How long does it take to complete the course?

The course contains 3 hours of video content. Depending on your pace and whether you engage in hands-on exercises, it should take approximately 3 to 5 hours to complete.

Can I preview a course before enrolling?

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

When will I have access to the lectures and assignments?

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

What will I get when I enroll?

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

When will I receive my Course Certificate?

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

Why can’t I audit this course?

This course is currently available only to learners who have paid or received financial aid, when available.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Ultimate Guide to Crafting AI Agents with Persistent Memory

抓住节省的机会！购买 Coursera Plus 3 个月课程可享受40% 的折扣，并可完全访问数千门课程。

Ultimate Guide to Crafting AI Agents with Persistent Memory

位教师：Packt - Course Instructors

包含在中

了解更多

7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

7 小时完成

灵活的计划

自行安排学习进度

7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

7 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Learn how to build AI agents that can store and recall information dynamically.
Master frameworks and libraries like LangMem and Mem0 to enhance agent memory capabilities.
Implement persistent memory strategies to create adaptive AI agents for real-world applications.
Explore advanced concepts like background memory updates and cognitive language agents.

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

该课程共有7个模块

This course features Coursera Coach!

A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This comprehensive course will empower you to create AI agents that can remember, reason, and adapt. You will learn how memory plays a crucial role in enabling AI agents to handle complex tasks and interact intelligently. By understanding the fundamentals and practical applications of memory in AI, you will be able to build persistent, context-aware agents from scratch or using frameworks like LangMem, Mem0, and Supabase. Starting with an introduction to AI agents and their memory needs, you will dive into both theoretical and practical aspects of memory systems in AI. The course takes you step-by-step through building AI agents with memory, covering hands-on exercises to implement memory storage and retrieval from scratch, as well as using pre-built frameworks. You will also get familiar with key AI memory solutions like LangMem and Mem0, exploring their integration with different platforms and databases. The course is structured in a way that builds upon your previous knowledge, starting with foundational concepts and advancing to more complex real-world projects. By the end, you will have worked on creating AI agents with memory that can be applied in practical settings, from customer support agents to health coaches. This course is perfect for anyone interested in developing smart AI systems that can adapt to ongoing interactions. No prior knowledge of memory frameworks is required, although familiarity with AI basics will be helpful. Whether you're a beginner or an intermediate learner, this course will guide you through essential concepts and hands-on techniques to help you build sophisticated AI agents. By the end of the course, you will be able to design and implement AI agents with memory, work with frameworks like LangMem and Mem0, and develop a variety of AI-driven applications with persistent, context-aware capabilities.

In this module, we will introduce the course, outline the prerequisites, and walk through the key skills you'll acquire by the end. You'll also get an overview of the course structure, development environment setup, and explore the crucial role memory plays in AI agents.

涵盖的内容

5个视频1篇阅读材料

In this module, we will delve into the concept of memory as a critical component in AI agent design. You’ll explore different types of memory and their impact on agent behavior, gaining a deep understanding of how memory enhances the user experience.

涵盖的内容

2个视频1个作业

In this module, we will guide you through the process of building an AI agent with memory, entirely from scratch. We’ll cover class setup, memory implementation, and testing, enabling you to create a fully functional agent without external frameworks or libraries.

涵盖的内容

5个视频1个作业

5个视频总计45分钟

Give AI Agents Memory Introduction 1分钟
HANDS-ON: Building an Agent with memory - No Framework 14分钟
HANDS-ON: Building AI Agent with Memory - No Framework - Class Setup 11分钟
HANDS-ON: Agents with Memory - Testing 13分钟
Agents with Memory - Visual Animation 7分钟

1个作业总计15分钟

Giving AI Agents Memory - No Frameworks or Libraries - Assessment 15分钟

In this module, we will introduce frameworks and libraries that simplify the creation of memory-enabled AI agents. You’ll work with LangMem and Langchain to build a customer support agent, applying memory to enhance interactions and optimize performance.

涵盖的内容

8个视频1个作业

8个视频总计59分钟

AI Agents Memory with Frameworks and Libraries - Overview 3分钟
Motivation and Key Features of LangMem by Langchain 3分钟
Cognitive Language Agent - Full Overview 8分钟
Memory Updating in Cognitive Language Agents - Hot Path & Background Memory 4分钟
CoALA Memory Update Approaches Comparison - Overview 3分钟
Customer Support Agent with Memory Architecture - Overview 3分钟
HANDS-ON: Customer Support Agent with Memory 19分钟
Customer Support Agent with Memory - Testing the Whole Workflow 16分钟

1个作业总计15分钟

Giving AI Agents Memory - With Frameworks and Libraries - Assessment 15分钟

In this module, we will dive deep into LangMem, exploring its features and capabilities. You’ll learn how to create memory-enabled agents, such as a health coach, and apply advanced memory processes to improve performance and personalization.

涵盖的内容

7个视频1个作业

7个视频总计46分钟

Introduction to LangMem - Overview 4分钟
Create a Simple Agent with Langmen that Remembers Things 7分钟
Understanding LangMem in the Background Memory - Full Overview 9分钟
HANDS-ON: LangMem - Health Coach with Memory 4分钟
HANDS-ON: Build a Health Fitness Coach with LangMem 14分钟
LangMem Background Memory Process - Summary 3分钟
Extra Use Case - Recipe Agent with Memory - with Visuals 6分钟

1个作业总计15分钟

LangMem Deep Dive - Assessment 15分钟

In this module, we will combine the power of Mem0 and Supabase to create a persistent memory framework for AI agents. Through hands-on exercises, you will build a final health coach agent, applying the skills you've learned to create a fully functional, memory-driven AI.

涵盖的内容

7个视频1个作业

7个视频总计47分钟

Introduction to Mem0 8分钟
Mem0 - Platform and Open Source Repo Overview 4分钟
Mem0 - Simple Agent with Memory 6分钟
Setting up Supabase - API Keys and Database URL 4分钟
Supabase and Mem0 - Testing 3分钟
Supabase and Mem0 - Implementation 10分钟
HANDS-ON: Final Health Coach Agent with Memory - Capstone 13分钟

1个作业总计15分钟

Giving AI Agents Memory with Frameworks with Mem0 and Supabase – Capstone - Assessment 15分钟

In this final module, we will summarize the key takeaways from the course and provide you with actionable next steps. Whether you’re looking to apply your knowledge in a professional setting or explore further learning paths, this module sets you up for continued success.

涵盖的内容

1个视频2个作业

位教师

Packt - Course Instructors

Packt

1,542 门课程 424,522 名学生

提供方

Packt

人们为什么选择 Coursera 来帮助自己实现职业发展

Felipe M.

自 2018开始学习的学生

''能够按照自己的速度和节奏学习课程是一次很棒的经历。只要符合自己的时间表和心情，我就可以学习。'

Jennifer J.

自 2020开始学习的学生

''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'

Larry W.

自 2021开始学习的学生

''如果我的大学不提供我需要的主题课程，Coursera 便是最好的去处之一。'

Chaitanya A.

''学习不仅仅是在工作中做的更好：它远不止于此。Coursera 让我无限制地学习。'

通过 Coursera Plus 开启新生涯

无限制访问 10,000+ 世界一流的课程、实践项目和就业就绪证书课程 - 所有这些都包含在您的订阅中

了解更多

通过在线学位推动您的职业生涯

获取世界一流大学的学位 - 100% 在线

探索学位

加入超过 3400 家选择 Coursera for Business 的全球公司

提升员工的技能，使其在数字经济中脱颖而出

了解更多

常见问题

AI agent memory refers to the ability of an artificial intelligence system to store and retrieve information over time, enabling it to recall past interactions and context to improve decision-making and user experience. This is crucial for creating intelligent agents that are context-aware and can exhibit more natural, coherent behaviors, making them highly relevant for applications in customer support, health coaching, and more.

This course focuses on teaching you how to design and develop AI agents with persistent memory. You'll learn both the theory and practical techniques for implementing memory in agents, using both traditional programming methods and modern frameworks like LangMem and Mem0. The course covers memory structures, the development of memory-capable agents from scratch, and the integration of popular libraries for more complex agents.

Upon completion of this course, you will be able to build AI agents that retain information over time, improving their ability to handle long-term interactions. You'll be proficient in creating custom memory-enabled agents, using frameworks like LangMem and Mem0, and implementing real-world applications like customer support and health coach agents.

This course assumes that you have basic programming knowledge, particularly in Python. Familiarity with concepts such as object-oriented programming will be helpful but not required. A basic understanding of artificial intelligence and machine learning will also be useful, although the course includes foundational explanations to get you up to speed.

This course is ideal for software developers, data scientists, and AI enthusiasts who are interested in taking their skills to the next level by learning how to build intelligent agents with memory. It's perfect for anyone wanting to delve into AI agent design, especially those interested in applications like virtual assistants, chatbots, and personalized customer experiences.