{"name":"iFixAi: Independently Audit AI Agents for Operational Alignment","description":"iFixAi is a Python CLI for independently auditing AI agents in simulations of business workflows. It probes behavior such as access control, prompt-injection resistance and policy adherence, then reports findings with evidence and a grade.","github":"https://github.com/ifixai-ai/iFixAi","url":"https://osrepos.com/repo/ifixai-ai-ifixai","source":"osrepos.com","sourceDescription":"This repository profile is provided by osrepos.com, an open source repository discovery platform.","repositoryProfile":"https://osrepos.com/repo/ifixai-ai-ifixai","generatedFor":"open source discovery and AI-assisted research","markdown":"https://osrepos.com/repo/ifixai-ai-ifixai.md","json":"https://osrepos.com/repo/ifixai-ai-ifixai.json","topics":["python","ai-agents","cli","evaluation","ai-governance","llm-security","prompt-injection"],"keywords":["python","ai-agents","cli","evaluation","ai-governance","llm-security","prompt-injection"],"stars":null,"summary":"iFixAi is a Python CLI for independently auditing AI agents in simulations of business workflows. It probes behavior such as access control, prompt-injection resistance and policy adherence, then reports findings with evidence and a grade.","content":"## Overview\n\niFixAi is a Python CLI for independent auditing of AI agents and operational alignment. It simulates business scenarios, including requests that test an agent's limits, and uses a judge model to assess whether the agent acts within its intended role and authority. The project frames this as broader than cybersecurity or compliance alone, combining red teaming, governance and assurance.\n\nIt is aimed at teams that operate chat-based agents and want evidence about how those agents respond to realistic requests before deployment or as part of ongoing checks. The README describes support for OpenAI-compatible agent endpoints and custom integrations, with judges selected from supported model providers separately from the agent's provider.\n\n## Key Features\n- Runs a default suite of 10 chat-based inspections, with a full-run option covering 61.\n- Tests authorization boundaries, prompt injection, policy violations, privacy, uncertainty, failure reporting and task adherence.\n- Grades results across five pillars: fabrication, manipulation, deception, unpredictability and opacity.\n- Shows evidence for each inspection and produces JSON and Markdown reports.\n- Offers a dry run to preview test and judge-call counts and checks that do not apply.\n- Provides a mock demo flow that runs without an API key, plus a CLI, Python API and integrations for coding agents.\n\n## Use Cases\n- An AI product team can test whether a support agent follows role-based permissions before connecting it to production tools.\n- A security or AI governance team can probe for prompt injection, unauthorized actions and cross-user data exposure.\n- A developer can check whether an agent reports errors honestly instead of claiming an action succeeded.\n- A team can compare JSON or Markdown audit reports across agent changes or use the minimum-score option to gate CI.\n\n## Our Take\niFixAi is an active, Apache-2.0-licensed audit toolkit with a substantial inspection suite, but teams should validate its simulation and grading approach against their own risks before treating results as assurance.\n\n**Good fit if:**\n- You operate a chat-based AI agent and need structured checks of authorization, policy adherence or reliability.\n- You can provide an agent endpoint or write a small Python integration, and can configure a separate judge provider for real audits.\n- You want an Apache-2.0 project with recent commits and releases and contributions from a broad group of developers.\n\n**Look elsewhere if:**\n- You need a turnkey audit for agents that cannot be reached through chat or integrated with the documented interfaces.\n- Your requirement is only to test a narrow security property, rather than assess behavior across the project's broader operational-alignment scope.\n- You need an audit that runs entirely without external model-provider credentials; only the mock demonstration is described as requiring no API key.\n\n## Project Health\n\n| Signal | Value |\n|---|---|\n| Status | **Active**: regular commits and releases |\n| Last commit | 2026-11-12 (today) |\n| Commits, last 90 days | 100+ |\n| Releases, last 12 months | 23 (latest v4.3.0, 2026-10-22) |\n| Contributors | 24 (top contributor: 63% of commits) |\n| Issues closed, last 90 days | 2+ |\n| Pull requests merged, last 90 days | 96 (typically merged in 3 days) |\n| Project age | 7 months |\n\nChecked on 2026-11-13 with the GitHub API.\n\n## Project Facts\n\n- Language: Python\n- License: Apache-2.0\n- Stars: 45.2k\n- Forks: 1.8k\n- Topics: agent-evaluation, ai, ai-alignment, ai-evaluation, ai-governance, ai-safety, cli, diagnostic-tool\n- Website: https://www.ifixai.ai\n- Archived: no\n\n## What You Need\n\nDetected in the repository:\n\n- Python >=3.10 (from pyproject.toml)\n- Automated checks on GitHub Actions\n\n## Getting Started\nRequires Python 3.10 or newer. Install and run the no-key demo:\n\n```bash\npip install ifixai\nifixai author reference -o reference.yaml\nifixai validate reference.yaml\nifixai run --provider mock --eval-mode self --fixture reference.yaml\n```\n\nFor real-agent setup, provider configuration and integration details, see the [README](https://github.com/ifixai-ai/iFixAi#readme) and [documentation](https://github.com/ifixai-ai/iFixAi/tree/main/docs).\n\n## License in Plain Words\n\n**Apache-2.0** (permissive).\n\n- Commercial use: yes\n- Modify and redistribute: yes\n- You must keep: the license, the NOTICE file and a note of your changes\n- Share your changes: no\n- Includes an explicit patent grant from the contributors\n\nA summary, not legal advice: the LICENSE file is what applies.\n\n## Alternatives\n\n- [agent-anvil](https://osrepos.com/repo/agent-axiom-agent-anvil): Agent Anvil evaluates tool-using agents with scenario runs, trace checks, and optional semantic grading, rather than auditing simulated business workflows for security and policy issues.\n- [skill-up](https://osrepos.com/repo/alibaba-skill-up): skill-up evaluates agents and Agent Skills using repeatable cases and grading, with support for external agent engines, rather than focusing on security audits of business workflows.\n- [agentevals](https://osrepos.com/repo/agentevals-dev-agentevals): agentevals scores existing OpenTelemetry traces without rerunning agents, while iFixAi probes agents in simulated workflows for security and policy failures.\n\n| Project | Language | License | Stars | Status |\n|---|---|---|---|---|\n| **iFixAi** | Python | Apache-2.0 | 45.2k | Active |\n| [agent-anvil](https://osrepos.com/repo/agent-axiom-agent-anvil) | Python | MIT | 9 | New |\n| [skill-up](https://osrepos.com/repo/alibaba-skill-up) | Go | Apache-2.0 | 1.4k | New |\n| [agentevals](https://osrepos.com/repo/agentevals-dev-agentevals) | Python | Apache-2.0 | 162 | Active |\n\n## Considerations\n- A real audit sends probes to the agent, so the README recommends using a staging copy. The judge also makes model calls, which may incur costs; use the dry-run option to inspect the plan first.\n- The documented interface is a chat endpoint, with a few lines of Python needed for non-OpenAI-compatible endpoints. Teams with other agent architectures should confirm integration effort before adoption.\n- The project is actively maintained, with frequent recent commits and releases and contributions from 24 people. This provides evidence of ongoing development, not independent validation of audit accuracy.\n- The README presents grades and inspection findings as audit outputs. Teams should assess their methodology and decide how much weight to place on those results in their own assurance process.","metrics":{"detailViews":2,"githubClicks":0},"dates":{"published":null,"modified":"2026-11-13T14:27:29.000Z"}}