--- title: "awesome-ai-guardrails vs Failed-ML" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/enguard-ai-awesome-ai-guardrails-vs-kennethleungty-failed-ml" tools: ["enguard-ai-awesome-ai-guardrails", "kennethleungty-failed-ml"] --- # awesome-ai-guardrails vs Failed-ML *GraphCanon updated Aug 9, 2026* ## Verdict Pick awesome-ai-guardrails if awesome-ai-guardrails offers a comprehensive list of tools focused on ensuring ethical and secure usage of AI technologies by tackling inappropriate content, offensive language, deepfakes, privacy violations, and more; pick Failed-ML if failed-ML is compiled around high-profile ML project failures across domains and includes detailed insights from these cases to help understand common pitfalls in implementing machine learning. [awesome-ai-guardrails](https://huggingface.co/collections/enguard/) reports 62 GitHub stars, 11 forks, and 1 open issues, last pushed Jul 30, 2026. [Failed-ML](https://towardsdatascience.com/when-ai-goes-astray-high-profile-machine-learning-mishaps-in-the-real-world-26bd58692195) has 753 stars, 51 forks, and 0 open issues, last pushed Jun 14, 2024. Figures are from public GitHub metadata via [awesome-ai-guardrails's repository](https://github.com/enguard-ai/awesome-ai-guardrails) and [Failed-ML's repository](https://github.com/kennethleungty/Failed-ML). | | [awesome-ai-guardrails](/tools/enguard-ai-awesome-ai-guardrails.md) | [Failed-ML](/tools/kennethleungty-failed-ml.md) | | --- | --- | --- | | Tagline | A curated list of materials on AI guardrails | Compilation of high-profile real-world examples of failed machine learning projects | | Stars | 62 | 753 | | Forks | 11 | 51 | | Open issues | 1 | 0 | | Language | Python | - | | Adopt for | awesome-ai-guardrails offers a comprehensive list of tools focused on ensuring ethical and secure usage of AI technologies by tackling inappropriate content, offensive language, deepfakes, privacy violations, and more. | Failed-ML is compiled around high-profile ML project failures across domains and includes detailed insights from these cases to help understand common pitfalls in implementing machine learning systems. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Data & Retrieval, Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-ai-guardrails](/tools/enguard-ai-awesome-ai-guardrails.md) | [Failed-ML](/tools/kennethleungty-failed-ml.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 10d | 777d | | Open issues (now) | 1 | 0 | | Owner type | Organization | User | | Full report | [trust report](/tools/enguard-ai-awesome-ai-guardrails/trust.md) | [trust report](/tools/kennethleungty-failed-ml/trust.md) | ## Decision facts: awesome-ai-guardrails - **Adopt for:** awesome-ai-guardrails offers a comprehensive list of tools focused on ensuring ethical and secure usage of AI technologies by tackling inappropriate content, offensive language, deepfakes, privacy violations, and more. ## Decision facts: Failed-ML - **Hosting:** self hosted - **Pricing:** freemium - Open source under MIT license but no additional paid features are mentioned. - **Requirements:** Not a software tool that requires installation. Informational repository intended for reading and learning. - **Adopt for:** Failed-ML is compiled around high-profile ML project failures across domains and includes detailed insights from these cases to help understand common pitfalls in implementing machine learning systems. ## Choose when ### Choose awesome-ai-guardrails if… - License: awesome-ai-guardrails is Apache-2.0, Failed-ML is MIT. - Tags unique to awesome-ai-guardrails: awesome, deepfake-detection, genai, guardrails. - Also covers Data & Retrieval. - When you need to implement robust mechanisms for blocking inappropriate content and offensive language in your AI applications. ### Choose Failed-ML if… - License: Failed-ML is MIT, awesome-ai-guardrails is Apache-2.0. - Pricing: Open source under MIT license but no additional paid features are mentioned.. - Requirements: Not a software tool that requires installation. Informational repository intended for reading and learning.. - Tags unique to Failed-ML: ai, artificial-intelligence, classification, computer-vision. - When you seek specific historical examples of where machine learning application went wrong, aiding in understanding the potential mistakes and challenges one might face. ## When NOT to use awesome-ai-guardrails - If you are looking for a tool that offers code samples for real-world implementations, as awesome-ai-guardrails primarily serves as a curated list of resources rather than providing executable code. - Do not use if your project requires continuous support or updates beyond the community-driven contributions maintained within this repository. ## When NOT to use Failed-ML - If you need a prescriptive guide for solving your current project's technical problems rather than learning from industry-wide mistakes. - When looking for detailed quantitative metrics on failed projects, Failed-ML focuses more on high-level analysis of failure causes rather than specific performance numbers. ## Common questions ### What is the difference between awesome-ai-guardrails and Failed-ML? awesome-ai-guardrails: A curated list of materials on AI guardrails. Failed-ML: Compilation of high-profile real-world examples of failed machine learning projects. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-ai-guardrails over Failed-ML? Choose awesome-ai-guardrails over Failed-ML when License: awesome-ai-guardrails is Apache-2.0, Failed-ML is MIT; Tags unique to awesome-ai-guardrails: awesome, deepfake-detection, genai, guardrails; Also covers Data & Retrieval; When you need to implement robust mechanisms for blocking inappropriate content and offensive language in your AI applications. ### When should I choose Failed-ML over awesome-ai-guardrails? Choose Failed-ML over awesome-ai-guardrails when License: Failed-ML is MIT, awesome-ai-guardrails is Apache-2.0; Pricing: Open source under MIT license but no additional paid features are mentioned.; Requirements: Not a software tool that requires installation. Informational repository intended for reading and learning.; Tags unique to Failed-ML: ai, artificial-intelligence, classification, computer-vision; When you seek specific historical examples of where machine learning application went wrong, aiding in understanding the potential mistakes and challenges one might face. ### When should I avoid awesome-ai-guardrails? If you are looking for a tool that offers code samples for real-world implementations, as awesome-ai-guardrails primarily serves as a curated list of resources rather than providing executable code. Do not use if your project requires continuous support or updates beyond the community-driven contributions maintained within this repository. ### When should I avoid Failed-ML? If you need a prescriptive guide for solving your current project's technical problems rather than learning from industry-wide mistakes. When looking for detailed quantitative metrics on failed projects, Failed-ML focuses more on high-level analysis of failure causes rather than specific performance numbers. ### Is awesome-ai-guardrails or Failed-ML more popular on GitHub? Failed-ML has more GitHub stars (753 vs 62). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-ai-guardrails and Failed-ML open source? Yes - both are open-source projects on GitHub (awesome-ai-guardrails: Apache-2.0, Failed-ML: MIT). ### Where can I find alternatives to awesome-ai-guardrails or Failed-ML? GraphCanon lists graph-backed alternatives at [awesome-ai-guardrails alternatives](/tools/enguard-ai-awesome-ai-guardrails/alternatives) and [Failed-ML alternatives](/tools/kennethleungty-failed-ml/alternatives) ([awesome-ai-guardrails markdown twin](/tools/enguard-ai-awesome-ai-guardrails/alternatives.md), [Failed-ML markdown twin](/tools/kennethleungty-failed-ml/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/enguard-ai-awesome-ai-guardrails-vs-kennethleungty-failed-ml.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-ai-guardrails or Failed-ML? awesome-ai-guardrails: Active. Failed-ML: Dormant. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for awesome-ai-guardrails and Failed-ML? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-ai-guardrails trust report](/tools/enguard-ai-awesome-ai-guardrails/trust); [Failed-ML trust report](/tools/kennethleungty-failed-ml/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=enguard-ai-awesome-ai-guardrails`](/api/graphcanon/graph?tool=enguard-ai-awesome-ai-guardrails) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_