AI Agents: Develop Autonomous AI Agents with LangGraph [NEW]

Name: AI Agents: Develop Autonomous AI Agents with LangGraph [NEW]
Rating: 4.5 (921 reviews)

Develop High-Performance, Autonomous, AI Agents, Using LangChain, LangGraph (Python)

Created byPaulo Dichone | Software Engineer, AWS Cloud Practitioner & Instructor

Last updated 2/2025

English

What you'll learn

Understand the Fundamentals of AI Agents
Build and Develop AI Agents Using LangGraph and other tools
Master LangGraph for Advanced AI Agent Development
Create a Full-Fledged Financial Report Writer/Researcher Agent
Optimize AI Agents for Performance and Scalability
Hands-On Projects and Practical Application

Course content

8 sections • 38 lectures • 3h 2m total length

Introduction2:45
Course Structure and the OpenAI Account1:50
Demo - What Will You Build in This Course5:12
Important Message2:09
This online course requires basic programming navigation and familiarity with AI prompts, LLMs, and ML. You can rewatch lectures, adjust playback, and share a review when you’re ready.

AI Agents Deep Dive - A Full Overview7:16
Agents Characteristics and Use Cases2:51
Building our First AI Agent - Project Setup (OpenAI API)3:35
Build our First AI Agent - Creating the Agent Class and Prompt10:33
Create your first ai agent by building a class that encapsulates the agent, manage system and user messages, and implement a prompt-driven loop of thought, action, pause, and observation.
First AI Agent - Running our First Agent and Seeing the Results7:36
Instantiate and run the agent, using system prompts and message stacking to drive the model to compute Earth's mass, observe results, and generate the final answer.
Passing Complex Queries Through the Agent11:51
First Agent - Using a Loop to Automate our Agent10:02
Automate an AI agent's workflow by looping through prompts, parsing actions with regex, and executing known actions (calculate and planet mass) via a query function to compute planetary masses.
Adding Interactive to Our Agent - Console App4:34
Section Summary3:30

LangGraph - Overview & Key Concepts5:25
Learn line graph concepts with LangGraph, using graph-based knowledge to represent and orchestrate AI agent workflows through nodes and edges, supported by natural language processing and reasoning.
How LangGraph Helps to Build AI Agents2:52
LangGraph Core Concepts - Simple Flow Diagrapm2:17
LangGraph - Data and State - Overview2:02
Explore how LangGraph attaches state and data to AI agents to preserve context and enable retrieval of past information for better responses.
Building a Simple Agent with LangChain11:13
LangGraph Simple Bot - Streaming Values - Console App5:00
Adding Tools to our Basic LangGraph Agent6:57
Learn to extend a basic LangGraph agent by attaching tools, wiring a Tavileh search tool, and running the model with tools to perform web queries and continue the workflow.
Adding tools to the Agent - Part 16:07
Explore adding tools to an AI agent by implementing a basic tool node, using tool messages to pass tool results, and routing with conditional edges in a LangGraph graph.
Adding Tools to the Agent - Using Built-in Tools - Part 24:35
Adding Memory to Our Agent State8:01
Add memory to your agent using a check pointer, threads, and a SQLite saver to save and restore context between interactions for fault-tolerant, long-running workflows.
Adding Human-in-the-loop to the AI Agent5:12
Section Summary1:23

AI Financial Report Writer Agent - Overview1:20
Agent State and Prompts Setup6:14
Develop an autonomous ai agent that ingests a financials csv with pandas, analyzes and compares profits to competitors, and uses defined prompts to generate a financial report.
Creating All Nodes - Functions10:20
Create all nodes as functions to gather financials, analyze data, research competitors, compare performance, and write a final report within a LangGraph autonomous agent.
Adding Nodes and Edges and Running our Agent11:29
Adding a GUI to the Agent with Streamlit7:45
Optimization Techniques - Overview3:21
Explore optimization techniques for AI agents, including model optimization, quantization, and fine tuning. Leverage parallel and batch processing, distributed computing, and caching to boost efficiency.
Course Summary1:32
Explore building autonomous AI agents from scratch and with the LangGraph framework, learn about line graph, building blocks, and finish with a financial report writer and Streamlit GUI.

Requirements

Must know Python (or any OOP, Modern Programming Language)
Basics of LLM and AI

Description

Master AI Agent Development: Build Scalable Agents with LangChain & LangGraph

Are you ready to revolutionize your skills and harness the power of AI to develop sophisticated agents?

This course is meticulously designed to elevate your understanding from beginner to advanced, enabling you to create scalable AI agents using LangChain and LangGraph.

Whether you're a developer, data scientist, or tech enthusiast, this course equips you with the expertise to build high-performance AI agents for a variety of applications.

What You'll Learn:

Building AI Agents: Understand the fundamentals of AI agents and their significance in various industries.
Agents Deep Dive: Explore the core principles, key characteristics, and diverse use cases of AI agents.
First Simple Agent: Learn to build your first simple AI agent using Large Language Models (LLMs) only.
Introduction to LangGraph: Delve into LangGraph, understanding its building blocks, main components, and how it empowers the development of sophisticated AI agents.
Building Agents with LangGraph: Step-by-step guidance on constructing agents using LangGraph, from basic concepts to advanced techniques.
Comprehensive Financial Report Writer/Researcher Agent: Develop a full-fledged AI agent that gathers, analyzes, and reports financial data, along with performing competitor research to provide actionable insights.
Advanced Optimization Techniques: Master advanced strategies to ensure your AI agents are efficient, scalable, and high-performing.

Who Should Enroll:

Developers: Enhance your programming capabilities with AI-powered tools and techniques.
Data Scientists: Apply your data science skills to create sophisticated AI agents for various applications.
Tech Enthusiasts: Explore the exciting world of AI agents and their applications across different industries.
Students and Academics: Gain practical skills and knowledge that complement your academic studies and research.

Why Choose This Course:

The demand for AI-driven solutions is growing rapidly across all sectors. By enrolling in this course, you'll gain a competitive edge in the job market and be well-prepared to tackle complex tasks with ease.

Whether you want to enhance your career, start a new venture, or expand your knowledge, this course provides all the tools you need to succeed.

Join us today and embark on your journey toward mastering AI agent development with LangChain and LangGraph!

Enroll now and transform your skills with AI!

Who this course is for:

Developers and Programmers
Data Scientists and Analysts
Finance Professionals
Tech Enthusiasts and Hobbyists
Entrepreneurs and Business Owners

AI Agents: Develop Autonomous AI Agents with LangGraph [NEW]

What you'll learn

Explore related topics

Course content

Introduction4 lectures • 12min

Code and Resources2 lectures • 1min

Development Environment Setup1 lecture • 1min

AI Agents - Deep Dive9 lectures • 1hr 2min

Check in1 lecture • 1min

Build AI Agents with LangGraph - Deep Dive12 lectures • 1hr 1min

Capstone Project - Build a Financial Report Writer AI Agent7 lectures • 42min

Wrap up2 lectures • 4min

Requirements

Description

Who this course is for: