--- title: "AgentGuard vs do-not-answer" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dipampaul17-agentguard-vs-libr-ai-do-not-answer" tools: ["dipampaul17-agentguard", "libr-ai-do-not-answer"] --- # AgentGuard vs do-not-answer *GraphCanon updated Aug 9, 2026* ## Verdict Pick AgentGuard if agentGuard is a budget-conscious observer for real-time token spending by AI agents and LLMs, integrating with major providers like OpenAI and Anthropic; pick do-not-answer if dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses. [AgentGuard](https://github.com/dipampaul17/AgentGuard) reports 171 GitHub stars, 10 forks, and 1 open issues, last pushed Jul 31, 2025. [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 [AgentGuard's repository](https://github.com/dipampaul17/AgentGuard) and [do-not-answer's repository](https://github.com/Libr-AI/do-not-answer). | | [AgentGuard](/tools/dipampaul17-agentguard.md) | [do-not-answer](/tools/libr-ai-do-not-answer.md) | | --- | --- | --- | | Tagline | Real-time guardrail that monitors token spend and manages LLM/agent loops in real time | A Dataset for Evaluating Safeguards in LLMs | | Stars | 171 | 339 | | Forks | 10 | 29 | | Open issues | 1 | 0 | | Language | JavaScript | Jupyter Notebook | | Adopt for | AgentGuard is a budget-conscious observer for real-time token spending by AI agents and LLMs, integrating with major providers like OpenAI and Anthropic. | Dataset for evaluating safeguards in LLMs to ensure ethical compliance, distributed under both Creative Commons and Apache licenses. | | Persona | - | - | | Runtime | - | - | | License | MIT | Dual licensing model, datasets under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License and source files under Apache 2.0 license. | | Categories | Evaluation & Observability, Inference & Serving | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [AgentGuard](/tools/dipampaul17-agentguard.md) | [do-not-answer](/tools/libr-ai-do-not-answer.md) | | --- | --- | --- | | Days since push | 373d | 788d | | Open issues (now) | 1 | 0 | | Owner type | User | Organization | | Full report | [trust report](/tools/dipampaul17-agentguard/trust.md) | [trust report](/tools/libr-ai-do-not-answer/trust.md) | ## Decision facts: AgentGuard - **Adopt for:** AgentGuard is a budget-conscious observer for real-time token spending by AI agents and LLMs, integrating with major providers like OpenAI and Anthropic. ## 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 AgentGuard if… - AgentGuard is primarily JavaScript; do-not-answer is Jupyter Notebook. - License: AgentGuard is MIT, do-not-answer is Apache-2.0. - Tags unique to AgentGuard: ai-agents, anthropic, cost-monitoring, observability. - Also covers Inference & Serving. - When you need precise control over spend and want live updates on token prices ### Choose do-not-answer if… - do-not-answer is primarily Jupyter Notebook; AgentGuard is JavaScript. - License: do-not-answer is Apache-2.0, AgentGuard is MIT. - 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 AgentGuard - If you prioritize a different language for your project and cannot use JavaScript - In cases requiring more elaborate fallback mechanisms than what AgentGuard offers ## 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 AgentGuard and do-not-answer? AgentGuard: Real-time guardrail that monitors token spend and manages LLM/agent loops in real time. 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 AgentGuard over do-not-answer? Choose AgentGuard over do-not-answer when AgentGuard is primarily JavaScript; do-not-answer is Jupyter Notebook; License: AgentGuard is MIT, do-not-answer is Apache-2.0; Tags unique to AgentGuard: ai-agents, anthropic, cost-monitoring, observability; Also covers Inference & Serving; When you need precise control over spend and want live updates on token prices. ### When should I choose do-not-answer over AgentGuard? Choose do-not-answer over AgentGuard when do-not-answer is primarily Jupyter Notebook; AgentGuard is JavaScript; License: do-not-answer is Apache-2.0, AgentGuard is MIT; 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 AgentGuard? If you prioritize a different language for your project and cannot use JavaScript In cases requiring more elaborate fallback mechanisms than what AgentGuard offers ### 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 AgentGuard or do-not-answer more popular on GitHub? do-not-answer has more GitHub stars (339 vs 171). Stars measure visibility, not whether either tool fits your constraints. ### Are AgentGuard and do-not-answer open source? Yes - both are open-source projects on GitHub (AgentGuard: MIT, do-not-answer: Apache-2.0). ### Where can I find alternatives to AgentGuard or do-not-answer? GraphCanon lists graph-backed alternatives at [AgentGuard alternatives](/tools/dipampaul17-agentguard/alternatives) and [do-not-answer alternatives](/tools/libr-ai-do-not-answer/alternatives) ([AgentGuard markdown twin](/tools/dipampaul17-agentguard/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/dipampaul17-agentguard-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, AgentGuard or do-not-answer? AgentGuard: Dormant. 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 AgentGuard and do-not-answer? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [AgentGuard trust report](/tools/dipampaul17-agentguard/trust); [do-not-answer trust report](/tools/libr-ai-do-not-answer/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dipampaul17-agentguard`](/api/graphcanon/graph?tool=dipampaul17-agentguard) - 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/_