--- title: "lmms-eval vs langfuse" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/evolvinglmms-lab-lmms-eval-vs-langfuse-langfuse" tools: ["evolvinglmms-lab-lmms-eval", "langfuse-langfuse"] --- # lmms-eval vs langfuse Neutral, constraint-first comparison with live GitHub stats. | | [lmms-eval](/tools/evolvinglmms-lab-lmms-eval.md) | [langfuse](/tools/langfuse-langfuse.md) | | --- | --- | --- | | Tagline | One-for-All Multimodal Evaluation Toolkit Across Text, Image, Video, and Audio Tasks | Open source AI engineering platform: LLM evals, observability, metrics, prompt management, playground, datasets. | | Stars | 4,292 | 30,693 | | Forks | 613 | 3,221 | | Open issues | 43 | 700 | | Language | Python | TypeScript | | Adopt for | - | Langfuse is an open-source AI engineering platform focused on the evaluation and monitoring of large language models (LLMs), offering a comprehensive set of tools including evaluations, observability features, metrics, a | | Persona | - | - | | Runtime | - | - | | License | Other | MIT License | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [lmms-eval](/tools/evolvinglmms-lab-lmms-eval.md) | [langfuse](/tools/langfuse-langfuse.md) | | --- | --- | --- | | Days since push | 1d | 0d | | Open issues (now) | 43 | 700 | | Security scan | Not scanned | No lockfile | | Full report | [trust report](/tools/evolvinglmms-lab-lmms-eval/trust.md) | [trust report](/tools/langfuse-langfuse/trust.md) | **Typed relationship:** lmms-eval _(alternative)_ langfuse Both tools focus on evaluating LLMs, but they offer different functionalities and approaches. Langfuse offers a broader platform for AI engineering with observability features, whereas lmms-eval is specialized in multimodal evaluations. ## Decision facts: langfuse - **Hosting:** self hosted - **Requirements:** Requires Docker - **Adopt for:** Langfuse is an open-source AI engineering platform focused on the evaluation and monitoring of large language models (LLMs), offering a comprehensive set of tools including evaluations, observability features, metrics, a - **License detail:** MIT License ## Choose when ### Choose lmms-eval if… - lmms-eval is primarily Python; langfuse is TypeScript. - Both tools focus on evaluating LLMs, but they offer different functionalities and approaches. Langfuse offers a broader platform for AI engineering with observability features, whereas lmms-eval is specialized in multimodal evaluations. - Tags unique to lmms-eval: benchmark, large-language-models, multimodal-evaluation, audio-evaluation. ### Choose langfuse if… - langfuse is primarily TypeScript; lmms-eval is Python. - Requirements: Requires Docker. - Both tools focus on evaluating LLMs, but they offer different functionalities and approaches. Langfuse offers a broader platform for AI engineering with observability features, whereas lmms-eval is specialized in multimodal evaluations. - Tags unique to langfuse: analytics, llm-observability, open-source. - langfuse ships Docker support for self-hosted deployment. - You need detailed observability insights specific to LLMs like tracking usage through integration with OpenTelemetry. ## When NOT to use lmms-eval - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ## When NOT to use langfuse - You require a proprietary solution that offers specialized features not available in open-source tools like Langfuse. - Your team is strictly bound to use technologies exclusively from major vendors and cannot accommodate external open-source dependencies. - The existing toolset in your tech stack does not benefit from integrations with OpenTelemetry, LangChain or the OpenAI SDK. ## Common questions ### What is the difference between lmms-eval and langfuse? lmms-eval: One-for-All Multimodal Evaluation Toolkit Across Text, Image, Video, and Audio Tasks. langfuse: Open source AI engineering platform: LLM evals, observability, metrics, prompt management, playground, datasets.. See the comparison table for live GitHub stats and shared categories. ### When should I choose lmms-eval over langfuse? Choose lmms-eval over langfuse when lmms-eval is primarily Python; langfuse is TypeScript; Both tools focus on evaluating LLMs, but they offer different functionalities and approaches. Langfuse offers a broader platform for AI engineering with observability features, whereas lmms-eval is specialized in multimodal evaluations; Tags unique to lmms-eval: benchmark, large-language-models, multimodal-evaluation, audio-evaluation. ### When should I choose langfuse over lmms-eval? Choose langfuse over lmms-eval when langfuse is primarily TypeScript; lmms-eval is Python; Requirements: Requires Docker; Both tools focus on evaluating LLMs, but they offer different functionalities and approaches. Langfuse offers a broader platform for AI engineering with observability features, whereas lmms-eval is specialized in multimodal evaluations; Tags unique to langfuse: analytics, llm-observability, open-source; langfuse ships Docker support for self-hosted deployment; You need detailed observability insights specific to LLMs like tracking usage through integration with OpenTelemetry. ### When should I avoid lmms-eval? Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. ### When should I avoid langfuse? You require a proprietary solution that offers specialized features not available in open-source tools like Langfuse. Your team is strictly bound to use technologies exclusively from major vendors and cannot accommodate external open-source dependencies. The existing toolset in your tech stack does not benefit from integrations with OpenTelemetry, LangChain or the OpenAI SDK. ### Is lmms-eval or langfuse more popular on GitHub? langfuse has more GitHub stars (30,693 vs 4,292). Stars measure visibility, not whether either tool fits your constraints. ### Are lmms-eval and langfuse open source? Yes - both are open-source projects on GitHub (lmms-eval: Other, langfuse: Other). ### Where can I find alternatives to lmms-eval or langfuse? GraphCanon lists graph-backed alternatives at /tools/evolvinglmms-lab-lmms-eval/alternatives and /tools/langfuse-langfuse/alternatives (/tools/evolvinglmms-lab-lmms-eval/alternatives.md, /tools/langfuse-langfuse/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/evolvinglmms-lab-lmms-eval-vs-langfuse-langfuse.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, lmms-eval or langfuse? lmms-eval: Very active. langfuse: 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 lmms-eval and langfuse? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: lmms-eval: /tools/evolvinglmms-lab-lmms-eval/trust; langfuse: /tools/langfuse-langfuse/trust. --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=evolvinglmms-lab-lmms-eval`](/api/graphcanon/graph?tool=evolvinglmms-lab-lmms-eval) - 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/_