This course lasts about 2 hours and introduces multi-agent development based on Microsoft’s AutoGen framework, exploring a new world of large model applications.
With the development of large models, generative AI is likely to become an important branch of future technology.
AI Agents can make autonomous decisions, perceive changes in the environment, call tools, and execute actions automatically, while LLMs can serve as the “brain” of AI Agents, enabling them to have more powerful capabilities.
We can let multiple AI Agents collaborate with us humans, acting as our assistants or even subordinates. For example, we can develop several Agents: Product Manager Agent, Architect Agent, Developer Agent, Code Review Agent, and Testing Agent, while we play the role of the boss, directing these Agents to work for us. This is very effective for tasks that are not particularly complex, such as developing a personal homepage.
Screenshot of a personal homepage generated entirely by Agent.
This course is based on Microsoft’s open-source AutoGen framework, taking a practical approach to guide students step by step into the field of multi-agent application development, providing practical cases to improve work efficiency. The course begins with a showcase of popular multi-agent applications and platforms, such as AI programmer Devin and fully automated code writing tool Bolt. It then unfolds with an interesting case: “Is your partner’s money your money?” leading to a debate competition among multi-agents around this topic, sparking students’ interest. Following that, Agentic AI and AutoGen are introduced, covering basic installation and usage, multi-agent-related knowledge, and practical cases, including exploring multi-agent-controlled robots. This course is suitable for students interested in multi-agent applications who have a basic understanding of Python development.
Pang Zhixiang (Contracted Author of Guyueju)
Application Engineer for Generative AI at an automotive company
Currently engaged in multi-agent research and using LLMs to enhance R&D efficiency.
2 — Interesting demo: Is your partner’s money your money?
3 — Public Cloud in the New Era
4 — What is Agentic AI?
5 — What is AutoGen?
6 — Installation of AutoGen
7 — First Case Study
8 — Automating Code Execution with AutoGen
9 — Large Models Can Perceive the Physical World with the Help of AutoGen
10 — Two Agents
11 — Group Chat: Collaborating with Multiple Agents
12 — Group Chat with Human Agents: Directing a Group of Agents to Develop a Target Detection Function Based on YOLO v5
13 — Group Chat with Human Agents: Letting Agents Develop a Personal Homepage for Us
14 — Connecting AutoGen to Domestic/Proxy Large Models
15 — What is RAG?
16 — A Simple Case of Making AutoGen a Personal Knowledge Assistant
17 — Tool Use in AutoGen: Expanding the Capabilities of Multi-Agents
18 — Making AutoGen a Personal Knowledge Assistant
19 — Simple Exploration of Controlling Robots with AutoGen
20 — Open Source Case of Controlling Robots with AutoGen
21 — Extended Learning: Focus on Multi-Agents Rather than AutoGen
For course materials, please reply “Embodied Intelligence Materials” to the WeChat public account “Guyueju”.
1) Installation and Initial Use of AutoGen
A detailed explanation of the installation process of AutoGen, including environment configuration and dependency installation. Through simple examples, it demonstrates how to create and manage agents using AutoGen, helping students to quickly master it.
2) Group Chat in AutoGen: Real Multi-Agent Collaboration
A deep analysis of the group chat function in AutoGen, presenting the collaboration principles among multiple agents. Using practical cases, it demonstrates how to achieve division of labor and collaboration for complex tasks through group chat.
3) Connecting AutoGen to Domestic/Proxy Large Models
Explains how to combine AutoGen with domestic or proxy large language models to enhance agent capabilities. Detailed explanations of configuration methods and precautions.
4) Developing a Personal Knowledge Assistant with AutoGen
Guides students to use AutoGen to build a personal knowledge assistant to meet specific information needs in a particular field. Through customizing agents, it achieves efficient information retrieval and processing.
Logic Diagram of Personal Knowledge Assistant
5) Exploring Control of Robots with AutoGen
Researches the application of AutoGen in the field of robot control. Through examples, it demonstrates how agents interact with physical devices to achieve automated control.
Logic of Multi-Agent Control of Robots
Recommends some learning resources, such as Fei-Fei Li’s overview of Agent AI (providing a mind map), understanding Prompt Engineering techniques,emphasizing that during the learning process, one should focus on the overall design and application of multi-agent systems, rather than being limited to the use of a specific framework.
By studying this course, students can master the preliminary applications of large model multi-agents and can use them to enhance their work efficiency, especially in coding and text tasks.
Original course price ￥59.8
Group of two ￥49.8 yuan，Group of three ￥39.8 yuan
Now newFirst 50 people to purchase can receive5 yuan discount coupon
Each person can only receive1
After receiving, it is valid for15 days
1、This course is mainly practical, interspersed with theoretical explanations, suitable for readers who want to master applications, not suitable for those who want to learn theory.
2、This course uses Python, requiring basic Python development skills, such as understanding basic syntax, installing dependency packages, and using virtual environments.
3、Due to the nature of this product being a video course, refunds are not provided after viewing.
4、Purchase Notice: After initiating a group purchase, exit is not supported temporarily.（If someone successfully groups with you, the course will enter the account you used to purchase and can be watched directly; if no one groups with you, you will automatically group after 24 hours, and the course will still enter your account, please wait a moment, thank you for your understanding and support~）
Recruitment Requirements
Complete the production of robot-related videos that meet the requirements
The total duration must reach over 3 hours
The video content must be high-quality and professional
Instructor Rewards
Enjoy course revenue sharing
Gift 2 courses from Guyue Academy (excluding training camps)
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