LangChain & LangGraph: Build Production AI Agents in Python

About this course

Learn how to build production-ready AI agents in Python using LangChain and LangGraph.

This course takes you from the fundamentals of AI agents all the way to building, evaluating, securing, and deploying complete agentic applications. You do not need previous AI or machine-learning experience. If you are comfortable with Python, you can follow the course from the beginning.

We start by understanding what AI agents actually are, how the agent loop works, when to use agents instead of traditional workflows, and how tools allow language models to interact with external systems.

You will then learn the essential building blocks of LangChain, including models, messages, prompts, streaming, structured outputs, and tool calling. From there, you will create your first real agent and learn why naive agent implementations often fail in production.

The course then moves into LangGraph, where you will build more reliable and controllable agent architectures using state, nodes, edges, routing, parallel execution, subgraphs, persistence, memory, and human-in-the-loop approval.

You will also build RAG agents capable of retrieving information from internal knowledge bases, improving search queries, reasoning over retrieved context, and generating grounded answers with citations.

But building an agent is only half the problem.

You will learn how to use LangSmith to trace agent execution, debug failures, create evaluation datasets, measure performance, and compare different versions of your agent.

Finally, we will cover the engineering required to move from a prototype to production: FastAPI, Docker, input validation, guardrails, secure tool design, least-privilege access, monitoring, cost control, deployment, and incident handling.

Throughout the course, you will progressively build components that come together in the final capstone: a complete AI

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