Comparison
Spearmint vs Awesome-LLMOps
Verdict
Pick Spearmint if a specialized package for performing Bayesian optimization, Spearmint automates experiment running and parameter tuning to minimize objectives efficiently; 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 · Spearmint alternatives · Awesome-LLMOps alternatives
GraphCanon updated 5d
vs
Trust & integrity
| Signal | Spearmint | Awesome-LLMOps |
|---|---|---|
| Maintenance | Dormant (2411d since push) As of 3w · github_public_v1 | Slowing (91d since push) As of 5d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3w · github_public_v1 | Not a fork · Organization account As of 5d · 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
- Spearmint
- Bayesian optimization codebase
- Awesome-LLMOps
- An awesome & curated list of best LLMOps tools for developers
Stars
- Spearmint
- 1.6k
- Awesome-LLMOps
- 5.9k
Forks
- Spearmint
- 327
- Awesome-LLMOps
- 993
Open issues
- Spearmint
- 77
- Awesome-LLMOps
- 247
Language
- Spearmint
- Python
- Awesome-LLMOps
- Shell
Adopt for
- Spearmint
- A specialized package for performing Bayesian optimization, Spearmint automates experiment running and parameter tuning to minimize objectives efficiently.
- 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
- Spearmint
- -
- Awesome-LLMOps
- -
Runtime
- Spearmint
- -
- Awesome-LLMOps
- -
License
- Spearmint
- Other
- Awesome-LLMOps
- CC0-1.0
Last pushed
- Spearmint
- Dec 27, 2019
- Awesome-LLMOps
- May 21, 2026
Categories
- Spearmint
- Model Training
- Awesome-LLMOps
- Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio
Trust and health
Maintenance
- Spearmint
- Dormant (18%)
- Awesome-LLMOps
- Slowing (36%)
Days since push
- Spearmint
- 2411d
- Awesome-LLMOps
- 91d
Open issues (now)
- Spearmint
- 77
- Awesome-LLMOps
- 247
Stars delta
- Spearmint
- Unknown
- Awesome-LLMOps
- +28 (30d)
Open issues delta
- Spearmint
- Unknown
- Awesome-LLMOps
- +66 (30d)
Full report
- Spearmint
- Trust report
- Awesome-LLMOps
- Trust report
Choose Spearmint if…
- Spearmint is primarily Python; Awesome-LLMOps is Shell.
- License: Spearmint is Other, Awesome-LLMOps is CC0-1.0.
- Tags unique to Spearmint: automated-experimentation, bayesian-optimization, hyperparameter-tuning.
- - When you require automated experimentation with parameters that can be iteratively adjusted
When NOT to use Spearmint
- - If your project requires a permissive license as Spearmint operates under an Academic and Non-Commercial Research Use License
- - If you need real-time or continuous parameter tuning outside of batch experimentation contexts as Spearmint is suited for controlled experiment setups
Choose Awesome-LLMOps if…
- Awesome-LLMOps is primarily Shell; Spearmint is Python.
- License: Awesome-LLMOps is CC0-1.0, Spearmint is Other.
- Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops.
- Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, 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 (HIPS/Spearmint) · observed Aug 4, 2026
- GitHub forks (HIPS/Spearmint) · observed Aug 4, 2026
- Last push (HIPS/Spearmint) · observed Dec 27, 2019
- License file (Other) · observed Aug 4, 2026
- Decision facts (enrichment) · observed Jul 12, 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: Spearmint 1.6k · Awesome-LLMOps 5.9k (synced Aug 4, 2026).
Common questions
- What is the difference between Spearmint and Awesome-LLMOps?
- Spearmint: Bayesian optimization codebase. 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 Spearmint over Awesome-LLMOps?
- Choose Spearmint over Awesome-LLMOps when Spearmint is primarily Python; Awesome-LLMOps is Shell; License: Spearmint is Other, Awesome-LLMOps is CC0-1.0; Tags unique to Spearmint: automated-experimentation, bayesian-optimization, hyperparameter-tuning; - When you require automated experimentation with parameters that can be iteratively adjusted.
- When should I choose Awesome-LLMOps over Spearmint?
- Choose Awesome-LLMOps over Spearmint when Awesome-LLMOps is primarily Shell; Spearmint is Python; License: Awesome-LLMOps is CC0-1.0, Spearmint is Other; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Speech & Audio; - When you need a comprehensive directory of tools specifically focused on LLM development, training, fine-tuning, and management.
- When should I avoid Spearmint?
- - If your project requires a permissive license as Spearmint operates under an Academic and Non-Commercial Research Use License - If you need real-time or continuous parameter tuning outside of batch experimentation contexts as Spearmint is suited for controlled experiment setups
- 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 Spearmint or Awesome-LLMOps more popular on GitHub?
- Awesome-LLMOps has more GitHub stars (5,915 vs 1,573). Stars measure visibility, not whether either tool fits your constraints.
- Are Spearmint and Awesome-LLMOps open source?
- Yes - both are open-source projects on GitHub (Spearmint: Other, Awesome-LLMOps: CC0-1.0).
- Where can I find alternatives to Spearmint or Awesome-LLMOps?
- GraphCanon lists graph-backed alternatives at Spearmint alternatives and Awesome-LLMOps alternatives (Spearmint 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, Spearmint or Awesome-LLMOps?
- Spearmint: Dormant. 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 Spearmint and Awesome-LLMOps?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: Spearmint trust report; Awesome-LLMOps trust report.