--- title: "cceval vs awesome-llm-apps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/amazon-science-cceval-vs-shubhamsaboo-awesome-llm-apps" tools: ["amazon-science-cceval", "shubhamsaboo-awesome-llm-apps"] --- # cceval vs awesome-llm-apps *GraphCanon updated Jul 11, 2026* ## Verdict Pick cceval when also covers Evaluation & Observability; pick awesome-llm-apps when pricing: Free with open-source licensing, but commercial exploitation is allowed.. [cceval](https://crosscodeeval.github.io/) reports 181 GitHub stars, 25 forks, and 5 open issues, last pushed Aug 15, 2025. [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 [cceval's repository](https://github.com/amazon-science/cceval) and [awesome-llm-apps's repository](https://github.com/Shubhamsaboo/awesome-llm-apps). | | [cceval](/tools/amazon-science-cceval.md) | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | | --- | --- | --- | | Tagline | CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion (NeurIPS 2023) | 100+ AI Agent & RAG apps you can actually run — clone, customize, ship. | | Stars | 181 | 117,774 | | Forks | 25 | 17,498 | | Open issues | 5 | 6 | | Language | Python | Python | | 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. | | 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 | Data & Retrieval, Evaluation & Observability | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [cceval](/tools/amazon-science-cceval.md) | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 330d | 0d | | Open issues (now) | 5 | 6 | | Owner type | Organization | User | | Security scan | 5 critical, 7 high, 8 medium, 21 low (5 critical, 7 high, 8 medium, 21 low) | No lockfile | | Full report | [trust report](/tools/amazon-science-cceval/trust.md) | [trust report](/tools/shubhamsaboo-awesome-llm-apps/trust.md) | ## Shared compatibility - **Python**: [cceval](/tools/amazon-science-cceval.md) - Python runtime; [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) - Python runtime ## 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 cceval if… - Also covers Evaluation & Observability. - Leaner open-issue backlog (5). ### Choose awesome-llm-apps if… - 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 cceval - Last GitHub push was 330 days ago (slowing maintenance, Aug 15, 2025). Validate activity before betting a new project on cceval. - Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## 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 cceval and awesome-llm-apps? cceval: CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion (NeurIPS 2023). 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 cceval over awesome-llm-apps? Choose cceval over awesome-llm-apps when Also covers Evaluation & Observability; Leaner open-issue backlog (5). ### When should I choose awesome-llm-apps over cceval? Choose awesome-llm-apps over cceval when 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 cceval? Last GitHub push was 330 days ago (slowing maintenance, Aug 15, 2025). Validate activity before betting a new project on cceval. Data & Retrieval: Skip a heavy ingestion framework when your corpus is small and static; a script plus the embedding API is enough. Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### 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 cceval or awesome-llm-apps more popular on GitHub? awesome-llm-apps has more GitHub stars (117,774 vs 181). Stars measure visibility, not whether either tool fits your constraints. ### Are cceval and awesome-llm-apps open source? Yes - both are open-source projects on GitHub (cceval: Apache-2.0, awesome-llm-apps: Apache-2.0). ### Where can I find alternatives to cceval or awesome-llm-apps? GraphCanon lists graph-backed alternatives at [cceval alternatives](/tools/amazon-science-cceval/alternatives) and [awesome-llm-apps alternatives](/tools/shubhamsaboo-awesome-llm-apps/alternatives) ([cceval markdown twin](/tools/amazon-science-cceval/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/amazon-science-cceval-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, cceval or awesome-llm-apps? cceval: Slowing. 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 cceval and awesome-llm-apps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [cceval trust report](/tools/amazon-science-cceval/trust); [awesome-llm-apps trust report](/tools/shubhamsaboo-awesome-llm-apps/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=amazon-science-cceval`](/api/graphcanon/graph?tool=amazon-science-cceval) - 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/_