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KRISHAI Technologies Private Limited and Krish Naik

Are you excited about the future of AI where intelligent agents can think, act, and collaborate to solve complex tasks autonomously? Welcome to the Complete Agentic AI Bootcamp with LangGraph and LangChain — your one-stop course to master the art of building agentic AI applications from scratch.

This course is designed to teach you everything you need to know about Agentic AI, LangGraph, and LangChain — two of the most powerful frameworks for building intelligent AI agents and multi-agent systems.

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Are you excited about the future of AI where intelligent agents can think, act, and collaborate to solve complex tasks autonomously? Welcome to the Complete Agentic AI Bootcamp with LangGraph and LangChain — your one-stop course to master the art of building agentic AI applications from scratch.

This course is designed to teach you everything you need to know about Agentic AI, LangGraph, and LangChain — two of the most powerful frameworks for building intelligent AI agents and multi-agent systems.

You will start by understanding the fundamentals of Agentic AI — how it differs from traditional AI models, the key components of agents (memory, tools, decision-making), and real-world use cases.We will then dive deep into LangGraph, a cutting-edge framework that helps you design complex agent workflows using graphs, events, and state transitions. You’ll also learn how to combine LangChain's power with LangGraph to build production-ready agent applications.

Throughout the course, you will build real-world projects step-by-step, including:

  • Creating single intelligent agents with memory and tool-usage capabilities.

  • Designing multi-agent collaboration systems with message passing and shared goals.

  • Implementing autonomous research assistants, task automation bots, and retrieval-augmented generation (RAG) agents.

You will not just learn theory — you will build and deploy multiple end-to-end agentic applications, gaining real-world experience in constructing powerful AI systems.

By the end of this course, you will have the skills and confidence to create your own AI agents and deploy complex agentic applications for various domains like search, research, task planning, customer support, and beyond.

What You Will Learn:

  • Core concepts behind Agentic AI and how intelligent agents operate.

  • Hands-on mastery of LangGraph and LangChain for building agent systems.

  • Building autonomous, event-driven AI workflows with memory, reasoning, and tools.

  • Deploying and optimizing single-agent and multi-agent applications.

  • Real-world project experience with RAG agents, auto-research agents, and more.

Why Take This Course?

  • Hands-on, Project-Based Learning: Build actual AI agent applications, not just toy examples.

  • Complete and Beginner-Friendly: Designed to take you from beginner to advanced agent builder.

  • Real-World Skills: Learn techniques that companies are starting to use for next-generation AI products.

  • Cutting-Edge Technologies: Master the latest innovations in AI agent orchestration with LangGraph and LangChain.

If you are a developer, data scientist, AI/ML engineer, or tech enthusiast looking to future-proof your skills and build cutting-edge AI applications, this is the course for you.

Enroll now and start building the future with intelligent AI agents today.

Enroll now

What's inside

Learning objectives

  • Understand the core principles of agentic ai and how to design intelligent, autonomous agents for real-world tasks.
  • Master building ai agents using langgraph, including creating workflows, managing agent state, memory, and event-driven behavior.
  • Develop and deploy multi-agent collaborative systems that can communicate, reason, and solve complex problems together.
  • Mplement hands-on projects to create powerful agentic applications like autonomous research agents, task automation systems, and knowledge retrieval assistants.

Syllabus

Working With File Paths
Introduction To the Course
Welcome
Installation Of Anaconda And VS Code IDE
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Career center

Learners who complete Complete Agentic AI Bootcamp With LangGraph and Langchain will develop knowledge and skills that may be useful to these careers:
AI Engineer
An AI Engineer is at the forefront of designing, developing, and deploying intelligent systems. This role requires a deep understanding of artificial intelligence principles and practical skills in building sophisticated applications. The Complete Agentic AI Bootcamp is exceptionally well-suited for an aspiring AI Engineer, as it provides hands-on mastery of cutting-edge frameworks like LangGraph and LangChain. Learners will gain experience creating single intelligent agents, designing multi-agent collaboration systems, and building autonomous research assistants. By learning to deploy production-ready agent applications, this course helps build the foundation for success in a field constantly seeking individuals who can transform theoretical AI concepts into functional, real-world solutions.
Generative Artificial Intelligence Developer
A Generative Artificial Intelligence Developer focuses on creating systems that can generate new content, insights, or behaviors, often leveraging large language models. This Complete Agentic AI Bootcamp is an excellent pathway for this career, providing direct experience in building sophisticated agentic applications. The course specifically teaches how to build "GENAI Apps" and deploy "Simple GenAI App Using Ollama." By mastering LangGraph and LangChain, developers learn to orchestrate complex AI workflows, integrate tools, and manage memory, which are critical skills for developing advanced RAG agents and other generative AI solutions capable of autonomous task completion and creative problem-solving.
Autonomous Systems Engineer
An Autonomous Systems Engineer designs and implements intelligent systems capable of operating independently, making decisions, and performing tasks without continuous human intervention. This Complete Agentic AI Bootcamp directly aligns with the competencies required for an Autonomous Systems Engineer. The course teaches the core principles of Agentic AI, focusing on how intelligent agents think, act, and collaborate autonomously to solve complex tasks. Learners will gain practical experience in developing autonomous research assistants and task automation bots, using LangGraph to design event-driven AI workflows with memory, reasoning, and tools, laying a robust foundation for a career in autonomous systems.
Machine Learning Engineer
A Machine Learning Engineer builds, trains, and deploys machine learning models and systems and, increasingly, intelligent agents. This Complete Agentic AI Bootcamp offers highly relevant skills for a Machine Learning Engineer looking to specialize in advanced AI paradigms. The course provides hands-on experience developing and deploying single-agent and multi-agent applications using LangGraph and LangChain. Understanding how to create autonomous, event-driven AI workflows with memory, reasoning, and tool integration is crucial for M.L. engineers who wish to build next-generation intelligent systems, moving beyond traditional model deployment to orchestrate complex, decision-making AI agents.
Software Developer
A Software Developer creates, maintains, and evolves software applications, and increasingly, those applications incorporate advanced AI capabilities. This Complete Agentic AI Bootcamp offers significant practical skills for a Software Developer aiming to specialize in cutting-edge AI. The course provides a strong foundation in Python programming, object-oriented concepts, and covers tools like Pydantic, which are standard in modern software development. More importantly, it teaches how to "build and deploy multiple end-to-end agentic applications" and deploy "Langserve Runnable And Chains As API," directly enhancing a developer's ability to build robust, intelligent software systems.
Natural Language Processing Engineer
A Natural Language Processing Engineer specializes in building systems that process and understand human language. This Complete Agentic AI Bootcamp is particularly relevant for those looking to advance into agentic NLP applications. The course provides hands-on experience with core LangChain components, including data ingestion, text splitters, embeddings, and vector stores, which are fundamental to developing sophisticated NLP systems. Critically, learners build "retrieval-augmented generation (RAG) agents" and "chatbots with message history," directly applying NLP principles to create intelligent, conversational AI agents capable of understanding context and providing nuanced responses in real-world scenarios.
Backend Developer
A Backend Developer builds and maintains the server-side logic and databases that power applications, often including API development and integration. This Complete Agentic AI Bootcamp may be very helpful for a Backend Developer interested in integrating advanced AI intelligence into their systems. The course covers Python programming, Pydantic for data validation, and includes modules on "Deploy Langserve Runnable And Chains As API," which directly teaches how to expose AI agent functionalities as robust API endpoints. This knowledge is crucial for backend developers to seamlessly incorporate complex agentic AI capabilities into larger software architectures, enabling intelligent automation and advanced processing.
Research and Development Engineer
A Research and Development Engineer explores new technologies, designs prototypes, and validates innovative solutions. This Complete Agentic AI Bootcamp is highly pertinent for a Research and Development Engineer, as it focuses on cutting-edge Agentic AI, LangGraph, and LangChain. The course emphasizes "project-based learning" where learners "build actual AI agent applications, not just toy examples," directly equipping individuals with the skills to experiment with and implement novel AI paradigms. This practical experience in constructing powerful AI systems from scratch, including autonomous task automation and multi-agent collaboration, is invaluable for propelling innovation in an R&D setting.
Prompt Engineer
A Prompt Engineer specializes in crafting effective prompts for large language models to elicit desired outputs and behaviors. This Complete Agentic AI Bootcamp provides valuable context and practical skills for a Prompt Engineer. The course teaches how to "build LLM Prompt And StrOutput Parser Chain With LCEL" and integrate prompt templates within LangChain to manage conversation history. By understanding how intelligent agents utilize memory, tools, and decision-making processes, a prompt engineer gains a holistic view beyond single prompts, learning how to design prompts that function effectively within complex, multi-step agentic workflows and achieving more sophisticated, goal-driven interactions.
Solutions Architect Artificial Intelligence
A Solutions Architect Artificial Intelligence designs and oversees the implementation of complex AI systems, ensuring they meet business and technical requirements. This Complete Agentic AI Bootcamp provides a technical foundation that would be very helpful for a Solutions Architect Artificial Intelligence. The course teaches how to "design complex agent workflows using graphs, events, and state transitions" and implement "multi-agent collaboration systems." Understanding the core components of intelligent agents, like memory, tools, and decision-making, enables an architect to design scalable, robust, and effective agentic AI solutions, articulating their structure and integration with clarity.
Technical Product Manager Artificial Intelligence
A Technical Product Manager Artificial Intelligence leads the development of AI-powered products, defining features and collaborating closely with engineering teams. This Complete Agentic AI Bootcamp may be helpful for a Technical Product Manager Artificial Intelligence by providing a deep understanding of the underlying technology. The course offers insights into building agentic AI applications from scratch, including understanding "the key components of agents (memory, tools, decision-making)" and "real-world use cases" across domains like customer support and task planning. This technical comprehension allows product managers to effectively scope, prioritize, and communicate about complex AI agent features, fostering successful product development.
Developer Advocate Artificial Intelligence
A Developer Advocate Artificial Intelligence educates and inspires developers to adopt AI technologies, requiring deep technical understanding and excellent communication skills. This Complete Agentic AI Bootcamp is very fitting for a Developer Advocate Artificial Intelligence. The course offers hands-on mastery of LangGraph and LangChain, covering how to "build and deploy multiple end-to-end agentic applications." With this experience, an advocate can create compelling demonstrations, provide practical guidance, and articulate the value of agentic AI for "various domains like search, research, task planning, customer support." The ability to build, teach, and troubleshoot these cutting-edge systems makes a developer advocate highly effective.
Data Scientist
A Data Scientist analyzes complex datasets to extract insights, build predictive models, and inform strategic decisions, often integrating advanced analytical tools. This Complete Agentic AI Bootcamp may be useful for a Data Scientist keen on expanding their capabilities into intelligent automation and insight generation. The course covers Python prerequisites, including Pandas and Numpy for data manipulation, and introduces "retrieval-augmented generation (RAG) agents" and "autonomous research assistants." These agentic skills can greatly enhance a Data Scientist's ability to automate data collection, refine research processes, and build more dynamic, interactive data-driven applications. An advanced degree is very often required for this role.
Artificial Intelligence Consultant
An Artificial Intelligence Consultant advises businesses on AI strategy, implementation, and adoption, requiring both technical insight and practical application knowledge. This Complete Agentic AI Bootcamp may be helpful for an Artificial Intelligence Consultant to grasp the practicalities of cutting-edge agentic systems. The course provides a comprehensive understanding of Agentic AI, LangGraph, and LangChain, enabling consultants to speak credibly about "building production-ready agent applications" and "real-world use cases" in areas like customer support and task planning. This allows consultants to offer informed recommendations and guide clients through the complexities of implementing next-generation intelligent agents effectively.
Artificial Intelligence Researcher
An Artificial Intelligence Researcher advances the state of the art in AI through theoretical exploration and experimental validation. This Complete Agentic AI Bootcamp may be useful for an Artificial Intelligence Researcher. While primarily practical, the course grounds learners in the "core concepts behind Agentic AI and how intelligent agents operate," including memory, tools, and decision-making. By mastering frameworks like LangGraph, researchers can efficiently prototype and test novel agent architectures and multi-agent collaboration systems, bridging theoretical concepts with practical implementation. A strong foundation in building real-world agentic applications can inform and accelerate innovative research directions. An advanced degree, often a PhD, is almost always required for this role.

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Complete Agentic AI Bootcamp With LangGraph and Langchain.
Is specifically dedicated to LangGraph and building AI agents using this framework. It provides a comprehensive guide with practical examples and resources for building dynamic AI agents. This book is highly relevant for anyone focusing on LangGraph and offers in-depth knowledge and practical skills.
Offers a rigorous and broad treatment of multi-agent systems from a computer science perspective, integrating concepts from game theory, economics, and logic. It is an excellent resource for advanced undergraduates and graduate students, providing a deeper theoretical understanding. It is often used as a textbook in academic settings.
Practical guide to using the LangChain framework, which foundational component for building applications with LangGraph. It covers the fundamentals of LLMs, generative AI, and prompt engineering within the context of LangChain. Understanding LangChain prerequisite for effectively using LangGraph, making this book essential for anyone looking to build applications in this space.
Is an excellent starting point for anyone new to Python or programming in general. It covers fundamental programming concepts and Python basics with a hands-on, project-based approach, making it very practical for beginners. The third edition is updated to cover newer Python versions and is widely recommended for its clear explanations and engaging projects. It's often used as an introductory textbook.
Ideal for beginners who want to quickly apply Python to practical tasks. focuses on using Python to automate everyday computer tasks, such as working with files, web scraping, and sending emails. It assumes no prior programming experience and is highly regarded for its clear, step-by-step instructions and focus on immediate productivity gains. The second edition widely used and practical resource.
Focuses on building production-ready AI agents and multi-agent systems using LLMs. It covers essential components of an agent, including knowledge management, memory, planning, and tool use. The book specifically mentions using state-of-the-art tools like LangChain, Prompt Flow, AutoGen, and CrewAI, which are highly relevant to the LangGraph ecosystem. This book serves as a practical guide for implementing the concepts that LangGraph facilitates.
Is widely considered a standard introductory textbook for multi-agent systems. It provides a comprehensive overview of the field, covering key concepts, architectures, and applications. It's highly suitable for undergraduates and those new to the field, laying a solid foundation for further study.
Provides a practical guide to using generative AI, covering the different techniques and tools available. It is written by two leading experts in the field, Josh Patterson and Adam Gibson.
Offers a comprehensive overview of LLMs, covering their theoretical foundations, practical applications, and future directions.
Is highly recommended for intermediate to advanced Python programmers looking to write more idiomatic and efficient code. It explores Python's often-overlooked features and best practices, delving into topics like data structures, the Python data model, and metaprogramming. It's a valuable resource for deepening understanding and is considered a must-read for those aiming for mastery.
Reinforcement learning key paradigm for developing intelligent agents that can learn to make sequential decisions by interacting with their environment. is the classic text on the subject, providing a comprehensive introduction to the core concepts and algorithms used in training agents. It must-read for anyone focusing on learning agents.
A hands-on guide specifically for developers, this book delves into the technology behind LLMs and provides practical guidance for building intelligent agents and applications. It's highly relevant for those looking to implement AI agents using LLMs.
This handbook provides a guide to integrating and implementing LLMs using the LangChain framework. It covers building various applications like chatbots and document analysis systems. As LangGraph builds upon LangChain, this handbook valuable resource for understanding the broader framework.
This forthcoming book (at the time of this response, available in early access) focuses on building multi-agent systems using the AutoGen framework. It is highly relevant for those interested in practical, contemporary applications of MAS and AI agents, particularly software developers and engineers.
Provides a practical introduction to machine learning using Python and the scikit-learn library. It's ideal for Python programmers interested in entering the field of machine learning and data science. It covers essential concepts and provides hands-on examples.
Offers a practical approach to designing and implementing single and multi-agent systems, particularly in the context of generative AI. It helps bridge the gap between theoretical concepts and real-world deployment of AI agents. It is highly relevant for understanding contemporary applications.
Focuses on building applications and agents using Large Language Models. It's a practical guide for developers looking to leverage LLMs in creating intelligent agents. It provides hands-on knowledge relevant to contemporary AI agent development.
Examines the transformative role of autonomous intelligent systems in business and the workplace. It explores the concepts of autonomy and collaborative intelligence in AI agents and their potential impact on various industries. It's a good resource for understanding the broader implications and applications of agentic AI.
Offers a collection of tips and tricks to help intermediate Python developers write more professional and Pythonic code. It provides concise explanations and practical examples of various Python features, making it a useful resource for leveling up coding skills and discovering best practices.
This book, expected in 2025, focuses on embodied multi-agent systems, particularly in robotics. It covers perception, action, and learning, offering a unified framework. It is highly relevant for graduate students and researchers in robotics and machine learning interested in physical agent systems.
This handbook provides a comprehensive overview of research on multi-agent systems, with a focus on organizational models. It covers various perspectives and valuable reference for researchers and advanced practitioners interested in the structure and dynamics of agent organizations.
This guide provides a collection of best practices, development workflows, and recommendations for writing idiomatic Python code. It's a valuable resource for developers who want to follow community conventions and improve their overall Python development process.
Provides a comprehensive overview of embedding methods for natural language processing, covering both theoretical foundations and practical applications.
A practical guide aimed at a broad audience, this book introduces the concept of AI agents powered by generative AI and LLMs. It covers the core building blocks and provides a framework for designing and deploying agents, making it suitable for beginners and those new to the technical aspects.
Published in 2020, this book focuses on the coordination and control of multi-agent systems, including topics like synchronization and consensus. It is particularly relevant for those interested in the control aspects of MAS and can be useful for graduate students and researchers in engineering and computer science.
A collection of recipes for common Python programming tasks. is an excellent reference for experienced Python developers looking for practical solutions to a wide range of problems. It covers various domains and provides idiomatic Python code examples.
This comprehensive handbook includes a chapter on LLMs, providing a thorough overview of their history, evolution, and applications.
Provides a business-oriented perspective on generative AI, discussing its potential impact on industries and how companies can use it to gain a competitive advantage. It is written by three leading experts in the field, Thomas Davenport, Rajeev Ronanki, and Nitin Mittal.
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Provides a comprehensive overview of vector GIS analysis, covering topics such as data models, spatial analysis techniques, and visualization. It valuable resource for GIS professionals and researchers.
Explores the application of iterative learning control (ILC) for coordinating multi-agent systems. It bridges theory and practice, showcasing applications in areas like power grids and sensor networks. It is suitable for graduate students and researchers interested in control theory applied to MAS.
Offers a practical approach to understanding and working with large language models. It covers concepts related to language understanding and generation, which are core to building applications with LangGraph. It provides hands-on examples to solidify understanding.
Explores various aspects of autonomous agents and multi-agent systems, covering theories, methodologies, tools, and applications. It valuable resource for researchers and practitioners, offering insights into the practical aspects of building and deploying MAS.
While not solely focused on agents, this book covers building AI applications with foundation models, which are integral to many modern AI agents. It provides essential context and knowledge for developing and deploying AI systems that can act as agents.
Focuses on the practical aspects of building and deploying LLMs for production use. It covers techniques like prompting, fine-tuning, and RAG (Retrieval-Augmented Generation), which are relevant when building sophisticated AI agents with frameworks like LangGraph. This book is geared towards engineers working on deploying LLMs in real-world applications.
Delves into the logical foundations for reasoning about the properties and behavior of rational agents, particularly focusing on the Belief-Desire-Intention (BDI) model. It is more theoretical and suited for those who want to understand the formal underpinnings of agent systems.
Covers both the theory and applications of multi-agent systems, providing a balanced perspective. It good resource for gaining a solid understanding of the fundamental concepts and how they are applied in various domains.
Focuses on building robust and scalable Python applications. It covers topics like testing, architecture, and deployment, which are crucial for professional software development. It's aimed at developers who want to write production-ready Python code.
This monograph offers a concise yet comprehensive introduction to multiagent systems and distributed artificial intelligence. It covers theoretical foundations and recent developments, suitable for a half-semester course. It's a good option for those seeking a focused overview.
Provides a broad introduction to multi-agent systems from a distributed artificial intelligence perspective. It covers a wide range of topics and can serve as a good starting point for understanding the scope of the field.
Uses Python to introduce fundamental computer science concepts. It's a good choice for students or self-learners who want to learn programming within the context of computer science principles. The 3rd edition provides a solid foundation in both Python and computational thinking.
Prompt engineering crucial skill for effectively interacting with LLMs and building AI agents. provides techniques and strategies for crafting effective prompts to obtain reliable outputs from generative AI models. While not directly about LangGraph, mastering prompt engineering is vital for building successful applications with LangGraph.
Offers a practical guide to building AI agents and chatbots, covering foundational concepts and advanced techniques. It provides real-world applications and insights for developers. This book is relevant for understanding the practical aspects of building the types of systems that can be enhanced with LangGraph.
Provides a gentle and visual introduction to common algorithms. It's a great resource for Python programmers who want to understand how algorithms work without getting bogged down in dense theory. The examples are easy to follow and help solidify understanding of essential computer science concepts applicable to Python.

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