Comparison
plexiglass vs Awesome-LLMOps
Verdict
Pick plexiglass if plexiglass is a toolkit for detecting and mitigating vulnerabilities in Large Language Models through adversarial attacks and deep-learning techniques; 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 · plexiglass alternatives · Awesome-LLMOps alternatives
GraphCanon updated 4d
vs
Trust & integrity
| Signal | plexiglass | Awesome-LLMOps |
|---|---|---|
| Maintenance | Slowing (178d since push) As of 3w · github_public_v1 | Slowing (91d since push) As of 4d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 4d · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · 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
- plexiglass
- A toolkit for detecting and protecting against vulnerabilities in Large Language Models (LLMs).
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- plexiglass
- 153
- Awesome-LLMOps
- 5.9k
Forks
- plexiglass
- 18
- Awesome-LLMOps
- 993
Open issues
- plexiglass
- 0
- Awesome-LLMOps
- 247
Language
- plexiglass
- Python
- Awesome-LLMOps
- Shell
Adopt for
- plexiglass
- Plexiglass is a toolkit for detecting and mitigating vulnerabilities in Large Language Models through adversarial attacks and deep-learning techniques.
- 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
- plexiglass
- -
- Awesome-LLMOps
- -
Runtime
- plexiglass
- -
- Awesome-LLMOps
- -
License
- plexiglass
- Apache-2.0
- Awesome-LLMOps
- CC0-1.0
Last pushed
- plexiglass
- Feb 4, 2026
- Awesome-LLMOps
- May 21, 2026
Categories
- plexiglass
- Evaluation & Observability
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Days since push
- plexiglass
- 178d
- Awesome-LLMOps
- 91d
Open issues (now)
- plexiglass
- 0
- Awesome-LLMOps
- 247
Stars delta
- plexiglass
- Unknown
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- plexiglass
- Unknown
- Awesome-LLMOps
- +66 (30d)
Full report
- plexiglass
- Trust report
- Awesome-LLMOps
- Trust report
Choose plexiglass if…
- plexiglass is primarily Python; Awesome-LLMOps is Shell.
- License: plexiglass is Apache-2.0, Awesome-LLMOps is CC0-1.0.
- Tags unique to plexiglass: adversarial-attacks, adversarial-machine-learning, cybersecurity, deep-learning.
- When a team needs to evaluate the robustness of their LLM against specific adversarial attack vectors within Python-based projects.
When NOT to use plexiglass
- If a project is not using Python, because Plexiglass is specifically built for Python environments.
- When the team does not have access to the deep-learning techniques required by Plexiglass, as it heavily relies on such methods to mitigate vulnerabilities.
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; plexiglass is Python.
- License: Awesome-LLMOps is CC0-1.0, plexiglass is Apache-2.0.
- Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Data & Retrieval, 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 (safellama/plexiglass) · observed Aug 2, 2026
- GitHub forks (safellama/plexiglass) · observed Aug 2, 2026
- Last push (safellama/plexiglass) · observed Feb 4, 2026
- License file (Apache-2.0) · observed Aug 2, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- GitHub forks (tensorchord/Awesome-LLMOps) · observed Aug 20, 2026
- Last push (tensorchord/Awesome-LLMOps) · observed May 21, 2026
- License file (CC0-1.0) · observed Aug 20, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: plexiglass 153 · Awesome-LLMOps 5.9k (synced Aug 2, 2026).
Common questions
- What is the difference between plexiglass and Awesome-LLMOps?
- plexiglass: A toolkit for detecting and protecting against vulnerabilities in Large Language Models (LLMs).. 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 plexiglass over Awesome-LLMOps?
- Choose plexiglass over Awesome-LLMOps when plexiglass is primarily Python; Awesome-LLMOps is Shell; License: plexiglass is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to plexiglass: adversarial-attacks, adversarial-machine-learning, cybersecurity, deep-learning; When a team needs to evaluate the robustness of their LLM against specific adversarial attack vectors within Python-based projects.
- When should I choose Awesome-LLMOps over plexiglass?
- Choose Awesome-LLMOps over plexiglass when Awesome-LLMOps is primarily Shell; plexiglass is Python; License: Awesome-LLMOps is CC0-1.0, plexiglass is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, 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 plexiglass?
- If a project is not using Python, because Plexiglass is specifically built for Python environments. When the team does not have access to the deep-learning techniques required by Plexiglass, as it heavily relies on such methods to mitigate vulnerabilities.
- 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 plexiglass or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,915 vs 153). Stars measure visibility, not whether either tool fits your constraints.
- Are plexiglass and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (plexiglass: Apache-2.0, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to plexiglass or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at plexiglass alternatives and Awesome-LLMOps alternatives (plexiglass 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, plexiglass or Awesome-LLMOps?
- plexiglass: Slowing. 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 plexiglass and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: plexiglass trust report; Awesome-LLMOps trust report.