LangGraph for beginners : Agentic Workflows in simple steps from Udemy

Are you ready to go beyond simple LLM apps and build powerful, stateful, and agentic workflows using LangGraph?

In this beginner-friendly course, you’ll master LangGraph, an open-source library built on top of LangChain, designed for orchestrating multi-agent applications using a graph-based architecture. Whether you’re building intelligent agents, dynamic RAG pipelines, or real-world enterprise solutions, this course gives you the solid foundation you need.

What You’ll Learn

• What LangGraph is and how it fits into the GenAI ecosystem

• Build your first LangGraph workflow using a state machine

• Validate and structure your state using Pydantic models

• Use async and streaming to build responsive applications

• Implement conditional routing based on LLM output

• Understand reducers and how they manage state transitions

• Master tool calling with LangGraph’s built-in ToolNode

• Learn about checkpointers, and apply both short-term (in-memory) and long-term (SQLite, Redis) memory storage

• Build Agentic RAG workflows using tools and retrievers

• Implement Human-in-the-Loop workflows using Interrupt and resume

• Modularize complex graphs using subgraphs

• Apply everything in a real-time Hospital Insurance Claim Management use case

• Add tracing and observability using LangSmith

• Explore essential agentic design patterns to scale your applications

Who This Course Is For

• AI developers looking to build production-grade agentic apps

• LangChain users who want to level up to graph-based orchestration

• Backend engineers interested in tool use, memory, and state control

• Anyone working on LLM workflows in real-world use cases

Prerequisites

• Basic Python knowledge

• Some familiarity with LangChain

By the end of this course, you’ll be able to:

• Confidently build, scale, and debug LangGraph workflows

• Integrate LLMs, tools, memory, and human feedback into your apps

• Apply LangGraph in real-world business use cases like claim processing, customer support, and document analysis

Ready to master LangGraph and take your LLM applications to the next level?

Enroll now and start building intelligent, interactive agentic systems with ease.

What's inside

Learning objectives

What langgraph is and how it fits into the genai ecosystem
Build your first langgraph workflow using a state machine
Validate and structure your state using pydantic models
Use async and streaming to build responsive applications
Implement conditional routing based on llm output
Understand reducers and how they manage state transitions
Master tool calling with langgraph’s built-in toolnode
Learn about checkpointers, and apply both short-term (in-memory) and long-term (sqlite, redis) memory storage
Build agentic rag workflows using tools and retrievers

Implement human-in-the-loop workflows using interrupt and resume
Modularize complex graphs using subgraphs
Apply everything in a real-time hospital insurance claim management use case
Add tracing and observability using langsmith
Explore essential agentic design patterns to scale your applications
All in simple steps
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What langgraph is and how it fits into the genai ecosystem
Build your first langgraph workflow using a state machine
Validate and structure your state using pydantic models
Use async and streaming to build responsive applications
Implement conditional routing based on llm output
Understand reducers and how they manage state transitions
Master tool calling with langgraph’s built-in toolnode
Learn about checkpointers, and apply both short-term (in-memory) and long-term (sqlite, redis) memory storage
Build agentic rag workflows using tools and retrievers
Implement human-in-the-loop workflows using interrupt and resume
Modularize complex graphs using subgraphs
Apply everything in a real-time hospital insurance claim management use case
Add tracing and observability using langsmith
Explore essential agentic design patterns to scale your applications
All in simple steps
Show more
Show less

Syllabus

Introduction

Download Completed Project

Download Slides

The Fundamentals

Career center

Learners who complete LangGraph for beginners : Agentic Workflows in simple steps will develop knowledge and skills that may be useful to these careers:

Generative Artificial Intelligence Engineer

A Generative Artificial Intelligence Engineer specializes in designing, developing, and deploying systems that leverage generative models, particularly large language models. The "LangGraph for beginners : Agentic Workflows in simple steps" course is an exceptional fit for this role, as it directly focuses on building powerful, stateful, and agentic workflows using LangGraph. Learners will master orchestrating multi-agent GenAI applications, implementing dynamic RAG pipelines, and applying essential agentic design patterns to scale their applications, all crucial skills for creating advanced, real-world enterprise GenAI solutions in this cutting-edge field.

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Artificial Intelligence Developer

An Artificial Intelligence Developer is responsible for designing and implementing AI applications, often focusing on intelligent agents and complex automation. This course is explicitly for "AI developers looking to build production-grade agentic apps." It provides a robust foundation in LangGraph for orchestrating multi-agent applications, integrating LLMs, tools, memory, and human feedback into sophisticated systems. You will learn to confidently build, scale, and debug LangGraph workflows, which is directly applicable to developing intelligent, interactive agentic systems for real-world business use cases like claim processing or customer support.

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Software Engineer (Artificial Intelligence)

A Software Engineer Artificial Intelligence designs, develops, and deploys software applications with integrated AI capabilities. The "LangGraph for beginners : Agentic Workflows in simple steps" course is directly applicable to this specialization, teaching how to build production-grade agentic applications. It covers state validation with Pydantic, async and streaming for responsive applications, conditional routing based on LLM output, tool calling, and memory management using checkpointers. The inclusion of FastAPI implementation and LangSmith for tracing ensures you develop robust, scalable, and observable AI-driven software, essential for modern intelligent systems.

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Backend Engineer

A Backend Engineer builds the server-side logic and infrastructure that powers applications, focusing on data storage, APIs, and business logic. The "LangGraph for beginners : Agentic Workflows in simple steps" course is particularly relevant, targeting "Backend engineers interested in tool use, memory, and state control." It delves into state validation using Pydantic, asynchronous invocation and streaming for responsive applications, and persistent memory storage with SQLite, Redis, and Postgres. Implementing FastAPI methods is also covered, providing crucial skills for integrating intelligent agentic workflows into robust backend systems.

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Natural Language Processing Engineer

A Natural Language Processing Engineer specializes in developing systems that understand, process, and generate human language. Building agentic applications deeply relies on these skills. The "LangGraph for beginners : Agentic Workflows in simple steps" course is highly relevant by focusing on orchestrating LLMs within graph-based architectures. You will master tool calling with LangGraph's built-in ToolNode, build Agentic RAG workflows using tools and retrievers, and implement conditional routing based on LLM output. These skills are fundamental for creating sophisticated language-driven agents capable of complex interactions and information retrieval.

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Machine Learning Engineer

A Machine Learning Engineer designs, builds, and deploys machine learning models and intelligent systems. The "LangGraph for beginners : Agentic Workflows in simple steps" course is highly relevant for those integrating generative AI capabilities into production systems. It provides a solid foundation in orchestrating multi-agent applications using graph-based architectures, mastering tool calling, and implementing persistent memory for stateful applications. This course helps in developing and scaling intelligent agents, ensuring robust deployment and integration of LangGraph workflows within complex machine learning pipelines, crucial for building advanced real-world AI solutions.

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Solutions Architect

A Solutions Architect designs high-level technical solutions that address complex business problems, requiring a deep understanding of system integration and scalability. The "LangGraph for beginners : Agentic Workflows in simple steps" course helps professionals design robust and scalable agentic systems. It covers multi-agent orchestration through graph-based architectures, state management, memory, and modularizing complex graphs using subgraphs. Applying these concepts in a real-time hospital insurance claim management use case provides practical insights into structuring complex GenAI solutions for enterprises, which is invaluable for a Solutions Architect.

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Artificial Intelligence Consultant

As an Artificial Intelligence Consultant, you advise businesses on AI strategy, implementation, and adoption. The "LangGraph for beginners : Agentic Workflows in simple steps" course provides a practical toolkit for understanding and demonstrating the power of agentic workflows. It covers building multi-agent applications, implementing human-in-the-loop workflows, and applying GenAI solutions to real-world business use cases like claim processing, customer support, and document analysis. This enables you to effectively recommend and oversee advanced AI strategies, translating technical capabilities into tangible business value for your clients.

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Full-Stack Developer

A Full Stack Developer is adept at building both the client-side and server-side components of web applications. While the "LangGraph for beginners : Agentic Workflows in simple steps" course focuses on the backend logic of intelligent systems, it provides a crucial skillset for any Full Stack Developer aiming to integrate sophisticated AI capabilities. The course covers backend aspects like state management, tool calling, memory, and FastAPI implementation, allowing you to build the intelligent core of your applications. This expertise in agentic workflows provides a powerful differentiator for developing interactive and dynamic user experiences.

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Research Scientist, Artificial Intelligence

A Research Scientist Artificial Intelligence conducts investigations to develop new AI theories, algorithms, and applications, often requiring an advanced degree. The "LangGraph for beginners : Agentic Workflows in simple steps" course can help by providing a practical framework for implementing and experimenting with agentic design patterns such as Reflection, Tree of Thought, and Parallel Execution. It also covers state management with reducers and checkpointers, as well as human-in-the-loop workflows, which are critical for building and refining robust research prototypes and advanced AI systems in academic or industrial research settings.

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Prompt Engineer

A Prompt Engineer focuses on crafting effective prompts for large language models to elicit desired outputs. Understanding agentic workflows and tool use from the "LangGraph for beginners : Agentic Workflows in simple steps" course may be useful for designing sophisticated prompts that steer complex agent interactions. The course's exploration of conditional routing based on LLM output and implementing human-in-the-loop workflows offers insights into how prompts can guide multi-step reasoning and integrate external feedback within agentic systems. This knowledge helps enhance the capabilities of intelligent applications and develop more robust agent prompting strategies effectively.

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DevOps Engineer

A DevOps Engineer bridges development and operations, focusing on continuous integration, deployment, and monitoring to ensure reliable software delivery. The "LangGraph for beginners : Agentic Workflows in simple steps" course may be helpful by introducing tracing and observability using LangSmith, which is essential for monitoring the performance and behavior of agentic applications in production environments. It also covers aspects of scaling applications and implementing FastAPI methods, which help ensure robust deployment, efficient resource utilization, and operational stability of intelligent systems within an agile development pipeline.

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Product Manager Artificial Intelligence

A Product Manager Artificial Intelligence defines the strategy, roadmap, and features for AI products, requiring a solid grasp of underlying technologies. The "LangGraph for beginners : Agentic Workflows in simple steps" course may be useful to gain a comprehensive understanding of how intelligent agentic systems are built and orchestrated. This knowledge aids in conceptualizing innovative features, understanding technical feasibility for advanced LLM workflows, and identifying opportunities for real-world enterprise solutions, such as claim processing, customer support, or document analysis, facilitating better product decisions and communication with engineering teams.

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Data Scientist

A Data Scientist analyzes complex datasets, builds predictive models, and extracts actionable insights, often contributing to data-driven product development. The "LangGraph for beginners : Agentic Workflows in simple steps" course may be useful for data scientists interested in operationalizing LLMs and building intelligent data-centric applications. It covers state management, conditional routing based on LLM output, and Agentic RAG workflows which are relevant for creating more dynamic and interactive data products. Understanding these agentic patterns can help in designing systems that intelligently process and act upon data for more sophisticated analytical solutions.

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Technical Project Manager

A Technical Project Manager oversees the planning, execution, and delivery of technical projects, requiring a strong understanding of the technologies involved. The "LangGraph for beginners : Agentic Workflows in simple steps" course may be useful for a Technical Project Manager by providing a deep understanding of multi-agent application development using a graph-based architecture. This knowledge helps in managing projects involving LLM workflows, understanding potential complexities like state management, tool integration, human-in-the-loop processes, and effectively assessing risks and timelines for enterprise AI solutions.

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