--- title: "langtrace vs ragas" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/scale3-labs-langtrace-vs-vibrantlabsai-ragas" tools: ["scale3-labs-langtrace", "vibrantlabsai-ragas"] --- # langtrace vs ragas Neutral, constraint-first comparison with live GitHub stats. | | [langtrace](/tools/scale3-labs-langtrace.md) | [ragas](/tools/vibrantlabsai-ragas.md) | | --- | --- | --- | | Tagline | Open Source Observability for LLM Applications | Supercharge Your LLM Application Evaluations | | Stars | 1,213 | 14,717 | | Forks | 124 | 1,539 | | Open issues | 1 | 478 | | Language | TypeScript | Python | | Adopt for | Langtrace is an open-source observability software that provides real-time monitoring and performance insights for applications leveraging LLM APIs, vector databases, and LLM-based frameworks through Open Telemetry. It's | Ragas is an essential toolkit for evaluating and improving Large Language Model applications through objective metrics, intelligent test generation, and seamless integration with popular frameworks. | | Persona | - | - | | Runtime | - | - | | License | AGPL-3.0 implies that any changes made to the software must be released under the same license, which might limit its use in commercial products closed off to users. | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [langtrace](/tools/scale3-labs-langtrace.md) | [ragas](/tools/vibrantlabsai-ragas.md) | | --- | --- | --- | | Days since push | 232d | 134d | | Open issues (now) | 1 | 478 | | Full report | [trust report](/tools/scale3-labs-langtrace/trust.md) | [trust report](/tools/vibrantlabsai-ragas/trust.md) | **Typed relationship:** langtrace _(alternative)_ ragas Ragas by VibrantLabsAI focuses on evaluation and observability for LLM applications, which aligns with the features offered by Langtrace. ## Shared compatibility - **Python**: [langtrace](/tools/scale3-labs-langtrace.md) - Python runtime; [ragas](/tools/vibrantlabsai-ragas.md) - Python runtime ## Decision facts: langtrace - **Requirements:** Self-hosting is available as an option if desired. - **Adopt for:** Langtrace is an open-source observability software that provides real-time monitoring and performance insights for applications leveraging LLM APIs, vector databases, and LLM-based frameworks through Open Telemetry. It's - **License detail:** AGPL-3.0 implies that any changes made to the software must be released under the same license, which might limit its use in commercial products closed off to users. ## Decision facts: ragas - **Adopt for:** Ragas is an essential toolkit for evaluating and improving Large Language Model applications through objective metrics, intelligent test generation, and seamless integration with popular frameworks. ## Choose when ### Choose langtrace if… - langtrace is primarily TypeScript; ragas is Python. - License: langtrace is AGPL-3.0, ragas is Apache-2.0. - Requirements: Self-hosting is available as an option if desired.. - Ragas by VibrantLabsAI focuses on evaluation and observability for LLM applications, which aligns with the features offered by Langtrace. - Tags unique to langtrace: evaluations, ai, datasets, observability. - langtrace ships Docker support for self-hosted deployment. - - When you are developing or managing applications that heavily rely on LLM APIs and want to capture, debug, and analyze traces and metrics in real-time using Open Telemetry. ### Choose ragas if… - ragas is primarily Python; langtrace is TypeScript. - License: ragas is Apache-2.0, langtrace is AGPL-3.0. - Ragas by VibrantLabsAI focuses on evaluation and observability for LLM applications, which aligns with the features offered by Langtrace. - Tags unique to ragas: llmops, evaluation. - - When you need to assess the performance of your LLM applications with quantitative metrics beyond subjective evaluations. ## When NOT to use langtrace - - If you need proprietary features absent from the AGPL-3.0 open-source license, as Langtrace's nature may limit its use in closed or commercial projects. - - For organizations with existing observability solutions deeply integrated into their infrastructure that do not benefit significantly from adding an additional layer of monitoring. ## When NOT to use ragas - - Avoid using Ragas if your LLM evaluation solely relies on qualitative assessments without the need for quantitative metrics. - - If you prefer a toolkit that does not offer out-of-the-box integrations with commonly used LLM frameworks like LangChain. - - When specific custom evaluations are needed outside of predefined templates such as Aspect Critique or prompt analysis. ## Common questions ### What is the difference between langtrace and ragas? langtrace: Open Source Observability for LLM Applications. ragas: Supercharge Your LLM Application Evaluations. See the comparison table for live GitHub stats and shared categories. ### When should I choose langtrace over ragas? Choose langtrace over ragas when langtrace is primarily TypeScript; ragas is Python; License: langtrace is AGPL-3.0, ragas is Apache-2.0; Requirements: Self-hosting is available as an option if desired.; Ragas by VibrantLabsAI focuses on evaluation and observability for LLM applications, which aligns with the features offered by Langtrace; Tags unique to langtrace: evaluations, ai, datasets, observability; langtrace ships Docker support for self-hosted deployment; - When you are developing or managing applications that heavily rely on LLM APIs and want to capture, debug, and analyze traces and metrics in real-time using Open Telemetry. ### When should I choose ragas over langtrace? Choose ragas over langtrace when ragas is primarily Python; langtrace is TypeScript; License: ragas is Apache-2.0, langtrace is AGPL-3.0; Ragas by VibrantLabsAI focuses on evaluation and observability for LLM applications, which aligns with the features offered by Langtrace; Tags unique to ragas: llmops, evaluation; - When you need to assess the performance of your LLM applications with quantitative metrics beyond subjective evaluations. ### When should I avoid langtrace? - If you need proprietary features absent from the AGPL-3.0 open-source license, as Langtrace's nature may limit its use in closed or commercial projects. - For organizations with existing observability solutions deeply integrated into their infrastructure that do not benefit significantly from adding an additional layer of monitoring. ### When should I avoid ragas? - Avoid using Ragas if your LLM evaluation solely relies on qualitative assessments without the need for quantitative metrics. - If you prefer a toolkit that does not offer out-of-the-box integrations with commonly used LLM frameworks like LangChain. - When specific custom evaluations are needed outside of predefined templates such as Aspect Critique or prompt analysis. ### Is langtrace or ragas more popular on GitHub? ragas has more GitHub stars (14,717 vs 1,213). Stars measure visibility, not whether either tool fits your constraints. ### Are langtrace and ragas open source? Yes - both are open-source projects on GitHub (langtrace: AGPL-3.0, ragas: Apache-2.0). ### Where can I find alternatives to langtrace or ragas? GraphCanon lists graph-backed alternatives at /tools/scale3-labs-langtrace/alternatives and /tools/vibrantlabsai-ragas/alternatives (/tools/scale3-labs-langtrace/alternatives.md, /tools/vibrantlabsai-ragas/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/scale3-labs-langtrace-vs-vibrantlabsai-ragas.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, langtrace or ragas? langtrace: Slowing. ragas: 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 langtrace and ragas? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: langtrace: /tools/scale3-labs-langtrace/trust; ragas: /tools/vibrantlabsai-ragas/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=scale3-labs-langtrace`](/api/graphcanon/graph?tool=scale3-labs-langtrace) - 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/_