LangGraph for Developers: From Zero to Hero

Build Production-Ready Agentic AI Systems using LangGraph, LLMs, MCP & FastAPI

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.

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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