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

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Ultimate Guide to Crafting AI Agents with Persistent Memory

Dozent: Packt - Course Instructors

Bei enthalten

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7 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

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Empfohlene Erfahrung

7 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

7 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

7 Stunden zu vervollständigen

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

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.

Kompetenzen, die Sie erwerben

Kategorie: Artificial Intelligence
Kategorie: Context Management
Kategorie: LLM Application
Kategorie: Generative AI Agents
Kategorie: Retrieval-Augmented Generation
Kategorie: Data Storage
Kategorie: Agentic systems
Kategorie: Embeddings
Kategorie: Databases
Kategorie: AI Personalization
Kategorie: Artificial Intelligence and Machine Learning (AI/ML)

Werkzeuge, die Sie lernen werden

Kategorie: Data Persistence
Kategorie: LangChain
Kategorie: Application Programming Interface (API)
Kategorie: Vector Databases

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Februar 2026

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7 Aufgaben

Unterrichtet in Englisch

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In diesem Kurs gibt es 7 Module

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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.

Das ist alles enthalten

5 Videos1 Lektüre

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.

Das ist alles enthalten

2 Videos1 Aufgabe

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.

Das ist alles enthalten

5 Videos1 Aufgabe

5 Videos Insgesamt 45 Minuten

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

1 Aufgabe Insgesamt 15 Minuten

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

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.

Das ist alles enthalten

8 Videos1 Aufgabe

8 Videos Insgesamt 59 Minuten

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

1 Aufgabe Insgesamt 15 Minuten

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

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.

Das ist alles enthalten

7 Videos1 Aufgabe

7 Videos Insgesamt 46 Minuten

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

1 Aufgabe Insgesamt 15 Minuten

LangMem Deep Dive - Assessment 15 Minuten

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.

Das ist alles enthalten

7 Videos1 Aufgabe

7 Videos Insgesamt 47 Minuten

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

1 Aufgabe Insgesamt 15 Minuten

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

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.

Das ist alles enthalten

1 Video2 Aufgaben

Dozent

Packt - Course Instructors

Packt

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Lernender seit 2018

„Es ist eine großartige Erfahrung, in meinem eigenen Tempo zu lernen. Ich kann lernen, wenn ich Zeit und Nerven dazu habe.“

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Lernender seit 2020

„Bei einem spannenden neuen Projekt konnte ich die neuen Kenntnisse und Kompetenzen aus den Kursen direkt bei der Arbeit anwenden.“

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Lernender seit 2021

„Wenn mir Kurse zu Themen fehlen, die meine Universität nicht anbietet, ist Coursera mit die beste Alternative.“

Chaitanya A.

„Man lernt nicht nur, um bei der Arbeit besser zu werden. Es geht noch um viel mehr. Bei Coursera kann ich ohne Grenzen lernen.“

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Häufig gestellte Fragen

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.