--- title: "onepanel vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/onepanelio-onepanel-vs-tensorchord-awesome-llmops" tools: ["onepanelio-onepanel", "tensorchord-awesome-llmops"] --- # onepanel vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## Verdict Pick onepanel if onepanel is an open source tool designed for computer vision projects with capabilities spanning from data labeling to model tuning and deployment, all supported by a Go-based codebase under the Apache-2.0 license; 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. [onepanel](https://docs.onepanel.ai/) reports 730 GitHub stars, 73 forks, and 102 open issues, last pushed Feb 25, 2023. [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 [onepanel's repository](https://github.com/onepanelio/onepanel) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [onepanel](/tools/onepanelio-onepanel.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | The open source, end-to-end computer vision platform. | An awesome & curated list of best LLMOps tools for developers | | Stars | 730 | 5,915 | | Forks | 73 | 993 | | Open issues | 102 | 247 | | Language | Go | Shell | | Adopt for | Onepanel is an open source tool designed for computer vision projects with capabilities spanning from data labeling to model tuning and deployment, all supported by a Go-based codebase under the Apache-2.0 license. | 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 | Computer Vision, Inference & Serving, 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._ | | [onepanel](/tools/onepanelio-onepanel.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 1252d | 91d | | Open issues (now) | 102 | 247 | | Stars delta | Unknown | +28 (30d) | | Open issues delta | Unknown | +66 (30d) | | Full report | [trust report](/tools/onepanelio-onepanel/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: onepanel - **Adopt for:** Onepanel is an open source tool designed for computer vision projects with capabilities spanning from data labeling to model tuning and deployment, all supported by a Go-based codebase under the Apache-2.0 license. ## 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 onepanel if… - onepanel is primarily Go; Awesome-LLMOps is Shell. - License: onepanel is Apache-2.0, Awesome-LLMOps is CC0-1.0. - Tags unique to onepanel: aiops, annotation, deeplearning, hyperparameter-tuning. - onepanel ships Docker support for self-hosted deployment. - When you need an end-to-end platform that supports multiple aspects of computer vision including advanced functionalities such as hyperparameter tuning and automated workflows. ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; onepanel is Go. - License: Awesome-LLMOps is CC0-1.0, onepanel is Apache-2.0. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops. - Also covers Data & Retrieval, Evaluation & Observability, 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 onepanel - Avoid using Onepanel for projects that require extensive Java-based development because it is written in Go. - Not suitable if you seek a platform focusing solely on model serving or inference without capabilities to go back upstream into data labeling and preprocessing stages. ## 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 onepanel and Awesome-LLMOps? onepanel: The open source, end-to-end computer vision platform.. 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 onepanel over Awesome-LLMOps? Choose onepanel over Awesome-LLMOps when onepanel is primarily Go; Awesome-LLMOps is Shell; License: onepanel is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to onepanel: aiops, annotation, deeplearning, hyperparameter-tuning; onepanel ships Docker support for self-hosted deployment; When you need an end-to-end platform that supports multiple aspects of computer vision including advanced functionalities such as hyperparameter tuning and automated workflows. ### When should I choose Awesome-LLMOps over onepanel? Choose Awesome-LLMOps over onepanel when Awesome-LLMOps is primarily Shell; onepanel is Go; License: Awesome-LLMOps is CC0-1.0, onepanel is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops; Also covers Data & Retrieval, Evaluation & Observability, 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 onepanel? Avoid using Onepanel for projects that require extensive Java-based development because it is written in Go. Not suitable if you seek a platform focusing solely on model serving or inference without capabilities to go back upstream into data labeling and preprocessing stages. ### 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 onepanel or Awesome-LLMOps more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,915 vs 730). Stars measure visibility, not whether either tool fits your constraints. ### Are onepanel and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (onepanel: Apache-2.0, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to onepanel or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [onepanel alternatives](/tools/onepanelio-onepanel/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([onepanel markdown twin](/tools/onepanelio-onepanel/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/onepanelio-onepanel-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, onepanel or Awesome-LLMOps? onepanel: 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 onepanel and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [onepanel trust report](/tools/onepanelio-onepanel/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=onepanelio-onepanel`](/api/graphcanon/graph?tool=onepanelio-onepanel) - 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/_