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Build Multi-Agent LLM Applications with AutoGen

person icon Shahzeb Naveed

4.2

Build Multi-Agent LLM Applications with AutoGen

Learn to Create Generative AI Agents using LLMs with AutoGen

updated on icon Updated on Jun, 2025

language icon Language - English

person icon Shahzeb Naveed

category icon IT and Software ,Other IT and Software,Python

Lectures -17

Duration -1 hours

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4.2

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Course Description

Welcome to the Build Multi-Agent LLM Applications with AutoGen !

Are you excited about exploring the world of Generative AI? In this course, we'll learn how to create conversable and customizable AI agents powered by Large Language Models. This is a hands-on course with exercises in Python. We'll cover how to combine external tools like APIs and web scrapers with agents. We'll cover advanced techniques like Retrieval Augmented Generation, Prompt Engineering (ReAct) and Task Decomposition. We'll also implement different conversational patterns like group chats and nested chats.                                                                                                                                                                            

Intended Audience:

This intermediate-level course is designed for data scientists, machine learning engineers and software engineers aiming to expand their skills into the LLM/Generative AI space.

Course Outline:

• Environment Setup

• Getting Started with AutoGen (Basic Concepts)

• Large Language Model Agents

• Agents with Human-in-the-Loop

• Agents with Code Execution Capability

• Agents with access to external tools like APIs and web scrapers

• Agents in different Conversational Patterns (Sequential, Group, Nested Chats)

• Agents with GPT-4 Turbto/DALL-E Image Generation Endpoints

• Prompt Engineering Techniques (ReAct) with Agents

• Retrieval Augmented Generation (RAG) using Chroma DB and LLM Agents

• Task Decomposition (Build Automated LLM Agents)

• Message Transformations for LLM Agents

• Using Non-OpenAI/Open Source Models with LM Studio


Join me on this journey to explore the world of LLM Agents and Generative AI!

  • Who this course is for:

  • Data Scientists and Machine Learning Engineers who'd like to integrate LLMs in various use-cases
  • Software Engineers who need a hands-on guide to develop LLM-based multi-agent workflows
  • Architects who need a high-level understanding of what's possible with agentic workflows

Goals

What you’ll learn:

  • Define LLM agents and its various components
  • Build multi-agent applications following different conversational patterns
  • Integrate web scraping, external APIs and image capabilities in agents
  • Create Retrieval Augment Generation (RAG) pipeline with AutoGen
  • Implement Prompt Engineering techniques with LLM agents

Prerequisites

  1. Python
  2. Experience using ChatGPT
Build Multi-Agent LLM Applications with AutoGen

Curriculum

Check out the detailed breakdown of what’s inside the course

Introduction

3 Lectures
  • play icon What You Should Know 00:22 00:22
  • play icon Environment Setup 01:14 01:14
  • play icon GitHub Repo

Agents and its Components

5 Lectures
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Conversational Patterns

3 Lectures
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Advanced Workflows

4 Lectures
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Using Non-Open AI Models

1 Lectures
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Next Steps

1 Lectures
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Instructor Details

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Shahzeb Naveed

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