Build AI agent workforce - Multi agent framework with MetaGPT & chatDev

Summary

Highlights

• AI agents are set to become part of workforces, handling specialized tasks such as design, development, and marketing. 📈
• The video explores projects like camel that simulate conversations between AI agents for tasks like building trading bots. 💬
• MetaGPT and chatDev are popular multi-agent frameworks that help orchestrate teams of agents, gaining popularity on GitHub. 🌟
• ChatDev provides customization with roles and phases, helping simulate various stages of a project, from idea generation to completion. 🔄
• Potential research by HubSpot and Jasper into the impacts of AI on content generation is briefly touched on. 📊

Key Takeaways

• AI agents can autonomously handle complex tasks with four key components: profile, memory, planning, and tool usage. 🤖
• Examples like camel and MetaGPT illustrate how multi-agent frameworks can create simulated environments for tasks like programming and stock trading. 🛠️
• The video guides you through customizing chatDev to create a specialized team of AI agents suited to different roles and tasks, such as content creation. 📚
• The importance of careful configuration in AI agent frameworks is highlighted, allowing for detailed task management and execution. 🔧
• Potential concerns exist around AI-generated content, such as search engine penalties, necessitating further research into best practices. 📉

Overview

Build AI agent workforce - Multi agent framework with MetaGPT & chatDev Transcription

• 00:00 - 00:30 in a world where we have multiple AI agents how will they work with each other ever since Auto GPT and PB AGI came out this year autonomous AI agents has been a Hot Topic AI agents is almost like a Aim Pro ease can do very complex tasks autonomously at high level AI agents has four big components one is the profile which Define who they are and what's their role and then memory where it can has both domain knowledge and also the shorter memory so it can remember what happened before an ability
• 00:30 - 01:00 to use large language model to do the planning so it can break down a big goal into subtasks in the end it has ability to use different tools and apis to complete the tasks this framework I'm showing here coming from a recent research paper called a survey on large language model based on autonomous agents which is very good paper if you want to dive into the world of Agents more and if you want to learn more technical details I also made a video before about how can you build autonomous agents that can do research for you so I'm very optimistic that in
• 01:00 - 01:30 the next 6 to 12 months we're starting people and companies how carrying AI agents as part of their Workforce you might get a specialized AI agents for different type of tasks like designers developers product management market and this reads the impression which is in a world where we have so many different agents how those agents work with each other because for a complex task and purchase it does require multiple different agents to work together and their few projects already explore this multi-agent's word one example is called camel which represents communicative agents for mind exploration of
• 01:30 - 02:00 large-scale language model Society fundamentally it provides a playground to simulate conversations between multiple different agents for example here you try to simulate a conversation between a Python programmer and a stock Trader with a goal to build a trading bot in the end and there are also other projects like Adrian verse which allows you to do those multi-agent simulation or you can simulate the whole classroom with the professor and five or six different students or the classic prisoner dilemma where you can simulate the conversation between the policemen and two different prisons and see how
• 02:00 - 02:30 the conversation involves they even can simulate Pokemon where the character will talk to each other and you can go to a specific character and start chatting with this character those are good for social experiments but the two projects that really color my eyes are meta GPT and chat Dev they are both multi-aging Frameworks they allow you to create team of Agents with different Specialties and Orchestra them to complete very complex progress both of them got really popular on GitHub in a very short period of time and this really gives us some good insights of
• 02:30 - 03:00 how the multi-agents were gonna look like and this is what I want to show you today how can you create your own teams of agents and let them work together so the one I want to dive a bit deeper is chat Dev because they provide a lot of interesting customizations and flexibilities there are three key components you can set up in chat Dev rows faces and chat chain and rows basically means you can Define different type of Agents from the boss product managers CTO and QA and then you will Define faces which basically means a specific task and Stage for example you
• 03:00 - 03:30 probably will start from demand analysis which involves find the requirements and scope and in the end they allow you to station together different phases from the demand analysis coding code review tests and documentation writing each phase will involve different AI agents and the default team the chat that provides is a software development team and the result is pretty stunning it is able to deliver compact software like a classic ping pong game Flappy Bird calculator 204a game and even image editor but the beauty of chat Dev is that you can fully customize it to any
• 03:30 - 04:00 other team you want so if you're a content creator like me you probably want to create a Content operation team who can work for you 24 7 from idea generation research to content writing and I'm going to show you a step-by-step guide of how can you create this content operation team with chat Dev but speaking of content generation about AI even though it is very powerful I often have questions like will Google penalize the AI content that I'm creating on my blogs and what kind of limitations I should be aware of that's why I want to introduce you to a research conducted by HubSpot and Jasper where they explore
• 04:00 - 04:30 all those important topics so we can understand what kind of limitations and pitfalls of using AI in our content creation process and also dive into how topirams in the world are scaling their content generation value AI so I think you will find this very useful if you are in content operation business I have put a link to download this free research paper in the description below so definitely go and check out and thanks HubSpot for sponsoring this video and provide this free resource now back to chat Dev so to set up the chat Dev on your computer is pretty simple first
• 04:30 - 05:00 they clone their GitHub repo you can do this either through the GitHub desktop app or command line and once you finish you can open the project folder in the visual studio code and open the terminal do this to line of code to set up the python environment and click enter once it's finished we're gonna install all the required dependencies so making sure you are in the project folder if not you can do CD chat Dev and do this pip install requirement and once you finish it should be all good the next step is set up your open AI API key if
• 05:00 - 05:30 you are on Mac do something like this export open AI API key with your API key here but if you're on Windows then you will do something like this that's pretty much it you can already give a task to the right development team by doing Python round.py and give a task name as well as a project folder for example I can ask it to build a snake game by doing python round.py task classic snake game with project name snake and project name Will basically be used to create a project folder under warehouse and I will click enter you can
• 05:30 - 06:00 see this already start working with all those conversations here it will probably take some time in the terminal you can actually see the conversation between different agents so this one agent that is giving comments to the original code delivered by the programmers in the programmer or try to iterate them in fixed bugs based on those comments once it's finished it will give you summary about the total cost which is less than 10 cents and the apps they did that will be under this folder called Warehouse you should be able to find this project called snake
• 06:00 - 06:30 and all we need to do just navigate to that specific folder where house and our copy the folder name here and inside this folder you can see that we have a few different files and our to Python main.py and here you go so we got a snake game and it's fully functional as you can see here I can play the game it has actually good visual as well so it is working pretty well inside this photo not only it has incremented game itself it also have a menu list all the details of how to play this game and this is
• 06:30 - 07:00 quite phenomenon they also provided you a simple web app where you can visualize how does this team work together so you can do this by navigate back to the root folder under the chat Dev and then we can do python online log app.py okay and then you get this web website URL you can click on and you'll get this page I will click on this chat replay and click on file upload and I will choose this log file under the Snake Game folder and click open then click replay and this will basically start lay out the whole conversation history from a CEO give the
• 07:00 - 07:30 brief and then CPO decide on the requirements and there's a little visualization on the left side as well so it's quite a fun to watch but this is not just it as I mentioned before you can actually customize the team of agents to be any other teams in any other standard procedures you have and the way it works is there's a folder called company config this is where you can set up a whole team so if you open that folder there should be a default folder and you will see three different files one is the raw config where you will Define the list of different agents
• 07:30 - 08:00 in their rows at default they have multiple different roles from CEO product managers CTO and even HR and then you can open the stagnant file which is the face config where you will Define different faces for example at the beginning they will Define the demand and Services where they will give a very special prompt about what this stage is and what's a go and scope and they will also Define a assistant role and user role because each stage here is actually simulate a conversation between two agents in the demand end of this
• 08:00 - 08:30 stage is a conversation between the CEO and the PM to come up with the product requirements and then for the next stage which is coding it will be a conversation between the CTO and Azure programmers where CTO will be giving the programmers a list of instructions about the tasks and goes on and on so each one of them is basically a specific tasks that need to be happened during this software development process and in the end you will have a chat chain config this is where you will Define the actual standard procedure to develop a software
• 08:30 - 09:00 it refers to demand analysis and the next turn step is negative one which means it only run once you can even turn on reflection as well so the CEO and the Consular can do a bit of refraction after each step for more complex phases like whole review you can do a compose face as well which means this step will be composed of a few sub steps like code review comment and code review modification and they will repeat this cycle for maximum three times in the end the documentation team will try to write
• 09:00 - 09:30 a user menu you will Define the list of AI agents need to be involved in this process so these three files is basically where you can Define the team the tasks and standard procedure in their documentation they actually have a pretty detailed instructions but I will show you a quick example of how can you create your own AI agent teams so here I create a new folder I name meta XYZ also duplicate the original files from default so use case I want is pretty simple I want to create an AI marketing agency where it still has CEO and
• 09:30 - 10:00 counselor to Define scope of work and do some reflection of each steps but the main role I will want to customize is marking directors whose responsibility is to come up with creative marketing campaign ideas and then another row which is marketing specialist whose job is taking the brief and idea from the marketing director and do the actual content writing and I remove all the other rows that I don't really need once I finish this I can also customize the faces and I simplify the faces to be only to one's ID generation which is basically conversation between the CEO
• 10:00 - 10:30 and marketing director to come up with the best marketing campaign ideas the second phase will be content generation whereas a marketing director will give the brief to the marketing specialist to do the actual content creating work there are some caveats about the prompt here so for the idea generation the last part is most important which only brings down the campaign idea and to not discuss anything else because this is actually simulating a conversation between this two person if you don't give the scope the conversation can go anywhere we also said wish rainstorm and
• 10:30 - 11:00 create tick on each other's idea after discussing more than 10 ideas any of us must actively terminate the discussion by picking up the best idea and reply with only one line which start with a simple word info followed by our latest content idea without any other words and what this does is when this special format show up that means the conversation has end so that we can move to next stage and apart from defining the faces here you will also need to go to the chat Dev and click on the face.py
• 11:00 - 11:30 and for each faces you added here you will actually need to Define class here but the format is very simple so you will Define the initialization which you don't need to change much and then you will Define update phase environment and this basically means passing on the global environment to this phase because if you remember in idea generation phase we will actually have a variable called task which is original task that the client gave us so in here we will need to define the variable called task where it will do chat environment dot environment that this is a Crux of this
• 11:30 - 12:00 phase if the lens of conversation is bigger than zero and info label is inside this conversation then we will set environment variable ideas to be the information it pass on and if there's no ideas then it will return I have no idea that's all you need to do and we will do both both ID generation and content generation here with same format and once we finish we will go back to this chat chin config and update the chains and where I will basically remove all the other steps that I don't really need and only keep this two steps idea generation and content generation and
• 12:00 - 12:30 face type will be simple faces because we don't have any sub steps and the recruitment will be this four persons that we defined earlier and the rest you can just keep guessing and that's pretty much it you can actually give a task to this new team that you just created and the way you would Define that is you will do python brown.py so you will do dash dash config meta XYZ which is the folder name here so this means your explicit however to use a new configuration folder that you created here and then task will be drive more people to subscribe to my newsletter
• 12:30 - 13:00 called AI Json where I share how to build AI products with a project name called AI Json newsletter so I will do this so here the star this conversation between different agents okay and now you can see it is finished so if we go to the warehouse you can receive this new folder here called aigs and newsletter because here I'm doing the content generation so there's no actual code it will be generated if I look at the log there will be a conversation between the marketing director and CEO where they start brainstorm a few different ideas and then the CEO pickups
• 13:00 - 13:30 the best idea which is an interactive quiz to test the user's knowledge of AI product development with a call to action to subscribe to a newsletter I think it's actually pretty good idea and once it's finished they move on to next phase where marketing director is giving a break to the marketing specialist about this is a go and this idea I came up with now please try to generate a social media post and then the marketing specialist will generate actual social media posts so this is a very quick example of this AI agent team you can build I'm super excited to see what kind of use case you start creating in this
• 13:30 - 14:00 multi-aging word so please comment below about the interesting agent use case you're creating if you do enjoy this video please consider giving me a subscribe thank you and I see you next time