Comparison
awesome-ai-guardrails vs do-not-answer
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.
Markdown twin · awesome-ai-guardrails alternatives · do-not-answer alternatives
GraphCanon updated 1w
vs
Trust & integrity
| Signal | awesome-ai-guardrails | do-not-answer |
|---|---|---|
| Maintenance | Active (10d since push) As of 1w · github_public_v1 | Dormant (788d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 1w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- awesome-ai-guardrails
- A curated list of materials on AI guardrails
- do-not-answer
- A Dataset for Evaluating Safeguards in LLMs
Stars
- awesome-ai-guardrails
- 62
- do-not-answer
- 339
Forks
- awesome-ai-guardrails
- 11
- do-not-answer
- 29
Open issues
- awesome-ai-guardrails
- 1
- do-not-answer
- 0
Language
- awesome-ai-guardrails
- Python
- do-not-answer
- Jupyter Notebook
Adopt for
- awesome-ai-guardrails
- 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.
- do-not-answer
- Dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses.
Persona
- awesome-ai-guardrails
- -
- do-not-answer
- -
Runtime
- awesome-ai-guardrails
- -
- do-not-answer
- -
License
- awesome-ai-guardrails
- Apache-2.0
- do-not-answer
- Dual licensing model, datasets under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License and source files under Apache 2.0 license.
Last pushed
- awesome-ai-guardrails
- Jul 30, 2026
- do-not-answer
- Jun 7, 2024
Categories
- awesome-ai-guardrails
- Data & Retrieval, Evaluation & Observability
- do-not-answer
- Evaluation & Observability
Trust and health
Maintenance
- awesome-ai-guardrails
- Active (82%)
- do-not-answer
- Dormant (18%)
Days since push
- awesome-ai-guardrails
- 10d
- do-not-answer
- 788d
Open issues (now)
- awesome-ai-guardrails
- 1
- do-not-answer
- 0
Full report
- awesome-ai-guardrails
- Trust report
- do-not-answer
- Trust report
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (enguard-ai/awesome-ai-guardrails) · observed Aug 9, 2026
- GitHub forks (enguard-ai/awesome-ai-guardrails) · observed Aug 9, 2026
- Last push (enguard-ai/awesome-ai-guardrails) · observed Jul 30, 2026
- License file (Apache-2.0) · observed Aug 9, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (Libr-AI/do-not-answer) · observed Aug 5, 2026
- GitHub forks (Libr-AI/do-not-answer) · observed Aug 5, 2026
- Last push (Libr-AI/do-not-answer) · observed Jun 7, 2024
- License file (Apache-2.0) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: awesome-ai-guardrails 62 · do-not-answer 339 (synced Aug 9, 2026).
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 and do-not-answer alternatives (awesome-ai-guardrails markdown twin, do-not-answer markdown twin), 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 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; do-not-answer trust report.