Langchain Tutorial - Stop Guessing! Trace and Debug LangChain Agents with LangSmith
About this lesson
Debugging AI Agents is hard—until now. If you’ve ever built a LangChain agent and wondered why it was slow, why a specific tool took too long, or why the model behaved unexpectedly, this video is for you. Traditional logging (adding print statements everywhere) isn't enough for complex, multi-agent workflows. In this tutorial, I show you how to integrate LangSmith into your Python projects to get full visibility into your execution flow. We’ll move beyond "black box" development and see exactly what happens inside every tool call and LLM interaction. What you will learn in this video: The Problem: Why traditional logging fails for agentic processes. The Solution: An introduction to LangSmith for tracing and debugging. Step-by-Step Setup: How to configure environment variables (LANGSMITH_TRACING, LANGSMITH_API_KEY, etc.). Project Management: Creating projects in LangSmith and selecting regions (US vs. EU). Live Demo: Analyzing a "Weather Agent" to see tool execution times (including handling time.sleep delays) and model inputs/outputs. Metadata Insights: How to check runtime versions, platform details (like macOS), and latency for every step. API Key Creation: A quick guide on generating personal access keys within the LangSmith settings. Whether you are using LangChain, LangGraph, CrewAI, or even TypeScript, LangSmith is the essential tool for building production-ready AI applications. Resources: Frameworks covered: LangChain, LangGraph, OpenAI. Environment: macOS with Python. Don't forget to LIKE and SUBSCRIBE for more deep dives into AI Agent orchestration! #LangChain, #LangSmith, #AIAgents, #PythonProgramming, #LLM, #LangGraph, #GenerativeAI, #AIDebugging, #MachineLearning, #SoftwareDevelopment, #OpenAI, #TechTutorial, #DataScience, #AIWorkflows
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