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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.

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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.

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Ultimate Guide to Crafting AI Agents with Persistent Memory

Ultimate Guide to Crafting AI Agents with Persistent Memory

Instructor: Packt - Course Instructors

7 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

7 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

7 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

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.

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

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There are 7 modules in this course

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.

What's included

5 videos1 reading

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.

What's included

2 videos1 assignment

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.

What's included

5 videos1 assignment

5 videos Total 45 minutes

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

1 assignment Total 15 minutes

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

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.

What's included

8 videos1 assignment

8 videos Total 59 minutes

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

1 assignment Total 15 minutes

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

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.

What's included

7 videos1 assignment

7 videos Total 46 minutes

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

1 assignment Total 15 minutes

LangMem Deep Dive - Assessment 15 minutes

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.

What's included

7 videos1 assignment

7 videos Total 47 minutes

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

1 assignment Total 15 minutes

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

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.

What's included

1 video2 assignments

Instructor

Packt - Course Instructors

Packt

1,395 Courses 375,627 learners

Offered by

Packt

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Frequently asked questions

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.