LangGraph Mastery: Develop LLM Agents with LangGraph

LangGraph Mastery: Develop LLM Agents with LangGraph, available at $54.99, has an average rating of 4.59, with 50 lectures, based on 12 reviews, and has 215 subscribers.

You will learn about How to create a ReAct agent from scratch. Gain a good understanding of LangGraph concepts and core components. Implement advanced agents. Dive deep into nodes, edges, and conditional edges. Learn about Tavily AI and agentic search. Understand LLM patterns such as reflection and reflexion. Explore LangSmith and learn how to debug and trace LLM applications with it. Learn about flow engineering. This will be a learning-by-doing experience. Together, we'll build agentic LLM apps step-by-step, line-by-line. Projects: 1. Create a ReAct agent from scratch 2. Develop a ChatBot app using LangGraph 3. Build a Tweet generator using reflection 4. Create an essay writer. This course is ideal for individuals who are Software Developers aiming to build Generative AI applications using LangChain and LangGraph. or Enthusiasts who want to explore the new frontier of LLM agents. It is particularly useful for Software Developers aiming to build Generative AI applications using LangChain and LangGraph. or Enthusiasts who want to explore the new frontier of LLM agents.

Enroll now: LangGraph Mastery: Develop LLM Agents with LangGraph

Summary

Title: LangGraph Mastery: Develop LLM Agents with LangGraph

Price: $54.99

Average Rating: 4.59

Number of Lectures: 50

Number of Published Lectures: 50

Number of Curriculum Items: 50

Number of Published Curriculum Objects: 50

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

How to create a ReAct agent from scratch.
Gain a good understanding of LangGraph concepts and core components.
Implement advanced agents.
Dive deep into nodes, edges, and conditional edges.
Learn about Tavily AI and agentic search.
Understand LLM patterns such as reflection and reflexion.
Explore LangSmith and learn how to debug and trace LLM applications with it.
Learn about flow engineering.
This will be a learning-by-doing experience. Together, we'll build agentic LLM apps step-by-step, line-by-line.
Projects: 1. Create a ReAct agent from scratch 2. Develop a ChatBot app using LangGraph 3. Build a Tweet generator using reflection 4. Create an essay writer.

Who Should Attend

Software Developers aiming to build Generative AI applications using LangChain and LangGraph.
Enthusiasts who want to explore the new frontier of LLM agents.

Target Audiences

Software Developers aiming to build Generative AI applications using LangChain and LangGraph.
Enthusiasts who want to explore the new frontier of LLM agents.

Welcome to this brand new course on LangGraph, which allows us to build agentic LLM applications!

By the end of this course, you will be equipped with the skills to seamlessly integrate LLM agents into your applications, opening up new possibilities and horizons.

We are witnessing a rapid ascent in AI capabilities, with groundbreaking advancements occurring annually. This swift progress has the potential to significantly reshape our world in the coming years.

Three pivotal advancements are poised to make a profound impact: Infinite Context Windows, Text to Action, and Agents.

Agents: The New Frontier in AI

Agents are autonomous intelligent entities designed to perform tasks, process information, and interact within a language-based framework. These agents are significantly expanding the potential of AI across various domains.

Agentic AI is revolutionizing industries, offering enhanced applications in fields such as legal document analysis, medical diagnostics, and software development. Imagine an army of skilled programmers working around the clock to develop software solutions for you.

In this course, we will delve into LangGraph, an extension of LangChain specifically designed for agent and multi-agent workflows. LangGraph enables highly customizable and controllable agent flows, ideal for complex scenarios.

We will also explore LangSmith, a platform for tracing and debugging your production-grade LLM applications.

Course Highlights:

Building a Simple ReAct Agent from Scratch
Introduction to Agents and ReAct
LangGraph concepts and core components
Nodes, edges, conditional edges, and state management
Visualizing the graph
Agentic search using Tavily AI
Enhancing agents with tool observation
Adding memory to agents
Patterns: reflection and reflexion
Setting up LangSmith
Debugging and tracing LLM apps with LangSmith

Hands-On Projects:

Create a ReAct agent from scratch.
Develop a ChatBot app using LangGraph.
Build a Tweet generator using the reflection pattern.
Create an essay writer using reflexion, Tavily AI, and tool observation.

Are you ready to dive into this exciting new world of AI? Enroll in this LangGraph mastery course and transform the way you integrate AI into your applications!

Looking forward to seeing you in the course!

Course Curriculum

Chapter 1: Getting Started

Lecture 1: Course Prerequisites

Lecture 2: Join Our Online Community!

Lecture 3: Course Resources

Chapter 2: Building a Simple ReAct Agent from Scratch

Lecture 1: Introduction to Agents and ReAct

Lecture 2: Creating the Agent Class

Lecture 3: Creating the ReAct Prompt

Lecture 4: Creating the Tools

Lecture 5: Testing the Agent

Lecture 6: Automating the Agent

Lecture 7: Project Source Code

Chapter 3: Building with LangGraph

Lecture 1: LangGraph Concepts and Core Components

Lecture 2: Building a ChatBot

Lecture 3: Visualizing the Graph

Lecture 4: Running the ChatBot

Lecture 5: Tavily AI

Lecture 6: Project Source Code

Chapter 4: Reflection – Tweet Generator

Lecture 1: Intro to Reflection

Lecture 2: Generate

Lecture 3: Reflect and Repeat

Lecture 4: Define the Graph – Part 1

Lecture 5: Define the Graph – Part 2

Lecture 6: Running the App

Lecture 7: Project Source Code

Chapter 5: LangSmith: Platform for Building Production-grade LLM Apps

Lecture 1: LangSmith

Lecture 2: Setting up LangSmith

Lecture 3: Tracing with LangSmith

Lecture 4: Tracing the Reflection Agentic App with LangSmith

Lecture 5: Project Source Code

Chapter 6: Reflexion – Essay Writer

Lecture 1: Project Overview

Lecture 2: Defining the AgentState and the Prompts

Lecture 3: Implementing the Agents and the Nodes

Lecture 4: Defining the Conditional Edge

Lecture 5: Defining the Graph

Lecture 6: Running the Agentic App

Lecture 7: Tracing the App with LangSmith

Lecture 8: Project Source Code

Chapter 7: Project: GPT-4o Research Agent in LangGraph

Lecture 1: GPT-4o Research Agent in LangGraph

Chapter 8: [Appendix]: Object Oriented Programming in Python

Lecture 1: README

Lecture 2: Intro to Object Oriented Programming (OOP)

Lecture 3: OOP Demonstration: The Turtle

Lecture 4: Defining Classes and Objects

Lecture 5: The __init__ Method

Lecture 6: The __del__ Method

Lecture 7: Instance Attributes and Class Attributes

Lecture 8: Magic Methods

Lecture 9: TypedDict: Type Hints for Dictionaries with a Fixed Set of Keys – Part 1

Lecture 10: TypedDict: Type Hints for Dictionaries with a Fixed Set of Keys – Part 2

Lecture 11: Python OOP Cheat Sheet

Chapter 9: BONUS SECTION

Lecture 1: Congratulations

Lecture 2: BONUS: THANK YOU GIFT!

Instructors

Andrei Dumitrescu
Software Engineer and Lead Instructor
Crystal Mind Academy
Teaching Cutting-Edge Technologies

Rating Distribution

1 stars: 1 votes
2 stars: 0 votes
3 stars: 0 votes
4 stars: 1 votes
5 stars: 10 votes

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