Langfuse vs. LangSmith: Everything You Need to Know Before Choosing

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If you're working with LLMs, you've probably heard of Langfuse and LangSmith, two powerful tools designed to bring structure, observability, and reliability to your AI workflows. But how do they really compare? What are their strengths, and which one fits best in your stack?

In this two-part series, we dive into prompt versioning and tracing, showing how each tool handles interaction tracking and offering hands-on examples with Python and LangChain and we tackle the topic of datasets and evaluation, a critical component for fine-tuning and testing LLM-based systems. We compare how each tool approaches dataset creation, experiment tracking, and evaluation flows.

Whether you're choosing a solution for observability, iterating faster on prompts, or setting up structured evaluations, this guide will give you the clarity you need to make the right decision 👇.

Langfuse vs LangSmith I: Prompt Versioning and Tracing

LangSmith and Langfuse are two essential tools in LLM engineering as they enable prompt versioning and interaction tracing management. In this post, we walk you through how to implement them step by step using Python and LangChain.

Langfuse vs. Langsmith II: datasets and evaluation

Creating incremental datasets with real-world cases is crucial to optimizing LLMs. Langfuse provides flexibility and self-hosted capabilities, while LangSmith integrates flawlessly with LangChain. Pros and cons? We’ve got you covered here

Leticia Martín-Fuertes

I have a classical philology degree from the Autonomous University of Madrid and work as a computational linguist at Paradigma. Before that, I worked as a computational linguist at Molino de Ideas, Bitext and Google in, among others, lexical richness analysis, sentiment analysis and conversational system projects. I have co-organised data science and natural language processing communities such as R-Ladies and Lingwars.

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Miguel López Campos

I am a native of both Almería and Granada and am in love with artificial intelligence and its applications. I have a PhD in natural language processing from the University of Granada and have been working on the application of generative AI in companies from different industries (health, banking…) for almost two years. I am also curious about how data science can enhance professional sports. In my spare time I like to eat popcorn at the cinema and practice that “sport” known as “pádel”.

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