--- title: "mage-ai vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/mage-ai-mage-ai-vs-tensorchord-awesome-llmops" tools: ["mage-ai-mage-ai", "tensorchord-awesome-llmops"] --- # mage-ai vs Awesome-LLMOps *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-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. [mage-ai](https://www.mage.ai) reports 8.8k GitHub stars, 990 forks, and 624 open issues, last pushed Sep 11, 2026. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 1.1k forks, and 317 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [mage-ai's repository](https://github.com/mage-ai/mage-ai) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [mage-ai](/tools/mage-ai-mage-ai.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | Build, run and manage data pipelines for integrating and transforming data | An awesome & curated list of best LLMOps tools for developers | | Stars | 8,823 | 5,941 | | Forks | 990 | 1,058 | | Open issues | 624 | 317 | | Language | Python | Shell | | Adopt for | Mage OSS offers a self-hosted Python-centric notebook-style UI for creating production-grade data pipelines with modular code blocks. | Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | CC0-1.0 | | Categories | Data & Retrieval | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [mage-ai](/tools/mage-ai-mage-ai.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 6d | 121d | | Open issues (now) | 624 | 317 | | Stars delta | +33 (30d) | +26 (30d) | | Open issues delta | +5 (30d) | +70 (30d) | | Full report | [trust report](/tools/mage-ai-mage-ai/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/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: Awesome-LLMOps - **Adopt for:** Awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training, serving, evaluation, deployment, and more. ## Choose when ### Choose mage-ai if… - mage-ai is primarily Python; Awesome-LLMOps is Shell. - License: mage-ai is Apache-2.0, Awesome-LLMOps is CC0-1.0. - Tags unique to mage-ai: artificial-intelligence, data-pipelines, machine-learning, 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 Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; mage-ai is Python. - License: Awesome-LLMOps is CC0-1.0, mage-ai is Apache-2.0. - Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops. - Also covers Computer Vision, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio. - - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ## 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-LLMOps - - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources. ## Common questions ### What is the difference between mage-ai and Awesome-LLMOps? mage-ai: Build, run and manage data pipelines for integrating and transforming data. Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. See the comparison table for live GitHub stats and shared categories. ### When should I choose mage-ai over Awesome-LLMOps? Choose mage-ai over Awesome-LLMOps when mage-ai is primarily Python; Awesome-LLMOps is Shell; License: mage-ai is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to mage-ai: artificial-intelligence, data-pipelines, machine-learning, 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 Awesome-LLMOps over mage-ai? Choose Awesome-LLMOps over mage-ai when Awesome-LLMOps is primarily Shell; mage-ai is Python; License: Awesome-LLMOps is CC0-1.0, mage-ai is Apache-2.0; Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops; Also covers Computer Vision, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management. ### 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-LLMOps? - When you are looking for a hands-on platform or framework for developing and deploying models rather than just a resource list. - If your focus is on general artificial intelligence development that includes areas beyond LLMOps like image processing, robotics, or federated learning without the need for LLM-specific resources. ### Is mage-ai or Awesome-LLMOps more popular on GitHub? mage-ai has more GitHub stars (8,823 vs 5,941). Stars measure visibility, not whether either tool fits your constraints. ### Are mage-ai and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (mage-ai: Apache-2.0, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to mage-ai or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [mage-ai alternatives](/tools/mage-ai-mage-ai/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([mage-ai markdown twin](/tools/mage-ai-mage-ai/alternatives.md), [Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/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-tensorchord-awesome-llmops.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-LLMOps? mage-ai: Very active. Awesome-LLMOps: Slowing. 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-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [mage-ai trust report](/tools/mage-ai-mage-ai/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/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/_