--- title: "vigil-llm vs autoguardrails" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deadbits-vigil-llm-vs-santanderai-autoguardrails" tools: ["deadbits-vigil-llm", "santanderai-autoguardrails"] --- # vigil-llm vs autoguardrails *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 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. [vigil-llm](https://vigil.deadbits.ai/) reports 496 GitHub stars, 56 forks, and 16 open issues, last pushed Jan 31, 2024. [autoguardrails](https://github.com/SantanderAI) has 128 stars, 35 forks, and 2 open issues, last pushed Aug 1, 2026. Figures are from public GitHub metadata via [vigil-llm's repository](https://github.com/deadbits/vigil-llm) and [autoguardrails's repository](https://github.com/SantanderAI/autoguardrails). | | [vigil-llm](/tools/deadbits-vigil-llm.md) | [autoguardrails](/tools/santanderai-autoguardrails.md) | | --- | --- | --- | | Tagline | Detect prompt injections and other risky inputs in LLMs | Alignment-research scaffold for LLM guardrails involving policy evaluation and content moderation | | Stars | 496 | 128 | | Forks | 56 | 35 | | Open issues | 16 | 2 | | Language | Python | Python | | Adopt for | Vigil-llm is designed for users who need robust security measures to protect against prompt injections and jailbreak attempts in LLMs. | 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 | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [vigil-llm](/tools/deadbits-vigil-llm.md) | [autoguardrails](/tools/santanderai-autoguardrails.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 932d | 8d | | Open issues (now) | 16 | 2 | | Stars delta | +5 (30d) | Unknown | | Open issues delta | 0 (30d) | Unknown | | Owner type | User | Organization | | Full report | [trust report](/tools/deadbits-vigil-llm/trust.md) | [trust report](/tools/santanderai-autoguardrails/trust.md) | ## Shared compatibility - **Python**: [vigil-llm](/tools/deadbits-vigil-llm.md) - Python runtime; [autoguardrails](/tools/santanderai-autoguardrails.md) - Python runtime ## 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: autoguardrails - **Requirements:** Requires Python 3.10 or higher.; No third-party runtimes; it is built completely on the standard Python library. - **Adopt for:** 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. ## Choose when ### Choose vigil-llm if… - 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 autoguardrails if… - 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 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 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. ## Common questions ### What is the difference between vigil-llm and autoguardrails? vigil-llm: Detect prompt injections and other risky inputs 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 vigil-llm over autoguardrails? Choose vigil-llm over autoguardrails when 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 autoguardrails over vigil-llm? Choose autoguardrails over vigil-llm when 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 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 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 vigil-llm or autoguardrails more popular on GitHub? vigil-llm has more GitHub stars (496 vs 128). Stars measure visibility, not whether either tool fits your constraints. ### Are vigil-llm and autoguardrails open source? Yes - both are open-source projects on GitHub (vigil-llm: Apache-2.0, autoguardrails: Apache-2.0). ### Where can I find alternatives to vigil-llm or autoguardrails? GraphCanon lists graph-backed alternatives at [vigil-llm alternatives](/tools/deadbits-vigil-llm/alternatives) and [autoguardrails alternatives](/tools/santanderai-autoguardrails/alternatives) ([vigil-llm markdown twin](/tools/deadbits-vigil-llm/alternatives.md), [autoguardrails markdown twin](/tools/santanderai-autoguardrails/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-santanderai-autoguardrails.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, vigil-llm or autoguardrails? vigil-llm: 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 vigil-llm and autoguardrails? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [vigil-llm trust report](/tools/deadbits-vigil-llm/trust); [autoguardrails trust report](/tools/santanderai-autoguardrails/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/_