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

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Building Your First AI Agent with LangChain

This course is part of Agentic AI Engineering Specialization

Instructor: Edureka

Included with

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4 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

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.

Skills you'll gain

Tools you'll learn

Details to know

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Build your subject-matter expertise

This course is part of the Agentic AI Engineering Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

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.

What's included

11 videos7 readings4 assignments

11 videosTotal 59 minutes

Specialization Introduction6 minutes
Course Introduction5 minutes
Introduction to Agentic AI6 minutes
Core Concepts of Agentic AI7 minutes
Real-World Agentic AI Use Cases5 minutes
What is LangChain v1.0?5 minutes
LangChain Architecture Deep Dive6 minutes
Key Components and Capabilities of LangChain5 minutes
Preparing a Modern AI Development Environment4 minutes
Demonstration: Gemini API Key Setup with AI Studio3 minutes
Demonstration: Setting up Virtual Environment and Configuring API Keys7 minutes

7 readingsTotal 100 minutes

Course Syllabus15 minutes
Agentic AI Systems: A Practical Overview15 minutes
Architectural Patterns for Autonomous and Collaborative AI Agents15 minutes
LangChain Evolution: From Early Releases to v1.015 minutes
LangChain v1.0: System Architecture and Design15 minutes
Setting Up a Reliable AI Development Environment 15 minutes
Module Summary: Getting Started with Agentic AI an the LangChain Ecosystem10 minutes

4 assignmentsTotal 33 minutes

Knowledge Check: Getting Started with Agentic AI and the LangChain Ecosystem15 minutes
Practice Assignment: Introduction to Agentic AI6 minutes
Practice Assignment: LangChain v1.0 Ecosystem6 minutes
Practice Assignment: Setting Up Your AI Development Environment6 minutes

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.

What's included

22 videos5 readings5 assignments

22 videosTotal 131 minutes

How LLMs Work in LangChain6 minutes
Comparing Leading LLM Providers7 minutes
Best Practices for Choosing the Right Model6 minutes
Demonstration: Building a Gemini-Powered CLI Tool6 minutes
Principles of Effective Prompt Engineering7 minutes
Core Prompting Techniques7 minutes
Designing Structured and Reliable Inputs5 minutes
Demonstration: Prompt Creation using LangChain's Prompt Templates7 minutes
Demonstration: Mastering Prompt Engineering with LangChain - I7 minutes
Demonstration: Mastering Prompt Engineering with LangChain - II3 minutes
Introduction to Context Engineering6 minutes
Types of Context in LLM-driven Applications6 minutes
Demonstration: Enhancing Model Responses with Context Engineering7 minutes
Demonstration: Tech Persona Context Injection using LangChain - I5 minutes
Demonstration: Tech Persona Context Injection using LangChain - II7 minutes
Building Pipelines Using LCEL5 minutes
Advanced LCEL Workflow Patterns4 minutes
Demonstration: Constructing Chains with LCEL7 minutes
Demonstration: Designing Multi-Step LCEL Workflows - I6 minutes
Demonstration: Designing Multi-Step LCEL Workflows - II7 minutes
Demonstration: Implementing Error-Resilient LCEL Pipelines - I4 minutes
Demonstration: Implementing Error-Resilient LCEL Pipelines - II6 minutes

5 readingsTotal 70 minutes

Optimizing LLM Provider Selection for Scalable and Cost-Efficient AI15 minutes
Best Practices in Prompt Engineering15 minutes
Designing Effective Context for Reliable LLM Outputs15 minutes
Designing Modular Workflows with LCEL15 minutes
Module Summary: Applied LLM Development: Prompting, Context Engineering and LCEL10 minutes

5 assignmentsTotal 39 minutes

Knowledge Check: Applied LLM Development: Prompting, Context Engineering and LCEL15 minutes
Practice Assignment: Working with Large Language Models6 minutes
Practice Assignment: Prompt Engineering Fundamentals6 minutes
Practice Assignment: Context Engineering and Persona Design6 minutes
Practice Assignment: LangChain Expression Language (LCEL) Workflows6 minutes

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.

What's included

11 videos3 readings3 assignments

11 videosTotal 66 minutes

Understanding the create_agent Framework6 minutes
Core Patterns in Agent Architecture6 minutes
Demonstration: Building Your First LangChain Agent - I5 minutes
Demonstration: Building Your First LangChain Agent - II6 minutes
Demonstration: Enhancing Agents with Memory7 minutes
Building and Using Tools in LangChain7 minutes
Structured Outputs with Pydantic and TypedDict7 minutes
Demonstration: Creating Tools with @tool7 minutes
Demonstration: Integrating External Tools into Your Agent5 minutes
Demonstration: Producing Validated Structured Outputs - I6 minutes
Demonstration: Producing Validated Structured Outputs - II4 minutes

3 readingsTotal 40 minutes

Advanced Considerations for Structured Output in create_agent15 minutes
Tool Design Principles for Scalable Agent Workflows15 minutes
Module Summary: Practical Agent Development with LangChain10 minutes

3 assignmentsTotal 27 minutes

Knowledge Check: Practical Agent Development with LangChain15 minutes
Practice Assignment: Building Agents with create_agent6 minutes
Practice Assignment: Tools and Structured Output in LangChain6 minutes

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.

What's included

1 video1 reading2 assignments1 discussion prompt

1 videoTotal 3 minutes

Course Summary3 minutes

1 readingTotal 30 minutes

Practice Project: Building an AI-Powered Developer Productivity Assistant30 minutes

2 assignmentsTotal 60 minutes

End Course Knowledge Check: Building Simple Agents with LangChain30 minutes
Designing an Intelligent Agent-Based Support Assistant Using LangChain30 minutes

1 discussion promptTotal 5 minutes

Describe Your Learning Journey5 minutes

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Instructor

Edureka

170 Courses151,746 learners

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Edureka

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Frequently asked questions

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.