--- title: "Spearmint vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hips-spearmint-vs-tensorchord-awesome-llmops" tools: ["hips-spearmint", "tensorchord-awesome-llmops"] --- # Spearmint vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## 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. [Spearmint](https://github.com/HIPS/Spearmint) reports 1.6k GitHub stars, 327 forks, and 77 open issues, last pushed Dec 27, 2019. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 993 forks, and 247 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [Spearmint's repository](https://github.com/HIPS/Spearmint) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [Spearmint](/tools/hips-spearmint.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | Bayesian optimization codebase | An awesome & curated list of best LLMOps tools for developers | | Stars | 1,573 | 5,915 | | Forks | 327 | 993 | | Open issues | 77 | 247 | | Language | Python | Shell | | Adopt for | A specialized package for performing Bayesian optimization, Spearmint automates experiment running and parameter tuning to minimize objectives efficiently. | 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 | Other | CC0-1.0 | | Categories | Model Training | 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._ | | [Spearmint](/tools/hips-spearmint.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 2411d | 91d | | Open issues (now) | 77 | 247 | | Stars delta | Unknown | +28 (30d) | | Open issues delta | Unknown | +66 (30d) | | Full report | [trust report](/tools/hips-spearmint/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: Spearmint - **Adopt for:** A specialized package for performing Bayesian optimization, Spearmint automates experiment running and parameter tuning to minimize objectives efficiently. ## 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 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 ### 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 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 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 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](/tools/hips-spearmint/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([Spearmint markdown twin](/tools/hips-spearmint/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/hips-spearmint-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, 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](/tools/hips-spearmint/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hips-spearmint`](/api/graphcanon/graph?tool=hips-spearmint) - 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/_