--- title: "awesome-ai-guardrails vs do-not-answer" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/enguard-ai-awesome-ai-guardrails-vs-libr-ai-do-not-answer" tools: ["enguard-ai-awesome-ai-guardrails", "libr-ai-do-not-answer"] --- # awesome-ai-guardrails vs do-not-answer *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 do-not-answer if dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses. [awesome-ai-guardrails](https://huggingface.co/collections/enguard/) reports 62 GitHub stars, 11 forks, and 1 open issues, last pushed Jul 30, 2026. [do-not-answer](https://github.com/Libr-AI/do-not-answer) has 339 stars, 29 forks, and 0 open issues, last pushed Jun 7, 2024. Figures are from public GitHub metadata via [awesome-ai-guardrails's repository](https://github.com/enguard-ai/awesome-ai-guardrails) and [do-not-answer's repository](https://github.com/Libr-AI/do-not-answer). | | [awesome-ai-guardrails](/tools/enguard-ai-awesome-ai-guardrails.md) | [do-not-answer](/tools/libr-ai-do-not-answer.md) | | --- | --- | --- | | Tagline | A curated list of materials on AI guardrails | A Dataset for Evaluating Safeguards in LLMs | | Stars | 62 | 339 | | Forks | 11 | 29 | | Open issues | 1 | 0 | | Language | Python | Jupyter Notebook | | 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. | Dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Dual licensing model, datasets under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License and source files under Apache 2.0 license. | | 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) | [do-not-answer](/tools/libr-ai-do-not-answer.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 10d | 788d | | Open issues (now) | 1 | 0 | | Full report | [trust report](/tools/enguard-ai-awesome-ai-guardrails/trust.md) | [trust report](/tools/libr-ai-do-not-answer/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: do-not-answer - **Adopt for:** Dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses. - **License detail:** Dual licensing model, datasets under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License and source files under Apache 2.0 license. ## Choose when ### Choose awesome-ai-guardrails if… - awesome-ai-guardrails is primarily Python; do-not-answer is Jupyter Notebook. - 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 do-not-answer if… - do-not-answer is primarily Jupyter Notebook; awesome-ai-guardrails is Python. - Tags unique to do-not-answer: datasets, ethical ai, llm-evaluation, safeguard testing. - To assess the reliability of safeguards implemented in your Large Language Model. ## 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 do-not-answer - If you require tools for direct implementation or fine-tuning LLMs rather than evaluating them. - Your project does not involve assessing ethical compliance or safeguard measures within language models. ## Common questions ### What is the difference between awesome-ai-guardrails and do-not-answer? awesome-ai-guardrails: A curated list of materials on AI guardrails. do-not-answer: A Dataset for Evaluating Safeguards in LLMs. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-ai-guardrails over do-not-answer? Choose awesome-ai-guardrails over do-not-answer when awesome-ai-guardrails is primarily Python; do-not-answer is Jupyter Notebook; 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 do-not-answer over awesome-ai-guardrails? Choose do-not-answer over awesome-ai-guardrails when do-not-answer is primarily Jupyter Notebook; awesome-ai-guardrails is Python; Tags unique to do-not-answer: datasets, ethical ai, llm-evaluation, safeguard testing; To assess the reliability of safeguards implemented in your Large Language Model. ### 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 do-not-answer? If you require tools for direct implementation or fine-tuning LLMs rather than evaluating them. Your project does not involve assessing ethical compliance or safeguard measures within language models. ### Is awesome-ai-guardrails or do-not-answer more popular on GitHub? do-not-answer has more GitHub stars (339 vs 62). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-ai-guardrails and do-not-answer open source? Yes - both are open-source projects on GitHub (awesome-ai-guardrails: Apache-2.0, do-not-answer: Apache-2.0). ### Where can I find alternatives to awesome-ai-guardrails or do-not-answer? GraphCanon lists graph-backed alternatives at [awesome-ai-guardrails alternatives](/tools/enguard-ai-awesome-ai-guardrails/alternatives) and [do-not-answer alternatives](/tools/libr-ai-do-not-answer/alternatives) ([awesome-ai-guardrails markdown twin](/tools/enguard-ai-awesome-ai-guardrails/alternatives.md), [do-not-answer markdown twin](/tools/libr-ai-do-not-answer/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-libr-ai-do-not-answer.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 do-not-answer? awesome-ai-guardrails: Active. do-not-answer: 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 do-not-answer? 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); [do-not-answer trust report](/tools/libr-ai-do-not-answer/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/_