LangGraph Tutorial - How to Build Advanced AI Agent Systems

Overview

Dive into building advanced AI agent systems with LangGraph, a professional framework offering more control and scalability than LangChain or LlamaIndex for production-ready applications.

Syllabus

• Introduction to LangGraph
Overview of LangGraph features
Comparison with other frameworks: LangChain and LlamaIndex
Installation and setup
• Core Concepts of AI Agent Systems
Understanding AI agents and their roles
Key components of AI agent systems
• Building Blocks of LangGraph
Nodes and edges in LangGraph
Graph data structures and manipulation
• Designing Scalable AI Agent Architectures
Best practices for scalable AI systems
Case studies of scalable AI agent systems
• Advanced Control Mechanisms in LangGraph
Customizing agent behaviors
Error handling and fallback strategies
• Integrating LangGraph with Existing Systems
API development and integration techniques
Data flow optimization
• Implementing and Testing Production-Ready Applications
Deployment strategies for LangGraph applications
Monitoring and maintenance of AI agent systems
• Practical Workshops
Building a sample AI agent system with LangGraph
Hands-on exercises: Advanced features and custom configurations
• Future Trends and Developments in AI Agent Systems
Upcoming features in LangGraph
Emerging trends in AI and their implications for LangGraph systems
• Course Review and Q&A
Summary of key concepts
Open session for questions and personalized guidance

Subjects

Computer Science