Complete Agentic AI Bootcamp With LangGraph and Langchain
Learn to build real-world AI agents, multi-agent workflows, and autonomous apps with LangGraph and LangChain
Lo que serás capaz de hacer
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!
- 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.
Temario
UNIDAD 1 - Introduction To the Course · 1 leccionesUNIDAD 1 - Introduction To the Course
UNIDAD 1 - Introduction To the Course
- Welcome
UNIDAD 2 - Installation Of Anaconda And VS Code IDE · 3 leccionesUNIDAD 2 - Installation Of Anaconda And VS Code IDE
UNIDAD 2 - Installation Of Anaconda And VS Code IDE
- Installation Of Anaconda And VS Code Editor
- Creating Virtual Environments Using Conda
- Creating Virtual Environments Using UV Package Manager
UNIDAD 3 - Python Prerequisites · 40 leccionesUNIDAD 3 - Python Prerequisites
UNIDAD 3 - Python Prerequisites
- Getting Started With VS Code
- Python Basics- Syntax And Semantics
- Variables In Python
- Basic Datatypes In Python
- Operators In Python
- Conditional Statements(if,elif,else)
- Loops In Python
- List And List Comprehension In Python
- Practical Exmaples Of List
- Sets In Python
- Dictionaries In Python
- Tuples In Python
- Getting Started With Functions
- More Coding Examples With Functions
- Python Lambda Funbction
- Maps Functions Python
- Filter Function In Python
- Import Modules And Package In Python
- Standard Library Overview
- File Operation In Python
- Working With File Paths
- Exception Handling
- Classes And Objects In Python
- Inheritance In OOPS
- Polymorphism In OOPS
- Encapsulations In OOPS
- Abstraction In OOPS
- Magic Methods In Python
- Operative Overloading In Python
- Custom Exception Handling
- Iterators In Python
- Generators In Python
- Fucntion Copy.Closures and Decorators
- Numpy In Python
- Pandas-DataFrame And Series
- Data Manipulation With Pandas And Numpy
- Reading Data From Various Data Source Using Pandas
- Logging Practical Implementation In Python
- Logging With Multiple Loggers
- Logging With A Real World Examples
UNIDAD 4 - Getting Started With Pydantic In Python · 2 leccionesUNIDAD 4 - Getting Started With Pydantic In Python
UNIDAD 4 - Getting Started With Pydantic In Python
- Introduction To Pydantic
- Pydantic Practical Implementation
UNIDAD 5 - Langchain Hands On · 13 leccionesUNIDAD 5 - Langchain Hands On
UNIDAD 5 - Langchain Hands On
- Getting Started With Langchain And Open AI
- Creating Virtual Environment
- Important Components Of LangChain
- Data Ingestion With Documents Loaders
- Recursive Character Text Splitter
- Character Text Splitter With Langchain
- HTML Header Text Splitter
- Recursive Json Text Splitter
- Introduction To OPENAI Embeddings
- Ollama Embeddings
- HuggingFace Embeddings
- Vector Stores-FAISS
- Vector Store And Retriever- Chroma DB
UNIDAD 6 - Getting Started With OpenAI And Ollama · 6 leccionesUNIDAD 6 - Getting Started With OpenAI And Ollama
UNIDAD 6 - Getting Started With OpenAI And Ollama
- Building Important Components Of Langchain
- Building GENAI Apps
- Understanding Retrievers And Chains
- Introduction To Ollama And Set Up
- Simple GenAI App Using Ollama
- Tracking GENAI App Using Langsmith
UNIDAD 7 - Building Basic LLM Application Using LCEL · 3 leccionesUNIDAD 7 - Building Basic LLM Application Using LCEL
UNIDAD 7 - Building Basic LLM Application Using LCEL
- Getting Started With Open Source Models Uing Groq API
- Building LLM Prompt And StrOutput Parser Chain With LCEL
- Deploy Langserve Runnable And Chains As API
UNIDAD 8 - Building AI agents With Conversation History Using Langchain · 4 leccionesUNIDAD 8 - Building AI agents With Conversation History Using Langchain
UNIDAD 8 - Building AI agents With Conversation History Using Langchain
- Building Chatbot With Message History Using Langchain
- Working With Prompt Template And Message ChatHistory Using LAngchain
- Managing the Chat Conversation History Using Langchain
- Working With VectorStore And Retriever
UNIDAD 9 - AI Agents Vs Agentic AI · 2 leccionesUNIDAD 9 - AI Agents Vs Agentic AI
UNIDAD 9 - AI Agents Vs Agentic AI
- What is Ai Agent Vs Agentic AI
- Some More Examples
UNIDAD 10 - Updated Langchain Version V1 · 12 leccionesUNIDAD 10 - Updated Langchain Version V1
UNIDAD 10 - Updated Langchain Version V1
- Langchain Updates V1
- Creating Virtual Environment Using UV Package
- Creating Agents Using Langchain
- Langchain LLM Model Integration
- Invoke And Batch Streaming Using Langchain
- Tools In Langchain
- Messages Types In Langchain
- LLM structured Output Using Pydantic
- LLM structured Output Using TypeDict
- LLM structured Output Using DataClass
- Middleware Summarization
- Human In The Loop MiddleWare
UNIDAD 11 - Getting Started With LangGraph · 8 leccionesUNIDAD 11 - Getting Started With LangGraph
UNIDAD 11 - Getting Started With LangGraph
- Introduction To LangGraph
- Getting Started LangGraph Application- Creating The Environment
- Setting Up OpenAI API Key
- Setting Up GROQ API KEY
- Setting Up LangSmith API Key
- Developing A Simple Graph or Workflow Using LangGraph- Building Nodes And Edges
- Building Simple Graph StateGraph And Graph Compiling
- Developing LLM Powered Simple Chatbot Using LangGraph
UNIDAD 12 - LangGraph Components · 13 leccionesUNIDAD 12 - LangGraph Components
UNIDAD 12 - LangGraph Components
- State Schema With DataClasses
- Pydantic
- Chain In LangGraph
- Routers In LangGraph
- Tools And ToolNode With Chain Integration- Part 1
- Tools And Tool Node With Chain Integration-Part 2
- Building Chatbot With Multiple Tools Integration- Part 1
- Building Chatbot With Multiple Tools Integration-Part 2
- Introduction To Agents And ReAct Agent Architecture In LangGraph
- ReAct Agent Architecture Implementation
- Agent With Memory In LangGraph
- Streaming In LangGraph
- Streaming using astream events Using Langgraph
UNIDAD 13 - Debugging LangGraph Application With LangSmith · 1 leccionesUNIDAD 13 - Debugging LangGraph Application With LangSmith
UNIDAD 13 - Debugging LangGraph Application With LangSmith
- LangGraph Studio
UNIDAD 14 - Different Workflows In LangGraph · 7 leccionesUNIDAD 14 - Different Workflows In LangGraph
UNIDAD 14 - Different Workflows In LangGraph
- Prompt Chaining
- Prompt Chaining Implementation With Langgraph
- Parallelization
- Routing
- Orchestrator-Worker
- Orchestrator Worker Implementation
- Evaluator-optimizer
UNIDAD 15 - Human In The Loop In LangGraph · 4 leccionesUNIDAD 15 - Human In The Loop In LangGraph
UNIDAD 15 - Human In The Loop In LangGraph
- Human In The Loop With LangGraph Workflows
- Human In the Loop Continuation
- Editing Human Feedback In Workflow
- Runtime Human Feedback In Workflow
UNIDAD 16 - RAG With LangGraph · 7 leccionesUNIDAD 16 - RAG With LangGraph
UNIDAD 16 - RAG With LangGraph
- Agentic RAG Theoretical Understanding
- Agentic RAG Implementation- Part 1
- Agentic RAG Implementation-Part 2
- Corrective RAG Theoretical Understanding
- Corrective RAG Practical Implementation
- Adaptive RAG Theoretical Understanding
- Adaptive RAG Implementation
UNIDAD 17 - Vectorless RAG · 2 leccionesUNIDAD 17 - Vectorless RAG
UNIDAD 17 - Vectorless RAG
- Vectorless RAG With PAgeIndex Tutorials
- Traditional Vs Vectorless RAG
UNIDAD 18 - Guardrails · 1 leccionesUNIDAD 18 - Guardrails
UNIDAD 18 - Guardrails
- Guardrails With Langchain
UNIDAD 19 - LLM Gateways · 1 leccionesUNIDAD 19 - LLM Gateways
UNIDAD 19 - LLM Gateways
- LLM Gateways Understanding And Implementation
UNIDAD 20 - End To End Agentic AI Projects With LangGraph · 10 leccionesUNIDAD 20 - End To End Agentic AI Projects With LangGraph
UNIDAD 20 - End To End Agentic AI Projects With LangGraph
- Introduction And Overview
- Project Set Up With VS Code
- Setting up The Github Repository
- Setting Up The Project Structure
- Designing The Front End Using streamlit
- Implementing The LLM Module In Graph Builder
- Implementing The Graph Builder Module
- Implementing The Node Implementation
- Integrating the Entire Pipeline With Front End
- Testing The End To End Agentic Application
UNIDAD 21 - End To End Agentic Chatbot With Web Search Functionality · 4 leccionesUNIDAD 21 - End To End Agentic Chatbot With Web Search Functionality
UNIDAD 21 - End To End Agentic Chatbot With Web Search Functionality
- Introduction To The Project
- Implementing The Front End With Streamlit
- Implementing GraphBuilder and Search Tools Pipeline
- Implementing Node Functionality With End To End Agentic Pipeline
UNIDAD 22 - AI News Summarizer End To End Agentic AI Projects · 7 leccionesUNIDAD 22 - AI News Summarizer End To End Agentic AI Projects
UNIDAD 22 - AI News Summarizer End To End Agentic AI Projects
- Project Introduction
- Building the Front End With Streamlit
- Building The AI News State Graph Builder
- Tavily Client Search Fetch News Node Implementation
- AI News Summarize Node Functionality Implementation
- Save Results Node Functionality Implementation
- Running The Entire AINEWS Agentic Workflow
UNIDAD 23 - End To End Blog Generation Agentic AI App · 10 leccionesUNIDAD 23 - End To End Blog Generation Agentic AI App
UNIDAD 23 - End To End Blog Generation Agentic AI App
- Introduction And Project Demo
- Building Project Structure Using UV Package
- Blog Generation Grpah Builder And State Implementation
- Blog Generation Node Implementation Definition
- Creating Blog Generating API Using FAST API
- Integrating Langgraph Studion For Debugging
- Blog Generation And Translation With Language
- Building Blog Generation And Translation Graph Builder
- Blog Generation And Translation Node Implementation
- Testing In Postman And Langgraph Studio
UNIDAD 24 - Model Context Protocol · 7 leccionesUNIDAD 24 - Model Context Protocol
UNIDAD 24 - Model Context Protocol
- Introduction To Model Context Protocol
- Important Components Of MCP
- Communication Between Components Of MCP
- Demo Of MCP With Claude Desktop
- Cursor IDE Installation
- Getting Started With Smithery AI
- Building MCP Servers With Tools And Client From Scratch Using Langchain
UNIDAD 25 - Claude Code · 7 leccionesUNIDAD 25 - Claude Code
UNIDAD 25 - Claude Code
- Introduction To Claude Ecosystem
- Claude Code Worrking And Setup
- Claude Code Agents
- Agent Views In Claude Code
- Agent Teams With Claude Code
- Hooks In Claude Code
- Skills And Plugins In Claude Code
UNIDAD 26 - Building Deep Agents With Langchain · 8 leccionesUNIDAD 26 - Building Deep Agents With Langchain
UNIDAD 26 - Building Deep Agents With Langchain
- Introduction To Deep Agents And Basics Implementation
- Deep Agents Customization
- Introduction To Backends In Deep Agents
- Deep Agents Vs Claude SDK
- Introduction To Context Engineering And Its Types
- Using Skills For Context Engineering
- Sub Agents In Deep Agents
- Implementating Project With All Features
UNIDAD 27 - Role Play · 1 leccionesUNIDAD 27 - Role Play
UNIDAD 27 - Role Play
- Multi-Agent Travel Assistant Using LangGraph
Tu certificado lo emite la SEP, no solo una plataforma
Al demostrar la competencia recibes un certificado emitido por la Secretaría de Educación Pública a través del Gobierno del Estado de México: un documento oficial que puedes presentar con confianza ante cualquier empleador o institución.
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- 1
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Pagas $2,800 MXN una sola vez y entras de inmediato al curso.
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Estudias a tu ritmo
27 unidades con videos, lecturas y actividades, disponibles 24/7 durante 12 meses.
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Cuestionarios por unidad
Cuestionarios para comprobar lo aprendido.
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Certificado
Emitido por la SEP a través del Gobierno del Estado de México, con folio verificable.
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Muestra · verificable por folio y QR
- Emitido por la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México. No es una constancia privada: es un documento oficial, y ese respaldo marca la diferencia frente a otras plataformas en línea.
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- Folio y QR verificables para compartirlo con quien te contrate.
Empieza hoy, a tu ritmo
Un solo pago y acceso inmediato al curso completo durante 12 meses.
- 27 unidades · 184 lecciones · 60 h
- 12 meses de acceso
- Certificado emitido por la SEP (Gob. del Estado de México) con folio verificable
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Puedes renovar tu cuenta tú mismo desde la plataforma, sin trámites ni esperas.
