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LangGraph 101: Learn to Build Agentic Workflows

IntermediateGuided Project

Create agentic workflows with LangGraph by learning key components like nodes, states, edges, and conditional edges through a user authentication use case. Learn how to design AI workflows that adapt to real-time inputs and manage state-driven logic effectively. You’ll explore how to code success and failure scenarios, build dynamic decision-making systems, and create workflows tailored to diverse conditions. Perfect for developers and AI enthusiasts, this project focuses on equipping you with practical skills to design flexible, autonomous AI systems for real-world applications.

Language

  • English

Topic

  • Artificial Intelligence

Skills You Will Learn

  • AI Agent, LangGraph, Agentic Flow, Generative AI, Python

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 1 hour

Platform

  • SkillsNetwork

Last Update

  • April 23, 2025
About this Guided Project
Learn to implement agentic workflows with LangGraph and discover how to build intelligent AI agents capable of making dynamic, context-aware decisions. This guided project will walk you through the core components of LangGraph, including nodes, states, edges, and conditional edges, to help you create flexible and autonomous systems. You’ll learn how to design and implement sophisticated workflows, such as user authentication systems, that adapt to different scenarios based on success, failure, or conditional logic.  

Through hands-on exercises, you’ll gain a deep understanding of how each component works and how to connect them to design seamless decision-making workflows. Whether you’re managing state transitions, setting up dynamic conditions, or creating failure-handling mechanisms, this project will empower you to create advanced AI systems with high levels of flexibility and autonomy.

What You’ll Learn

  • LangGraph Essentials: Understand the key components—nodes, states, edges, and conditional edges—that power decision-making in AI systems.
  • Dynamic Workflows: Learn how to build user authentication systems and other workflows with flexible decision logic, tailored to different user states and conditions.
  • State-driven Logic: Explore how to manage state transitions and conditional logic to ensure smooth execution of tasks, handling both success and failure scenarios.
  • Building Autonomous Systems: Develop skills to create autonomous agents that adapt based on real-time inputs, making intelligent decisions that reflect changing conditions.

Why It Matters
LangGraph represents a powerful framework for building AI agents with robust decision-making capabilities. By mastering LangGraph, you will:
  • Design Flexible Workflows: Create intelligent systems that react and adapt to user input and evolving scenarios.
  • Enable Autonomous Systems: Build systems capable of acting independently, managing success, failure, and conditions based on real-time data.
  • Enhance Decision-making: Make data-driven decisions by integrating dynamic decision logic and understanding the flow of information between nodes, states, and edges.

Who Should Register
This guided project is ideal for:
  • Developers: Those interested in building intelligent AI systems that require dynamic decision-making.
  • AI Enthusiasts: Anyone looking to deepen their understanding of AI workflows and how to build flexible, autonomous systems.
  • Software Engineers: Developers working on user authentication or similar workflows that require decision-based logic and state management.

What You’ll Need
Before starting this project, ensure you have the following:
  • Basic Programming Knowledge: Familiarity with basic programming concepts will be helpful, especially in Python.
  • Understanding of Decision-making: While prior experience with decision-making workflows is a plus, it’s not required.
  • A Computer with a Modern Browser: Chrome, Edge, Firefox, or Safari to run and interact with the project tools.

What You’ll Build
By the end of this guided project, you will have:
  • Mastered LangGraph: A deep understanding of how to build and connect the essential components of LangGraph for creating AI workflows.
  • Built Dynamic Workflows: Created user authentication workflows that handle success and failure scenarios and adapt to changing conditions.
  • Developed Autonomous Systems: Gained hands-on experience in designing systems that make real-time decisions based on data inputs and conditions.

Embark on this exciting journey with LangGraph to build intelligent AI agents that think, adapt, and make decisions in real-time. Elevate your AI development skills and start building dynamic decision-making systems today!

Instructors

Kunal Makwana

Data Scientist

I’m a passionate Data Scientist and AI enthusiast, currently working at IBM on innovative projects in Generative AI and machine learning. My journey began with a deep interest in mathematics and coding, which inspired me to explore how data can solve real-world problems. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and leveraging cloud technologies to extract meaningful insights from complex datasets.

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Faranak Heidari

Data Scientist at IBM

Detail-oriented data scientist and engineer, with a strong background in GenAI, applied machine learning and data analytics. Experienced in managing complex data to establish business insights and foster data-driven decision-making in complex settings such as healthcare. I implemented LLM, time-series forecasting models and scalable ML pipelines. Enthusiastic about leveraging my skills and passion for technology to drive innovative machine learning solutions in challenging contexts, I enjoy collaborating with multidisciplinary teams to integrate AI into their workflows and sharing my knowledge.

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Contributors

Karan Goswami

Data Scientist

I am a dedicated Data Scientist and an AI enthusiast, currently working at IBM's Skills Builder Network. Learning how some simple mathematical operations could be used to make predictions and discover patterns sparked my curiosity, leading me to explore the exciting world of AI. Over the years, I’ve gained hands-on experience in building scalable AI solutions, fine-tuning models, and extracting meaningful insights from complex datasets. I'm driven by a desire to apply these skills to solve real-world problems and make a meaningful impact through AI.

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Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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Wilbur Elbouni

Software Developer

I think computers are pretty cool!

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