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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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/idfomd/complete
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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.
- 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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Syllabus
Working With File Paths
Introduction To the Course
Welcome
Installation Of Anaconda And VS Code IDE
Read more
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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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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.
For more career information including salaries, visit:
OpenCourser.com/course/idfomd/complete
Reading list
We haven't picked any books for this reading list yet.
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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.
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.
Explores the fundamental concepts, technologies, and practical applications of LLMs for building intelligent apps and agents. It discusses mainstream architectural frameworks and specifically mentions using AI orchestrators like LangChain to create intelligent agents. This book provides a broader context for the types of applications that can be built using tools like LangChain and LangGraph.
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.
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.
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Provides a broader perspective on building AI applications with foundation models, including LLMs. It covers essential concepts and techniques for developing and deploying AI systems. While not specific to LangGraph, it offers valuable context on the landscape of AI engineering and how frameworks like LangGraph fit in.
Delves into the inner workings of LLMs by guiding the reader through building one from scratch. Understanding how LLMs are built provides a deeper appreciation for the technology that powers frameworks like LangChain and LangGraph. This book is more theoretical and provides foundational knowledge.
Transformers are the architecture behind most modern LLMs. provides a practical introduction to using the Hugging Face library for building NLP applications with transformers. While not directly about LangGraph, it offers essential background on the models that LangGraph utilizes.
Considered a classic in the field of NLP, this book provides a comprehensive introduction to the fundamental concepts and techniques in natural language processing. While it predates the latest advancements in LLMs and frameworks like LangGraph, it offers essential foundational knowledge in the broader field.
Another classic in NLP, this book provides a strong theoretical foundation in statistical methods for natural language processing. While it may not cover the latest deep learning techniques, it offers crucial background knowledge for understanding the evolution of NLP that led to LLMs and agent frameworks.
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.
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 concise introduction to the fundamental concepts of machine learning. While not directly related to LangGraph, a basic understanding of machine learning is beneficial for working with LLMs and AI agents. This book serves as a good quick reference for core ML ideas.
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This comprehensive and foundational text on deep learning, the technology behind LLMs. It covers the theoretical underpinnings of neural networks and deep learning architectures. is highly technical and provides deep background knowledge for those who want to understand the core technology.
Reinforcement learning paradigm relevant to training agents that can learn from their environment. While not directly used in LangGraph's core functionality, understanding RL can provide insights into agent behavior and decision-making processes. This classic text in the field of RL.
Provides a practical introduction to NLP using the NLTK library in Python. It covers fundamental NLP tasks and techniques. While it may not cover the latest LLM-based approaches, it offers a solid foundation in working with text data programmatically.
Offers a practical look at building real-world NLP applications, covering the entire project lifecycle. It provides task-specific case studies and domain-specific instructions. This book is useful for understanding the practical considerations of developing NLP systems that might involve components orchestrated by LangGraph.
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Introduces the field of agent-based AI systems with a focus on accessibility. It explains the evolution from traditional AI models to AI agents and covers the core building blocks of agents. This book is suitable for those new to the concept of AI agents and provides a practical foundation.
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/idfomd/complete
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