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Currently Playing: LangChain Agent Middleware Tutorial with Python | Logging & Retries

Learn LangChain Agent Middleware with a practical, hands-on Python example. In this tutorial, we build an AI inventory agent and add model-call logging, automatic retries, and exponential backoff using LangChain middleware. You’ll learn how middleware helps keep agent code clean by separating business logic from operational concerns such as observability, reliability, validation, caching, and model fallbacks. ✅ What you’ll learn: - How the LangChain agent loop works - What agent middleware is and when to use it - Creating an agent with `create_agent` - Using `before_model` to inspect and log model calls - Using `wrap_model_call` to intercept model requests - Implementing retries with exponential backoff - Keeping tools focused on business logic - When to use custom versus prebuilt middleware - Practical production recommendations for reliable AI agents In the hands-on Python demo, we create an inventory agent with a `get_stock` tool. We then add middleware to reveal the hidden model-and-tool loop and automatically retry temporary model failures. 📚 LangChain documentation: Middleware overview: https://docs.langchain.com/oss/python/langchain/middleware/overview Custom middleware: https://docs.langchain.com/oss/python/langchain/middleware/custom If this tutorial helped you, please like the video, share it with other Python and AI developers, and subscribe to Naveen TechHub for more practical AI, cloud, LangChain, and software engineering tutorials. What type of LangChain middleware would you like to see next—guardrails, human approval, model fallback, PII protection, or tool retries? Let me know in the comments. #LangChain #Python #aiagents #naveentechhub