Apr 28
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In this video, I demonstrate how to use **LangSmith** to trace and monitor LLM (Large Language Model) requests in real time. You will learn how to generate a LangSmith API key, integrate it into your project, and track LLM calls for debugging, performance monitoring, and observability. This tutorial is designed for developers working with **LangChain, FastAPI, OpenAI, or Gemini**, and anyone building production-ready AI applications who wants to monitor LLM responses, latency, token usage, and errors. 🚀 What you will learn in this video: • What is LangSmith and why it is important for LLM monitoring • How to generate a LangSmith API key • How to connect LangSmith to your Python project • How to trace LLM requests and responses • How to monitor performance and debug issues • How to view logs and execution details in the LangSmith dashboard This tutorial is especially useful for developers building: • AI chatbots • RAG (Retrieval-Augmented Generation) systems • LLM APIs using FastAPI • Production AI applications • Agent-based systems By the end of this video, you will understand how to use LangSmith to improve reliability, debugging, and monitoring of your AI applications. 💻 Technologies used: Python LangChain LangSmith FastAPI LLM APIs If you found this video helpful, please Like 👍, Share 🔁, and Subscribe 🔔 for more practical AI and backend development tutorials. #LangSmith #LLM #LangChain #FastAPI #AI #Python #Tracing #Monitoring #Observability langsmith - https://smith.langchain.com/