--- title: "fact-checker vs cupel" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jagilley-fact-checker-vs-tolitius-cupel" tools: ["jagilley-fact-checker", "tolitius-cupel"] --- # fact-checker vs cupel *GraphCanon updated Sep 20, 2026* ## Verdict 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; pick cupel if cupel is a JavaScript-based toolkit for discovering and evaluating the performance of large language models using configurable prompts, scoring mechanisms, multi-turn dialogues, and local inference server discovery. [fact-checker](https://github.com/jagilley/fact-checker) reports 314 GitHub stars, 39 forks, and 0 open issues, last pushed Oct 23, 2023. [cupel](https://cupel.run) has 64 stars, 0 forks, and 2 open issues, last pushed Aug 31, 2026. Figures are from public GitHub metadata via [fact-checker's repository](https://github.com/jagilley/fact-checker) and [cupel's repository](https://github.com/tolitius/cupel). | | [fact-checker](/tools/jagilley-fact-checker.md) | [cupel](/tools/tolitius-cupel.md) | | --- | --- | --- | | Tagline | Fact-checking LLM outputs with self-ask | discovery tool for evaluating LLM performance | | Stars | 314 | 64 | | Forks | 39 | 0 | | Open issues | 0 | 2 | | Language | Jupyter Notebook | Python | | Adopt for | `fact-checker` utilizes prompt chaining in Jupyter Notebook to fact-check Language Model outputs, enhancing the accuracy and reliability of responses. | Cupel is a JavaScript-based toolkit for discovering and evaluating the performance of large language models using configurable prompts, scoring mechanisms, multi-turn dialogues, and local inference server discovery. | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [fact-checker](/tools/jagilley-fact-checker.md) | [cupel](/tools/tolitius-cupel.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 1062d | 10d | | Open issues (now) | 0 | 2 | | Stars delta | +1 (30d) | +13 (30d) | | Full report | [trust report](/tools/jagilley-fact-checker/trust.md) | [trust report](/tools/tolitius-cupel/trust.md) | ## Shared compatibility - **Python**: [fact-checker](/tools/jagilley-fact-checker.md) - Python runtime; [cupel](/tools/tolitius-cupel.md) - Python runtime ## 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. ## Decision facts: cupel - **Adopt for:** Cupel is a JavaScript-based toolkit for discovering and evaluating the performance of large language models using configurable prompts, scoring mechanisms, multi-turn dialogues, and local inference server discovery. ## Choose when ### Choose fact-checker if… - fact-checker is primarily Jupyter Notebook; cupel 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. ### Choose cupel if… - cupel is primarily Python; fact-checker is Jupyter Notebook. - Tags unique to cupel: inference-servers-discovery, llm-evaluation, local-llm, multi-turn-dialogue. - When aiming to evaluate LLMs on local servers due to its auto-discovery feature for known ports of inference servers ## 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. ## When NOT to use cupel - If you require a solution that supports a non-JavaScript runtime environment, as Cupel is JavaScript-exclusive - When you need a tool without UI capabilities since Cupel's UI is bundled in the package and may not suit headless operations ## Common questions ### What is the difference between fact-checker and cupel? fact-checker: Fact-checking LLM outputs with self-ask. cupel: discovery tool for evaluating LLM performance. See the comparison table for live GitHub stats and shared categories. ### When should I choose fact-checker over cupel? Choose fact-checker over cupel when fact-checker is primarily Jupyter Notebook; cupel 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 choose cupel over fact-checker? Choose cupel over fact-checker when cupel is primarily Python; fact-checker is Jupyter Notebook; Tags unique to cupel: inference-servers-discovery, llm-evaluation, local-llm, multi-turn-dialogue; When aiming to evaluate LLMs on local servers due to its auto-discovery feature for known ports of inference servers. ### 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. ### When should I avoid cupel? If you require a solution that supports a non-JavaScript runtime environment, as Cupel is JavaScript-exclusive When you need a tool without UI capabilities since Cupel's UI is bundled in the package and may not suit headless operations ### Is fact-checker or cupel more popular on GitHub? fact-checker has more GitHub stars (314 vs 64). Stars measure visibility, not whether either tool fits your constraints. ### Are fact-checker and cupel open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to fact-checker or cupel? GraphCanon lists graph-backed alternatives at [fact-checker alternatives](/tools/jagilley-fact-checker/alternatives) and [cupel alternatives](/tools/tolitius-cupel/alternatives) ([fact-checker markdown twin](/tools/jagilley-fact-checker/alternatives.md), [cupel markdown twin](/tools/tolitius-cupel/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/jagilley-fact-checker-vs-tolitius-cupel.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, fact-checker or cupel? fact-checker: Dormant. cupel: 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 fact-checker and cupel? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [fact-checker trust report](/tools/jagilley-fact-checker/trust); [cupel trust report](/tools/tolitius-cupel/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=jagilley-fact-checker`](/api/graphcanon/graph?tool=jagilley-fact-checker) - 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/_