--- title: "mage-ai vs awesome-llm-apps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mage-ai-mage-ai-vs-shubhamsaboo-awesome-llm-apps" tools: ["mage-ai-mage-ai", "shubhamsaboo-awesome-llm-apps"] --- # mage-ai vs awesome-llm-apps *GraphCanon updated Sep 20, 2026* ## Verdict Pick mage-ai if mage OSS offers a self-hosted Python-centric notebook-style UI for creating production-grade data pipelines with modular code blocks; 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 cases in Python. [mage-ai](https://www.mage.ai) reports 8.8k GitHub stars, 990 forks, and 624 open issues, last pushed Sep 11, 2026. [awesome-llm-apps](https://www.theunwindai.com) has 136k stars, 20k forks, and 10 open issues, last pushed Sep 2, 2026. Figures are from public GitHub metadata via [mage-ai's repository](https://github.com/mage-ai/mage-ai) and [awesome-llm-apps's repository](https://github.com/Shubhamsaboo/awesome-llm-apps). | | [mage-ai](/tools/mage-ai-mage-ai.md) | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | | --- | --- | --- | | Tagline | Build, run and manage data pipelines for integrating and transforming data | Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy. | | Stars | 8,823 | 136,444 | | Forks | 990 | 20,078 | | Open issues | 624 | 10 | | Language | Python | Python | | Adopt for | Mage OSS offers a self-hosted Python-centric notebook-style UI for creating production-grade data pipelines with modular code blocks. | 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 | AI Agents, Data & Retrieval | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [mage-ai](/tools/mage-ai-mage-ai.md) | [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) | | --- | --- | --- | | Days since push | 6d | 4d | | Open issues (now) | 624 | 10 | | Stars delta | +33 (30d) | +5.2k (30d) | | Open issues delta | +5 (30d) | -3 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/mage-ai-mage-ai/trust.md) | [trust report](/tools/shubhamsaboo-awesome-llm-apps/trust.md) | ## Shared compatibility - **Python**: [mage-ai](/tools/mage-ai-mage-ai.md) - Python runtime; [awesome-llm-apps](/tools/shubhamsaboo-awesome-llm-apps.md) - Python runtime ## Decision facts: mage-ai - **Adopt for:** Mage OSS offers a self-hosted Python-centric notebook-style UI for creating production-grade data pipelines with modular code blocks. ## 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 mage-ai if… - Tags unique to mage-ai: artificial-intelligence, data-pipelines, machine-learning. - mage-ai ships Docker support for self-hosted deployment. - You need a local, self-hosted solution for building ETL tasks or orchestrating transformations. ### 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 mage-ai - You need a cloud-hosted service with pre-provisioned storage and compute resources. - Looking for real-time collaboration features beyond the notebook-style interface. - Need support for non-Python, SQL, R languages in pipeline creation. ## 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 mage-ai and awesome-llm-apps? mage-ai: Build, run and manage data pipelines for integrating and transforming data. awesome-llm-apps: Over 100 runnable AI Agent and RAG apps to clone, tweak, and deploy.. See the comparison table for live GitHub stats and shared categories. ### When should I choose mage-ai over awesome-llm-apps? Choose mage-ai over awesome-llm-apps when Tags unique to mage-ai: artificial-intelligence, data-pipelines, machine-learning; mage-ai ships Docker support for self-hosted deployment; You need a local, self-hosted solution for building ETL tasks or orchestrating transformations. ### When should I choose awesome-llm-apps over mage-ai? Choose awesome-llm-apps over mage-ai 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 mage-ai? You need a cloud-hosted service with pre-provisioned storage and compute resources. Looking for real-time collaboration features beyond the notebook-style interface. Need support for non-Python, SQL, R languages in pipeline creation. ### 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 mage-ai or awesome-llm-apps more popular on GitHub? awesome-llm-apps has more GitHub stars (136,444 vs 8,823). Stars measure visibility, not whether either tool fits your constraints. ### Are mage-ai and awesome-llm-apps open source? Yes - both are open-source projects on GitHub (mage-ai: Apache-2.0, awesome-llm-apps: Apache-2.0). ### Where can I find alternatives to mage-ai or awesome-llm-apps? GraphCanon lists graph-backed alternatives at [mage-ai alternatives](/tools/mage-ai-mage-ai/alternatives) and [awesome-llm-apps alternatives](/tools/shubhamsaboo-awesome-llm-apps/alternatives) ([mage-ai markdown twin](/tools/mage-ai-mage-ai/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/mage-ai-mage-ai-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, mage-ai or awesome-llm-apps? mage-ai: 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 mage-ai and awesome-llm-apps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [mage-ai trust report](/tools/mage-ai-mage-ai/trust); [awesome-llm-apps trust report](/tools/shubhamsaboo-awesome-llm-apps/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=mage-ai-mage-ai`](/api/graphcanon/graph?tool=mage-ai-mage-ai) - 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/_