, and the difference is structural rather than cosmetic. lm-evaluation-harness is oriented around running a large catalog of tasks against locally loaded Hugging Face models, with task definitions expressed in its own YAML and Python task format. HELM's orientation, as the README describes it, is standardized datasets plus a unified interface to hosted providers (OpenAI, Anthropic, Google Gemini) plus a web UI and leaderboard layer on top. That means the two tools answer different questions. If your model weights are on disk and you want to score them across many academic tasks with minimal provider plumbing, the harness is the more natural starting point. If your models are behind APIs and you want per-prompt inspection and a summary view that resembles the published HELM leaderboards, HELM's pipeline is the closer match. The overlap is real: both run MMLU-style benchmarks and both produce accuracy numbers. The divergence shows up in the surrounding workflow. HELM's suite abstraction and helm-server assume you are producing a comparison artifact; the harness assumes you are producing a number for a training run. Neither is a superset of the other, and a team with both hosted and local models may end up running both rather than picking one.
Licence, upgrade cost, and what maintenance mode means for a pinned version
HELM is Apache-2.0, which permits commercial use, modification and redistribution provided the licence and notices are preserved, and it includes an explicit patent grant. That is a permissive licence and it does not obligate you to publish your modifications. This is a description of the licence text, not legal advice; if you are redistributing HELM inside a product, have counsel read the actual LICENSE file and the NOTICE requirements. On upgrade cost, the release cadence visible in the supplied material is weekly to monthly patch releases in the v0.5.x line, which suggests incremental change rather than a stable long-term-support branch. Under maintenance mode, the sensible posture is to pin a specific crfm-helm version and treat upgrades as deliberate events tied to a reason (a benchmark fix, a provider change) rather than routine. The leaderboard results published on the HELM site are tied to specific framework versions, so reproducing them means matching versions, not just matching commands. Because the framework is a Python package installed from PyPI, the pinned version in your requirements file is the reproducibility boundary. Record it alongside your suite name and run entries, because a suite name alone does not identify the code that produced it. What the material does not establish is how long the maintenance-mode policy commits the maintainers to accepting security or compatibility fixes, so that question belongs in the policy document before you build a dependency on it.
Editorial conclusion
Adopt HELM if you need to reproduce a published HELM leaderboard or evaluate a model against a fixed set of standardized benchmarks with a unified provider interface, and if you accept that the project entered maintenance mode on June 1, 2026, so new features and new benchmark integrations should not be expected. Do not adopt it if your roadmap depends on benchmarks that are not already in the framework, or if you need an actively evolving evaluation tool. Before committing, read the Maintenance Mode Policy linked from the README, check that the run entries you need exist for your suite, and confirm the crfm-helm version on PyPI matches the release you intend to pin.
Community notes