--- title: "awesome-ai-apps vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/rohitg00-awesome-ai-apps-vs-wangrongsheng-awesome-llm-resources" tools: ["rohitg00-awesome-ai-apps", "wangrongsheng-awesome-llm-resources"] --- # awesome-ai-apps vs awesome-LLM-resources *GraphCanon updated Sep 20, 2026* ## Verdict Pick awesome-ai-apps if awesome-ai-apps offers curated AI application examples with diverse tech stacks including OpenAI, Gemini, and local models; pick awesome-LLM-resources if awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. [awesome-ai-apps](https://agenstskills.com) reports 828 GitHub stars, 177 forks, and 33 open issues, last pushed Feb 10, 2026. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) has 9.0k stars, 993 forks, and 40 open issues, last pushed Sep 14, 2026. Figures are from public GitHub metadata via [awesome-ai-apps's repository](https://github.com/rohitg00/awesome-ai-apps) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [awesome-ai-apps](/tools/rohitg00-awesome-ai-apps.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | A curated collection of AI Agents and LLM Apps with various tech stacks | Summary of the world's best LLM resources. | | Stars | 828 | 8,968 | | Forks | 177 | 993 | | Open issues | 33 | 40 | | Language | HTML | - | | Adopt for | awesome-ai-apps offers curated AI application examples with diverse tech stacks including OpenAI, Gemini, and local models. | awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution. | | Categories | AI Agents, LLM Frameworks | AI Agents, Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-ai-apps](/tools/rohitg00-awesome-ai-apps.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 221d | 3d | | Open issues (now) | 33 | 40 | | Stars delta | +11 (30d) | +123 (30d) | | Open issues delta | +6 (30d) | +17 (30d) | | Full report | [trust report](/tools/rohitg00-awesome-ai-apps/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) | ## Decision facts: awesome-ai-apps - **Adopt for:** awesome-ai-apps offers curated AI application examples with diverse tech stacks including OpenAI, Gemini, and local models. ## Decision facts: awesome-LLM-resources - **Pricing:** freemium - The repository itself is free to use, but some linked resources may require payment or have associated costs. - **Requirements:** The repository does not specify any technical requirements for accessing its content. - **Adopt for:** awesome-LLM-resources is a curated list of resources related to large language models, covering a wide range of topics from multimodal generation to model training and inference. - **License detail:** The repository is licensed under Apache-2.0, allowing for free use, modification, and distribution. ## Choose when ### Choose awesome-ai-apps if… - Tags unique to awesome-ai-apps: agents, ai, apps, automation. - For exploring real-world implementations of AI agents across different technologies - Leaner open-issue backlog (33). ### Choose awesome-LLM-resources if… - Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs.. - Requirements: The repository does not specify any technical requirements for accessing its content.. - Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models. - Also covers Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, Model Training. - When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more. ## When NOT to use awesome-ai-apps - When seeking detailed implementation steps specific to one technology stack - In scenarios demanding a deep dive into proprietary or less publicly-known application codes ## When NOT to use awesome-LLM-resources - If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider. - When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support. ## Common questions ### What is the difference between awesome-ai-apps and awesome-LLM-resources? awesome-ai-apps: A curated collection of AI Agents and LLM Apps with various tech stacks. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-ai-apps over awesome-LLM-resources? Choose awesome-ai-apps over awesome-LLM-resources when Tags unique to awesome-ai-apps: agents, ai, apps, automation; For exploring real-world implementations of AI agents across different technologies; Leaner open-issue backlog (33). ### When should I choose awesome-LLM-resources over awesome-ai-apps? Choose awesome-LLM-resources over awesome-ai-apps when Pricing: The repository itself is free to use, but some linked resources may require payment or have associated costs.; Requirements: The repository does not specify any technical requirements for accessing its content.; Tags unique to awesome-LLM-resources: awesome-list, book, course, large-language-models; Also covers Computer Vision, Data & Retrieval, Developer Tools, Evaluation & Observability, Inference & Serving, Model Training; When you need a comprehensive list of resources for large language models, including multimodal generation, agents, programming assistance, and more. ### When should I avoid awesome-ai-apps? When seeking detailed implementation steps specific to one technology stack In scenarios demanding a deep dive into proprietary or less publicly-known application codes ### When should I avoid awesome-LLM-resources? If you are looking for a tool that provides direct access to LLM APIs or services, as this repository is a list of resources rather than a service provider. When you need real-time support or a community forum for troubleshooting LLM-related issues, as this repository is a static list of resources without interactive support. ### Is awesome-ai-apps or awesome-LLM-resources more popular on GitHub? awesome-LLM-resources has more GitHub stars (8,968 vs 828). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-ai-apps and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub (awesome-ai-apps: Apache-2.0, awesome-LLM-resources: Apache-2.0). ### Where can I find alternatives to awesome-ai-apps or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [awesome-ai-apps alternatives](/tools/rohitg00-awesome-ai-apps/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([awesome-ai-apps markdown twin](/tools/rohitg00-awesome-ai-apps/alternatives.md), [awesome-LLM-resources markdown twin](/tools/wangrongsheng-awesome-llm-resources/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/rohitg00-awesome-ai-apps-vs-wangrongsheng-awesome-llm-resources.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-ai-apps or awesome-LLM-resources? awesome-ai-apps: Slowing. awesome-LLM-resources: 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 awesome-ai-apps and awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-ai-apps trust report](/tools/rohitg00-awesome-ai-apps/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=rohitg00-awesome-ai-apps`](/api/graphcanon/graph?tool=rohitg00-awesome-ai-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/_