The short answer
iFixAi is an open-source command-line diagnostic that runs 32 alignment inspections across five categories against any LLM-backed agent. It installs with pip, runs in about five minutes in Standard mode, and refuses to produce cross-vendor comparative scores unless you supply credentials for at least two providers. That refusal is the most intellectually honest design choice in current AI tooling.
If you ship anything that calls a large language model in production, iFixAi is worth thirty minutes of your Tuesday. Not because it will fix your AI. It will not. It is a diagnostic. It tells you what is broken, runs 32 distinct inspections across five categories, and writes the receipts to disk so you can defend the result to anyone who asks.
Most of the AI tooling space could learn something from how it is designed. That is the actual story.
What it is
iFixAi is an open-source command-line tool, installed via pip, that evaluates an LLM-backed system against a defined set of alignment inspections. You install it, you set API keys for whichever providers you want it to talk to (OpenAI, Anthropic, Gemini, Azure, Bedrock, HuggingFace, or a generic HTTP endpoint), and you run a single command. Standard mode finishes in about five minutes. Full mode is slower and more thorough, but it is the one you run before a launch or after a model change.
Output lands in a runs/<run_id>/ directory: manifests of what was tested, transcripts of every prompt and response, and a final scorecard. You can hand that directory to a regulator or an internal audit team and the work defends itself.
The quickstart, in two minutes
pip install ifixai[openai,anthropic]
export OPENAI_API_KEY=...
export ANTHROPIC_API_KEY=...
ifixai run --mode standard
That is it. The smoke test takes about thirty seconds. The Standard mode suite is a five-minute coffee. The Full suite is dinner. The docs walk through extending the tool with a custom ChatProvider if your system is not on the supported list.
The thing nobody else does
Here is the design choice that elevates this tool above the rest of the alignment-tooling crowd.
If you only set one provider's credentials, iFixAi flags every cross-vendor comparative score it produces with an explicit bias warning. It refuses to let you quietly run a "we compared model A against model B" report unless you actually had credentials for both. If you try, the manifest itself disclaims the result.
This sounds small. It is not. Half the AI-tooling marketplace is built on letting customers run "comparisons" with a single vendor's keys, then publishing scorecards that systematically favor whichever vendor footed the bill. iFixAi will not let you do that, and it documents why it will not, in the run manifest, every time. That is the most intellectually honest piece of AI tooling I have seen ship this year.
The lesson is broader than the tool. If your vendors do not refuse to produce numbers they cannot defend, their numbers are not defending anything.
Who should actually use this
Engineers and teams that ship LLM-backed agents to production. Specifically: anyone responsible for the version that ends up in front of a customer, anyone who has to answer for the system's behavior after deployment, anyone whose company is starting to get serious about AI governance.
It is not a business-buyer tool. A mid-market marketing director does not run this. Their vendor's engineering team should be running it, and the marketing director should be asking for the run output as part of vendor due diligence. That is a different conversation, covered in the companion white paper on evaluating AI vendor safety claims.
What it does not do
It does not fix anything. It will not tune your prompts, retrain your model, or rewrite your guardrails. It tells you which inspections failed and gives you the transcripts. Acting on that is on you.
It does not audit your training data or your fine-tuning pipeline. The scope is the deployed system's behavior, not how that system came to be.
It does not replace governance. Running iFixAi monthly does not satisfy any actual compliance regime by itself. It produces evidence that fits into a regime.
What I would do with it on a Tuesday
If I had an agent in production, I would run the Full suite once against the current model, save the run directory as the baseline, and re-run on every model swap or significant prompt change. Then I would publish the run identifier and the high-level scorecard in our internal documentation, with the manifest available on request.
That is not a heavy lift. It is enough to defend the system to a board, a regulator, or a customer who asks. And every team that ships AI is going to be asked, eventually.
The verdict
Adopt it if you ship LLM agents. Study its design discipline if you build AI tooling of any kind. The 32 inspections matter, but the willingness to refuse a number it cannot honestly produce matters more. The bar is here now. Tools that do not clear it should be evaluated accordingly.
Companion read for the business side, what to demand of your AI vendors and how to make sure the answer is defensible: How to Evaluate AI Vendor Safety Claims.
How to Evaluate AI Vendor Safety Claims, A Framework Your Board Can Sign Off On
Your AI vendors all claim 'safety testing.' Almost none of them mean the same thing. This is the five-question framework your risk team and your CFO can both defend, with an open-source diagnostic as a concrete example of what good looks like.
Read the white paper →Get a free AI-powered SEO audit of your site
We'll crawl your site, benchmark your local pack, and hand you a prioritized fix list in minutes. No call required.
Run my free audit