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LangGraph for Developers: From Zero to Hero
Rating: 4.1 out of 5(2 ratings)
17 students

LangGraph for Developers: From Zero to Hero

Build Production-Ready Agentic AI Systems using LangGraph, LLMs, MCP & FastAPI
Created byArnab Das
Last updated 5/2026
English

What you'll learn

  • Understand agentic systems and clearly differentiate them from traditional LLM applications.
  • Design AI agent architectures using modern agentic patterns, memory, and tool based reasoning.
  • Build production AI agents with LangGraph, MCP pipelines, and multi-agent collaboration.
  • Secure, evaluate, and monitor AI agents using guardrails, Langfuse, observability, authentication, and performance metrics.
  • Deploy scalable agentic systems to the cloud using Docker, FastAPI, and real world production workflows.
  • Architect end-to-end AI Agent APIs, from LLM integration and tool orchestration to backend connectivity and real-world system deployment.

Course content

9 sections • 37 lectures • 6h 35m total length

  • Why Learn AI Agents ?0:45
  • About Instructor1:36
  • Coding Resources0:39

Requirements

  • Basic Python programming knowledge (functions, classes, virtual environments)
  • Familiarity with REST APIs and JSON
  • Fundamental understanding of LLMs or prior experience using ChatGPT or similar tools
  • A laptop or desktop with internet access
  • Willingness to install Python, VS Code, Pycharm and required libraries

Description

Master LangGraph, Agentic AI, Stateful Workflows & Production-Ready AI Systems

In this comprehensive LangGraph course, you will learn how to design, build, and deploy production-ready Agentic AI systems using LangGraph, Large Language Models (LLMs), MCP, and FastAPI.

This course is built specifically for developers who want to master graph-based LLM orchestration and move beyond simple chatbot demos.

What You’ll Learn


By the end of this course, you will be able to:


  • Build stateful AI agents using LangGraph

  • Design graph-based LLM workflows with nodes, edges, and reducers

  • Work with OpenAI and other LLM providers

  • Implement control flow and conditional routing

  • Add memory, persistence, and interrupt handling

  • Use streaming and tool-calling capabilities

  • Design Agentic AI architectures

  • Implement Model Context Protocol (MCP)

  • Build MCP-enabled tool discovery systems

  • Develop and deploy AI Agent APIs using FastAPI


Core Topics Covered


  • LangGraph Fundamentals

  • State, Nodes, Edges & Reducers

  • Control Flow & Conditional Execution

  • Tool Calling & Streaming

  • Persistence & Time Travel Debugging

  • Memory & Sub-Graphs

  • Agentic Design Patterns

  • LangChain vs LangGraph Architecture

  • Model Context Protocol (MCP)

  • MCP Server Integration

  • Production API Development

  • FastAPI Integration

If you want to become an Agentic AI Developer and build real-world, production-ready AI systems using LangGraph, this course will take you from beginner to advanced, step by step.

Who this course is for:

  • Software developers and backend engineers who want to build real world AI agents and agentic systems
  • Full-stack developers looking to add production-grade AI agent skills to their toolkit
  • AI/ML practitioners who want hands-on experience with LangGraph, MCP, and multi-agent architectures
  • Technical architects interested in designing scalable, secure agentic systems
  • Developers moving beyond basic LLM apps and chatbots into autonomous AI systems
  • Professionals preparing for advanced AI engineering or agent architecture roles

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