LangSmith + LangChain v1: Full AI Agent Tracing & Debugging Tutorial

In this video, we integrate LangSmith observability into our existing LangChain AI agent so you can clearly trace, debug, and monitor every step of your agent’s execution and reasoning process.

You’ll learn:
✅ What LangSmith observability is and why it’s important
✅ How to enable tracing and logging in your LangChain application
✅ How to inspect execution traces, tool calls, prompts, and responses
✅ Why observability is essential when building production-ready AI agents

Add the following configuration to your .env file:

LANGSMITH_TRACING=true
LANGSMITH_API_KEY=your_api_key_here
LANGSMITH_PROJECT=your_project_name
LANGSMITH_ENDPOINT=https://api.smith.langchain.com

🔗 LangSmith Observability Documentation:
https://www.langchain.com/langsmith/observability

🔗 LangSmith Dashboard (Create Free Account):
https://smith.langchain.com/

🔗 Useful Links
📌 Previous Videos (Recommended):

1️⃣ Setting Up Ollama Locally
https://youtu.be/AZsh5kTXz84

2️⃣ Creating an Agent & Connecting it with Ollama (Local Setup)
https://youtu.be/b0HRiv-qMDM

3️⃣ Build an AI Agent with LangChain v1 (Groq, OpenAI, Anthropic, Gemini & More!)
https://youtu.be/Qe2r4XLeSe8

This tutorial builds on the earlier videos in this playlist where we covered:
• Installing and configuring Ollama
• Connecting LangChain with local and cloud LLM providers
• Creating and integrating tools into our AI agent

If you found this video helpful:
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#LangSmith #LangChain #AIAgent #DebugAI #Observability #Ollama #Python #LocalLLM

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