Mastering Autogen: Building Multi-Agent Systems

Mastering Autogen: Building Multi-Agent Systems
Published 7/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.63 GB | Duration: 3h 28m

Mastering Multi-Agent Systems for Research Automation and Visualization with AutoGen

What you'll learn

Understand and Implement Multi-Agent Systems

Automate Research Paper Retrieval and Analysis

Apply Agentic Design Patterns in Real-World Scenarios

Customize Multi-Agent Systems with AutoGen

Requirements

Basic Python Programming

Familiarity with Natural Language Processing (NLP) Concepts and LLM, ML

Description

In this hands-on course, you will explore the power of AutoGen to build and customize multi-agent systems for automating complex workflows. This comprehensive guide will take you through the fundamental concepts of multi-agent systems, effective implementation strategies, and best practices for using AutoGen. You will learn how to configure and deploy various types of agents, such as AssistantAgent and UserProxyAgent, and see how these agents can collaborate to accomplish sophisticated tasks.What You Will Learn:Multi-Agent Systems: Understand the core principles of multi-agent systems and their benefits in automating complex workflows.Agentic Design Patterns: Learn about different agentic design patterns and how to apply them to solve real-world problems efficiently.Automation of Research Tasks: Discover how to automate the retrieval, analysis, and visualization of research papers, enhancing productivity and insight generation.Advanced NLP and LLM Techniques: Gain practical knowledge in configuring and utilizing large language models (LLMs) and natural language processing (NLP) techniques to process and analyze textual data.Visualization and Data Presentation: Master the creation of visual tools such as bar charts to present your analysis results effectively.Enterprise Use Cases: Explore enterprise-level use cases and best practices for integrating AutoGen into professional workflows.If want to master AutoGen and build multi-agent systems that are highly customizable, then this course is for you.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course Structure and OpenAI Account Setup

Section 2: Development Environment Setup

Lecture 3 Setup OpenAI API Key

Lecture 4 Python Installation - Instructions

Section 3: Download Course Source Code

Lecture 5 Download course source code and resources

Section 4: OPTIONAL - Agents Crash Course

Lecture 6 Agents Crash Course

Lecture 7 Agents Characteristics & Use Cases

Section 5: AutoGen Deep Dive

Lecture 8 AutoGen Overview and Building Blocks and Key Features

Lecture 9 Hands-on - Create our First AutoGen Agent

Lecture 10 AutoGen Building Blocks & Multi-Agent Conversations Agent Types - Deep Overview

Lecture 11 UserProxyAgent and AssistantAgent - Chat

Lecture 12 Multi-Agent Conversation Framework Flow - Diagram Overview and Explanations

Lecture 13 Code Executors in AutoGen - Local and Docker

Lecture 14 Hands-on - Simple Code Executor to Plot a Graph

Lecture 15 Adding Human Input to Get Different Plottings

Lecture 16 UserProxyAgent and AssistantAgent Inherite from ConversableAgent

Lecture 17 Best Practices - UserProxyAgent and AssistantAgent

Lecture 18 Human Feedback in Agents - Full Overview

Lecture 19 Summary

Section 6: Hands-on Human Input Modes

Lecture 20 Human Input Modes - Overview

Lecture 21 Hands-on - NEVER Human Input Mode

Lecture 22 Hands-on - ALWAYS Human Input Mode

Lecture 23 TERMINATE - Human Input Mode

Lecture 24 LLM Caching - Overview

Section 7: AutoGen and Tools

Lecture 25 AutoGen and Tools - Overview

Lecture 26 Hands-on - AutoGen Simple tool - Add and Multiply Numbers

Lecture 27 Hands-on - Travel Advice Agents with Tools - Real world Use Case - 1

Lecture 28 Hands-on - Travel Planner Agents Workflow - Real world Use case - 2

Lecture 29 Summary

Section 8: AutoGen Conversation Patterns

Lecture 30 Conversation Patterns & Two-Agent Chat - Overview

Lecture 31 Hands-on - Two-Agent Conversation Deep Dive - The initiate_chat method

Lecture 32 Sequential Chats - Overview

Lecture 33 Hands-on - Sequential Chat

Lecture 34 GroupChat and GroupChatManager Overview

Lecture 35 Hands-on - GroupChat Agents in Action

Lecture 36 Hands-on - Adding GroupChat into Sequential Chat

Lecture 37 Nested Chat

Lecture 38 Hands-on - Nested Chats - Writer Assistant and Critic

Lecture 39 Summary

Section 9: Hands-on - Real World Use Cases

Lecture 40 Customer Service Automation Use Case

Lecture 41 Financial Report Writer Use Case

Lecture 42 Research Paper Automation User Case

Section 10: Wrap up and Next Steps

Lecture 43 Wrap up and Next Steps

Data Scientists and Analysts,AI and Machine Learning Enthusiasts,Software Developers and Engineers

We need your support!
Make a donation to help us stay online
        
Bitcoin (BTC)
bc1q08g9d22cxkawsjlf8etuek2pc9n2a3hs4cdrld
	
Bitcoin Cash (BCH)
qqvwexzhvgauxq2apgc4j0ewvcak6hh6lsnzmvtkem

Ethereum (ETH)
0xb55513D2c91A6e3c497621644ec99e206CDaf239

Litecoin (LTC)
ltc1qt6g2trfv9tjs4qj68sqc4uf0ukvc9jpnsyt59u

USDT (ERC20)
0xb55513D2c91A6e3c497621644ec99e206CDaf239

USDT (TRC20)
TYdPNrz7v1P9riWBWZ317oBgJueheGjATm