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LangGraph: From LangChain User to Agent Builder
Master LangGraph to build reliable, stateful AI workflows with memory, loops, and routing. Upgrade your skills from linear chains to advanced control flow.
22 Lessons
7h
Updated this week
Join 3 million developers at
Join 3 million developers at
LEARNING OBJECTIVES
- Explain the fundamentals of graph-based workflows and their advantages over sequential task execution.
- Design scalable workflows using state management and control flow techniques in LangGraph.
- Build complete LangGraph applications that incorporate memory, routing, and tool integration.
- Implement conditional routing patterns and quality loops to enhance agent performance.
- Utilize debugging and observability techniques to maintain and improve LangGraph applications.
- Evaluate and apply human-in-the-loop strategies for approval processes within agent workflows.
KEY OUTCOMES
Build Advanced LangGraph Applications
Create sophisticated agent workflows that leverage memory, state, and control flow for real-world applications.
Design Scalable Workflows
Architect workflows that efficiently manage state and control flow, ensuring reliability and performance in production environments
Implement Conditional Routing
Apply conditional routing patterns to enhance decision-making processes within agent workflows, improving user interactions.
Debug and Optimize LangGraph Systems
Utilize debugging tools and observability techniques to troubleshoot and enhance the performance of LangGraph applications.
Why choose this course?
Are You Struggling with AI Complexity?
As a developer, you may feel overwhelmed by the limitations of simple task sequences. The stakes are high; without advanced skills, your projects may falter in complexity.
Why Traditional Methods Fall Short
Even skilled developers hit a wall when faced with the need for stateful, controllable AI systems. Without mastering graph-based orchestration, your projects may lack the resilience and reliability required in production.
Transform Your Skills with Practical Learning
This course offers hands-on experience with LangGraph, guiding you through building a research assistant agent. You'll learn to implement memory, routing, and control flow effectively.
Elevate Your Career Today
Join a community of forward-thinking developers and gain the skills to tackle complex applications. Enroll now and take a decisive step towards mastering advanced agent building.
Learning Roadmap
2.
From Chains to Graphs
From Chains to Graphs
Understand why LangGraph exists and learn graph fundamentals to transition your mindset from linear chains to stateful systems.
3.
Control Flow and Agent Patterns
Control Flow and Agent Patterns
5 Lessons
5 Lessons
Enhance workflow efficiency with structured routing, quality assurance, and adaptive agent systems.
4.
Reliable Real-World Systems
Reliable Real-World Systems
5 Lessons
5 Lessons
Master conversation memory, persistence, human approvals, debugging, and graph design for effective LangGraph workflows.
5.
Capstone Build, Guided Walkthrough
Capstone Build, Guided Walkthrough
5 Lessons
5 Lessons
Develop a reliable research assistant that clarifies queries and synthesizes structured responses.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
As AI applications transition from prototypes to production systems, the limitations of linear chaining become apparent. Real-world agents require memory, branching logic, retry mechanisms, and human-in-the-loop control. These requirements exceed the capabilities of basic LangChain workflows. LangGraph is designed to support the development of stateful, controllable AI systems. To transition from experimental agents to reliable, production-ready architectures, understanding graph-based orchestration is essential.
I built this course based on my research and work in intelligent systems, as well as my work on adaptive AI and service-oriented architectures. One consistent gap I’ve seen is that developers can assemble simple chains but face challenges when systems require state, control flow, and resilience. This course presents a structured approach to designing agents, treating them not as sequences of calls, but as systems with state, decision logic, and feedback loops.
You’ll learn how to model AI workflows as graphs using LangGraph, starting from simple nodes and shared state, then progressively adding routing, tool usage, memory, and interrupts. The course is built around a single evolving project: a research assistant agent that grows in complexity as you move forward. Instead of isolated examples, each concept is introduced as part of a larger system, with LangSmith used for debugging, tracing, and evaluation throughout.
Developers are rapidly adopting graph-based approaches to build more reliable AI agents across industries. If you’re ready to go beyond basic chaining and build systems that behave predictably under real conditions, this course gives you the exact framework and hands-on experience to get there.
Trusted by 3 million developers working at companies
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Evan Dunbar
ML Engineer
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Software Developer
Carlos Matias La Borde
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Front-end Developer
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Vinay Krishnaiah
Software Developer
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