--- title: "oss-llmops-stack vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/langfuse-oss-llmops-stack-vs-tensorchord-awesome-llmops" tools: ["langfuse-oss-llmops-stack", "tensorchord-awesome-llmops"] --- # oss-llmops-stack vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## Verdict Pick oss-llmops-stack if the OSS LLMOps Stack is designed for managing and unifying LLM APIs with LiteLLM, and providing detailed observability through Langfuse; 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. [oss-llmops-stack](https://oss-llmops-stack.com) reports 142 GitHub stars, 7 forks, and 1 open issues, last pushed Jul 28, 2026. [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 [oss-llmops-stack's repository](https://github.com/langfuse/oss-llmops-stack) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [oss-llmops-stack](/tools/langfuse-oss-llmops-stack.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | Modular open source LLMOps stack for LLM API unification, observability and prompt management | An awesome & curated list of best LLMOps tools for developers | | Stars | 142 | 5,915 | | Forks | 7 | 993 | | Open issues | 1 | 247 | | Language | - | Shell | | Adopt for | The OSS LLMOps Stack is designed for managing and unifying LLM APIs with LiteLLM, and providing detailed observability through Langfuse. | 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 | MIT | CC0-1.0 | | Categories | Evaluation & Observability, Inference & Serving | 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._ | | [oss-llmops-stack](/tools/langfuse-oss-llmops-stack.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 91d | | Open issues (now) | 1 | 247 | | Stars delta | Unknown | +28 (30d) | | Open issues delta | Unknown | +66 (30d) | | Full report | [trust report](/tools/langfuse-oss-llmops-stack/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: oss-llmops-stack - **Requirements:** Ensure your environment supports both LiteLLM and Langfuse functionalities for seamless operation of the OSS LLMOps Stack.; Consider server capacity to handle the additional load introduced by using this stack for API unification and observability services. - **Adopt for:** The OSS LLMOps Stack is designed for managing and unifying LLM APIs with LiteLLM, and providing detailed observability through Langfuse. ## 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 oss-llmops-stack if… - License: oss-llmops-stack is MIT, Awesome-LLMOps is CC0-1.0. - Requirements: Ensure your environment supports both LiteLLM and Langfuse functionalities for seamless operation of the OSS LLMOps Stack.; Consider server capacity to handle the additional load introduced by using this stack for API unification and observability services.. - Tags unique to oss-llmops-stack: ai-gateway, llm-evaluation, open-source, prompt management. - When you need to unify Multiple Large Language Model (LLM) APIs using LiteLLM's API mediation capabilities for efficient routing, cost control, and high-availability support. ### Choose Awesome-LLMOps if… - License: Awesome-LLMOps is CC0-1.0, oss-llmops-stack is MIT. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops. - Also covers Computer Vision, Data & Retrieval, 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 oss-llmops-stack - If your operational requirements are simple and you do not need comprehensive observability metrics or advanced LLM API unification capabilities provided by the stack. - In scenarios where you prefer a proprietary software solution over an open-source tool for security, support, or compliance reasons. ## 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 oss-llmops-stack and Awesome-LLMOps? oss-llmops-stack: Modular open source LLMOps stack for LLM API unification, observability and prompt management. 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 oss-llmops-stack over Awesome-LLMOps? Choose oss-llmops-stack over Awesome-LLMOps when License: oss-llmops-stack is MIT, Awesome-LLMOps is CC0-1.0; Requirements: Ensure your environment supports both LiteLLM and Langfuse functionalities for seamless operation of the OSS LLMOps Stack.; Consider server capacity to handle the additional load introduced by using this stack for API unification and observability services.; Tags unique to oss-llmops-stack: ai-gateway, llm-evaluation, open-source, prompt management; When you need to unify Multiple Large Language Model (LLM) APIs using LiteLLM's API mediation capabilities for efficient routing, cost control, and high-availability support. ### When should I choose Awesome-LLMOps over oss-llmops-stack? Choose Awesome-LLMOps over oss-llmops-stack when License: Awesome-LLMOps is CC0-1.0, oss-llmops-stack is MIT; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, llmops, mlops; Also covers Computer Vision, Data & Retrieval, 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 oss-llmops-stack? If your operational requirements are simple and you do not need comprehensive observability metrics or advanced LLM API unification capabilities provided by the stack. In scenarios where you prefer a proprietary software solution over an open-source tool for security, support, or compliance reasons. ### 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 oss-llmops-stack or Awesome-LLMOps more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,915 vs 142). Stars measure visibility, not whether either tool fits your constraints. ### Are oss-llmops-stack and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (oss-llmops-stack: MIT, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to oss-llmops-stack or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [oss-llmops-stack alternatives](/tools/langfuse-oss-llmops-stack/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([oss-llmops-stack markdown twin](/tools/langfuse-oss-llmops-stack/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/langfuse-oss-llmops-stack-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, oss-llmops-stack or Awesome-LLMOps? oss-llmops-stack: 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 oss-llmops-stack and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [oss-llmops-stack trust report](/tools/langfuse-oss-llmops-stack/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=langfuse-oss-llmops-stack`](/api/graphcanon/graph?tool=langfuse-oss-llmops-stack) - 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/_