--- title: "apps vs awesome-llm-apps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hendrycks-apps-vs-shubhamsaboo-awesome-llm-apps" tools: ["hendrycks-apps", "shubhamsaboo-awesome-llm-apps"] --- # apps vs awesome-llm-apps *GraphCanon updated Aug 7, 2026* ## Verdict Pick apps if aPPS offers a benchmark to evaluate the competence of large language models on coding challenges using its datasets; pick awesome-llm-apps if awesome-llm-apps is a collection of over 100 AI Agent and Retrieval Augmented Generation (RAG) applications that enable users to quickly implement, customize, and deploy practical use cases in Python. [apps](https://github.com/hendrycks/apps) reports 534 GitHub stars, 70 forks, and 4 open issues, last pushed Jun 19, 2024. [awesome-llm-apps](https://www.theunwindai.com) has 131k stars, 19k forks, and 13 open issues, last pushed Aug 3, 2026. Figures are from public GitHub metadata via [apps's repository](https://github.com/hendrycks/apps) and [awesome-llm-apps's repository](https://github.com/Shubhamsaboo/awesome-llm-apps). | | [apps](/tools/hendrycks-apps.md) | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | | --- | --- | --- | | Tagline | APPS: Automated Programming Progress Standard | Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy. | | Stars | 534 | 131,230 | | Forks | 70 | 19,346 | | Open issues | 4 | 13 | | Language | Python | Python | | Adopt for | APPS offers a benchmark to evaluate the competence of large language models on coding challenges using its datasets. | awesome-llm-apps is a collection of over 100 AI Agent and Retrieval Augmented Generation (RAG) applications that enable users to quickly implement, customize, and deploy practical use cases in Python. | | Persona | - | - | | Runtime | - | - | | License | MIT | The Apache-2.0 license allows users to freely use, modify, and distribute the projects found in awesome-llm-apps under specific conditions outlined by the license. | | Categories | Data & Retrieval, Evaluation & Observability | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [apps](/tools/hendrycks-apps.md) | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 777d | 4d | | Open issues (now) | 4 | 13 | | Stars delta | Unknown | +14k (30d) | | Open issues delta | Unknown | +6 (30d) | | Full report | [trust report](/tools/hendrycks-apps/trust.md) | [trust report](/tools/shubhamsaboo-awesome-llm-apps/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. ## Decision facts: awesome-llm-apps - **Pricing:** freemium - Free with open-source licensing, but commercial exploitation is allowed. - **Adopt for:** awesome-llm-apps is a collection of over 100 AI Agent and Retrieval Augmented Generation (RAG) applications that enable users to quickly implement, customize, and deploy practical use cases in Python. - **License detail:** The Apache-2.0 license allows users to freely use, modify, and distribute the projects found in awesome-llm-apps under specific conditions outlined by the license. ## Choose when ### Choose apps if… - License: apps is MIT, awesome-llm-apps is Apache-2.0. - Tags unique to apps: code generation, program-synthesis. - Also covers Evaluation & Observability. - When you need benchmarking datasets specifically tailored for assessing the performance of your AI in solving programming tasks ### Choose awesome-llm-apps if… - License: awesome-llm-apps is Apache-2.0, apps is MIT. - Pricing: Free with open-source licensing, but commercial exploitation is allowed.. - Tags unique to awesome-llm-apps: agents, applications, customizable, deployable. - Also covers AI Agents. - When you need quick implementations of various real-world use cases for AI Agents and RAG. ## 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 awesome-llm-apps - If your project requires highly specialized customization beyond what the provided apps can offer out-of-the-box, as deep integration might be required from scratch. - When you are looking for a fully managed service or support directly from developers; this repository is more about self-service and community interaction. ## Common questions ### What is the difference between apps and awesome-llm-apps? apps: APPS: Automated Programming Progress Standard. awesome-llm-apps: Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy.. See the comparison table for live GitHub stats and shared categories. ### When should I choose apps over awesome-llm-apps? Choose apps over awesome-llm-apps when License: apps is MIT, awesome-llm-apps is Apache-2.0; Tags unique to apps: code generation, program-synthesis; Also covers Evaluation & Observability; When you need benchmarking datasets specifically tailored for assessing the performance of your AI in solving programming tasks. ### When should I choose awesome-llm-apps over apps? Choose awesome-llm-apps over apps when License: awesome-llm-apps is Apache-2.0, apps is MIT; Pricing: Free with open-source licensing, but commercial exploitation is allowed.; Tags unique to awesome-llm-apps: agents, applications, customizable, deployable; Also covers AI Agents; When you need quick implementations of various real-world use cases for AI Agents and RAG. ### 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 awesome-llm-apps? If your project requires highly specialized customization beyond what the provided apps can offer out-of-the-box, as deep integration might be required from scratch. When you are looking for a fully managed service or support directly from developers; this repository is more about self-service and community interaction. ### Is apps or awesome-llm-apps more popular on GitHub? awesome-llm-apps has more GitHub stars (131,230 vs 534). Stars measure visibility, not whether either tool fits your constraints. ### Are apps and awesome-llm-apps open source? Yes - both are open-source projects on GitHub (apps: MIT, awesome-llm-apps: Apache-2.0). ### Where can I find alternatives to apps or awesome-llm-apps? GraphCanon lists graph-backed alternatives at [apps alternatives](/tools/hendrycks-apps/alternatives) and [awesome-llm-apps alternatives](/tools/shubhamsaboo-awesome-llm-apps/alternatives) ([apps markdown twin](/tools/hendrycks-apps/alternatives.md), [awesome-llm-apps markdown twin](/tools/shubhamsaboo-awesome-llm-apps/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-shubhamsaboo-awesome-llm-apps.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, apps or awesome-llm-apps? apps: Dormant. awesome-llm-apps: Very 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 apps and awesome-llm-apps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [apps trust report](/tools/hendrycks-apps/trust); [awesome-llm-apps trust report](/tools/shubhamsaboo-awesome-llm-apps/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/_