--- title: "Awesome-LLMOps vs openllmetry" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/tensorchord-awesome-llmops-vs-traceloop-openllmetry" tools: ["tensorchord-awesome-llmops", "traceloop-openllmetry"] --- # Awesome-LLMOps vs openllmetry *GraphCanon updated Sep 20, 2026* ## Verdict 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; pick openllmetry if openLLMetry is an open-source observability tool for GenAI and LLM applications, built on OpenTelemetry, offering Python SDK for easy integration. [Awesome-LLMOps](https://github.com/tensorchord/Awesome-LLMOps) reports 5.9k GitHub stars, 1.1k forks, and 317 open issues, last pushed May 21, 2026. [openllmetry](https://www.traceloop.com/openllmetry) has 7.4k stars, 1.1k forks, and 679 open issues, last pushed Sep 17, 2026. Figures are from public GitHub metadata via [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps) and [openllmetry's repository](https://github.com/traceloop/openllmetry). | | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | [openllmetry](/tools/traceloop-openllmetry.md) | | --- | --- | --- | | Tagline | An awesome & curated list of best LLMOps tools for developers | Open-source observability for GenAI or LLM applications based on OpenTelemetry | | Stars | 5,941 | 7,438 | | Forks | 1,058 | 1,082 | | Open issues | 317 | 679 | | Language | Shell | Python | | 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. | OpenLLMetry is an open-source observability tool for GenAI and LLM applications, built on OpenTelemetry, offering Python SDK for easy integration. | | Persona | - | - | | Runtime | - | - | | License | CC0-1.0 | Apache-2.0 | | Categories | Computer Vision, Data & Retrieval, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training, Speech & Audio | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | [openllmetry](/tools/traceloop-openllmetry.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 121d | 0d | | Open issues (now) | 317 | 679 | | Stars delta | +26 (30d) | +61 (30d) | | Open issues delta | +70 (30d) | +41 (30d) | | Full report | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | [trust report](/tools/traceloop-openllmetry/trust.md) | **Typed relationship:** Awesome-LLMOps _(related)_ openllmetry 'OpenLLMeterY' targets observability within LLMOps, an area covered generally by 'Awesome-LLMOps'. ## 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. ## Decision facts: openllmetry - **Adopt for:** OpenLLMetry is an open-source observability tool for GenAI and LLM applications, built on OpenTelemetry, offering Python SDK for easy integration. ## Choose when ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; openllmetry is Python. - License: Awesome-LLMOps is CC0-1.0, openllmetry is Apache-2.0. - 'OpenLLMeterY' targets observability within LLMOps, an area covered generally by 'Awesome-LLMOps'. - 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. ### Choose openllmetry if… - openllmetry is primarily Python; Awesome-LLMOps is Shell. - License: openllmetry is Apache-2.0, Awesome-LLMOps is CC0-1.0. - 'OpenLLMeterY' targets observability within LLMOps, an area covered generally by 'Awesome-LLMOps'. - Tags unique to openllmetry: artifical-intelligence, datascience, generative-ai, llm. - When you are working on GenAI or LLM applications and need observability features that are specifically tailored for these types of applications. ## 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. ## When NOT to use openllmetry - If your application is not in Python, as OpenLLMetry's primary support is for Python applications, limiting its utility for other languages. - When you need a tool that offers more than just observability features, as OpenLLMetry focuses specifically on monitoring and metrics for GenAI and LLM applications and may not cover other aspects of ## Common questions ### What is the difference between Awesome-LLMOps and openllmetry? Awesome-LLMOps: An awesome & curated list of best LLMOps tools for developers. openllmetry: Open-source observability for GenAI or LLM applications based on OpenTelemetry. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLMOps over openllmetry? Choose Awesome-LLMOps over openllmetry when Awesome-LLMOps is primarily Shell; openllmetry is Python; License: Awesome-LLMOps is CC0-1.0, openllmetry is Apache-2.0; 'OpenLLMeterY' targets observability within LLMOps, an area covered generally by 'Awesome-LLMOps'; 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 choose openllmetry over Awesome-LLMOps? Choose openllmetry over Awesome-LLMOps when openllmetry is primarily Python; Awesome-LLMOps is Shell; License: openllmetry is Apache-2.0, Awesome-LLMOps is CC0-1.0; 'OpenLLMeterY' targets observability within LLMOps, an area covered generally by 'Awesome-LLMOps'; Tags unique to openllmetry: artifical-intelligence, datascience, generative-ai, llm; When you are working on GenAI or LLM applications and need observability features that are specifically tailored for these types of applications. ### 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. ### When should I avoid openllmetry? If your application is not in Python, as OpenLLMetry's primary support is for Python applications, limiting its utility for other languages. When you need a tool that offers more than just observability features, as OpenLLMetry focuses specifically on monitoring and metrics for GenAI and LLM applications and may not cover other aspects of ### Is Awesome-LLMOps or openllmetry more popular on GitHub? openllmetry has more GitHub stars (7,438 vs 5,941). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLMOps and openllmetry open source? Yes - both are open-source projects on GitHub (Awesome-LLMOps: CC0-1.0, openllmetry: Apache-2.0). ### Where can I find alternatives to Awesome-LLMOps or openllmetry? GraphCanon lists graph-backed alternatives at [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) and [openllmetry alternatives](/tools/traceloop-openllmetry/alternatives) ([Awesome-LLMOps markdown twin](/tools/tensorchord-awesome-llmops/alternatives.md), [openllmetry markdown twin](/tools/traceloop-openllmetry/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/tensorchord-awesome-llmops-vs-traceloop-openllmetry.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-LLMOps or openllmetry? Awesome-LLMOps: Slowing. openllmetry: Very active. 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 Awesome-LLMOps and openllmetry? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust); [openllmetry trust report](/tools/traceloop-openllmetry/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=tensorchord-awesome-llmops`](/api/graphcanon/graph?tool=tensorchord-awesome-llmops) - 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/_