--- title: "awesome-ai-apps vs onyx" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/arindam200-awesome-ai-apps-vs-onyx-dot-app-onyx" tools: ["arindam200-awesome-ai-apps", "onyx-dot-app-onyx"] --- # awesome-ai-apps vs onyx *GraphCanon updated Aug 16, 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 onyx if onyx is an open-source platform tailored for developing AI chat applications that can integrate with various large language models (LLMs). It caters to developers and enterprises needing. [awesome-ai-apps](https://raah.dev) reports 13k GitHub stars, 1.7k forks, and 89 open issues, last pushed Jul 23, 2026. [onyx](https://onyx.app) has 32k stars, 4.4k forks, and 401 open issues, last pushed Aug 16, 2026. Figures are from public GitHub metadata via [awesome-ai-apps's repository](https://github.com/Arindam200/awesome-ai-apps) and [onyx's repository](https://github.com/onyx-dot-app/onyx). | | [awesome-ai-apps](/tools/arindam200-awesome-ai-apps.md) | [onyx](/tools/onyx-dot-app-onyx.md) | | --- | --- | --- | | Tagline | A curated list of AI applications showcasing RAG, agents, and workflows. | Open Source AI Platform - AI Chat with advanced features that works with every LLM | | Stars | 13,268 | 31,617 | | Forks | 1,721 | 4,351 | | Open issues | 89 | 401 | | Language | Python | 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. | Onyx is an open-source platform tailored for developing AI chat applications that can integrate with various large language models (LLMs). It caters to developers and enterprises needing flexible, customizable solutions. | | Persona | - | - | | Runtime | - | - | | License | MIT License ensures easy integration into both open source and proprietary projects without restrictions. | Other (Specific license details not provided here) | | Categories | AI Agents, LLM Frameworks | AI Agents, Data & Retrieval, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-ai-apps](/tools/arindam200-awesome-ai-apps.md) | [onyx](/tools/onyx-dot-app-onyx.md) | | --- | --- | --- | | Days since push | 2d | 0d | | Open issues (now) | 89 | 401 | | Stars delta | Unknown | +685 (30d) | | Open issues delta | Unknown | -94 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/arindam200-awesome-ai-apps/trust.md) | [trust report](/tools/onyx-dot-app-onyx/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: onyx - **Requirements:** Requires Python environment - **Adopt for:** Onyx is an open-source platform tailored for developing AI chat applications that can integrate with various large language models (LLMs). It caters to developers and enterprises needing flexible, customizable solutions. - **License detail:** Other (Specific license details not provided here) ## Choose when ### Choose awesome-ai-apps if… - License: awesome-ai-apps is MIT, onyx is Other. - 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, llm. - 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 onyx if… - License: onyx is Other, awesome-ai-apps is MIT. - Requirements: Requires Python environment. - Tags unique to onyx: ai-chat, enterprise-search, llm-ui, rag. - Also covers Data & Retrieval. - When you need a versatile framework that supports integration with multiple LLMs to develop customized AI chat platforms. ## 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 onyx - When you are already committed to a specific proprietary LLM framework with specialized needs not covered by Onyx. - If your project does not require extensive customization or support for multiple LLMs; this could introduce unnecessary complexity. - For scenarios where real-time collaboration and direct customer support on the platform itself are critical, as Onyx may have limitations in these areas compared to more managed solutions. ## Common questions ### What is the difference between awesome-ai-apps and onyx? awesome-ai-apps: A curated list of AI applications showcasing RAG, agents, and workflows.. onyx: Open Source AI Platform - AI Chat with advanced features that works with every LLM. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-ai-apps over onyx? Choose awesome-ai-apps over onyx when License: awesome-ai-apps is MIT, onyx is Other; 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, llm; 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 onyx over awesome-ai-apps? Choose onyx over awesome-ai-apps when License: onyx is Other, awesome-ai-apps is MIT; Requirements: Requires Python environment; Tags unique to onyx: ai-chat, enterprise-search, llm-ui, rag; Also covers Data & Retrieval; When you need a versatile framework that supports integration with multiple LLMs to develop customized AI chat platforms. ### 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 onyx? When you are already committed to a specific proprietary LLM framework with specialized needs not covered by Onyx. If your project does not require extensive customization or support for multiple LLMs; this could introduce unnecessary complexity. For scenarios where real-time collaboration and direct customer support on the platform itself are critical, as Onyx may have limitations in these areas compared to more managed solutions. ### Is awesome-ai-apps or onyx more popular on GitHub? onyx has more GitHub stars (31,617 vs 13,268). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-ai-apps and onyx open source? Yes - both are open-source projects on GitHub (awesome-ai-apps: MIT, onyx: Other). ### Where can I find alternatives to awesome-ai-apps or onyx? GraphCanon lists graph-backed alternatives at [awesome-ai-apps alternatives](/tools/arindam200-awesome-ai-apps/alternatives) and [onyx alternatives](/tools/onyx-dot-app-onyx/alternatives) ([awesome-ai-apps markdown twin](/tools/arindam200-awesome-ai-apps/alternatives.md), [onyx markdown twin](/tools/onyx-dot-app-onyx/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-onyx-dot-app-onyx.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 onyx? awesome-ai-apps: Very active. onyx: 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 onyx? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-ai-apps trust report](/tools/arindam200-awesome-ai-apps/trust); [onyx trust report](/tools/onyx-dot-app-onyx/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/_