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Agentic AI with LangChain and LangGraph

Agentic AI with LangChain and LangGraph

This course is part of multiple programs.

Instructors: Faranak Heidari

17,605 already enrolled

Included with

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3 modules

Gain insight into a topic and learn the fundamentals.

76 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

76 reviews

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Build agentic AI systems using LangChain and LangGraph to support memory, iteration, and conditional logic
Design and implement self-improving agents using Reflection, Reflexion, and ReAct architectures
Apply agent orchestration techniques to build collaborative multi-agent systems
Implement agentic RAG systems that route queries and support retrieval-enhanced reasoning

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

11 assignments

Taught in English

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There are 3 modules in this course

Ready to build intelligent AI agents that can reason, improve, and collaborate? This hands-on course gives you the skills to build agentic AI systems using LangChain and LangGraph in just 3 weeks.

You’ll design stateful workflows that support memory, iteration, and conditional logic. You’ll explore how to build self-improving agents using Reflection, Reflexion, and ReAct architectures, empowering your agents to reason about their outputs and refine them over time. Plus, you’ll work on guided labs where you’ll structure agent feedback, integrate external data, and generate context-aware responses through step-by-step reasoning. You’ll then develop collaborative multi-agent systems that coordinate tasks, retrieve relevant data, and solve complex problems using agentic RAG. Plus, you'll gain experience in agent orchestration, query routing, and governance strategies for building robust, scalable AI applications. By the end of the course, you’ll have built working prototypes of agentic systems and gained hands-on skills to design reliable, adaptable agents. Enroll today and get ready to power up your portfolio!

This module introduces LangGraph for building intelligent, stateful AI agents that support memory, iteration, and conditional logic. You’ll explore how nodes, edges, and shared state enable dynamic workflows, and how LangGraph extends LangChain for advanced control. Through foundational concepts and hands-on practice, you’ll learn to design, build, and execute workflows that reflect real-world agentic behavior

What's included

6 videos2 readings4 assignments1 app item5 plugins

6 videos Total 37 minutes

Course Introduction 3 minutes
RAG and Agentic AI Professional Certificate Overview 6 minutes
Generative versus Agentic AI 7 minutes
Core Components of LangGraph 4 minutes
LangGraph versus LangChain: When to Use What  10 minutes
Getting Started with LangGraph 101 7 minutes

2 readings Total 6 minutes

Course Overview 3 minutes
Summary and Highlights 3 minutes

4 assignments Total 39 minutes

Practice Quiz: Introduction to Agentic AI 6 minutes
Practice Quiz: LangGraph versus LangChain 6 minutes
Practice Quiz: Build a LangGraph Workflow 6 minutes
Graded Quiz: Introduction to LangGraph 21 minutes

1 app item Total 60 minutes

Lab: LangGraph 101: Building Stateful AI Workflows 60 minutes

5 plugins Total 50 minutes

Reading: Helpful Tips for Course Completion 5 minutes
Reading: Agentic AI 15 minutes
Reading: LangGraph Architecture: Designing Effective Workflows 10 minutes
Reading: LangGraph versus LangChain: Pros, Cons, and Practical Considerations  10 minutes
Cheat Sheet: Introduction to LangGraph 10 minutes

This module focuses on building self-improving AI agents using LangGraph. You’ll explore and implement Reflection, Reflexion, and ReAct agent architectures to design workflows that evaluate and refine their own outputs. Through guided labs, you’ll gain hands-on experience creating agents that reason, integrate feedback, and improve performance using structured approaches grounded in reflection and prompt engineering.

What's included

5 videos1 reading4 assignments3 app items2 plugins

5 videos Total 42 minutes

Overview: Types of AI Agents 10 minutes
The Art of AI Self-Improvement: Building Reflection Agents 8 minutes
Understanding Reflexion Agents 6 minutes
Building Reflexion Agents 8 minutes
ReAct: Building Agents that Reason Before Acting  9 minutes

1 reading Total 3 minutes

Summary and Highlights 3 minutes

4 assignments Total 39 minutes

Practice Quiz: Build Reflection Agents 6 minutes
Practice Quiz: Advanced Self-Improvement with Reflexion Agents 6 minutes
Practice Quiz: ReAct: Integrating Reasoning and Action 6 minutes
Graded Quiz: Build Self-Improving Agents with LangGraph 21 minutes

3 app items Total 165 minutes

Lab: Building a Reflection Agent with LangGraph 45 minutes
Lab: Building a Reflexion Agent with External Knowledge Integration  30 minutes
Lab: ReAct: Build Reasoning and Acting AI Agents with LangGraph 90 minutes

2 plugins Total 20 minutes

Reading: Structuring LLM Tool Calls with Pydantic and JSON Serialization 10 minutes
Cheat Sheet: Build Self-Improving Agents with LangGraph 10 minutes

This module focuses on designing and implementing multi-agent systems using LangGraph. You’ll explore how specialized agents can collaborate to solve complex problems through structured orchestration. Key topics include core principles of multi-agent systems, collaboration patterns, and governance considerations. Through hands-on practice, you’ll build a multi-agent RAG system that dynamically routes queries to relevant data sources, gaining practical experience in coordinating specialized agents to enhance retrieval and reasoning. 

What's included

4 videos3 readings3 assignments1 app item3 plugins

4 videos Total 25 minutes

Introduction to Multi-Agent Systems 8 minutes
Risks of Agentic AI: What You Need to Know About Autonomous AI 7 minutes
Agentic RAG: Enhance Retrieval with Multi-Agent Systems 6 minutes
Course Wrap-up 5 minutes

3 readings Total 7 minutes

Summary and Highlights 3 minutes
Congratulations and Next Steps 2 minutes
Team and Acknowledgments 2 minutes

3 assignments Total 33 minutes

Practice Quiz: The Evolution from Single to Multi-Agent Systems 6 minutes
Practice Quiz: Build Multi-Agent Applications 6 minutes
Graded Quiz: Multi-Agent Systems and Agentic RAG with LangGraph 21 minutes

1 app item Total 60 minutes

Lab: DocChat: Build a Multi-Agent RAG System 60 minutes

3 plugins Total 40 minutes

Reading: Multi-Agent LLM System Fundamentals 10 minutes
Reading: Building Multi-Agent Systems with LangGraph 15 minutes
Cheat Sheet: Multi-Agent Systems and Agentic RAG with LangGraph 15 minutes

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Instructors

Instructor ratings

(22 ratings)

Faranak Heidari

IBM

3 Courses 39,113 learners

Offered by

IBM

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Reviewed on Aug 21, 2025

Great overview and the insight into the coding aspects was invaluable!

Reviewed on Jan 20, 2026

Great course with intro to agent & Muti agent.

Reviewed on Nov 1, 2025

Good, Make it a little more clear on teaching the code and specific functions for agentic systems

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Frequently asked questions

Skills in agentic AI development are highly valuable for roles such as Software Developer, Data Scientist, Machine Learning Engineer, AI Engineer, and Automation Specialist. These positions involve building intelligent systems that use language models to reason, interact with tools, and automate complex workflows. These capabilities are increasingly in demand across industries where adaptive, language-driven automation is transforming how work gets done.

No prior machine learning (ML) experience is required. If you're comfortable with Python, you're ready to go. This course focuses on building practical agentic AI systems that reflect, improve, and act. No complex ML understanding is required.

Traditional development builds static applications, and prompt engineering fine-tunes LLM responses. But agentic AI development focuses on designing autonomous, stateful systems that can evaluate their outputs, manage memory, and interact intelligently over time. You'll learn how to architect systems that think, adapt, and collaborate, using tools such as LangGraph to build workflows with cycles, conditionals, and inter-agent communication.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.