--- title: "judgeval vs awesome-llm-apps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/judgmentlabs-judgeval-vs-shubhamsaboo-awesome-llm-apps" tools: ["judgmentlabs-judgeval", "shubhamsaboo-awesome-llm-apps"] --- # judgeval vs awesome-llm-apps *GraphCanon updated Jul 12, 2026* ## Verdict Pick judgeval if judgeval is a Python tool that aids in the continuous improvement of AI agents through comprehensive environment data and evaluations, supporting methodologies like reinforcement learning and prompt engineering; 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. [judgeval](https://judgmentlabs.ai/) reports 1.0k GitHub stars, 93 forks, and 18 open issues, last pushed Jul 7, 2026. [awesome-llm-apps](https://www.theunwindai.com) has 118k stars, 17k forks, and 6 open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [judgeval's repository](https://github.com/JudgmentLabs/judgeval) and [awesome-llm-apps's repository](https://github.com/Shubhamsaboo/awesome-llm-apps). | | [judgeval](/tools/judgmentlabs-judgeval.md) | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | | --- | --- | --- | | Tagline | The Continuous-Improvement Stack for Agents | 100+ AI Agent & RAG apps you can actually run — clone, customize, ship. | | Stars | 1,040 | 117,774 | | Forks | 93 | 17,498 | | Open issues | 18 | 6 | | Language | Python | Python | | Adopt for | Judgeval is a Python tool that aids in the continuous improvement of AI agents through comprehensive environment data and evaluations, supporting methodologies like reinforcement learning and prompt engineering. | 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 | Apache-2.0 | 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 | AI Agents, Evaluation & Observability | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [judgeval](/tools/judgmentlabs-judgeval.md) | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | | --- | --- | --- | | Days since push | 3d | 0d | | Open issues (now) | 18 | 6 | | Owner type | Organization | User | | Full report | [trust report](/tools/judgmentlabs-judgeval/trust.md) | [trust report](/tools/shubhamsaboo-awesome-llm-apps/trust.md) | ## Decision facts: judgeval - **Adopt for:** Judgeval is a Python tool that aids in the continuous improvement of AI agents through comprehensive environment data and evaluations, supporting methodologies like reinforcement learning and prompt engineering. ## 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 judgeval if… - Tags unique to judgeval: agent, agentic-ai, grpo, langchain. - Also covers Evaluation & Observability. - You are working on an AI project where continuous monitoring and enhancement of your agent's performance are critical. ### Choose awesome-llm-apps if… - Pricing: Free with open-source licensing, but commercial exploitation is allowed.. - Tags unique to awesome-llm-apps: applications, customizable, deployable, llms. - Also covers Data & Retrieval. - When you need quick implementations of various real-world use cases for AI Agents and RAG. ## When NOT to use judgeval - If you are looking for a tool focused solely on the theoretical aspects of AI development without practical, continuous improvement methodologies. - You require a solution that only supports evaluation metrics and does not offer integrated environment data support, diverging from Judgeval’s comprehensive approach. ## 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 judgeval and awesome-llm-apps? judgeval: The Continuous-Improvement Stack for Agents. awesome-llm-apps: 100+ AI Agent & RAG apps you can actually run — clone, customize, ship.. See the comparison table for live GitHub stats and shared categories. ### When should I choose judgeval over awesome-llm-apps? Choose judgeval over awesome-llm-apps when Tags unique to judgeval: agent, agentic-ai, grpo, langchain; Also covers Evaluation & Observability; You are working on an AI project where continuous monitoring and enhancement of your agent's performance are critical. ### When should I choose awesome-llm-apps over judgeval? Choose awesome-llm-apps over judgeval when Pricing: Free with open-source licensing, but commercial exploitation is allowed.; Tags unique to awesome-llm-apps: applications, customizable, deployable, llms; Also covers Data & Retrieval; When you need quick implementations of various real-world use cases for AI Agents and RAG. ### When should I avoid judgeval? If you are looking for a tool focused solely on the theoretical aspects of AI development without practical, continuous improvement methodologies. You require a solution that only supports evaluation metrics and does not offer integrated environment data support, diverging from Judgeval’s comprehensive approach. ### 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 judgeval or awesome-llm-apps more popular on GitHub? awesome-llm-apps has more GitHub stars (117,774 vs 1,040). Stars measure visibility, not whether either tool fits your constraints. ### Are judgeval and awesome-llm-apps open source? Yes - both are open-source projects on GitHub (judgeval: Apache-2.0, awesome-llm-apps: Apache-2.0). ### Where can I find alternatives to judgeval or awesome-llm-apps? GraphCanon lists graph-backed alternatives at [judgeval alternatives](/tools/judgmentlabs-judgeval/alternatives) and [awesome-llm-apps alternatives](/tools/shubhamsaboo-awesome-llm-apps/alternatives) ([judgeval markdown twin](/tools/judgmentlabs-judgeval/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/judgmentlabs-judgeval-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, judgeval or awesome-llm-apps? judgeval: Very active. 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 judgeval and awesome-llm-apps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [judgeval trust report](/tools/judgmentlabs-judgeval/trust); [awesome-llm-apps trust report](/tools/shubhamsaboo-awesome-llm-apps/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=judgmentlabs-judgeval`](/api/graphcanon/graph?tool=judgmentlabs-judgeval) - 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/_