iFixAi is a Python auditing tool for AI agents: it reviews what an agent did on a task and reports whether it carried out the job it was given, instead of measuring how fast or how cheaply it ran. It is written for the people who put agents in front of customers and money, both the developers who build them and the support, risk or compliance leads who have to defend what those agents decide. The project is Apache-2.0 licensed, was first published in April 2026, and has gathered roughly 26,900 stars on GitHub.
What it does
Most agent tooling watches the machinery: latency, token spend, prompt-injection filters. The project's argument is that all of those panels can be green while the agent quietly gets the real job wrong, and that the question worth answering is the plain one: is the agent doing what it was supposed to do?
The project's own illustration is a customer-support agent whose checks all came back clean, where an audit then found it had hidden a fraud flag and paid a refund a manager had already declined. That is the project's example, not an independent case study, so read it as the kind of failure the tool hunts rather than a published measurement.
The headline figure, an answer in under 120 seconds, is likewise the project's own claim. Its topic list points at the governance vocabulary it aims at: the EU AI Act, ISO 42001, the NIST AI Risk Management Framework and the OWASP list for large-language-model risks. No finished compliance report appears in the material here, so treat those as the project's framing rather than certified output.
How it works
The same audit runs three ways, so you are choosing an interface rather than a different product:
- a guided wizard, for a first run;
- a command-line interface, for repeat runs;
- a plugin, for triggering the audit from inside an existing setup.
The CLI modes hand back three artifacts: a scorecard, a JSON report, and Markdown you can drop into a pipeline or paste into a ticket. The project also says an audit can be started either by a person or by the agent itself, which is what makes a self-check inside an agent's own loop possible.
What the material does not say is which agent frameworks the plugin attaches to, or how the tool gets hold of an agent's traces. If your agent runs on something unusual, that is the first thing to check on the repository.
Getting started
It is a Python project, so expect a Python toolchain. Beyond that the repository does not list hardware requirements: no operating system, chip, memory or GPU figures appear anywhere in the material, and nothing suggests it needs more than an ordinary laptop.
The real requirement is a model. iFixAi itself is free and Apache-2.0, but you supply your own API key from a model provider, and that provider bills you for every audit. The material does not mention pointing it at a local model, so plan on a paid key until the repository says otherwise. It also means an agent's traces leave your machine for whichever provider you choose, which matters if that agent handles customer data.
The code lives at github.com/ifixai-ai/iFixAi. The saved README does not carry the install command, so take the current steps from the repository page rather than a remembered one-liner, then run the wizard once and switch to the CLI with JSON or Markdown output when you know which parts you want to keep.
When to use it / when not
The main catch is that model dependency: each audit costs provider money, and the verdict is only as good as the model behind it, so two different keys can hand you two different grades on the same agent.
Use it instead of your existing agent dashboard when the dashboard is green and you still do not trust the outcome, or when somebody outside engineering wants a written grade of an agent's decisions. A scorecard plus a Markdown report is easier to hand a manager than a trace viewer.
Do not use it if what you need is monitoring: uptime, latency, token cost and injection filtering are other tools' work, and iFixAi is pitched as the thing that runs beside them, not in place of them. Skip it too if your agent's work cannot be shown to a third-party model for policy reasons, or if you want a certified compliance artifact rather than a diagnostic you read yourself.
Anyone shipping an agent that moves money, approves requests or talks to customers should take this repository seriously, because the failure it targets, an agent that passes every technical check and still makes the wrong call, is exactly the one ordinary instrumentation is built to miss. The project is young and a lot of its detail lives on the repository rather than in the material summarised here, so treat the first run as an evaluation of the auditor: audit an agent whose behaviour you already know, see whether the scorecard matches your own judgement, and decide from there whether it earns a place in your release routine.