--- title: "onyx vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/onyx-dot-app-onyx-vs-tensorchord-awesome-llmops" tools: ["onyx-dot-app-onyx", "tensorchord-awesome-llmops"] --- # onyx vs Awesome-LLMOps *GraphCanon updated Sep 20, 2026* ## Verdict Pick onyx if onyx is an open-source AI platform that supports AI chat functionalities with advanced features, suitable for deployment in various environments including Docker, Kubernetes, and cloud providers. It offers both Lite and,; 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. [onyx](https://onyx.app) reports 32k GitHub stars, 4.5k forks, and 470 open issues, last pushed Sep 18, 2026. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) has 5.9k stars, 1.1k forks, and 317 open issues, last pushed May 21, 2026. Figures are from public GitHub metadata via [onyx's repository](https://github.com/onyx-dot-app/onyx) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [onyx](/tools/onyx-dot-app-onyx.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | Open Source AI Platform - AI Chat with advanced features that works with every LLM | An awesome & curated list of best LLMOps tools for developers | | Stars | 32,153 | 5,941 | | Forks | 4,477 | 1,058 | | Open issues | 470 | 317 | | Language | Python | Shell | | Adopt for | Onyx is an open-source AI platform that supports AI chat functionalities with advanced features, suitable for deployment in various environments including Docker, Kubernetes, and cloud providers. It offers both Lite and, | 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 | Onyx is available in two editions: Community Edition (CE) under the MIT license, which covers core features, and Enterprise Edition (EE) with additional features for larger organizations. | CC0-1.0 | | Categories | AI Agents, Data & Retrieval, Developer Tools, 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._ | | [onyx](/tools/onyx-dot-app-onyx.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 121d | | Open issues (now) | 470 | 317 | | Stars delta | +536 (30d) | +26 (30d) | | Open issues delta | +69 (30d) | +70 (30d) | | Full report | [trust report](/tools/onyx-dot-app-onyx/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: onyx - **Pricing:** freemium - Onyx Community Edition is free under the MIT license, while the Enterprise Edition includes extra features for larger organizations and requires a paid subscription. - **Requirements:** Min 1 GB RAM; Lite mode requires under 1GB of memory, while Standard mode requires more resources for its additional features. - **Adopt for:** Onyx is an open-source AI platform that supports AI chat functionalities with advanced features, suitable for deployment in various environments including Docker, Kubernetes, and cloud providers. It offers both Lite and, - **License detail:** Onyx is available in two editions: Community Edition (CE) under the MIT license, which covers core features, and Enterprise Edition (EE) with additional features for larger organizations. ## 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 onyx if… - onyx is primarily Python; Awesome-LLMOps is Shell. - License: onyx is Other, Awesome-LLMOps is CC0-1.0. - Pricing: Onyx Community Edition is free under the MIT license, while the Enterprise Edition includes extra features for larger organizations and requires a paid subscription.. - Requirements: Min 1 GB RAM; Lite mode requires under 1GB of memory, while Standard mode requires more resources for its additional features.. - Tags unique to onyx: ai-chat, chatui, enterprise-search, gen-ai. - Also covers AI Agents, Developer Tools. - use Onyx when you need a platform that supports both lightweight and full-featured deployments, including vector and keyword indexing, and performance optimizations for large-scale use. ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; onyx is Python. - License: Awesome-LLMOps is CC0-1.0, onyx is Other. - Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops. - Also covers Computer Vision, 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 onyx - avoid Onyx if you are looking for a platform that requires minimal setup and resource usage beyond what the Lite mode offers. - do not use Onyx if your project does not benefit from vector and keyword indexing, AI model inference servers, or performance optimizations, as these features are only available in the Standard mode. ## 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 onyx and Awesome-LLMOps? onyx: Open Source AI Platform - AI Chat with advanced features that works with every LLM. 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 onyx over Awesome-LLMOps? Choose onyx over Awesome-LLMOps when onyx is primarily Python; Awesome-LLMOps is Shell; License: onyx is Other, Awesome-LLMOps is CC0-1.0; Pricing: Onyx Community Edition is free under the MIT license, while the Enterprise Edition includes extra features for larger organizations and requires a paid subscription.; Requirements: Min 1 GB RAM; Lite mode requires under 1GB of memory, while Standard mode requires more resources for its additional features.; Tags unique to onyx: ai-chat, chatui, enterprise-search, gen-ai; Also covers AI Agents, Developer Tools; use Onyx when you need a platform that supports both lightweight and full-featured deployments, including vector and keyword indexing, and performance optimizations for large-scale use. ### When should I choose Awesome-LLMOps over onyx? Choose Awesome-LLMOps over onyx when Awesome-LLMOps is primarily Shell; onyx is Python; License: Awesome-LLMOps is CC0-1.0, onyx is Other; Tags unique to Awesome-LLMOps: ai development tools, awesome-list, llmops, mlops; Also covers Computer Vision, 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 onyx? avoid Onyx if you are looking for a platform that requires minimal setup and resource usage beyond what the Lite mode offers. do not use Onyx if your project does not benefit from vector and keyword indexing, AI model inference servers, or performance optimizations, as these features are only available in the Standard mode. ### 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 onyx or Awesome-LLMOps more popular on GitHub? onyx has more GitHub stars (32,153 vs 5,941). Stars measure visibility, not whether either tool fits your constraints. ### Are onyx and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (onyx: Other, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to onyx or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [onyx alternatives](/tools/onyx-dot-app-onyx/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([onyx markdown twin](/tools/onyx-dot-app-onyx/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/onyx-dot-app-onyx-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, onyx or Awesome-LLMOps? onyx: 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 onyx and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [onyx trust report](/tools/onyx-dot-app-onyx/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=onyx-dot-app-onyx`](/api/graphcanon/graph?tool=onyx-dot-app-onyx) - 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/_