Will I get hands-on practice with LangChain and AI tools?

Absolutely! This course is built around hands-on demos, coding exercises, and practice assignments. You will work with LangChain, Gemini, LCEL, and other tools to create real-world applications like intelligent agents, workflows, and error-resilient systems. The course is designed to ensure that you gain practical experience throughout.

How long will it take to complete the course?

The course is structured to be completed in 4 weeks with a recommended study pace of 3–4 hours per week. You can work at your own pace, revisiting content as needed. This flexibility allows you to balance your learning with your professional schedule.

Will I receive a certificate upon completion?

Yes, after successfully completing all the modules, assignments, and the final project, you will receive a Certificate of Completion. This certificate validates your skills in LangChain development, Agentic AI, and intelligent agent design, enhancing your professional profile.

Is this course suitable for someone with no prior experience in AI?

Yes, this course is designed for both beginners and intermediate learners. It provides foundational knowledge in Agentic AI and LangChain, so even if you are new to AI, you will gain the skills needed to build intelligent agents from scratch. The course ensures that no prior knowledge is necessary to get started.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

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.

Building Your First AI Agent with LangChain

本课程是 Agentic AI Engineering 专项课程的一部分

位教师：Edureka

包含在中

了解更多

4个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Define core principles of Agentic AI and LangChain ecosystem, including architecture and components.
Apply LangChain frameworks to set up AI environments and build intelligent agents.
Analyze prompt engineering, context design, and LCEL workflows to optimize agent behavior.
Design and evaluate multi-step agent workflows, integrating external tools to solve real-world tasks.

您将获得的技能

要了解的详细信息

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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 Agentic AI Engineering 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4个模块

This program introduces you to Building Simple Agents with LangChain, designed for developers and AI enthusiasts seeking to create intelligent agents powered by LangChain. You’ll begin by mastering the foundational concepts of Agentic AI and the LangChain ecosystem, including understanding its architecture, key components, and capabilities.

Next, you’ll dive into LLM development, focusing on prompting, context engineering, and persona design. You’ll learn to create effective prompts, engineer context to guide model behavior, and design powerful, multi-step workflows using LangChain Expression Language (LCEL). Through hands-on demonstrations, you'll build and optimize intelligent agent systems that can interact with various data sources and tools. As you progress, you’ll explore practical agent development with create_agent, and understand how to enhance agents with memory and external tools. You’ll also learn to produce structured outputs with Pydantic and TypedDict, ensuring that your agents can handle complex tasks with precision. By the end of the program, you will be able to: - Define the core principles of Agentic AI and the LangChain ecosystem. - Apply LangChain’s create_agent framework to build and customize intelligent agents. - Analyze prompt engineering and context engineering techniques to influence model behavior. - Design multi-step workflows and error-resilient pipelines using LangChain Expression Language. - Integrate external tools and synthesize structured outputs for solving complex tasks. - Optimize agents to handle real-world applications, from querying data to generating actionable insights. This program is ideal for developers, AI enthusiasts, and technical professionals looking to dive into the world of intelligent agent development. Prior experience with Python programming and basic AI concepts will help maximize your learning experience. Learners need a reliable internet connection, a modern web browser, and access to Python tools. The course uses AI tools like LangChain and Gemini API, which don't require specialized hardware. Basic knowledge of Python and AI concepts is recommended. Join us and learn to build powerful, responsive agents that can automate tasks, optimize workflows, and unlock new capabilities in AI-driven applications.

Learn the fundamentals of agentic AI and how it differs from traditional prompt-based systems. Explore how autonomous agents reason, plan, and act, and examine real-world use cases where agentic systems are applied. Gain an understanding of the LangChain v1.0 ecosystem, its core components, and architecture. Build a solid technical foundation by setting up a modern AI development environment with API access and virtual environments, preparing you for hands-on agent development.

涵盖的内容

11个视频7篇阅读材料4个作业

11个视频总计59分钟

Specialization Introduction 6分钟
Course Introduction 5分钟
Introduction to Agentic AI 6分钟
Core Concepts of Agentic AI 7分钟
Real-World Agentic AI Use Cases 5分钟
What is LangChain v1.0? 5分钟
LangChain Architecture Deep Dive 6分钟
Key Components and Capabilities of LangChain 5分钟
Preparing a Modern AI Development Environment 4分钟
Demonstration: Gemini API Key Setup with AI Studio 3分钟
Demonstration: Setting up Virtual Environment and Configuring API Keys 7分钟

7篇阅读材料总计100分钟

Course Syllabus 15分钟
Agentic AI Systems: A Practical Overview 15分钟
Architectural Patterns for Autonomous and Collaborative AI Agents 15分钟
LangChain Evolution: From Early Releases to v1.0 15分钟
LangChain v1.0: System Architecture and Design 15分钟
Setting Up a Reliable AI Development Environment 15分钟
Module Summary: Getting Started with Agentic AI an the LangChain Ecosystem 10分钟

4个作业总计33分钟

Practice Assignment: Introduction to Agentic AI 6分钟
Practice Assignment: LangChain v1.0 Ecosystem 6分钟
Practice Assignment: Setting Up Your AI Development Environment 6分钟
Knowledge Check: Getting Started with Agentic AI and the LangChain Ecosystem 15分钟

Discover how to work effectively with large language models using LangChain. Learn prompt engineering best practices, structured prompting techniques, and how context and persona design influence model behavior. Explore LangChain Expression Language (LCEL) to build modular, multi-step, and error-resilient workflows. Develop practical skills to design reusable pipelines that replace fragile, monolithic prompts with maintainable LLM workflows.

涵盖的内容

22个视频5篇阅读材料5个作业

22个视频总计131分钟

How LLMs Work in LangChain 6分钟
Comparing Leading LLM Providers 7分钟
Best Practices for Choosing the Right Model 6分钟
Demonstration: Building a Gemini-Powered CLI Tool 6分钟
Principles of Effective Prompt Engineering 7分钟
Core Prompting Techniques 7分钟
Designing Structured and Reliable Inputs 5分钟
Demonstration: Prompt Creation using LangChain's Prompt Templates 7分钟
Demonstration: Mastering Prompt Engineering with LangChain - I 7分钟
Demonstration: Mastering Prompt Engineering with LangChain - II 3分钟
Introduction to Context Engineering 6分钟
Types of Context in LLM-driven Applications 6分钟
Demonstration: Enhancing Model Responses with Context Engineering 7分钟
Demonstration: Tech Persona Context Injection using LangChain - I 5分钟
Demonstration: Tech Persona Context Injection using LangChain - II 7分钟
Building Pipelines Using LCEL 5分钟
Advanced LCEL Workflow Patterns 4分钟
Demonstration: Constructing Chains with LCEL 7分钟
Demonstration: Designing Multi-Step LCEL Workflows - I 6分钟
Demonstration: Designing Multi-Step LCEL Workflows - II 7分钟
Demonstration: Implementing Error-Resilient LCEL Pipelines - I 4分钟
Demonstration: Implementing Error-Resilient LCEL Pipelines - II 6分钟

5篇阅读材料总计70分钟

Optimizing LLM Provider Selection for Scalable and Cost-Efficient AI 15分钟
Best Practices in Prompt Engineering 15分钟
Designing Effective Context for Reliable LLM Outputs 15分钟
Designing Modular Workflows with LCEL 15分钟
Module Summary: Applied LLM Development: Prompting, Context Engineering and LCEL 10分钟

5个作业总计39分钟

Practice Assignment: Working with Large Language Models 6分钟
Practice Assignment: Prompt Engineering Fundamentals 6分钟
Practice Assignment: Context Engineering and Persona Design 6分钟
Practice Assignment: LangChain Expression Language (LCEL) Workflows 6分钟
Knowledge Check: Applied LLM Development: Prompting, Context Engineering and LCEL 15分钟

Learn how to build intelligent agents using LangChain’s create_agent framework. Explore core agent architecture patterns, multi-step reasoning, and memory integration for conversational continuity. Gain hands-on experience creating and integrating tools, and producing reliable, validated structured outputs using Pydantic and TypedDict. Build practical skills to design agents that reason, act, and interact with external systems.

涵盖的内容

11个视频3篇阅读材料3个作业

11个视频总计66分钟

Understanding the create_agent Framework 6分钟
Core Patterns in Agent Architecture 6分钟
Demonstration: Building Your First LangChain Agent - I 5分钟
Demonstration: Building Your First LangChain Agent - II 6分钟
Demonstration: Enhancing Agents with Memory 7分钟
Building and Using Tools in LangChain 7分钟
Structured Outputs with Pydantic and TypedDict 7分钟
Demonstration: Creating Tools with @tool 7分钟
Demonstration: Integrating External Tools into Your Agent 5分钟
Demonstration: Producing Validated Structured Outputs - I 6分钟
Demonstration: Producing Validated Structured Outputs - II 4分钟

3篇阅读材料总计40分钟

Advanced Considerations for Structured Output in create_agent 15分钟
Tool Design Principles for Scalable Agent Workflows 15分钟
Module Summary: Practical Agent Development with LangChain 10分钟

3个作业总计27分钟

Practice Assignment: Building Agents with create_agent 6分钟
Practice Assignment: Tools and Structured Output in LangChain 6分钟
Knowledge Check: Practical Agent Development with LangChain 15分钟

Consolidate your learning across the entire course and reflect on your growth in agentic AI and LangChain development. Apply your skills in a hands-on practice project, building a beginner intelligent agent that combines prompting, workflows, tools, and memory. Complete a graded end-of-course assessment to demonstrate your ability to design and reason about agent-based AI systems and prepare for more advanced agentic applications.

涵盖的内容

1个视频1篇阅读材料2个作业1个讨论话题

1个视频总计3分钟

Course Summary 3分钟

1篇阅读材料总计30分钟

Practice Project: Building an AI-Powered Developer Productivity Assistant 30分钟

2个作业总计60分钟

End Course Knowledge Check: Building Simple Agents with LangChain 30分钟
Designing an Intelligent Agent-Based Support Assistant Using LangChain 30分钟

1个讨论话题总计5分钟

Describe Your Learning Journey 5分钟

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将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。

位教师

Edureka

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常见问题

This course is designed for AI developers, data scientists, and software engineers interested in building intelligent agents using LangChain. Whether you’re a beginner or have prior experience with AI, the course offers foundational knowledge in Agentic AI and LangChain ecosystem, making it accessible even without a programming background.

Throughout the course, you will learn to create intelligent agents using LangChain. You’ll dive into prompt engineering, context engineering, and the use of LCEL for building robust workflows. Topics also include working with LLMs (Large Language Models), creating and enhancing agents with memory, and integrating external tools into your agents for increased functionality. By the end of the course, you'll be well-equipped to design complex agents and workflows.

The course covers LangChain, Gemini, LCEL, Python, and tools like Pydantic and TypedDict. These tools will be used to help you build agents, create structured outputs, and enhance model behavior in various applications.

No prior programming experience is required. This course is designed for beginners and intermediate learners. We will walk you through each step, from understanding the core concepts of Agentic AI and LangChain to building your first intelligent agent. The course provides everything you need, including explanations of programming concepts and hands-on coding exercises.

This course stands out by focusing specifically on Agentic AI and the LangChain framework, two powerful tools for building intelligent agents. Unlike other AI courses, it emphasizes practical, hands-on learning through real-world applications such as building agents, creating automated workflows, and integrating external tools like Gemini and @tool for added functionality.

Upon completion of this course, you will be ready for roles such as AI Developer, Machine Learning Engineer, Intelligent Systems Architect, and Automation Specialist. This course will prepare you to build AI-powered agents, automate workflows, and implement intelligent systems in various industries, opening doors to career advancement in the growing AI and software development fields.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.