Keywords AI

Langfuse vs Ragas

Compare Langfuse and Ragas side by side. Both are tools in the Observability, Prompts & Evals category.

Quick Comparison

	Langfuse	Ragas
Category	Observability, Prompts & Evals	Observability, Prompts & Evals
Pricing	Open Source	Open Source
Best For	Teams who want open-source LLM observability they can self-host and customize	Developers building RAG applications who need specialized evaluation metrics
Website	langfuse.com	ragas.io
Key Features	Open-source LLM observability Detailed trace and span tracking Prompt management Evaluation scoring Self-hosted and cloud options	RAG-specific evaluation framework Component-wise metrics for RAG Synthetic test data generation LLM-as-judge evaluators Open-source Python library
Use Cases	Self-hosted LLM monitoring Open-source tracing for AI applications Prompt versioning and management Cost tracking across providers Community-driven observability	Evaluating RAG pipeline quality end-to-end Measuring retrieval precision and recall Testing faithfulness and answer relevance Generating synthetic evaluation datasets Benchmarking RAG across configurations

When to Choose Langfuse vs Ragas

Choose Langfuse if you need

Self-hosted LLM monitoring
Open-source tracing for AI applications
Prompt versioning and management

Pricing: Open Source

Choose Ragas if you need

Evaluating RAG pipeline quality end-to-end
Measuring retrieval precision and recall
Testing faithfulness and answer relevance

Pricing: Open Source

About Langfuse

Langfuse is an open-source LLM observability platform that provides tracing, analytics, prompt management, and evaluation for AI applications. It captures detailed traces of LLM calls, supports custom scoring, and integrates with LangChain, LlamaIndex, Vercel AI SDK, and raw API calls. Langfuse can be self-hosted for data privacy or used as a managed cloud service. Its open-source model and generous free tier make it popular with startups and developers.

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About Ragas

Ragas is an open-source evaluation framework specifically designed for RAG (Retrieval-Augmented Generation) pipelines. It provides metrics for context precision, context recall, faithfulness, and answer relevancy, helping teams measure and improve the quality of their RAG systems. Ragas has become the standard evaluation toolkit for teams building production RAG applications.

View Ragas profile →Visit website

What is Observability, Prompts & Evals?

Tools for monitoring LLM applications in production, managing and versioning prompts, and evaluating model outputs. Includes tracing, logging, cost tracking, prompt engineering platforms, automated evaluation frameworks, and human annotation workflows.

Browse all Observability, Prompts & Evals tools →