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Building Your First AI Agent with LangChain

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Building Your First AI Agent with LangChain

Instructor: Edureka

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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
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.

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Recently updated!

February 2026

Assessments

14 assignments¹

AI Graded see disclaimer
Taught in English

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 videos•Total 59 minutes
  • Specialization Introduction•6 minutes
  • Course Introduction•5 minutes
  • Introduction to Agentic AI•6 minutes
  • Core Concepts of Agentic AI•7 minutes
  • Real-World Agentic AI Use Cases•5 minutes
  • What is LangChain v1.0?•5 minutes
  • LangChain Architecture Deep Dive•6 minutes
  • Key Components and Capabilities of LangChain•5 minutes
  • Preparing a Modern AI Development Environment•4 minutes
  • Demonstration: Gemini API Key Setup with AI Studio•3 minutes
  • Demonstration: Setting up Virtual Environment and Configuring API Keys•7 minutes
7 readings•Total 100 minutes
  • Course Syllabus•15 minutes
  • Agentic AI Systems: A Practical Overview•15 minutes
  • Architectural Patterns for Autonomous and Collaborative AI Agents•15 minutes
  • LangChain Evolution: From Early Releases to v1.0•15 minutes
  • LangChain v1.0: System Architecture and Design•15 minutes
  • Setting Up a Reliable AI Development Environment •15 minutes
  • Module Summary: Getting Started with Agentic AI an the LangChain Ecosystem•10 minutes
4 assignments•Total 33 minutes
  • Knowledge Check: Getting Started with Agentic AI and the LangChain Ecosystem•15 minutes
  • Practice Assignment: Introduction to Agentic AI•6 minutes
  • Practice Assignment: LangChain v1.0 Ecosystem•6 minutes
  • Practice Assignment: Setting Up Your AI Development Environment•6 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 videos•Total 131 minutes
  • How LLMs Work in LangChain•6 minutes
  • Comparing Leading LLM Providers•7 minutes
  • Best Practices for Choosing the Right Model•6 minutes
  • Demonstration: Building a Gemini-Powered CLI Tool•6 minutes
  • Principles of Effective Prompt Engineering•7 minutes
  • Core Prompting Techniques•7 minutes
  • Designing Structured and Reliable Inputs•5 minutes
  • Demonstration: Prompt Creation using LangChain's Prompt Templates•7 minutes
  • Demonstration: Mastering Prompt Engineering with LangChain - I•7 minutes
  • Demonstration: Mastering Prompt Engineering with LangChain - II•3 minutes
  • Introduction to Context Engineering•6 minutes
  • Types of Context in LLM-driven Applications•6 minutes
  • Demonstration: Enhancing Model Responses with Context Engineering•7 minutes
  • Demonstration: Tech Persona Context Injection using LangChain - I•5 minutes
  • Demonstration: Tech Persona Context Injection using LangChain - II•7 minutes
  • Building Pipelines Using LCEL•5 minutes
  • Advanced LCEL Workflow Patterns•4 minutes
  • Demonstration: Constructing Chains with LCEL•7 minutes
  • Demonstration: Designing Multi-Step LCEL Workflows - I•6 minutes
  • Demonstration: Designing Multi-Step LCEL Workflows - II•7 minutes
  • Demonstration: Implementing Error-Resilient LCEL Pipelines - I•4 minutes
  • Demonstration: Implementing Error-Resilient LCEL Pipelines - II•6 minutes
5 readings•Total 70 minutes
  • Optimizing LLM Provider Selection for Scalable and Cost-Efficient AI•15 minutes
  • Best Practices in Prompt Engineering•15 minutes
  • Designing Effective Context for Reliable LLM Outputs•15 minutes
  • Designing Modular Workflows with LCEL•15 minutes
  • Module Summary: Applied LLM Development: Prompting, Context Engineering and LCEL•10 minutes
5 assignments•Total 39 minutes
  • Knowledge Check: Applied LLM Development: Prompting, Context Engineering and LCEL•15 minutes
  • Practice Assignment: Working with Large Language Models•6 minutes
  • Practice Assignment: Prompt Engineering Fundamentals•6 minutes
  • Practice Assignment: Context Engineering and Persona Design•6 minutes
  • Practice Assignment: LangChain Expression Language (LCEL) Workflows•6 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 videos•Total 66 minutes
  • Understanding the create_agent Framework•6 minutes
  • Core Patterns in Agent Architecture•6 minutes
  • Demonstration: Building Your First LangChain Agent - I•5 minutes
  • Demonstration: Building Your First LangChain Agent - II•6 minutes
  • Demonstration: Enhancing Agents with Memory•7 minutes
  • Building and Using Tools in LangChain•7 minutes
  • Structured Outputs with Pydantic and TypedDict•7 minutes
  • Demonstration: Creating Tools with @tool•7 minutes
  • Demonstration: Integrating External Tools into Your Agent•5 minutes
  • Demonstration: Producing Validated Structured Outputs - I•6 minutes
  • Demonstration: Producing Validated Structured Outputs - II•4 minutes
3 readings•Total 40 minutes
  • Advanced Considerations for Structured Output in create_agent•15 minutes
  • Tool Design Principles for Scalable Agent Workflows•15 minutes
  • Module Summary: Practical Agent Development with LangChain•10 minutes
3 assignments•Total 27 minutes
  • Knowledge Check: Practical Agent Development with LangChain•15 minutes
  • Practice Assignment: Building Agents with create_agent•6 minutes
  • Practice Assignment: Tools and Structured Output in LangChain•6 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 video•Total 3 minutes
  • Course Summary•3 minutes
1 reading•Total 30 minutes
  • Practice Project: Building an AI-Powered Developer Productivity Assistant•30 minutes
2 assignments•Total 60 minutes
  • End Course Knowledge Check: Building Simple Agents with LangChain•30 minutes
  • Designing an Intelligent Agent-Based Support Assistant Using LangChain•30 minutes
1 discussion prompt•Total 5 minutes
  • Describe Your Learning Journey•5 minutes

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Instructor

Edureka
203 Courses•185,285 learners

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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.

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.

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.

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.

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.

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.

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.

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.

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.

Financial aid available,

¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.