Build Production-Ready Agentic AI Systems using LangGraph, LLMs, MCP & FastAPI
Master LangGraph, Agentic AI, Stateful Workflows & Production-Ready AI Systems
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
- Welcome –> 3 lectures • 2min.
- Introduction to Agentic System –> 7 lectures • 53min.
- LLM Provider –> 1 lecture • 4min.
- LangGraph Basics –> 8 lectures • 2hr 21min.
- Langfuse Basics –> 5 lectures • 28min.
- Capstone Project –> 8 lectures • 2hr 26min.
- Let’s review our learnings –> 0.
- Conclusion –> 1 lecture • 3min.
Requirements
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.