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iFixAi
iFixAi is a Python diagnostic for checking how a language model or AI agent behaves across a fixed set of behavioral and governance inspections, then producing a scorecard and reviewable run artifacts.
The project ships 32 core and 13 extended inspections, provider adapters, guided and scriptable CLI paths, a Codex and Claude Code plugin, an agent-skill installer, and JSON and Markdown reports. Its documentation describes the result as a repeatable diagnostic, not a certification or safety guarantee. Use this as a first read, not a recommendation. Open the original project before trusting details like terms, limits, privacy, cost, setup, or safety.
What it is
A fixed inspection suite for AI behavior
iFixAi runs selected or full suites against a bare model API or an agent reached through an adapter. The repository includes inspection definitions, rubrics, fixtures, scoring code, provider connections, and report generation rather than only a hosted dashboard.
Why it stands out
Repeatable runs with inspectable evidence
Runs can record inputs in a manifest and produce JSON and Markdown outputs. Standard mode can use a judge from a different provider, while Full mode supports multiple judges and records their verdicts for later review.
Availability
Python package, CLI, plugin, and skill
The project is available as an Apache-2.0 GitHub repository and a Python 3.10+ package. Readers can start with a built-in mock run, configure a provider through the CLI, use the Codex or Claude Code plugin, or scaffold a command for other coding agents.
Why it matters
What makes it useful
A model can answer ordinary prompts well and still fail when tools, permissions, citations, retrieval, long sessions, or human escalation enter the workflow. iFixAi packages those questions into repeatable inspections, so a reader can rerun the same suite after changing a model, prompt, provider, policy, or agent setup and review what moved.
What to know
Where it fits
This belongs in the evaluation and regression-testing layer, not the runtime-control layer. Unlike Microsoft ASSERT, which generates scenarios from written requirements, iFixAi supplies a fixed inspection catalog. Unlike Microsoft Agent Governance Toolkit, it diagnoses behavior around controls rather than installing those controls around an agent.
Notable points
What stands out
The repository documents suites for smoke, strategic, core, extended, and full runs. It supports model-provider adapters and agent adapters, but inspections that need audit trails, authorization hooks, retrieval sources, or other control-plane evidence can return insufficient evidence unless the tested system exposes those capabilities.
Before using
What to review
Start with the built-in mock path if the goal is to check the local pipeline without sending prompts or spending provider credits.
Review which model or agent is under test, which provider judges it, what prompts or responses those services receive, and the cost estimate before a real run.
Treat self-judged runs as smoke tests. The project reserves its cross-provider framing for runs where a different provider judges the system under test.
Check whether a governance result was measured from runtime hooks, supplied through a declared fixture, synthesized from configuration, or marked insufficient evidence.
Remember that the adversarial corpora are public, individual runs can vary, and scorecards from different fixtures are not directly comparable.
Review the default pseudonymous telemetry before running outside CI. The project documents the fields, indefinite event retention, and opt-out paths including --no-telemetry, IFIXAI_TELEMETRY=0, and DO_NOT_TRACK=1.
Reader fit
Who may find it relevant
People maintaining an AI agent who want repeatable regression checks after changing prompts, models, tools, retrieval, permissions, or providers.
Developers comparing how the same fixture behaves across model and judge combinations.
Teams that want JSON, Markdown, manifest, and scorecard artifacts they can keep with an evaluation run.
Less relevant for readers looking for a consumer chatbot, a no-code repair button, or proof that an AI system is safe or certified.
Editorial note
Why LifeHubber lists it
iFixAi is useful when an agent setup keeps changing but the questions used to challenge it should stay repeatable. Its fixed suites, provider separation, manifests, and report artifacts make regressions easier to revisit without turning one grade into a safety verdict.
Source links
Source materials
Reader note
Before relying on this entry
LifeHubber lists entries to help readers inspect AI projects, not to endorse them or prove they are safe, suitable, accurate, maintained, or right for a specific use. We do not verify every entry in depth. Before relying on anything listed, review the original materials, terms, privacy practices, limits, and risks that matter for your situation.
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