--- title: "vigil-llm vs AgentGuard" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deadbits-vigil-llm-vs-dipampaul17-agentguard" tools: ["deadbits-vigil-llm", "dipampaul17-agentguard"] --- # vigil-llm vs AgentGuard *GraphCanon updated Aug 21, 2026* ## Verdict Pick vigil-llm if vigil-llm is designed for users who need robust security measures to protect against prompt injections and jailbreak attempts in LLMs; 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. [vigil-llm](https://vigil.deadbits.ai/) reports 496 GitHub stars, 56 forks, and 16 open issues, last pushed Jan 31, 2024. [AgentGuard](https://github.com/dipampaul17/AgentGuard) has 171 stars, 10 forks, and 1 open issues, last pushed Jul 31, 2025. Figures are from public GitHub metadata via [vigil-llm's repository](https://github.com/deadbits/vigil-llm) and [AgentGuard's repository](https://github.com/dipampaul17/AgentGuard). | | [vigil-llm](/tools/deadbits-vigil-llm.md) | [AgentGuard](/tools/dipampaul17-agentguard.md) | | --- | --- | --- | | Tagline | Detect prompt injections and other risky inputs in LLMs | Real-time guardrail that monitors token spend and manages LLM/agent loops in real time | | Stars | 496 | 171 | | Forks | 56 | 10 | | Open issues | 16 | 1 | | Language | Python | JavaScript | | Adopt for | Vigil-llm is designed for users who need robust security measures to protect against prompt injections and jailbreak attempts in LLMs. | AgentGuard is a budget-conscious observer for real-time token spending by AI agents and LLMs, integrating with major providers like OpenAI and Anthropic. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Evaluation & Observability | Evaluation & Observability, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [vigil-llm](/tools/deadbits-vigil-llm.md) | [AgentGuard](/tools/dipampaul17-agentguard.md) | | --- | --- | --- | | Days since push | 932d | 373d | | Open issues (now) | 16 | 1 | | Stars delta | +5 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Full report | [trust report](/tools/deadbits-vigil-llm/trust.md) | [trust report](/tools/dipampaul17-agentguard/trust.md) | ## Decision facts: vigil-llm - **Adopt for:** Vigil-llm is designed for users who need robust security measures to protect against prompt injections and jailbreak attempts in LLMs. ## 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. ## Choose when ### Choose vigil-llm if… - vigil-llm is primarily Python; AgentGuard is JavaScript. - License: vigil-llm is Apache-2.0, AgentGuard is MIT. - Tags unique to vigil-llm: adversarial-attacks, large language models, llm security, prompt-injection. - vigil-llm ships Docker support for self-hosted deployment. - When deploying large language models that require high levels of input security, vigil-llm can be employed to detect maliciously crafted inputs intended to manipulate model behavior. ### Choose AgentGuard if… - AgentGuard is primarily JavaScript; vigil-llm is Python. - License: AgentGuard is MIT, vigil-llm 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 NOT to use vigil-llm - If your application does not require high security against malicious inputs or if the risks of prompt injection are minimal due to controlled input sources, vigil-llm might be unnecessary. - For projects that focus on optimizing output speed rather than input robustness, other tools might be more appropriate as vigil-llm could add significant processing overhead. ## 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 ## Common questions ### What is the difference between vigil-llm and AgentGuard? vigil-llm: Detect prompt injections and other risky inputs in LLMs. AgentGuard: Real-time guardrail that monitors token spend and manages LLM/agent loops in real time. See the comparison table for live GitHub stats and shared categories. ### When should I choose vigil-llm over AgentGuard? Choose vigil-llm over AgentGuard when vigil-llm is primarily Python; AgentGuard is JavaScript; License: vigil-llm is Apache-2.0, AgentGuard is MIT; Tags unique to vigil-llm: adversarial-attacks, large language models, llm security, prompt-injection; vigil-llm ships Docker support for self-hosted deployment; When deploying large language models that require high levels of input security, vigil-llm can be employed to detect maliciously crafted inputs intended to manipulate model behavior. ### When should I choose AgentGuard over vigil-llm? Choose AgentGuard over vigil-llm when AgentGuard is primarily JavaScript; vigil-llm is Python; License: AgentGuard is MIT, vigil-llm 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 avoid vigil-llm? If your application does not require high security against malicious inputs or if the risks of prompt injection are minimal due to controlled input sources, vigil-llm might be unnecessary. For projects that focus on optimizing output speed rather than input robustness, other tools might be more appropriate as vigil-llm could add significant processing overhead. ### 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 ### Is vigil-llm or AgentGuard more popular on GitHub? vigil-llm has more GitHub stars (496 vs 171). Stars measure visibility, not whether either tool fits your constraints. ### Are vigil-llm and AgentGuard open source? Yes - both are open-source projects on GitHub (vigil-llm: Apache-2.0, AgentGuard: MIT). ### Where can I find alternatives to vigil-llm or AgentGuard? GraphCanon lists graph-backed alternatives at [vigil-llm alternatives](/tools/deadbits-vigil-llm/alternatives) and [AgentGuard alternatives](/tools/dipampaul17-agentguard/alternatives) ([vigil-llm markdown twin](/tools/deadbits-vigil-llm/alternatives.md), [AgentGuard markdown twin](/tools/dipampaul17-agentguard/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/deadbits-vigil-llm-vs-dipampaul17-agentguard.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, vigil-llm or AgentGuard? vigil-llm: Dormant. AgentGuard: 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 vigil-llm and AgentGuard? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [vigil-llm trust report](/tools/deadbits-vigil-llm/trust); [AgentGuard trust report](/tools/dipampaul17-agentguard/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=deadbits-vigil-llm`](/api/graphcanon/graph?tool=deadbits-vigil-llm) - 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/_