--- title: "mage-ai vs llm-app" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mage-ai-mage-ai-vs-pathwaycom-llm-app" tools: ["mage-ai-mage-ai", "pathwaycom-llm-app"] --- # mage-ai vs llm-app *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 llm-app if llm-app offers cloud templates for RAG, AI pipelines, and enterprise search, supporting integration with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs. [mage-ai](https://www.mage.ai) reports 8.8k GitHub stars, 990 forks, and 624 open issues, last pushed Sep 11, 2026. [llm-app](https://pathway.com/developers/templates/) has 59k stars, 1.5k forks, and 8 open issues, last pushed Jul 5, 2026. Figures are from public GitHub metadata via [mage-ai's repository](https://github.com/mage-ai/mage-ai) and [llm-app's repository](https://github.com/pathwaycom/llm-app). | | [mage-ai](/tools/mage-ai-mage-ai.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Tagline | Build, run and manage data pipelines for integrating and transforming data | Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data | | Stars | 8,823 | 58,920 | | Forks | 990 | 1,498 | | Open issues | 624 | 8 | | Language | Python | Jupyter Notebook | | Adopt for | Mage OSS offers a self-hosted Python-centric notebook-style UI for creating production-grade data pipelines with modular code blocks. | llm-app offers cloud templates for RAG, AI pipelines, and enterprise search, supporting integration with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT License | | Categories | Data & Retrieval | Data & Retrieval, Evaluation & Observability, Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [mage-ai](/tools/mage-ai-mage-ai.md) | [llm-app](/tools/pathwaycom-llm-app.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 6d | 74d | | Open issues (now) | 624 | 8 | | Stars delta | +33 (30d) | -117 (30d) | | Open issues delta | +5 (30d) | 0 (30d) | | Full report | [trust report](/tools/mage-ai-mage-ai/trust.md) | [trust report](/tools/pathwaycom-llm-app/trust.md) | ## 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: llm-app - **Pricing:** freemium - The repository is open-source under the MIT License, but additional services or support might incur costs. - **Requirements:** Min 4 GB RAM; Requires Docker; Requires Docker for running the cloud templates.; Supports integration with a variety of data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs. - **Adopt for:** llm-app offers cloud templates for RAG, AI pipelines, and enterprise search, supporting integration with various data sources like Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs. - **License detail:** MIT License ## Choose when ### Choose mage-ai if… - mage-ai is primarily Python; llm-app is Jupyter Notebook. - License: mage-ai is Apache-2.0, llm-app is MIT. - Tags unique to mage-ai: artificial-intelligence, data-pipelines, python. - mage-ai ships Docker support for self-hosted deployment. - You need a local, self-hosted solution for building ETL tasks or orchestrating transformations. ### Choose llm-app if… - llm-app is primarily Jupyter Notebook; mage-ai is Python. - License: llm-app is MIT, mage-ai is Apache-2.0. - Pricing: The repository is open-source under the MIT License, but additional services or support might incur costs.. - Requirements: Min 4 GB RAM; Requires Docker; Requires Docker for running the cloud templates.; Supports integration with a variety of data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs.. - Tags unique to llm-app: chatbot, hugging-face, llm, llm-local. - Also covers Evaluation & Observability, Inference & Serving, Model Training. - When you need ready-to-run cloud templates for RAG, AI pipelines, and enterprise search that integrate seamlessly with data sources such as Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-ti ## 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 llm-app - Avoid using llm-app if your project does not require integration with specific data sources like Sharepoint or Google Drive, as the tool's strength lies in its broad data source support. - Do not use llm-app if you are looking for a tool that focuses solely on model training or inference without the need for cloud templates or enterprise search capabilities. ## Common questions ### What is the difference between mage-ai and llm-app? mage-ai: Build, run and manage data pipelines for integrating and transforming data. llm-app: Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. See the comparison table for live GitHub stats and shared categories. ### When should I choose mage-ai over llm-app? Choose mage-ai over llm-app when mage-ai is primarily Python; llm-app is Jupyter Notebook; License: mage-ai is Apache-2.0, llm-app is MIT; Tags unique to mage-ai: artificial-intelligence, data-pipelines, python; 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 llm-app over mage-ai? Choose llm-app over mage-ai when llm-app is primarily Jupyter Notebook; mage-ai is Python; License: llm-app is MIT, mage-ai is Apache-2.0; Pricing: The repository is open-source under the MIT License, but additional services or support might incur costs.; Requirements: Min 4 GB RAM; Requires Docker; Requires Docker for running the cloud templates.; Supports integration with a variety of data sources including Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-time data APIs.; Tags unique to llm-app: chatbot, hugging-face, llm, llm-local; Also covers Evaluation & Observability, Inference & Serving, Model Training; When you need ready-to-run cloud templates for RAG, AI pipelines, and enterprise search that integrate seamlessly with data sources such as Sharepoint, Google Drive, S3, Kafka, PostgreSQL, and real-ti. ### 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 llm-app? Avoid using llm-app if your project does not require integration with specific data sources like Sharepoint or Google Drive, as the tool's strength lies in its broad data source support. Do not use llm-app if you are looking for a tool that focuses solely on model training or inference without the need for cloud templates or enterprise search capabilities. ### Is mage-ai or llm-app more popular on GitHub? llm-app has more GitHub stars (58,920 vs 8,823). Stars measure visibility, not whether either tool fits your constraints. ### Are mage-ai and llm-app open source? Yes - both are open-source projects on GitHub (mage-ai: Apache-2.0, llm-app: MIT). ### Where can I find alternatives to mage-ai or llm-app? GraphCanon lists graph-backed alternatives at [mage-ai alternatives](/tools/mage-ai-mage-ai/alternatives) and [llm-app alternatives](/tools/pathwaycom-llm-app/alternatives) ([mage-ai markdown twin](/tools/mage-ai-mage-ai/alternatives.md), [llm-app markdown twin](/tools/pathwaycom-llm-app/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-pathwaycom-llm-app.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, mage-ai or llm-app? mage-ai: Very active. llm-app: Steady. 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 llm-app? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [mage-ai trust report](/tools/mage-ai-mage-ai/trust); [llm-app trust report](/tools/pathwaycom-llm-app/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/_