--- title: "openlit vs Awesome-LLMOps" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/openlit-openlit-vs-tensorchord-awesome-llmops" tools: ["openlit-openlit", "tensorchord-awesome-llmops"] --- # openlit vs Awesome-LLMOps *GraphCanon updated Aug 20, 2026* ## Verdict Pick openlit if decision-critical facts for OpenLIT are centered around its unique features in LLM observability, GPU monitoring, and extensive integration capabilities; 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. [openlit](https://docs.openlit.io) reports 2.7k GitHub stars, 342 forks, and 48 open issues, last pushed Jul 31, 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 [openlit's repository](https://github.com/openlit/openlit) and [Awesome-LLMOps's repository](https://github.com/tensorchord/Awesome-LLMOps). | | [openlit](/tools/openlit-openlit.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Tagline | A comprehensive open-source platform for AI Engineering with LLM Observability, Monitoring, and Management | An awesome & curated list of best LLMOps tools for developers | | Stars | 2,664 | 5,915 | | Forks | 342 | 993 | | Open issues | 48 | 247 | | Language | TypeScript | Shell | | Adopt for | Decision-critical facts for OpenLIT are centered around its unique features in LLM observability, GPU monitoring, and extensive integration capabilities. | 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, 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._ | | [openlit](/tools/openlit-openlit.md) | [Awesome-LLMOps](/tools/tensorchord-awesome-llmops.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 91d | | Open issues (now) | 48 | 247 | | Stars delta | Unknown | +28 (30d) | | Open issues delta | Unknown | +66 (30d) | | Full report | [trust report](/tools/openlit-openlit/trust.md) | [trust report](/tools/tensorchord-awesome-llmops/trust.md) | **Typed relationship:** openlit _(related)_ Awesome-LLMOps Given the emphasis on AI engineering observability by OpenLIT and general LLMOps tools curated in 'Awesome-LLMOps', they are tangentially related. ## Decision facts: openlit - **Pricing:** freemium - **Adopt for:** Decision-critical facts for OpenLIT are centered around its unique features in LLM observability, GPU monitoring, and extensive integration capabilities. - **License detail:** Apache-2.0 ## 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 openlit if… - openlit is primarily TypeScript; Awesome-LLMOps is Shell. - License: openlit is Apache-2.0, Awesome-LLMOps is CC0-1.0. - Given the emphasis on AI engineering observability by OpenLIT and general LLMOps tools curated in 'Awesome-LLMOps', they are tangentially related. - Tags unique to openlit: ai-observability, gpu-monitoring, langchain, monitoring-tool. - openlit ships Docker support for self-hosted deployment. - When you need comprehensive observability features native to OpenTelemetry, allowing seamless trace and metric management with an out-of-the-box solution. ### Choose Awesome-LLMOps if… - Awesome-LLMOps is primarily Shell; openlit is TypeScript. - License: Awesome-LLMOps is CC0-1.0, openlit is Apache-2.0. - Given the emphasis on AI engineering observability by OpenLIT and general LLMOps tools curated in 'Awesome-LLMOps', they are tangentially related. - Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, 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 openlit - If your project strictly requires a proprietary tool or if you have specific requirements that are not covered by OpenLIT's integrations, such as unique vector databases not yet supported. - When the team lacks the expertise in TypeScript or Python SDK to efficiently manage and implement observability into their current workflows with OpenLIT. ## 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 openlit and Awesome-LLMOps? openlit: A comprehensive open-source platform for AI Engineering with LLM Observability, Monitoring, and 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 openlit over Awesome-LLMOps? Choose openlit over Awesome-LLMOps when openlit is primarily TypeScript; Awesome-LLMOps is Shell; License: openlit is Apache-2.0, Awesome-LLMOps is CC0-1.0; Given the emphasis on AI engineering observability by OpenLIT and general LLMOps tools curated in 'Awesome-LLMOps', they are tangentially related; Tags unique to openlit: ai-observability, gpu-monitoring, langchain, monitoring-tool; openlit ships Docker support for self-hosted deployment; When you need comprehensive observability features native to OpenTelemetry, allowing seamless trace and metric management with an out-of-the-box solution. ### When should I choose Awesome-LLMOps over openlit? Choose Awesome-LLMOps over openlit when Awesome-LLMOps is primarily Shell; openlit is TypeScript; License: Awesome-LLMOps is CC0-1.0, openlit is Apache-2.0; Given the emphasis on AI engineering observability by OpenLIT and general LLMOps tools curated in 'Awesome-LLMOps', they are tangentially related; Tags unique to Awesome-LLMOps: ai-development-tools, awesome-list, 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 openlit? If your project strictly requires a proprietary tool or if you have specific requirements that are not covered by OpenLIT's integrations, such as unique vector databases not yet supported. When the team lacks the expertise in TypeScript or Python SDK to efficiently manage and implement observability into their current workflows with OpenLIT. ### 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 openlit or Awesome-LLMOps more popular on GitHub? Awesome-LLMOps has more GitHub stars (5,915 vs 2,664). Stars measure visibility, not whether either tool fits your constraints. ### Are openlit and Awesome-LLMOps open source? Yes - both are open-source projects on GitHub (openlit: Apache-2.0, Awesome-LLMOps: CC0-1.0). ### Where can I find alternatives to openlit or Awesome-LLMOps? GraphCanon lists graph-backed alternatives at [openlit alternatives](/tools/openlit-openlit/alternatives) and [Awesome-LLMOps alternatives](/tools/tensorchord-awesome-llmops/alternatives) ([openlit markdown twin](/tools/openlit-openlit/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/openlit-openlit-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, openlit or Awesome-LLMOps? openlit: 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 openlit and Awesome-LLMOps? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [openlit trust report](/tools/openlit-openlit/trust); [Awesome-LLMOps trust report](/tools/tensorchord-awesome-llmops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=openlit-openlit`](/api/graphcanon/graph?tool=openlit-openlit) - 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/_