--- title: "vigil-llm vs fact-checker" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deadbits-vigil-llm-vs-jagilley-fact-checker" tools: ["deadbits-vigil-llm", "jagilley-fact-checker"] --- # vigil-llm vs fact-checker *GraphCanon updated Jul 12, 2026* ## Verdict Pick vigil-llm if vigil-LLM is specifically designed for detecting prompt injections and jailbreaks in LLM inputs, offering unique capabilities not found in all security tools; pick fact-checker if `fact-checker` utilizes prompt chaining in Jupyter Notebook to fact-check Language Model outputs, enhancing the accuracy and reliability of responses. [vigil-llm](https://vigil.deadbits.ai/) reports 489 GitHub stars, 55 forks, and 16 open issues, last pushed Jan 31, 2024. [fact-checker](https://github.com/jagilley/fact-checker) has 308 stars, 40 forks, and 0 open issues, last pushed Oct 23, 2023. Figures are from public GitHub metadata via [vigil-llm's repository](https://github.com/deadbits/vigil-llm) and [fact-checker's repository](https://github.com/jagilley/fact-checker). | | [vigil-llm](/tools/deadbits-vigil-llm.md) | [fact-checker](/tools/jagilley-fact-checker.md) | | --- | --- | --- | | Tagline | ⚡ Vigil ⚡ Detect prompt injections, jailbreaks, and other potentially risky Large Language Model (LLM) inputs | Fact-checking LLM outputs with self-ask | | Stars | 489 | 308 | | Forks | 55 | 40 | | Open issues | 16 | 0 | | Language | Python | Jupyter Notebook | | Adopt for | Vigil-LLM is specifically designed for detecting prompt injections and jailbreaks in LLM inputs, offering unique capabilities not found in all security tools. | `fact-checker` utilizes prompt chaining in Jupyter Notebook to fact-check Language Model outputs, enhancing the accuracy and reliability of responses. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | - | | Categories | Evaluation & Observability, LLM Frameworks, Vector Databases | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [vigil-llm](/tools/deadbits-vigil-llm.md) | [fact-checker](/tools/jagilley-fact-checker.md) | | --- | --- | --- | | Days since push | 891d | 991d | | Open issues (now) | 16 | 0 | | Full report | [trust report](/tools/deadbits-vigil-llm/trust.md) | [trust report](/tools/jagilley-fact-checker/trust.md) | ## Decision facts: vigil-llm - **Pricing:** freemium - Vigil-LLM is open-source under the Apache License 2.0 and freely available to download. However, additional support or services may be offered on a paid basis depending on the context of usage. - **Requirements:** Min 4 GB RAM - **Adopt for:** Vigil-LLM is specifically designed for detecting prompt injections and jailbreaks in LLM inputs, offering unique capabilities not found in all security tools. ## Decision facts: fact-checker - **Pricing:** unknown - The licensing information for `fact-checker` is unclear, indicating that further investigation into its legal usage might be required before implementation. - **Requirements:** Requires Python and possibly Jupyter Notebook environment for running the provided IPython notebook script or command-line script. - **Adopt for:** `fact-checker` utilizes prompt chaining in Jupyter Notebook to fact-check Language Model outputs, enhancing the accuracy and reliability of responses. ## Choose when ### Choose vigil-llm if… - vigil-llm is primarily Python; fact-checker is Jupyter Notebook. - Pricing: Vigil-LLM is open-source under the Apache License 2.0 and freely available to download. However, additional support or services may be offered on a paid basis depending on the context of usage.. - Requirements: Min 4 GB RAM. - Tags unique to vigil-llm: adversarial-attacks, adversarial-machine-learning, large-language-models, llm-security. - Also covers LLM Frameworks, Vector Databases. - When integrating large language models into applications that need robust protection against prompt injection attacks. ### Choose fact-checker if… - fact-checker is primarily Jupyter Notebook; vigil-llm is Python. - Pricing: The licensing information for `fact-checker` is unclear, indicating that further investigation into its legal usage might be required before implementation.. - Requirements: Requires Python and possibly Jupyter Notebook environment for running the provided IPython notebook script or command-line script.. - Tags unique to fact-checker: fact-checking, llm, prompt-chaining, python. - - When you need to verify the accuracy of assumptions made by a Language Model’s initial response through self-ask methodologies. ## When NOT to use vigil-llm - If the focus of security efforts is solely on data breaches and not specifically on securing against adversarial machine learning techniques like prompt injections or jailbreaks. - When your environment is set up without Python or YARA v4.3.2, as these are prerequisites for using Vigil-LLM effectively. ## When NOT to use fact-checker - - If an immediate answer is required without the step-by-step reassessment process, as `fact-checker` involves sequential validation that could be time-consuming. - - In situations where real-time interaction is critical and a delay from additional self-interrogation steps would not be beneficial for user experience. ## Common questions ### What is the difference between vigil-llm and fact-checker? vigil-llm: ⚡ Vigil ⚡ Detect prompt injections, jailbreaks, and other potentially risky Large Language Model (LLM) inputs. fact-checker: Fact-checking LLM outputs with self-ask. See the comparison table for live GitHub stats and shared categories. ### When should I choose vigil-llm over fact-checker? Choose vigil-llm over fact-checker when vigil-llm is primarily Python; fact-checker is Jupyter Notebook; Pricing: Vigil-LLM is open-source under the Apache License 2.0 and freely available to download. However, additional support or services may be offered on a paid basis depending on the context of usage.; Requirements: Min 4 GB RAM; Tags unique to vigil-llm: adversarial-attacks, adversarial-machine-learning, large-language-models, llm-security; Also covers LLM Frameworks, Vector Databases; When integrating large language models into applications that need robust protection against prompt injection attacks. ### When should I choose fact-checker over vigil-llm? Choose fact-checker over vigil-llm when fact-checker is primarily Jupyter Notebook; vigil-llm is Python; Pricing: The licensing information for `fact-checker` is unclear, indicating that further investigation into its legal usage might be required before implementation.; Requirements: Requires Python and possibly Jupyter Notebook environment for running the provided IPython notebook script or command-line script.; Tags unique to fact-checker: fact-checking, llm, prompt-chaining, python; - When you need to verify the accuracy of assumptions made by a Language Model’s initial response through self-ask methodologies. ### When should I avoid vigil-llm? If the focus of security efforts is solely on data breaches and not specifically on securing against adversarial machine learning techniques like prompt injections or jailbreaks. When your environment is set up without Python or YARA v4.3.2, as these are prerequisites for using Vigil-LLM effectively. ### When should I avoid fact-checker? - If an immediate answer is required without the step-by-step reassessment process, as `fact-checker` involves sequential validation that could be time-consuming. - In situations where real-time interaction is critical and a delay from additional self-interrogation steps would not be beneficial for user experience. ### Is vigil-llm or fact-checker more popular on GitHub? vigil-llm has more GitHub stars (489 vs 308). Stars measure visibility, not whether either tool fits your constraints. ### Are vigil-llm and fact-checker open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to vigil-llm or fact-checker? GraphCanon lists graph-backed alternatives at [vigil-llm alternatives](/tools/deadbits-vigil-llm/alternatives) and [fact-checker alternatives](/tools/jagilley-fact-checker/alternatives) ([vigil-llm markdown twin](/tools/deadbits-vigil-llm/alternatives.md), [fact-checker markdown twin](/tools/jagilley-fact-checker/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-jagilley-fact-checker.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, vigil-llm or fact-checker? vigil-llm: Dormant. fact-checker: 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 fact-checker? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [vigil-llm trust report](/tools/deadbits-vigil-llm/trust); [fact-checker trust report](/tools/jagilley-fact-checker/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/_