Hugging Face and Langfuse are two platforms that facilitate the development and operation of large language model (LLM) applications, and this guide outlines five ways to integrate them for more streamlined open-source LLM development. Hugging Face is a machine learning and AI platform offering tools for building, training, and deploying models, while Langfuse is an open-source platform focused on LLM engineering, providing capabilities for tracing and monitoring AI agents. The guide details methods such as tracing Hugging Face models and smolagents using Langfuse, utilizing Hugging Face models in Langfuse's Playground and Evaluators, employing Hugging Face datasets in Langfuse Dataset Experiments, and deploying Langfuse on Hugging Face Spaces. These integrations aim to enhance the observability, debugging, and optimization of AI products, offering developers the ability to trace model calls, evaluate model performance, and conduct dataset experiments to compare different models and settings.