Comparison
mage-ai vs Awesome-LLMOps
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.
Markdown twin · mage-ai alternatives · Awesome-LLMOps alternatives
GraphCanon updated Sep 20, 2026
8views this month
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Trust & integrity
| Signal | mage-ai | Awesome-LLMOps |
|---|---|---|
| Maintenance | Very active (6d since push) As of Sep 18, 2026 · github_public_v1 | Slowing (121d since push) As of Sep 20, 2026 · github_public_v1 |
| Provenance | Not a fork · Organization account As of Sep 18, 2026 · github_public_v1 | Not a fork · Organization account As of Sep 20, 2026 · github_public_v1 |
| OSV dependency advisories | Published findings As of Jul 15, 2026 · osv@v1 | No lockfile (source not queried) As of Jul 11, 2026 · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- 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
Stars
- mage-ai
- 8.8k
- Awesome-LLMOps
- 5.9k
Forks
- mage-ai
- 990
- Awesome-LLMOps
- 1.1k
Open issues
- mage-ai
- 624
- Awesome-LLMOps
- 317
Language
- mage-ai
- Python
- Awesome-LLMOps
- Shell
Adopt for
- mage-ai
- Mage OSS offers a self-hosted Python-centric notebook-style UI for creating production-grade data pipelines with modular code blocks.
- Awesome-LLMOps
- 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
- mage-ai
- -
- Awesome-LLMOps
- -
Runtime
- mage-ai
- -
- Awesome-LLMOps
- -
License
- mage-ai
- Apache-2.0
- Awesome-LLMOps
- CC0-1.0
Last pushed
- mage-ai
- Sep 11, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- mage-ai
- Data & Retrieval
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- mage-ai
- Very active (96%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- mage-ai
- 6d
- Awesome-LLMOps
- 121d
Open issues (now)
- mage-ai
- 624
- Awesome-LLMOps
- 317
Stars delta
- mage-ai
- +33 (30d)
- Awesome-LLMOps
- +26 (30d)
Open issues delta
- mage-ai
- +5 (30d)
- Awesome-LLMOps
- +70 (30d)
OSV dependency advisories
- mage-ai
- Published findings
- Awesome-LLMOps
- No lockfile (source not queried)
Full report
- mage-ai
- Trust report
- Awesome-LLMOps
- Trust report
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.
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.
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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (mage-ai/mage-ai) · observed Sep 20, 2026
- GitHub forks (mage-ai/mage-ai) · observed Sep 20, 2026
- Last push (mage-ai/mage-ai) · observed Sep 11, 2026
- License file (Apache-2.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Sep 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Sep 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Sep 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: mage-ai 8.8k · Awesome-LLMOps 5.9k (synced Sep 20, 2026).
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 and Awesome-LLMOps alternatives (mage-ai markdown twin, Awesome-LLMOps markdown twin), 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 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; Awesome-LLMOps trust report.