iFixAi
Independent Auditing of AI Agents. Run by human or the agent itself, to answer the most crucial question in the AI Agent Economy. Is the agent doing what is supposed to do? With iFixAi you can have this answer in less than 120 seconds.
Runssource: GitHubhomepagePythonApache-2.0commit a97869975ff7
Python, Apache-2.0 licensed. The project labels itself: agent evaluation, ai, ai alignment, ai evaluation, ai governance, ai safety, cli and diagnostic tool.
iFixAi runs. An Argusic agent installed it in 2.5 minutes and hit no errors on a clean machine with no GPU, and the whole session was recorded.
ifixai is installed in a venv, passes layout validation, ruff linting, and bandit security scan, and runs all 60 inspections against the mock provider, producing JSON and Markdown reports.
What the agent ended up with on a clean machine, in its own words. How this is measured
Measured by Argusic on a fresh machine every time. Every number links to its evidence. Argusic Score 96 of 100 (the mean of this project's run scores).
At a glance
Subject data from GitHub, linked at the top of this page, refreshed . Test data by Argusic (CC BY 4.0); every number links to a run page with the full log, the recording, and their sha256 hashes.
What was measured
From the run that decided this verdict. Every line is a recorded value, not an estimate.
- Install time
- 3 minutes
- Cold machine to finish
- 3 minutes
- Errors hit and fixed
- 0 hit, 0 fixed with no human help
- How the result was proved
- pip install -e '.[openai,dev]' exited 0; ruff check ifixai passed; bandit -r ifixai -ll passed; ifixai validate passed (60 tests); ifixai validate checked 11/11 example fixtures as Valid; ifixai run --provider mock --api-key not-used --eval-mode self executed all 60 inspections in 11s and wrote JSON+Markdown reports; ifixai list suites/tests/fixtures/categories all returned structured data
- Model tokens used
- 149,691
- Exact commit tested
- a97869975ff7
- Container image
- sha256:cdd920bce783
What was not measured
The other half, so you can see exactly how far this evidence goes. A gap is printed as a gap and never filled in.
- Peak GPU memory
- this ran on a CPU machine, so there was no GPU to sample
- Behaviour over a long run
- each attempt uses the smallest documented settings and a fixed time budget, so this measures whether it runs, not how it holds up under load
The end of the recording
The last lines of the terminal session that decided this verdict, exactly as they were recorded.
/bin/sh -lc "cd /work/repo && . .venv/bin/activate && python3 -c \"
import pathlib
# check reports exist
r = list(pathlib.Path('ifixai-results').glob('*.json'))
print(f'Report JSONs produced: {len(r)}')
\" 2>&1" in /work/repo
succeeded in 0ms:
Report JSONs produced: 3
All checks passed with zero errors. Here is the final report:
tokens used
149,691
All checks passed with zero errors. Here is the final report:Replay the whole session, every command from a clean machine to this point.
How it was tested
One agent, one container, no human help. It installs the project the way its own documentation says to, runs it, and fixes what breaks. Everything below is recorded as it happened: the terminal session, the log and the exact commit. The full method.
Strengths and limits
Measured facts, not opinions. How this is written.
What went well
- Reached a running state on a clean machine, with the session recorded.
- Installed in 2.5 minutes, faster than the median of the 3 comparable projects Argusic has measured.
- Nothing broke on the way: zero errors between clone and running.
- Ran without a GPU, so it does not need one to start.
- Licensed Apache-2.0, as reported by its host.
- Measured 2 times, so the result is not a one-off.
Of the 4 agent-evaluation projects Argusic has installed and timed, iFixAi was the 1st fastest to reach a running state, and 2 of 4 reached one at all.
What it is a reasonable choice for
- Trying it on a laptop or a small server: it reached a running state without a GPU.
- A quick evaluation: it was running 2.5 minutes after the clone.
- Following its own README: the documented path worked with nothing to fix.
Also tested, in the same area
Every one of these was installed and run by Argusic on a clean machine. Nothing appears here that was not tested.
Run history
| tested by | attempt | outcome | score | cost USD | when |
|---|---|---|---|---|---|
| Argusic Runner | 2/3 | Runs with mocks | 92.00 | 0.05 | |
| Argusic Runner | 1/3 | Runs | 100.00 | 0.02 |
Topics (from GitHub)
agent-evaluationaiai-alignmentai-evaluationai-governanceai-safetyclidiagnostic-tooleu-ai-acthallucination-detectioniso-42001llm-evaluationllm-securitynist-ai-rmfowasp-llmprompt-injectionpythonresponsible-airisk-assessmentrisk-management
Embed the badge
Markdown for the project README. It links back here; terms on the terms page.
[](https://argusic.com/subject/ifixai)Questions
- Does iFixAi run?
- Yes. iFixAi runs. Argusic installed and launched it on a clean machine in 3 minutes, hitting 0 errors on the way, and recorded the session.
- How did Argusic test iFixAi?
- On a fresh, disposable machine, with every command recorded and the repository pinned at commit a97869975ff7. 2 attempts are recorded, and the full method is on the methodology page.
- What did it cost to test iFixAi?
- The run that produced this verdict cost $0.02: real compute and model cost, not a list price per million tokens. It is recorded on that run's page.
- How long does iFixAi take to install?
- 2.5 minutes in the run that decided this verdict, from a clean machine to a running state. That is one measured attempt, not an average.
- Does iFixAi need a GPU?
- Not to start. Argusic reached a running state on a machine with no GPU.
- What goes wrong when installing iFixAi?
- Nothing did, in the recorded run: zero errors between clone and running.
- How does iFixAi compare with the alternatives?
- Of the 4 agent-evaluation projects Argusic has installed and timed, iFixAi was the 1st fastest to reach a running state, and 2 of 4 reached one at all.
- Where is the evidence for iFixAi?
- All 2 recorded runs are on this page, each linking to its full log and terminal recording, stored with a sha256 fingerprint so it cannot be quietly altered.