Comparison
do-not-answer vs autoguardrails
Verdict
Pick do-not-answer if dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses; pick autoguardrails if autoguardrails is an evaluation and development framework for AI policy creation and review. It enables the iterative adjustment and testing of guardrail policies in alignment research through a controlled workflow.
Markdown twin · do-not-answer alternatives · autoguardrails alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | do-not-answer | autoguardrails |
|---|---|---|
| Maintenance | Dormant (788d since push) As of 2w · github_public_v1 | Active (8d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · 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
- do-not-answer
- A Dataset for Evaluating Safeguards in LLMs
- autoguardrails
- Alignment-research scaffold for LLM guardrails involving policy evaluation and content moderation
Stars
- do-not-answer
- 339
- autoguardrails
- 128
Forks
- do-not-answer
- 29
- autoguardrails
- 35
Open issues
- do-not-answer
- 0
- autoguardrails
- 2
Language
- do-not-answer
- Jupyter Notebook
- autoguardrails
- Python
Adopt for
- do-not-answer
- Dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses.
- autoguardrails
- Autoguardrails is an evaluation and development framework for AI policy creation and review. It enables the iterative adjustment and testing of guardrail policies in alignment research through a controlled workflow.
Persona
- do-not-answer
- -
- autoguardrails
- -
Runtime
- do-not-answer
- -
- autoguardrails
- -
License
- 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.
- autoguardrails
- Apache-2.0
Last pushed
- do-not-answer
- Jun 7, 2024
- autoguardrails
- Aug 1, 2026
Categories
- do-not-answer
- Evaluation & Observability
- autoguardrails
- Evaluation & Observability, LLM Frameworks
Trust and health
Maintenance
- do-not-answer
- Dormant (18%)
- autoguardrails
- Active (82%)
Days since push
- do-not-answer
- 788d
- autoguardrails
- 8d
Open issues (now)
- do-not-answer
- 0
- autoguardrails
- 2
Full report
- do-not-answer
- Trust report
- autoguardrails
- Trust report
Choose do-not-answer if…
- do-not-answer is primarily Jupyter Notebook; autoguardrails 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.
Choose autoguardrails if…
- autoguardrails is primarily Python; do-not-answer is Jupyter Notebook.
- Requirements: Requires Python 3.10 or higher.; No third-party runtimes; it is built completely on the standard Python library..
- Tags unique to autoguardrails: ai safety, alignment, autoresearch, content-moderation.
- Also covers LLM Frameworks.
- When you are conducting alignment research that requires systematic iteration on LLM safeguard policies.
When NOT to use autoguardrails
- Autoguardrails may not suit needs requiring real-time or dynamic policy adjustments outside its autoresearch workflow.
- Avoid using Autoguardrails if you cannot accept offline operation as it is built on the Python standard library and runs without third-party runtime dependencies.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- 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 (SantanderAI/autoguardrails) · observed Aug 9, 2026
- GitHub forks (SantanderAI/autoguardrails) · observed Aug 9, 2026
- Last push (SantanderAI/autoguardrails) · observed Aug 1, 2026
- License file (Apache-2.0) · observed Aug 9, 2026
- Decision facts (enrichment) · observed Jul 15, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: do-not-answer 339 · autoguardrails 128 (synced Aug 5, 2026).
Common questions
- What is the difference between do-not-answer and autoguardrails?
- do-not-answer: A Dataset for Evaluating Safeguards in LLMs. autoguardrails: Alignment-research scaffold for LLM guardrails involving policy evaluation and content moderation. See the comparison table for live GitHub stats and shared categories.
- When should I choose do-not-answer over autoguardrails?
- Choose do-not-answer over autoguardrails when do-not-answer is primarily Jupyter Notebook; autoguardrails 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 choose autoguardrails over do-not-answer?
- Choose autoguardrails over do-not-answer when autoguardrails is primarily Python; do-not-answer is Jupyter Notebook; Requirements: Requires Python 3.10 or higher.; No third-party runtimes; it is built completely on the standard Python library.; Tags unique to autoguardrails: ai safety, alignment, autoresearch, content-moderation; Also covers LLM Frameworks; When you are conducting alignment research that requires systematic iteration on LLM safeguard policies.
- 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.
- When should I avoid autoguardrails?
- Autoguardrails may not suit needs requiring real-time or dynamic policy adjustments outside its autoresearch workflow. Avoid using Autoguardrails if you cannot accept offline operation as it is built on the Python standard library and runs without third-party runtime dependencies.
- Is do-not-answer or autoguardrails more popular on GitHub?
- do-not-answer has more GitHub stars (339 vs 128). Stars measure visibility, not whether either tool fits your constraints.
- Are do-not-answer and autoguardrails open source?
- Yes - both are open-source projects on GitHub (do-not-answer: Apache-2.0, autoguardrails: Apache-2.0).
- Where can I find alternatives to do-not-answer or autoguardrails?
- GraphCanon lists graph-backed alternatives at do-not-answer alternatives and autoguardrails alternatives (do-not-answer markdown twin, autoguardrails 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, do-not-answer or autoguardrails?
- do-not-answer: Dormant. autoguardrails: Active. 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 do-not-answer and autoguardrails?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: do-not-answer trust report; autoguardrails trust report.