--- title: "openlit vs openllmetry" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/openlit-openlit-vs-traceloop-openllmetry" tools: ["openlit-openlit", "traceloop-openllmetry"] --- # openlit vs openllmetry Both OpenLIT and OpenLLMetry offer observability solutions for AI applications but cater to slightly different needs. | | [openlit](/tools/openlit-openlit.md) | [openllmetry](/tools/traceloop-openllmetry.md) | | --- | --- | --- | | Tagline | Platform for AI Engineering Observability | Open-source observability for your LLM application | | Stars | 2,581 | 7,281 | | Forks | 319 | 1,016 | | Open issues | 53 | 591 | | Language | TypeScript | Python | | Adopt for | Platform for AI Engineering Observability, integrating OpenTelemetry-native observability SDKs and supporting a wide array of LLM providers and GPU monitoring. | OpenLLMetry is an open-source observability tool based on OpenTelemetry for monitoring and metrics in GenAI and LLM applications. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 license grants permission to use, study, modify, and distribute the tool freely. | | Categories | Evaluation & Observability, Inference & Serving | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [openlit](/tools/openlit-openlit.md) | [openllmetry](/tools/traceloop-openllmetry.md) | | --- | --- | --- | | Open issues (now) | 53 | 591 | | Security scan | No lockfile | 29 low (29 low) | | Full report | [trust report](/tools/openlit-openlit/trust.md) | [trust report](/tools/traceloop-openllmetry/trust.md) | ## Shared compatibility - **Python**: [openlit](/tools/openlit-openlit.md) - Python runtime; [openllmetry](/tools/traceloop-openllmetry.md) - Python runtime ## Decision facts: openlit - **Adopt for:** Platform for AI Engineering Observability, integrating OpenTelemetry-native observability SDKs and supporting a wide array of LLM providers and GPU monitoring. ## Decision facts: openllmetry - **Requirements:** Built on top of OpenTelemetry; compatible with various observability solutions like Datadog and Honeycomb - **Adopt for:** OpenLLMetry is an open-source observability tool based on OpenTelemetry for monitoring and metrics in GenAI and LLM applications. - **License detail:** Apache-2.0 license grants permission to use, study, modify, and distribute the tool freely. ## Choose when ### Choose openlit if… - openlit is primarily TypeScript; openllmetry is Python. - Tags unique to openlit: gpu-monitoring, monitoring-tool, ai-observability, opentelemetry. - Also covers Inference & Serving. - openlit ships Docker support for self-hosted deployment. - - When you need comprehensive monitoring tailored specifically for AI engineering tasks such as managing prompts, handling exceptions, and evaluating model performance with built-in evaluation types. ### Choose openllmetry if… - openllmetry is primarily Python; openlit is TypeScript. - Requirements: Built on top of OpenTelemetry; compatible with various observability solutions like Datadog and Honeycomb. - Tags unique to openllmetry: ml, artifical-intelligence, llm, datascience. - - Use it if you are working with Large Language Model (LLM) or Generative AI applications that require detailed observations, including model oversight and performance metrics. ## When NOT to use openlit - - When looking for a tool exclusively focused on training models, as OpenLIT primarily caters to inference and serving phases with monitoring and evaluation functionalities. - - If you are working in an environment where the Apache-2.0 license is not compliant or acceptable. ## When NOT to use openllmetry - - Avoid using it for applications where existing comprehensive proprietary observability solutions already meet all requirements without the need for integration flexibility offered by OpenTelemetry. - - Not recommended if your team has limited experience with OpenTelemetry, which could complicate adoption and integration with other tools unless there is a dedicated effort to adapt. ## Common questions ### What should I consider when choosing between OpenLIT and OpenLLMetry? Consider the specific needs of your AI application. If you require comprehensive monitoring tailored for managing prompts, handling exceptions, and evaluating model performance with a focus on inference and serving phases, along with integration into existing observability infrastructure using Apache-2.0 license compliance, then OpenLIT might be suitable. On the other hand, if your project focuses ### What is the difference between openlit and openllmetry? openlit: Platform for AI Engineering Observability. openllmetry: Open-source observability for your LLM application. See the comparison table for live GitHub stats and shared categories. ### When should I choose openlit over openllmetry? Choose openlit over openllmetry when openlit is primarily TypeScript; openllmetry is Python; Tags unique to openlit: gpu-monitoring, monitoring-tool, ai-observability, opentelemetry; Also covers Inference & Serving; openlit ships Docker support for self-hosted deployment; - When you need comprehensive monitoring tailored specifically for AI engineering tasks such as managing prompts, handling exceptions, and evaluating model performance with built-in evaluation types. ### When should I choose openllmetry over openlit? Choose openllmetry over openlit when openllmetry is primarily Python; openlit is TypeScript; Requirements: Built on top of OpenTelemetry; compatible with various observability solutions like Datadog and Honeycomb; Tags unique to openllmetry: ml, artifical-intelligence, llm, datascience; - Use it if you are working with Large Language Model (LLM) or Generative AI applications that require detailed observations, including model oversight and performance metrics. ### When should I avoid openlit? - When looking for a tool exclusively focused on training models, as OpenLIT primarily caters to inference and serving phases with monitoring and evaluation functionalities. - If you are working in an environment where the Apache-2.0 license is not compliant or acceptable. ### When should I avoid openllmetry? - Avoid using it for applications where existing comprehensive proprietary observability solutions already meet all requirements without the need for integration flexibility offered by OpenTelemetry. - Not recommended if your team has limited experience with OpenTelemetry, which could complicate adoption and integration with other tools unless there is a dedicated effort to adapt. ### Is openlit or openllmetry more popular on GitHub? openllmetry has more GitHub stars (7,281 vs 2,581). Stars measure visibility, not whether either tool fits your constraints. ### Are openlit and openllmetry open source? Yes - both are open-source projects on GitHub (openlit: Apache-2.0, openllmetry: Apache-2.0). ### Where can I find alternatives to openlit or openllmetry? GraphCanon lists graph-backed alternatives at /tools/openlit-openlit/alternatives and /tools/traceloop-openllmetry/alternatives (/tools/openlit-openlit/alternatives.md, /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 /compare/openlit-openlit-vs-traceloop-openllmetry.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, openlit or openllmetry? openlit: Very active. 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 openlit and openllmetry? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: openlit: /tools/openlit-openlit/trust; openllmetry: /tools/traceloop-openllmetry/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/_