LangGraph in Action: Develop Advanced AI Agents with LLMs from Udemy

What to Expect from This CourseWelcome to LangGraph in Action, your ultimate guide to mastering the design and deployment of advanced AI agents using LangGraph. In this course, you’ll explore the fundamentals of building modular, scalable, and production-ready agents, all with a hands-on approach. From understanding the basics of LangGraph’s state-based design to creating a full-stack application, you’ll gain the skills needed to bring AI agents to life.

Course Highlights

State-Based Design: Dive into LangGraph’s core philosophy of nodes and edges to create structured, maintainable agents.
Memory Management: Explore short-term memory with checkpointers and long-term memory with the Store object to enable agents that adapt and learn.
Advanced Workflows: Build human-in-the-loop systems, implement parallel execution, and master multi-agent patterns.
Production-Ready Development: Learn asynchronous operations, subgraphs, and create full-stack applications using FastAPI and Docker.

By the end of the course, you’ll not only have a strong theoretical understanding but also the practical skills to deploy AI agents anywhere, entirely with open-source tools. Whether you're a developer aiming to stay ahead of the curve or a seasoned engineer looking to expand your AI toolkit, this course equips you for the rapidly growing field of AI agents.

With the increasing adoption of AI agents in real-world applications, this course ensures you're prepared to design, build, and deploy advanced systems that solve practical challenges. Let’s start building and shaping the future of AI together.

What's inside

Learning objectives

Understand the core functions and concepts of langgraph, including nodes, edges, and checkpointers
Develop an ai agent with langgraph that effectively uses both short-term and long-term memory

Implement advanced multi-agent workflows and subgraphs for handling complex real-world scenarios
Build production-ready ai agents using fastapi, docker, and unit testing for maintainable workflows

Understand the core functions and concepts of langgraph, including nodes, edges, and checkpointers
Develop an ai agent with langgraph that effectively uses both short-term and long-term memory
Implement advanced multi-agent workflows and subgraphs for handling complex real-world scenarios
Build production-ready ai agents using fastapi, docker, and unit testing for maintainable workflows

Syllabus

Introduction

Why this course and why should be listen to me?

What you will learn and what will you not learn

Prerequisites

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Explores LangGraph's state-based design, which promotes structured and maintainable agent development, making it easier to manage complex AI systems

Covers memory management techniques, including short-term memory with checkpointers and long-term memory with the Store object, enabling agents to adapt and learn effectively

Teaches advanced workflows, such as human-in-the-loop systems and parallel execution, which are essential for building robust and responsive AI applications

Includes production-ready development techniques using FastAPI and Docker, which are standard tools for deploying AI agents in real-world environments

Requires learners to clone a repository and set up an environment, which may pose a challenge for those unfamiliar with version control systems and environment management

Features tool calling, connecting agents to the real world, which is a crucial aspect of developing practical and functional AI applications for various industries

Reviews summary

Practical langgraph guide for advanced agents

According to students, this course is a highly effective guide for mastering LangGraph. Learners particularly praise the instructor's clear explanations and the course's strong emphasis on practical, hands-on coding examples. The content covers advanced concepts like multi-agent patterns, asynchronous operations, and preparing agents for production deployment using tools like FastAPI and Docker, which many found invaluable for real-world application. While the course moves at a fast pace and assumes a solid foundation in Python, those with the necessary prerequisites found it to be a comprehensive resource for building sophisticated AI agents.

Instructor explains complex topics well.

"The instructor's explanations are very clear, making challenging LangGraph topics accessible."

"Highly knowledgeable instructor who guides you through the material step-by-step logically."

"Found the lectures easy to follow thanks to the instructor's clear and engaging teaching style."

"The way the instructor broke down complex ideas was extremely helpful."

Deep dive into complex agent patterns.

"The sections on multi-agent systems and subgraphs were particularly insightful for building complex workflows."

"Excellent coverage of turning theoretical agents into production-ready applications using FastAPI and Docker."

"Understanding async agents and parallel execution was a game-changer for my work, boosting performance."

"This course provided the advanced patterns I needed to build sophisticated AI agent solutions."

Course excels with hands-on coding demos.

"The hands-on coding and projects are the strongest part of the course for me."

"Really appreciate the practical approach with runnable code demos throughout the course."

"Building along with the instructor solidified my understanding greatly, much better than just theory."

"I learned how to apply concepts immediately through the practical exercises provided."

Some initial hurdles reported.

"Had a few issues getting the environment set up correctly at the very start of the course."

"The setup instructions could be a little clearer, especially regarding specific library versions."

"Needed to troubleshoot dependencies a bit to get the course code running smoothly."

"Getting the required packages installed took longer than expected for me."

Requires prior knowledge and attention.

"The pace is quite fast, so definitely recommend having solid Python experience beforehand."

"Assumes you are comfortable with coding and can pick up new LangGraph concepts quickly."

"Needed to pause and rewatch sections frequently due to the speed of the material being covered."

"If you're not already familiar with LangChain basics, you might find it challenging."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in LangGraph in Action: Develop Advanced AI Agents with LLMs with these activities:

Review State Machine Concepts

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Reviewing state machine concepts will provide a solid foundation for understanding LangGraph's state-based design and workflows.

Browse courses on State Machines

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Read articles or watch videos explaining state machines.
Draw state diagrams for simple processes.
Implement a basic state machine in Python.

Read 'Generative AI with LangChain'

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Reading this book will expand your knowledge of generative AI and its applications in LangGraph.

View Melania on Amazon

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Read the chapters related to text and code generation.
Experiment with the LangChain code examples for generative AI.
Consider how these techniques can be applied to LangGraph agents.

Read 'Building LLM Applications with LangChain'

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Reading this book will provide a broader context for LangGraph and its relationship to LangChain.

View Melania on Amazon

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Read the chapters related to agents and workflows.
Experiment with the LangChain code examples.
Compare LangChain's approach to LangGraph's.

Four other activities

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Build a Simple Chatbot with LangGraph

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Building a chatbot will allow you to apply the concepts learned in the course and solidify your understanding of LangGraph's architecture.

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Define the chatbot's functionality and scope.
Design the state machine for the chatbot's conversation flow.
Implement the chatbot using LangGraph's nodes and edges.
Test and refine the chatbot's performance.

Write a Blog Post on LangGraph's Advantages

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Writing a blog post will force you to articulate the benefits of LangGraph and deepen your understanding of its strengths.

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Research LangGraph's features and benefits.
Compare LangGraph to other agent frameworks.
Write a clear and concise blog post explaining LangGraph's advantages.
Publish the blog post on a relevant platform.

Create a LangGraph Agent Demo

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Creating a demo will showcase your ability to build and deploy LangGraph agents in a practical setting.

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Choose a real-world problem to solve with a LangGraph agent.
Design and implement the agent using LangGraph.
Create a presentation or video demonstrating the agent's functionality.
Share the demo with others and gather feedback.

Contribute to a LangGraph Project

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Contributing to open source will expose you to real-world LangGraph projects and allow you to learn from experienced developers.

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Find an open-source LangGraph project on GitHub.
Identify a bug or feature to work on.
Submit a pull request with your changes.
Respond to feedback and iterate on your code.

Career center

Learners who complete LangGraph in Action: Develop Advanced AI Agents with LLMs will develop knowledge and skills that may be useful to these careers:

AI Agent Developer

An AI Agent Developer designs, builds, and deploys intelligent agents that can perform tasks autonomously. This career demands a strong understanding of AI principles and the ability to translate complex problems into functional code. This course on LangGraph helps those looking to become AI Agent Developers. It provides the practical experience necessary to build modular, scalable, and production-ready agents. The course provides insight into state-based design, memory management, and advanced workflows, which are all critical for developing effective AI agents. It also details how to construct full-stack applications using FastAPI and Docker, ensuring your agents are deployable in real-world scenarios.

See salaries and explore the career path for AI Agent Developer

Machine Learning Engineer

A Machine Learning Engineer focuses on building and deploying machine learning models into production systems. This often requires a deep understanding of software engineering principles and a proficiency in various machine learning frameworks. The course on LangGraph may be useful for designing advanced AI agents. It provides hands-on experience with modular design, memory management, and advanced workflows. These are essential skills for building robust and scalable AI solutions. Especially useful are the lessons on using FastAPI and Docker to create full-stack applications, along with the knowledge of asynchronous operations and subgraphs.

See salaries and explore the career path for Machine Learning Engineer

AI Solutions Architect

An AI Solutions Architect designs and oversees the implementation of AI-driven solutions for businesses. This involves understanding business needs and translating them into technical requirements. This course may be helpful for building a strong foundation in the practical aspects of AI agent development. The course provides insights into state-based design, memory management, and advanced workflows. These are crucial for designing scalable and maintainable AI systems. Gaining experience in using FastAPI and Docker for creating full-stack applications ensures that the architect understands the deployment challenges and can design solutions tailored for production environments.

See salaries and explore the career path for AI Solutions Architect

Software Engineer

A Software Engineer designs, develops, and maintains software systems, often working on complex projects that require a broad range of technical skills. This course can help Software Engineers to become more effective at building AI-powered applications by providing specific skills in AI agent development. Specifically, the course's focus on LangGraph empowers engineers to craft modular, scalable, and production-ready agents. It is a practical introduction to AI agent design, memory management, and advanced workflows. The full-stack application development using FastAPI and Docker provides valuable experience in deploying complex AI systems.

See salaries and explore the career path for Software Engineer

Data Scientist

A Data Scientist analyzes large datasets to extract meaningful insights and build predictive models. While primarily focused on data analysis, the knowledge of AI agent development can greatly enhance a data scientist's capability to automate data-driven decision-making processes. The LangGraph course is valuable because it provides the practical skills to design and deploy AI agents. The course provides a deep dive into state-based design, memory management, and advanced workflows, ensuring that data scientists can create agents that adapt and learn. The experience gained in building full-stack applications using FastAPI and Docker allows them to deploy their models more effectively.

See salaries and explore the career path for Data Scientist

AI Consultant

An AI Consultant advises businesses on how to leverage AI technologies to solve their specific problems and improve their operations. The ability to demonstrate practical skills in AI agent development can make a consultant more credible and effective in providing tailored solutions. The LangGraph course helps consultants gain the practical knowledge needed to design and deploy advanced AI systems. The focus on modular design, memory management, and advanced workflows helps the consultant to understand the complexities involved in building AI agents. The experience in building full-stack applications using FastAPI and Docker provides them with insights into the deployment challenges of AI solutions.

See salaries and explore the career path for AI Consultant

Robotics Engineer

A Robotics Engineer designs, builds, and programs robots for various applications. These robots often require sophisticated AI to perform complex tasks autonomously. This course may be useful for Robotics Engineers by providing the skills to develop advanced AI agents that can control and coordinate robotic systems. The course delves into modular design, memory management, and advanced workflows. These are crucial for creating intelligent robotic systems. The knowledge gained in building full-stack applications using FastAPI and Docker ensures that the engineer can integrate these agents into larger robotic ecosystems.

See salaries and explore the career path for Robotics Engineer

Natural Language Processing Engineer

A Natural Language Processing Engineer develops systems that can understand, interpret, and generate human language, often incorporating these systems into AI agents and chatbots. This course may be useful in training NLP Engineers to design conversational agents and AI-driven language systems. The hands-on approach of the course, focusing on modular design, memory management, and advanced workflows, facilitates the development of intelligent language processing agents. Experience with FastAPI and Docker is also invaluable for deploying NLP models into production.

See salaries and explore the career path for Natural Language Processing Engineer

Automation Engineer

An Automation Engineer designs and implements automated systems that improve efficiency and reduce human intervention. This course might be helpful for automating more complex tasks using AI agents. The course explores state-based design, memory management, and advanced workflows. These are essential for creating robust automation systems. The practical skills acquired in building full-stack applications using FastAPI and Docker would enable them to integrate these agents into existing automation infrastructure.

See salaries and explore the career path for Automation Engineer

Data Engineer

A Data Engineer builds and maintains the infrastructure required for data storage, processing, and analysis. While not directly involved in AI agent development, a data engineer who understands the principles of AI can better support the data needs of AI systems. This course may be useful. It discusses the practical aspects of building AI agents. The focus on modular design, memory management, and advanced workflows allows the data engineer to understand the data requirements of these systems. Knowledge of FastAPI and Docker can help them design data pipelines that support real-time AI applications.

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Research Scientist

A Research Scientist investigates and develops new AI algorithms and techniques, often working in academic or industrial research labs. This course may be useful for providing the practical skills to implement and test their research ideas. The emphasis on modular design, memory management, and advanced workflows may allow research scientists to quickly prototype and evaluate new AI agents. The focus on FastAPI and Docker helps in deploying these agents for real-world testing and validation.

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Technical Lead

A Technical Lead manages and guides a team of engineers, ensuring the successful delivery of technical projects. This course may be useful for Technical Leads as it provides a solid understanding of AI agent development. The course covers modular design, memory management, and advanced workflows. These are critical components in AI systems. The material on full-stack application development with FastAPI and Docker helps the lead understand the practical challenges of deploying AI solutions.

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AI Product Manager

An AI Product Manager defines the vision, strategy, and roadmap for AI-powered products. While not directly involved in the technical development, a product manager who understands the capabilities and limitations of AI can make more informed decisions. This course may be useful because it provides insight into AI agent development. This will help them to understand the practical considerations of building AI agents. The focus on modular design, memory management, and advanced workflows may allow them to communicate more effectively with the engineering teams.

See salaries and explore the career path for AI Product Manager

Business Intelligence Analyst

A Business Intelligence Analyst analyzes data to identify trends and insights that can improve business performance. The knowledge of AI agent development can help them automate data analysis and decision-making processes. This course may be useful for understanding how AI agents can be used to automate tasks. It looks into modular design, memory management, and advanced workflows. These can be applied to building intelligent data analysis systems. The experience with FastAPI and Docker allows them to deploy these systems effectively.

See salaries and explore the career path for Business Intelligence Analyst

Full-Stack Developer

A Full Stack Developer is proficient in both front-end and back-end development, capable of building complete web applications. This course may be useful as it empowers Full Stack Developers to integrate AI agents into their applications. The course helps them understand modular design, memory management, and advanced workflows. These are crucial for building AI-driven features. The hands-on experience with FastAPI and Docker helps in deploying these agents as part of the full-stack application.

See salaries and explore the career path for Full-Stack Developer

Reading list

We've selected one books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in LangGraph in Action: Develop Advanced AI Agents with LLMs.

LangGraph in Action

Develop Advanced AI Agents with LLMs

What's inside

Learning objectives

Syllabus

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Reviews summary

Practical langgraph guide for advanced agents

Activities

Career center

Reading list

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