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LangGraph Framework

本课程是 Hands-on Agentic AI: Building Intelligent Agents 专项课程的一部分

位教师：Hurix Digital

包含在中

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3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

4 小时完成

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

4 小时完成

灵活的计划

自行安排学习进度

您将获得的技能

您将学习的工具

要了解的详细信息

可分享的证书

添加到您的领英档案

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

积累特定领域的专业知识

本课程是 Hands-on Agentic AI: Building Intelligent Agents 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

向行业专家学习新概念
获得对主题或工具的基础理解
通过实践项目培养工作相关技能
获得可共享的职业证书

该课程共有3个模块

LangGraph Framework is an intermediate-level course designed for developers and AI engineers who want to build production-ready, stateful AI systems that go beyond simple prompt-response interactions. In today's AI landscape, the most powerful applications aren't single agents working in isolation—they're coordinated systems that maintain context, make intelligent decisions, and collaborate to solve complex problems. This course teaches you to harness LangGraph's graph-based architecture to create AI workflows with persistent memory, conditional logic, and multi-agent coordination. Through hands-on labs, real-world case studies from companies like Klarna, CyberArk, and Replit, and practical projects, you'll learn to build systems that maintain context across interactions, handle failures gracefully, and coordinate multiple specialized agents to create emergent intelligence. Whether you're building customer service automation, research assistants, or complex business workflows, this course equips you with the skills to create AI systems that are not just intelligent, but reliable, maintainable, and production-ready.

单元详情

In this introductory lesson, learners will explore the fundamental architecture of LangGraph and understand how it differs from traditional agent frameworks. They'll examine the core concepts of graph-based state management and learn why LangGraph provides superior control and reliability for AI applications compared to stateless approaches.

涵盖的内容

4个视频3篇阅读材料1个作业

4个视频总计26分钟

Introduction and Welcome4分钟
The LangGraph Advantage: Beyond Traditional Agent Frameworks6分钟
Core Architecture: Nodes, Edges, and State Management6分钟
Getting Started: Your First LangGraph Implementation10分钟

3篇阅读材料总计21分钟

Welcome to the Course: Course Overview3分钟
Real-World LangGraph Implementations: Production Case Studies8分钟
LangGraph Development Environment and Tools10分钟

1个作业总计15分钟

HOL: Building Your First LangGraph Workflow15分钟

In this lesson, learners will master the practical implementation of LangGraph's state management system. They'll learn to design persistent workflows with memory, implement conditional logic for dynamic routing, and create robust error handling mechanisms. Through hands-on exercises, learners will build workflows that maintain context across complex multi-step processes and handle real-world edge cases effectively.

涵盖的内容

3个视频1篇阅读材料1个作业

In this final lesson, learners will master the design and implementation of sophisticated multi-agent systems using LangGraph. They'll learn to coordinate autonomous AI agents through event-driven flows, implement inter-agent communication patterns, and create systems where specialized agents collaborate to solve complex problems. The lesson culminates with a comprehensive capstone project that demonstrates production-ready multi-agent coordination.