| Product | iFixAi |
| GitHub | ifixai-ai/iFixAi |
| Category | AI Safety / Agent Auditing |
| License | Open Source |
| Tests | 32 inspections across 5 risk categories |
| Providers | OpenAI, Anthropic, Bedrock, Azure, Gemini, and more |
What Is iFixAi?
iFixAi is an open-source auditing framework that runs 32 alignment tests against any AI agent and reports where its behavior diverges from expected norms. The tests are grouped into five categories of misalignment risk: fabrication, manipulation, deception, unpredictability, and opacity.
The core question iFixAi answers is straightforward: is this agent doing what it is supposed to do? Not in a theoretical sense — in a measurable, reproducible sense. You run the audit, get a letter grade, and see exactly which tests passed or failed. The entire run takes under five minutes.
Key Features
32 Alignment Tests
Each test targets a specific misalignment behavior. Fabrication tests check whether the agent invents facts or citations. Manipulation tests probe whether it tries to influence user decisions in ways that serve its own objectives. Deception tests look for deliberate misleading. Unpredictability tests measure consistency across repeated runs. Opacity tests evaluate whether the agent is transparent about its reasoning and limitations.
Provider-Agnostic
iFixAi works with OpenAI, Anthropic, AWS Bedrock, Azure, Google Gemini, and other providers. You point it at any agent endpoint and it runs the same battery of tests regardless of the underlying model. This makes it useful for comparing alignment behavior across different providers and models.
Three Modes of Operation
You can run iFixAi as a CLI guided wizard (interactive, step by step), with explicit CLI flags (for scripting and CI/CD integration), or as a plugin where the agent itself discovers and runs the audit. The third mode is notable: the agent audits itself, which is useful for continuous monitoring in production.
Content-Addressed Manifests
Every audit run produces a content-addressed manifest that enables bit-identical replay. This means you can reproduce any audit result exactly, which matters for compliance, for comparing results over time, and for verifying that a model update did not introduce new alignment issues.
Who Is This For?
- Teams deploying AI agents in production who need to verify alignment before and after each release
- Enterprise compliance teams that need auditable, reproducible evidence of AI behavior
- AI safety researchers who want a standardized benchmark for agent alignment
- Anyone evaluating AI providers and wants to compare alignment across models
Pros
- Open source and self-hostable
- 32 tests across 5 well-defined risk categories
- Provider-agnostic — works with any major AI provider
- Reproducible results with content-addressed manifests
- Self-audit mode for continuous monitoring
- Runs in under 5 minutes
- Letter grade output for quick assessment
Cons
- Tests reflect current alignment research — may miss novel misalignment patterns
- Agent self-audit mode raises questions about testing objectivity
- Young project (created April 2026)
- 32 tests may not cover domain-specific alignment requirements
- Requires API access to the agent being tested
Verdict
iFixAi fills a gap that most teams deploying AI agents know exists but few have addressed: systematic, reproducible alignment testing. The 32-test framework gives you a concrete baseline rather than anecdotal impressions of agent behavior. The content-addressed manifests make results auditable and comparable over time.
The provider-agnostic design is the right call — alignment testing should not be locked to one vendor. And the self-audit mode, while philosophically interesting (can an agent objectively audit itself?), is practically useful for continuous monitoring in production pipelines.
For any team shipping AI agents, running iFixAi before deployment is a minimal investment (under 5 minutes, open source) with meaningful return: a documented, reproducible assessment of your agent’s alignment behavior.
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