> ## Documentation Index > Fetch the complete documentation index at: https://docs.withwhile.com/llms.txt > Use this file to discover all available pages before exploring further. > ## Agent Instructions > Install with `pip install whileai`; import as `import whileai as wai`. > Run the offline path first (`simulator=False`, `wai.seeded_agent`, a callable judge); no key is needed for it. > Report every pass rate with its interval and n, as `scored.pass_at` prints it. # Constitution > What whileai is, what we believe, and where each belief is enforced in the code. Repeatable science, replicated papers as proof, your keys, ergonomics as the product. This page mirrors [`CONSTITUTION.md`](https://github.com/whilehq/whileai-sdk/blob/main/CONSTITUTION.md) at the repository root, which is the canonical copy; a test keeps the two identical. The three agents that apply it on demand live in [`.claude/agents/`](https://github.com/whilehq/whileai-sdk/tree/main/.claude/agents): `style-guide` (the SDK surface against the [style guide](/reference/style) and the book), `docs-designer` (these docs and the website, with a designer's page contract), `user-sim` (a researcher running a recipe on their own keys and compute, filing what got in the way). What `whileai` is, what we believe, and how that shows up in the code. Read it before you add a public name, write a page, or run a recipe. The routines that maintain this repo read it too. ## What we are `whileai` is a scientific post-training library for language models: SFT and RL, on open models, with the measurement that says whether training helped. Simulate, grade, measure with intervals, select, train, prove on a held-out set, serve, and feed the new traces back in. Build self-improving systems. It is for AI researchers, ML engineers and applied-AI developers, and the goal is that it sits in every applied-AI and research department the way PyTorch does. The platform (`whileai.platform`) is a separate, optional service for hosted training and serving. The library needs no account. ## What we believe 1. **Repeatable science.** A number is a result only with its interval, its noise floor, its seed and the versions that produced it. A mean alone is not a result. A flat result is a result. (`pass_at`, `eval_variance`, `delta_report`, `holdout_size`.) 2. **Replicated papers are the proof.** We show the library works by reproducing recent post-training research in it, one recipe per paper, under an hour on one GPU, with the number it moved and the number it did not. Every reproduced paper is a post. The proof point is the recipe, not the pitch. (`recipes/papers/`.) 3. **The book is the map, the paper is the citation.** Every default is named, sourced and tunable from the call. [rlhfbook.com](https://rlhfbook.com) (Lambert) is the map of the field; the originating paper is the reference. A default with no source says "convention, untested". (`defaults.py`, `scripts/check_no_hardcoding.py`.) 4. **Bring your own keys.** Your models, your compute, your accounts. Modal and Prime Intellect are first-class: a `whileai` environment becomes a `verifiers` environment and back, selected rows become a trainer's prompt set, eval results flow back into measurement with intervals. Nothing in the loop requires our hosting. 5. **Developer ergonomics are the product.** The code reads like PyTorch, DSPy and Unsloth: one import, objects carry configuration, calls carry data, reports print themselves, errors name the fix, and a first-time reader can guess the next line. Rigor lives behind a default, never behind a flag. (`docs/reference/style.md`, the ratchet test.) 6. **Plain words, then the mechanism, then the proof.** Every page, every docstring, every README section in that order. Book vocabulary stays in the docstring that cites the chapter, never in a public name. 7. **Mass experimentation.** A PhD or an engineer runs many experiments from one import, on their own compute, and every run leaves a record that a person can decide from. 8. **Never big-bang.** The internals carry the science and the tests. Change the front door, migrate callers mechanically, keep the old name working for one release with a warning that says the new one. ## How it shows up | Belief | Where it is enforced | | ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Repeatable science | `recipes/papers/check.py` refuses "moved" without an interval that excludes zero, three base re-runs, a clean holdout, and a proxy-vs-target verdict | | Replicated papers | `recipes/papers/README.md`: one paper, one recipe, one command, one `post.md` | | Sourced defaults | `scripts/check_no_hardcoding.py` in CI; `tests/api/test_readme_defaults.py` | | Ergonomics | `docs/reference/style.md`; `tests/api/test_style_ratchet.py` pins the retired shapes | | Docs order | `docs/` on Mintlify; the docs routine's one PR a day | | Bring your own keys | `wai.configure(agent=, judge=, api_key=)`, backend objects whose repr names the key source; the Modal and Prime Intellect researcher routines run on their own accounts twice a day | ## Who reads this People: contributors, before their first public name. Agents: the style guide routine, the docs and site routines, the paper recipes routine, and the two researcher routines, at the top of every run. When this file and another file disagree, this file wins and the other file gets a PR.