--- title: "awesome-ai-apps vs open-llms" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/arindam200-awesome-ai-apps-vs-eugeneyan-open-llms" tools: ["arindam200-awesome-ai-apps", "eugeneyan-open-llms"] --- # awesome-ai-apps vs open-llms *GraphCanon updated Aug 17, 2026* ## Verdict Pick awesome-ai-apps if awesome-ai-apps is a curated list of projects focusing on AI applications and innovations such as RAG technologies, AI agents, and workflows, emphasizing large language models using Python; pick open-llms if critical Facts for 'open-llms' Tool Usage. [awesome-ai-apps](https://raah.dev) reports 13k GitHub stars, 1.7k forks, and 89 open issues, last pushed Jul 23, 2026. [open-llms](https://github.com/eugeneyan/open-llms) has 13k stars, 985 forks, and 11 open issues, last pushed Feb 13, 2025. Figures are from public GitHub metadata via [awesome-ai-apps's repository](https://github.com/Arindam200/awesome-ai-apps) and [open-llms's repository](https://github.com/eugeneyan/open-llms). | | [awesome-ai-apps](/tools/arindam200-awesome-ai-apps.md) | [open-llms](/tools/eugeneyan-open-llms.md) | | --- | --- | --- | | Tagline | A curated list of AI applications showcasing RAG, agents, and workflows. | A list of open LLMs available for commercial use. | | Stars | 13,268 | 12,849 | | Forks | 1,721 | 985 | | Open issues | 89 | 11 | | Language | Python | - | | Adopt for | awesome-ai-apps is a curated list of projects focusing on AI applications and innovations such as RAG technologies, AI agents, and workflows, emphasizing large language models using Python. | Critical Facts for 'open-llms' Tool Usage | | Persona | - | - | | Runtime | - | - | | License | MIT License ensures easy integration into both open source and proprietary projects without restrictions. | The repository itself is licensed under the permissive Apache-2.0 license; however, it aggregates data about various LLMs that may have differing licensing conditions including but not limited to the | | Categories | AI Agents, LLM Frameworks | LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-ai-apps](/tools/arindam200-awesome-ai-apps.md) | [open-llms](/tools/eugeneyan-open-llms.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 2d | 549d | | Open issues (now) | 89 | 11 | | Stars delta | Unknown | +18 (30d) | | Open issues delta | Unknown | -2 (30d) | | Full report | [trust report](/tools/arindam200-awesome-ai-apps/trust.md) | [trust report](/tools/eugeneyan-open-llms/trust.md) | ## Decision facts: awesome-ai-apps - **Pricing:** freemium - As an open-source project under the MIT License, awesome-ai-apps is free to use. There are no paid plans beyond potential third-party service integrations or support contracts. - **Requirements:** Requires understanding of Python and familiarity with large language models and RAG technologies to benefit fully from the projects listed. - **Adopt for:** awesome-ai-apps is a curated list of projects focusing on AI applications and innovations such as RAG technologies, AI agents, and workflows, emphasizing large language models using Python. - **License detail:** MIT License ensures easy integration into both open source and proprietary projects without restrictions. ## Decision facts: open-llms - **Pricing:** freemium - Free and open-source models for commercial use are listed here. Usage of the models themselves varies by license, with some possibly requiring contributions or acknowledgements. - **Adopt for:** Critical Facts for 'open-llms' Tool Usage - **License detail:** The repository itself is licensed under the permissive Apache-2.0 license; however, it aggregates data about various LLMs that may have differing licensing conditions including but not limited to the ## Choose when ### Choose awesome-ai-apps if… - License: awesome-ai-apps is MIT, open-llms is Apache-2.0. - Pricing: As an open-source project under the MIT License, awesome-ai-apps is free to use. There are no paid plans beyond potential third-party service integrations or support contracts.. - Requirements: Requires understanding of Python and familiarity with large language models and RAG technologies to benefit fully from the projects listed.. - Tags unique to awesome-ai-apps: agents, ai, hacktoberfest, mcp. - Also covers AI Agents. - Use awesome-ai-apps when looking to explore or implement Retrieval-Augmented Generation (RAG) in Python projects focused on enhancing search-based question answering. ### Choose open-llms if… - License: open-llms is Apache-2.0, awesome-ai-apps is MIT. - Pricing: Free and open-source models for commercial use are listed here. Usage of the models themselves varies by license, with some possibly requiring contributions or acknowledgements.. - Tags unique to open-llms: commercial, large language models, llms. - When you need a curated list of open-source large language models (LLMs) that are specifically licensed for commercial use. ## When NOT to use awesome-ai-apps - Avoid awesome-ai-apps if your project requires non-Python support, as all the included applications are built using Python. - Do not use this repository if your focus is on backend-only AI services that do not involve RAG technologies or AI agents. ## When NOT to use open-llms - If you require proprietary or closed-source LLMs as this repository exclusively lists open-source models that are available for commercial use under permissive licenses such as Apache 2.0. - For projects needing a detailed technical implementation guide of each listed LLM, since 'open-llms' acts primarily as an index rather than providing in-depth tutorials on implementing and training L4 ## Common questions ### What is the difference between awesome-ai-apps and open-llms? awesome-ai-apps: A curated list of AI applications showcasing RAG, agents, and workflows.. open-llms: A list of open LLMs available for commercial use.. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-ai-apps over open-llms? Choose awesome-ai-apps over open-llms when License: awesome-ai-apps is MIT, open-llms is Apache-2.0; Pricing: As an open-source project under the MIT License, awesome-ai-apps is free to use. There are no paid plans beyond potential third-party service integrations or support contracts.; Requirements: Requires understanding of Python and familiarity with large language models and RAG technologies to benefit fully from the projects listed.; Tags unique to awesome-ai-apps: agents, ai, hacktoberfest, mcp; Also covers AI Agents; Use awesome-ai-apps when looking to explore or implement Retrieval-Augmented Generation (RAG) in Python projects focused on enhancing search-based question answering. ### When should I choose open-llms over awesome-ai-apps? Choose open-llms over awesome-ai-apps when License: open-llms is Apache-2.0, awesome-ai-apps is MIT; Pricing: Free and open-source models for commercial use are listed here. Usage of the models themselves varies by license, with some possibly requiring contributions or acknowledgements.; Tags unique to open-llms: commercial, large language models, llms; When you need a curated list of open-source large language models (LLMs) that are specifically licensed for commercial use. ### When should I avoid awesome-ai-apps? Avoid awesome-ai-apps if your project requires non-Python support, as all the included applications are built using Python. Do not use this repository if your focus is on backend-only AI services that do not involve RAG technologies or AI agents. ### When should I avoid open-llms? If you require proprietary or closed-source LLMs as this repository exclusively lists open-source models that are available for commercial use under permissive licenses such as Apache 2.0. For projects needing a detailed technical implementation guide of each listed LLM, since 'open-llms' acts primarily as an index rather than providing in-depth tutorials on implementing and training L4 ### Is awesome-ai-apps or open-llms more popular on GitHub? awesome-ai-apps has more GitHub stars (13,268 vs 12,849). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-ai-apps and open-llms open source? Yes - both are open-source projects on GitHub (awesome-ai-apps: MIT, open-llms: Apache-2.0). ### Where can I find alternatives to awesome-ai-apps or open-llms? GraphCanon lists graph-backed alternatives at [awesome-ai-apps alternatives](/tools/arindam200-awesome-ai-apps/alternatives) and [open-llms alternatives](/tools/eugeneyan-open-llms/alternatives) ([awesome-ai-apps markdown twin](/tools/arindam200-awesome-ai-apps/alternatives.md), [open-llms markdown twin](/tools/eugeneyan-open-llms/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/arindam200-awesome-ai-apps-vs-eugeneyan-open-llms.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 open-llms? awesome-ai-apps: Very active. open-llms: Dormant. 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 open-llms? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-ai-apps trust report](/tools/arindam200-awesome-ai-apps/trust); [open-llms trust report](/tools/eugeneyan-open-llms/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=arindam200-awesome-ai-apps`](/api/graphcanon/graph?tool=arindam200-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/_