--- title: "openinference vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/arize-ai-openinference-vs-tensorchord-awesome-llmops" tools: ["arize-ai-openinference", "tensorchord-awesome-llmops"] --- # openinference vs Awesome-LLMOps *GraphCanon updated Aug 21, 2026* ## Verdict Pick openinference if openInference is a tool focused on providing observability for AI systems using OpenTelemetry, designed to complement it by enabling tracing of AI applications. It is natively supported by Arize Phoenix and AX but can be; pick Awesome-LLMOps if awesome-LLMOps is a curated list tailored for developers working with Large Language Models (LLMs), providing resources for model training. [openinference](https://arize-ai.github.io/openinference/) reports 1.2k GitHub stars, 299 forks, and 238 open issues, last pushed Aug 20, 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 [openinference's repository](https://github.com/Arize-ai/openinference) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [openinference](/tools/arize-ai-openinference.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | OpenTelemetry Instrumentation for AI Observability | An awesome & curated list of best LLMOps tools for developers | | Stars | 1,159 | 5,915 | | Forks | 299 | 993 | | Open issues | 238 | 247 | | Language | Python | Shell | | Adopt for | OpenInference is a tool focused on providing observability for AI systems using OpenTelemetry, designed to complement it by enabling tracing of AI applications. It is natively supported by Arize Phoenix and AX but can be | 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 | Evaluation & Observability | 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._ | | [openinference](/tools/arize-ai-openinference.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 91d | | Open issues (now) | 238 | 247 | | Stars delta | +55 (30d) | +28 (30d) | | Open issues delta | +5 (30d) | +66 (30d) | | Full report | [trust report](/tools/arize-ai-openinference/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | ## Decision facts: openinference - **Adopt for:** OpenInference is a tool focused on providing observability for AI systems using OpenTelemetry, designed to complement it by enabling tracing of AI applications. It is natively supported by Arize Phoenix and AX but can be ## 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 openinference if… - openinference is primarily Python; Awesome-LLMOps is Shell. - License: openinference is Apache-2.0, Awesome-LLMOps is CC0-1.0. - Tags unique to openinference: aiops, openinference, telemetry, tracing. - When you are working with AI applications that need detailed tracing capabilities alongside observability features provided by OpenTelemetry. ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; openinference is Python. - License: Awesome-LLMOps is CC0-1.0, openinference is Apache-2.0. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, mlops. - Also covers Computer Vision, Data & Retrieval, Inference & Serving, 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 openinference - When your AI applications do not require the specific tracing features supported by OpenInference and can operate effectively with standard OpenTelemetry capabilities alone. - If you are looking for alternatives that offer more generic observability features without needing to integrate Arize-specific tools, as this might make OpenInference less beneficial. ## 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 openinference and Awesome-LLMOps? openinference: OpenTelemetry Instrumentation for AI Observability. 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 openinference over Awesome-LLMOps? Choose openinference over Awesome-LLMOps when openinference is primarily Python; Awesome-LLMOps is Shell; License: openinference is Apache-2.0, Awesome-LLMOps is CC0-1.0; Tags unique to openinference: aiops, openinference, telemetry, tracing; When you are working with AI applications that need detailed tracing capabilities alongside observability features provided by OpenTelemetry. ### When should I choose Awesome-LLMOps over openinference? Choose Awesome-LLMOps over openinference when Awesome-LLMOps is primarily Shell; openinference is Python; License: Awesome-LLMOps is CC0-1.0, openinference is Apache-2.0; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, mlops; Also covers Computer Vision, Data & Retrieval, Inference & Serving, 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 openinference? When your AI applications do not require the specific tracing features supported by OpenInference and can operate effectively with standard OpenTelemetry capabilities alone. If you are looking for alternatives that offer more generic observability features without needing to integrate Arize-specific tools, as this might make OpenInference less beneficial. ### 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 openinference or Awesome-LLMOps more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,915 vs 1,159). Stars measure visibility, not whether either tool fits your constraints. ### Are openinference and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (openinference: Apache-2.0, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to openinference or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [openinference alternatives](/tools/arize-ai-openinference/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([openinference markdown twin](/tools/arize-ai-openinference/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/arize-ai-openinference-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, openinference or Awesome-LLMOps? openinference: 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 openinference and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [openinference trust report](/tools/arize-ai-openinference/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=arize-ai-openinference`](/api/graphcanon/graph?tool=arize-ai-openinference) - 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/_