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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
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