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Building Your First AI Agent with LangChain · LearnSpace
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Building Your First AI Agent with LangChain

Курс от Edureka
Начальный≈ 12.1 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

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.

Навыки, которые вы освоите

Tool CallingAgentic WorkflowsContext EngineeringAI OrchestrationGenerative AIData ValidationArtificial IntelligenceEmbeddingsWorkflow ManagementPython ProgrammingGeminiGoogle GeminiModel OptimizationRetrieval-Augmented Generation

Программа курса

4 модулей · 82 учебных материалов

01Getting Started with Agentic AI and the LangChain Ecosystem24 материалов

Introduction to Agentic AI

Specialization IntroductionВидеоCourse IntroductionВидеоCourse SyllabusЧтениеYour Starting Point with Agentic AI and LangChainDIALOGUE

Учитесь у экспертов

Edureka

Преподаватель курса

Building Your First AI Agent with LangChain
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Обучение на Coursera

≈ 12.1 ч

4 модулей

Язык: Английский

Часть программы вашего университета
Introduction to Agentic AIВидео
Core Concepts of Agentic AIВидео
Real-World Agentic AI Use CasesВидео
Agentic AI Systems: A Practical OverviewЧтение
Architectural Patterns for Autonomous and Collaborative AI AgentsЧтение
Practice Assignment: Introduction to Agentic AIЗадание

LangChain v1.0 Ecosystem

What is LangChain v1.0?ВидеоLangChain Evolution: From Early Releases to v1.0ЧтениеLangChain Architecture Deep DiveВидеоKey Components and Capabilities of LangChainВидеоLangChain v1.0: System Architecture and DesignЧтениеPractice Assignment: LangChain v1.0 EcosystemЗадание

Setting Up Your AI Development Environment

Preparing a Modern AI Development EnvironmentВидеоDemonstration: Gemini API Key Setup with AI StudioВидеоDemonstration: Setting up Virtual Environment and Configuring API KeysВидеоSetting Up a Reliable AI Development Environment ЧтениеPractice Assignment: Setting Up Your AI Development EnvironmentЗадание

Module Wrap-Up and Assessment

Module Summary: Getting Started with Agentic AI an the LangChain EcosystemЧтениеKnowledge Check: Getting Started with Agentic AI and the LangChain EcosystemЗаданиеOnboarding a New Team Member to LangChain and Agentic AIDIALOGUE
02Applied LLM Development: Prompting, Context Engineering and LCEL33 материалов

Working with Large Language Models

How LLMs Work in LangChainВидеоComparing Leading LLM ProvidersВидеоBest Practices for Choosing the Right ModelВидеоDemonstration: Building a Gemini-Powered CLI ToolВидеоOptimizing LLM Provider Selection for Scalable and Cost-Efficient AIЧтениеPractice Assignment: Working with Large Language ModelsЗадание

Prompt Engineering Fundamentals

Principles of Effective Prompt EngineeringВидеоCore Prompting TechniquesВидеоDesigning Structured and Reliable InputsВидеоDemonstration: Prompt Creation using LangChain's Prompt TemplatesВидеоDemonstration: Mastering Prompt Engineering with LangChain - IВидеоDemonstration: Mastering Prompt Engineering with LangChain - IIВидео

Context Engineering and Persona Design

Introduction to Context EngineeringВидеоTypes of Context in LLM-driven ApplicationsВидеоDemonstration: Enhancing Model Responses with Context EngineeringВидеоDemonstration: Tech Persona Context Injection using LangChain - IВидеоDemonstration: Tech Persona Context Injection using LangChain - IIВидеоDesigning Effective Context for Reliable LLM OutputsЧтение

LangChain Expression Language (LCEL) Workflows

Building Pipelines Using LCELВидеоAdvanced LCEL Workflow PatternsВидеоDemonstration: Constructing Chains with LCELВидеоDemonstration: Designing Multi-Step LCEL Workflows - IВидеоDemonstration: Designing Multi-Step LCEL Workflows - IIВидеоDemonstration: Implementing Error-Resilient LCEL Pipelines - IВидео

Module Wrap-Up and Assessment

Module Summary: Applied LLM Development: Prompting, Context Engineering and LCELЧтениеKnowledge Check: Applied LLM Development: Prompting, Context Engineering and LCELЗаданиеGuiding a Colleague Through LLM Prompting and Context EngineeringDIALOGUE
03Practical Agent Development with LangChain18 материалов

Building Agents with create_agent

Understanding the create_agent FrameworkВидеоCore Patterns in Agent ArchitectureВидеоDemonstration: Building Your First LangChain Agent - IВидеоDemonstration: Building Your First LangChain Agent - IIВидеоDemonstration: Enhancing Agents with MemoryВидеоAdvanced Considerations for Structured Output in create_agentЧтениеPractice Assignment: Building Agents with create_agentЗадание

Tools and Structured Output in LangChain

Building and Using Tools in LangChainВидеоStructured Outputs with Pydantic and TypedDictВидеоDemonstration: Creating Tools with @toolВидеоDemonstration: Integrating External Tools into Your AgentВидеоDemonstration: Producing Validated Structured Outputs - IВидеоDemonstration: Producing Validated Structured Outputs - IIВидео

Module Wrap-Up and Assessment

Module Summary: Practical Agent Development with LangChainЧтениеKnowledge Check: Practical Agent Development with LangChainЗаданиеBuilding and Enhancing Intelligent Agents with LangChainDIALOGUE
04Course Wrap-Up and Assessment7 материалов

Course Wrap-Up and Assessment

Reflecting on Your Agentic AI and LangChain JourneyDIALOGUEPractice Project: Building an AI-Powered Developer Productivity AssistantЧтениеEnd Course Knowledge Check: Building Simple Agents with LangChainЗаданиеDesigning an Intelligent Agent-Based Support Assistant Using LangChainЗаданиеInterview - Demonstrating Expertise in LangChain Agent DevelopmentDIALOGUECourse SummaryВидеоDescribe Your Learning JourneyОбсуждение
Best Practices in Prompt EngineeringЧтение
Practice Assignment: Prompt Engineering FundamentalsЗадание
Practice Assignment: Context Engineering and Persona DesignЗадание
Demonstration: Implementing Error-Resilient LCEL Pipelines - IIВидео
Designing Modular Workflows with LCELЧтение
Practice Assignment: LangChain Expression Language (LCEL) WorkflowsЗадание
Tool Design Principles for Scalable Agent WorkflowsЧтение
Practice Assignment: Tools and Structured Output in LangChainЗадание