ridi-check

See what a model or prompt update really changed in your assistant's answers, before you ship it.

A new model or an edited prompt can change what your assistant or agent answers without anyone noticing: average accuracy barely moves while individual answers flip. ridi-check runs the old and the new version on the same questions, more than once, and shows which answers changed and which of those changes hold on re-run. You need no test set, no answer key and no model judge.

Try it in your browser, with nothing to install: adeebnoor.github.io/ridi-check. It opens on two synthetic examples: a library handbook, and made-up medicines where mean accuracy rises while three answers break in every run.

What you will see · Who it is for · Start in one minute · Use it in CI · Connect your assistant · FAQ · Roadmap · Limitations · Reference

What you will see

In your terminal or CI log, ridi-check gate lists every answer that was right and is now wrong in every run, and exits 1 so the pipeline stops:

ridi-check gate: ridi-demo/ridi-out/results.csv, 30 cases, answer-key mode
stable_regression  case q02
stable_regression  case q09
stable_regression  case q18
stable_regression  case q25
counts: stable_regression=4 stable_improvement=1 regression_unconfirmed=0 improvement_unconfirmed=0 unstable=4 wrong_both=4 correct_both=17 incomplete=0
stable regressions: 4 (limit 0) - over the limit
gate: a limit was exceeded

In the report, each changed case shows the question, both answers, what the second run gave and the passages only one version used:

Every question ends in one group:

The report is one self-contained HTML file. It works offline, on a phone, in light and dark, in English and Arabic. The numbers above come from ridi-check demo, which uses synthetic data.

Who it is for

• You are about to swap the model, edit the prompt or change retrieval and want to know which answers move, not only whether an average moved.
• You have questions but no answer key. The report shows what changed; you judge only the changes that held on re-run.
• You want a CI check that fails when an answer that was right becomes wrong in every run.
• You only have answers in a spreadsheet. The browser version reads pasted cells and needs no install.

It is not for proving that answers are correct or that a system is ready to deploy, or for explaining why an answer changed. Agents that use tools or keep state can be wrapped, but that has not been tried on a production agent. See Limitations and the use cases.

Start in one minute

Python 3.10 or later, no dependencies. The demo needs no network and no API key; its data is synthetic.

pip install ridi-check

ridi-check demo --open

Then check your own assistant:

ridi-check init my-check

It writes questions.csv, my_assistant.py (one function to fill in), ridi-check.json and ASK_YOUR_AI.md, a prompt that lets your AI coding assistant do the wiring. In that folder, try five questions with ridi-check run --limit 5, then run everything with ridi-check run. The user guide walks through each step and how to read the report.

No install at all: open the browser version and paste your results (CSV text or cells copied from a spreadsheet). Your data stays in the page: it sends nothing anywhere. To use it offline, download the single file web/ridi-update-check.html.

Development version: pip install git+https://github.com/adeebnoor/ridi-check installs the latest commit instead of the latest release.

Use it in CI

- run: pip install ridi-check
- run: ridi-check run --config ridi-check.json     # calls both versions, writes ridi-out/results.csv
- run: ridi-check gate ridi-out/results.csv        # exits 1 on a stable regression

A complete GitHub Actions workflow to copy: examples/ci/ridi-check.yml. gate prints counts and the offending cases; it never prints a verdict about your system. By default it does not count answers that did not hold on re-run; where a wrong answer is costly, add --max-unstable 0. Limits such as --max-stable-regressions 2 are in the reference.

Connect your assistant

ridi-check has no model provider built in. Your assistant is the system under test, reached through one function:

def answer(question: str) -> dict:
    return {"answer": "text", "context": ["doc-12", "doc-40"]}   # context is optional

Ready-made examples in examples/adapters: an OpenAI-compatible HTTP API (no code), the Claude API through its official SDK, and a shell command with curl. Each reads its key from an environment variable and is tested against a local stand-in server.

The two versions should differ only in what the update changes. Details: developer guide.

FAQ

Do I need a test set or an answer key? No. Without an expected column the report shows which answers differ and you judge only the stable changes. With one, it speaks of regressions and improvements.

Does my data go anywhere? No. ridi-check calls only your assistant and writes local files. The report and the browser version make no network request.

Does it use a model to judge answers? No. A judge adds its own error. Scoring is exact match after normalisation, or a function you write.

How many questions and runs? 30 to 100 real user questions is a good first set. Two runs per version is the minimum for the stable label; use more when the decision is expensive. Calls = questions × 2 × runs.

Almost everything is "unstable" or "changed". Why? Your assistant words the same answer differently each time. Add expected answers with a scoring function, or use --similarity 0.9.

Which providers and frameworks does it support? Any. It talks to a function, a command or an HTTP endpoint, not to a provider.

Can I use it on an agent? Yes, wrapped as one function: a question or a whole scripted conversation goes in, the final answer comes out, and the tool calls are returned as context, so the report shows which calls only one version made. examples/adapters has a working stand-in agent. It has not been tried on a production agent yet.

What happens when my provider limits or refuses calls? A Retry-After header is obeyed when it asks for up to 120 seconds, and no other call starts during that wait. HTTP 400, 401, 402, 403 and 404 are not tried again, unless the refusal itself carries Retry-After. After ten refusals in a row (a used-up quota, a missing key, a rate limit) the run stops with exit status 2, and the same command continues from there later. A failure that belongs to one question, such as a 400, a 500 or a timeout, is recorded and never stops the run. This was tried live against one free public model service: its limit arrived as HTTP 402 with Retry-After, and 48 calls all finished, five of them on a second attempt after waits of 3 to 42 seconds. That is one service on one day; yours may behave differently. Details.

Can I open the CSV files in Excel? Yes. A cell that starts with =, +, - or @ is written with an apostrophe in front ('=1+1), so a spreadsheet does not run answer text as a formula. The tool takes the apostrophe off again when it reads the file, and raw_outputs.jsonl keeps every answer as it was given. Going the other way, save your sheet as "CSV UTF-8"; Excel's "Unicode Text" is read too, and other encodings are refused with a message that says how to save the file.

Roadmap

No dates and no promises; this is what is planned and what is not.

• Done: a release on PyPI, a hosted browser version, adapter examples for common APIs.
• Next: fixes and examples that real use asks for. Nothing else is scheduled.
• Being considered: intervals for the rates, multi-turn conversations, tool-using agents, more report languages.
• Not planned: model-based judging, or any hosted service that receives your data.

Tell us what you need in an issue.

Limitations

• It finds change, not correctness. A version that is wrong the same way every time looks stable, and cases wrong in both versions are listed separately.
• Without an answer key or your judgment, a difference is a change, not a regression.
• Passages are shown as they differ and are not graded; an ungraded passage can still carry the evidence.
• Two runs is a thin estimate of re-run variation. More runs make the stable label firmer.
• It is not a readiness or safety certificate. No output says a system is fit to deploy; the gate prints counts and the decision stays yours.
• Agents that use tools or keep state have a documented pattern and a stand-in example, but the tool has not been tried on a production agent. Not in this version: model-based judging, statistical tests.
• Size was measured on Linux with Python 3.13 only, not on Windows or macOS and not in the browser version. There, 100,000 cases with three runs of each version take about 6 seconds and 552 MB in report; the HTML report for them is 17.9 MB and opens in about 7 seconds; and a cell above 64 MB is refused with a message. The whole file is held in memory. The measured limits have the full table.

Documentation

• User guide: step by step, and how to read the report
• Use cases: thirteen situations and how to set each one up
• Developer guide: adapters, files, CI, library use
• Reference: every command and option, results.csv, the labelling rules
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ridi-check follows the RIDI method.

Contributing

Bug reports, questions and pull requests are welcome: see CONTRIBUTING.md and the changelog. Everyone taking part follows the code of conduct. Report a vulnerability privately, as SECURITY.md describes.

Licence

MIT. See LICENSE.