--- title: "apps vs auto-evaluator" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hendrycks-apps-vs-langchain-ai-auto-evaluator" tools: ["hendrycks-apps", "langchain-ai-auto-evaluator"] --- # apps vs auto-evaluator *GraphCanon updated Aug 8, 2026* ## Verdict Pick apps when apps is primarily Python; auto-evaluator is TypeScript; pick auto-evaluator when auto-evaluator is primarily TypeScript; apps is Python. [apps](https://github.com/hendrycks/apps) reports 534 GitHub stars, 70 forks, and 4 open issues, last pushed Jun 19, 2024. [auto-evaluator](https://autoevaluator.langchain.com/) has 783 stars, 102 forks, and 21 open issues, last pushed Jun 26, 2025. Figures are from public GitHub metadata via [apps's repository](https://github.com/hendrycks/apps) and [auto-evaluator's repository](https://github.com/langchain-ai/auto-evaluator). | | [apps](/tools/hendrycks-apps.md) | [auto-evaluator](/tools/langchain-ai-auto-evaluator.md) | | --- | --- | --- | | Tagline | APPS: Automated Programming Progress Standard | auto-evaluator | | Stars | 534 | 783 | | Forks | 70 | 102 | | Open issues | 4 | 21 | | Language | Python | TypeScript | | Adopt for | APPS offers a benchmark to evaluate the competence of large language models on coding challenges using its datasets. | - | | Persona | - | - | | Runtime | - | - | | License | MIT | Other | | Categories | Data & Retrieval, Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [apps](/tools/hendrycks-apps.md) | [auto-evaluator](/tools/langchain-ai-auto-evaluator.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Archived (8%) | | Days since push | 777d | 408d | | Archived on GitHub | No | Yes | | Open issues (now) | 4 | 21 | | Owner type | User | Organization | | Full report | [trust report](/tools/hendrycks-apps/trust.md) | [trust report](/tools/langchain-ai-auto-evaluator/trust.md) | ## Decision facts: apps - **Adopt for:** APPS offers a benchmark to evaluate the competence of large language models on coding challenges using its datasets. ## Choose when ### Choose apps if… - apps is primarily Python; auto-evaluator is TypeScript. - License: apps is MIT, auto-evaluator is Other. - Tags unique to apps: code generation, program-synthesis. - Also covers Data & Retrieval. - When you need benchmarking datasets specifically tailored for assessing the performance of your AI in solving programming tasks ### Choose auto-evaluator if… - auto-evaluator is primarily TypeScript; apps is Python. - License: auto-evaluator is Other, apps is MIT. - Tags unique to auto-evaluator: auto-evaluation, railway, typescript, vercel. - Use auto-evaluator when you are working with TypeScript and need an integrated solution for evaluating AI models ## When NOT to use apps - If you solely require general datasets without a focus on coding challenges - When your use case does not involve using Python-based tools for developing machine learning applications that include program synthesis and code generation ## When NOT to use auto-evaluator - Avoid using auto-evaluator if you require a multi-language support environment, as it focuses solely on TypeScript - Do not use this tool if your project's hosting requirements do not align with using Vercel or Railway ## Common questions ### What is the difference between apps and auto-evaluator? apps: APPS: Automated Programming Progress Standard. auto-evaluator: auto-evaluator. See the comparison table for live GitHub stats and shared categories. ### When should I choose apps over auto-evaluator? Choose apps over auto-evaluator when apps is primarily Python; auto-evaluator is TypeScript; License: apps is MIT, auto-evaluator is Other; Tags unique to apps: code generation, program-synthesis; Also covers Data & Retrieval; When you need benchmarking datasets specifically tailored for assessing the performance of your AI in solving programming tasks. ### When should I choose auto-evaluator over apps? Choose auto-evaluator over apps when auto-evaluator is primarily TypeScript; apps is Python; License: auto-evaluator is Other, apps is MIT; Tags unique to auto-evaluator: auto-evaluation, railway, typescript, vercel; Use auto-evaluator when you are working with TypeScript and need an integrated solution for evaluating AI models. ### When should I avoid apps? If you solely require general datasets without a focus on coding challenges When your use case does not involve using Python-based tools for developing machine learning applications that include program synthesis and code generation ### When should I avoid auto-evaluator? Avoid using auto-evaluator if you require a multi-language support environment, as it focuses solely on TypeScript Do not use this tool if your project's hosting requirements do not align with using Vercel or Railway ### Is apps or auto-evaluator more popular on GitHub? auto-evaluator has more GitHub stars (783 vs 534). Stars measure visibility, not whether either tool fits your constraints. ### Are apps and auto-evaluator open source? Yes - both are open-source projects on GitHub (apps: MIT, auto-evaluator: Other). ### Where can I find alternatives to apps or auto-evaluator? GraphCanon lists graph-backed alternatives at [apps alternatives](/tools/hendrycks-apps/alternatives) and [auto-evaluator alternatives](/tools/langchain-ai-auto-evaluator/alternatives) ([apps markdown twin](/tools/hendrycks-apps/alternatives.md), [auto-evaluator markdown twin](/tools/langchain-ai-auto-evaluator/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/hendrycks-apps-vs-langchain-ai-auto-evaluator.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, apps or auto-evaluator? apps: Dormant. auto-evaluator: 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 apps and auto-evaluator? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [apps trust report](/tools/hendrycks-apps/trust); [auto-evaluator trust report](/tools/langchain-ai-auto-evaluator/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hendrycks-apps`](/api/graphcanon/graph?tool=hendrycks-apps) - 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/_