--- title: "AgentGuard vs llm-guard" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dipampaul17-agentguard-vs-protectai-llm-guard" tools: ["dipampaul17-agentguard", "protectai-llm-guard"] --- # AgentGuard vs llm-guard *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 llm-guard if lLM Guard is a toolkit tailored for securing interactions with large language models, focusing on safeguarding against prompt injection and adversarial attacks. [AgentGuard](https://github.com/dipampaul17/AgentGuard) reports 171 GitHub stars, 10 forks, and 1 open issues, last pushed Jul 31, 2025. [llm-guard](https://protectai.github.io/llm-guard/) has 3.2k stars, 435 forks, and 40 open issues, last pushed Jul 8, 2026. Figures are from public GitHub metadata via [AgentGuard's repository](https://github.com/dipampaul17/AgentGuard) and [llm-guard's repository](https://github.com/protectai/llm-guard). | | [AgentGuard](/tools/dipampaul17-agentguard.md) | [llm-guard](/tools/protectai-llm-guard.md) | | --- | --- | --- | | Tagline | Real-time guardrail that monitors token spend and manages LLM/agent loops in real time | The Security Toolkit for LLM Interactions | | Stars | 171 | 3,202 | | Forks | 10 | 435 | | Open issues | 1 | 40 | | Language | JavaScript | Python | | 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. | LLM Guard is a toolkit tailored for securing interactions with large language models, focusing on safeguarding against prompt injection and adversarial attacks. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | Evaluation & Observability, Inference & Serving | Developer Tools, Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [AgentGuard](/tools/dipampaul17-agentguard.md) | [llm-guard](/tools/protectai-llm-guard.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Archived (8%) | | Days since push | 373d | 27d | | Archived on GitHub | No | Yes | | Open issues (now) | 1 | 40 | | Owner type | User | Organization | | Full report | [trust report](/tools/dipampaul17-agentguard/trust.md) | [trust report](/tools/protectai-llm-guard/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: llm-guard - **Requirements:** Min 2 GB RAM; Ensure you have Python version 3.9 or higher.; Some advanced features require additional libraries which are automatically installed as needed. - **Adopt for:** LLM Guard is a toolkit tailored for securing interactions with large language models, focusing on safeguarding against prompt injection and adversarial attacks. ## Choose when ### Choose AgentGuard if… - AgentGuard is primarily JavaScript; llm-guard is Python. - 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 llm-guard if… - llm-guard is primarily Python; AgentGuard is JavaScript. - Requirements: Min 2 GB RAM; Ensure you have Python version 3.9 or higher.; Some advanced features require additional libraries which are automatically installed as needed.. - Tags unique to llm-guard: adversarial-machine-learning, chatgpt, large language models, llm security. - Also covers Developer Tools. - - You need to secure your application from sophisticated prompt injection techniques. ## 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 llm-guard - - If you are working in a low-security environment or with small-scale projects where advanced security mechanisms are not necessary. - - In cases where integrating external libraries and ensuring Python version compatibility may introduce complexities that outweigh the benefits. ## Common questions ### What is the difference between AgentGuard and llm-guard? AgentGuard: Real-time guardrail that monitors token spend and manages LLM/agent loops in real time. llm-guard: The Security Toolkit for LLM Interactions. See the comparison table for live GitHub stats and shared categories. ### When should I choose AgentGuard over llm-guard? Choose AgentGuard over llm-guard when AgentGuard is primarily JavaScript; llm-guard is Python; 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 llm-guard over AgentGuard? Choose llm-guard over AgentGuard when llm-guard is primarily Python; AgentGuard is JavaScript; Requirements: Min 2 GB RAM; Ensure you have Python version 3.9 or higher.; Some advanced features require additional libraries which are automatically installed as needed.; Tags unique to llm-guard: adversarial-machine-learning, chatgpt, large language models, llm security; Also covers Developer Tools; - You need to secure your application from sophisticated prompt injection techniques. ### 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 llm-guard? - If you are working in a low-security environment or with small-scale projects where advanced security mechanisms are not necessary. - In cases where integrating external libraries and ensuring Python version compatibility may introduce complexities that outweigh the benefits. ### Is AgentGuard or llm-guard more popular on GitHub? llm-guard has more GitHub stars (3,202 vs 171). Stars measure visibility, not whether either tool fits your constraints. ### Are AgentGuard and llm-guard open source? Yes - both are open-source projects on GitHub (AgentGuard: MIT, llm-guard: MIT). ### Where can I find alternatives to AgentGuard or llm-guard? GraphCanon lists graph-backed alternatives at [AgentGuard alternatives](/tools/dipampaul17-agentguard/alternatives) and [llm-guard alternatives](/tools/protectai-llm-guard/alternatives) ([AgentGuard markdown twin](/tools/dipampaul17-agentguard/alternatives.md), [llm-guard markdown twin](/tools/protectai-llm-guard/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-protectai-llm-guard.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, AgentGuard or llm-guard? AgentGuard: Dormant. llm-guard: Archived. 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 llm-guard? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [AgentGuard trust report](/tools/dipampaul17-agentguard/trust); [llm-guard trust report](/tools/protectai-llm-guard/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/_