Build an AI Workforce 🤖💬🤖 Multiple Conversable Agents

Summary

This video tutorial explores enhancing an Autogen application by integrating multiple assistant agents, each with a distinct role, such as a coder or product manager. It delves into the concept of caching, or 'seeding', which significantly boosts performance by storing previous results. The video demonstrates how to create a new cache by altering the seed value and discusses the impact on performance. It also guides viewers on setting up a group chat for agents to communicate, introducing the Group Chat Manager to coordinate interactions. Lastly, it touches on the 'human input mode' setting, which controls user feedback frequency, and concludes with a live demo of agents collaborating to solve a coding task and propose product applications.

Takeaways

• 😀 The video discusses enhancing an autogen application by adding multiple assistant agents, each with a distinct role.
• 🔧 Autogen's caching mechanism, facilitated by a 'cache' folder, significantly improves performance by storing results from previous runs.
• ⏱️ The first execution of an autogen application is slower due to model API calls, but subsequent executions are faster thanks to caching.
• 🗂️ To bypass caching, one can either delete the cache folder or change the 'seed' value in the llm config, which generates a new cache folder.
• 💻 The script demonstrates how to create a new assistant agent, such as a 'product manager', and specify its role through a system message.
• 🗣️ Communication between multiple agents is managed through a 'group chat' setup, allowing for coordinated interactions.
• 🔄 The 'group chat manager' is crucial for coordinating conversations between different agents within the group chat.
• 🛠️ The 'human input mode' on the user proxy agent determines whether the program pauses for user feedback after each step or runs continuously.
• 🔄 The video shows a practical example where the coder agent writes code, the user proxy agent executes it, and the product manager suggests applications based on the output.
• 🎯 The video concludes with a demonstration of how the agents collaborate, from coding to problem-solving and application suggestion, showcasing the power of multi-agent coordination.

Q & A

What is Autogen and what was discussed in the previous video?

Autogen is a system that allows the creation of applications with multiple assistant agents, each serving a different purpose. In the previous video, the setup of Autogen was discussed, and the creation of a first application using a user proxy agent and a single assistant agent was demonstrated.

What is the benefit of having multiple assistant agents in an Autogen application?

Multiple assistant agents allow for specialized roles within an application, such as a coder, project manager, or tester, which can enhance the functionality and efficiency of the application.

What is caching in the context of Autogen, and why is it important?

Caching in Autogen refers to the storage of previous run results in a 'cache' folder. It is important because it significantly improves the performance and reduces costs by allowing Autogen to retrieve results from the cache instead of calling model APIs every time.

How can the caching mechanism in Autogen be controlled?

The caching mechanism can be controlled by either deleting the cache folder or changing the seed value in the 'llm config' dictionary. Changing the seed creates a new cache folder and forces the APIs to be called from scratch.

What is the purpose of the 'seed' in Autogen's caching system?

The 'seed' in Autogen's caching system determines which cache folder is used. Changing the seed value creates a new cache folder, effectively resetting the cache and causing the application to call APIs from scratch.

How can additional agents be added to an Autogen application?

Additional agents can be added to an Autogen application by creating new variables for each agent, specifying their roles, and then including them in a group chat managed by a group chat manager.

What is a group chat in Autogen, and how does it facilitate communication between agents?

A group chat in Autogen is a setup that allows multiple agents to communicate with each other. It is facilitated by a group chat manager, which coordinates the conversation between different agents.

What is the role of the group chat manager in an Autogen application?

The group chat manager in an Autogen application is responsible for coordinating the conversation between different agents, ensuring that messages are passed between them effectively.

How does the 'human input mode' argument affect the execution of an Autogen application?

The 'human input mode' argument determines whether the application will ask for user feedback after each step. Setting it to 'always' means the application will prompt for feedback, while setting it to 'never' will run without stopping for feedback, potentially leading to infinite loops if not managed properly.

How can an Autogen application handle the installation of missing packages during execution?

An Autogen application can handle the installation of missing packages by having the coder agent recognize the missing package and instruct the user proxy agent to install it. Once installed, the application can continue with the execution.

What is the significance of the product manager agent's involvement in the Autogen application demo?

The product manager agent's involvement in the demo signifies the collaborative nature of Autogen applications, where different agents can work together. The product manager agent suggests potential applications based on the results provided by the coder agent, showcasing the agents' coordinated efforts.

Outlines

00:00

😀 Enhancing Autogen Applications with Multiple Assistant Agents

This paragraph introduces the concept of adding multiple assistant agents to an Autogen application, each with a distinct role. The video demonstrates how to integrate a product manager agent alongside a coder agent. It also explains the importance of caching in Autogen, which significantly improves performance by reusing results from previous runs. The process of changing the 'seed' to generate new cache folders and the impact on performance is discussed. The paragraph concludes with a demonstration of executing the application and the effects of modifying the cache.

05:03

🔧 Setting Up Group Chats for Agent Interaction in Autogen

The second paragraph delves into the setup of a group chat within Autogen, allowing multiple agents to communicate. It details the creation of a 'group chat' class instance that includes a list of agents such as the user proxy, coder, and product manager. The paragraph explains the role of a 'group chat manager' in coordinating conversations between agents. It also touches on the 'human input mode' setting, which can be adjusted to control the level of user interaction during the execution of the application. The paragraph concludes with a live demonstration of the agents interacting in a group chat, handling tasks, and responding to issues such as missing packages.

10:04

📈 Exploring the Collaborative Potential of Autogen Agents

The final paragraph showcases the collaborative capabilities of different agents working together within an Autogen application. It highlights how the product manager agent can suggest potential applications based on the results from the coder agent's execution. The paragraph emphasizes the synergy between agents and encourages viewers to engage with the content by liking and subscribing. It ends with a farewell note, signaling the end of the video.

Keywords

💡Autogen

Autogen refers to a software development tool or framework that automates the generation of code or applications. In the video, it is used to create applications with multiple assistant agents, each serving a different purpose. The script discusses setting up Autogen and creating an application with a user proxy agent and a single assistant agent, highlighting its role in automating and streamlining the development process.

💡User Proxy Agent

A user proxy agent in the context of the video is a type of agent within the Autogen framework that acts on behalf of the user. It is the interface through which the user interacts with the system. The script mentions creating a user proxy agent and using it to initiate chats with other agents, demonstrating its role in facilitating user interactions within the application.

💡Assistant Agent

Assistant agents are components within the Autogen framework designed to perform specific tasks or roles within an application. The video script discusses adding multiple assistant agents, such as a coder agent and a product manager, to handle different aspects of the application's functionality. These agents enhance the application's capabilities by specializing in various tasks.

💡Caching

Caching in the video refers to the process of storing previously computed results to improve performance. The script explains that Autogen creates a 'cache' folder in the project, which stores results from previous runs. This allows the application to retrieve results quickly without needing to call model APIs again, thus reducing execution time and costs. Caching is crucial for optimizing the performance of applications built with Autogen.

💡Seeding

Seeding in the context of the video is the process of initializing or resetting the cache in Autogen by specifying a numeric value. The script describes how changing the seed value creates a new cache folder, which forces the application to call model APIs from scratch, as opposed to using cached results. This is useful for ensuring that the application behaves differently or generates new outputs.

💡Group Chat

A group chat in the video is a feature of the Autogen framework that allows multiple agents to communicate with each other. The script illustrates how to set up a group chat by adding agents to a chat room, enabling them to interact and coordinate tasks. This is essential for applications with multiple assistant agents, as it facilitates collaboration and communication between them.

💡Group Chat Manager

The group chat manager is an agent within the Autogen framework responsible for coordinating conversations between different agents in a group chat. The script explains how to create a group chat manager and how it directs the flow of messages between agents, ensuring that tasks are managed effectively. This role is pivotal for maintaining order and efficiency in applications with multiple interacting agents.

💡Human Input Mode

Human input mode is a setting on the user proxy agent that determines when the application requests user feedback. The video script mentions that the default setting is 'always,' meaning the application will pause after each step to get user input. The script also discusses the option to set it to 'never,' which allows the application to run without stopping for user feedback, but cautions that this could lead to infinite loops if not managed properly.

💡Product Manager

In the video, a product manager is one of the assistant agents within the Autogen application, representing a role that focuses on product development and management. The script shows how the product manager agent interacts with the group chat, providing insights and suggestions based on the outcomes of other agents' tasks, such as the coder agent's code execution results. This illustrates the collaborative nature of the application and the diverse roles that assistant agents can fulfill.

💡Coder Agent

The coder agent is an assistant agent in the Autogen framework specialized in writing code. The video script describes how the coder agent is added to the application and how it contributes by providing Python code solutions and handling code execution. The coder agent's interactions in the group chat demonstrate its role in the development process, working alongside other agents like the product manager.

Highlights

Introduction to adding multiple assistant agents in Autogen applications for diverse roles like coder, project manager, and tester.

Explanation of caching or seeding in Autogen, which improves performance by reusing results from previous runs.

Demonstration of how to create a cache folder in Autogen and its role in enhancing application performance.

The impact of changing the seed value on cache performance and how it affects the execution of Autogen applications.

Step-by-step guide on executing Autogen applications and the importance of virtual environments.

How to add a product manager agent to an Autogen application and set its role through a system message.

The process of enabling communication between multiple agents using a group chat setup in Autogen.

Introduction to the Group Chat Manager in Autogen and its role in coordinating conversations between agents.

A practical example of how to initiate a chat with multiple agents in a group chat environment.

The significance of the 'human input mode' argument in the User Proxy Agent and its effect on feedback loops.

A demonstration of how agents collaborate to solve a problem, including code execution and error handling.

How the Product Manager agent gets involved in the conversation to suggest potential applications based on code results.

The importance of feedback in the Autogen process and how it affects the flow of the conversation between agents.

A summary of how different agents work together in an Autogen application to achieve a common goal.

Encouragement for viewers to like and subscribe to the channel for more content on Autogen and its applications.

Transcripts

hello and welcome back in the previous

video we had a look at setting up

autogen on our machines and we created

our first autogen application using a

user proxy agent and a single assistant

agent one of the strengths of using

autogen is the ability to add multiple

assistant agents in our applications and

each of these assistants can have a

different purpose for instance we could

have a coder agent as well as a project

manager a tester Etc so in this video we

will have a look at adding additional

assistant agents to our application and

we will also have a look at some other

key important features of these classes

before we add an additional agent I

first want to explain caching or seeding

when you executed autogen you would have

noticed this cach folder was created in

your project if I open up this folder I

can see a subfolder with a numeric value

in my example 41 caching plays an

extremely important role in autogene you

might have noticed that the first time

we executed our autogen application it

took quite a long time to get results

back from the models however when we

executed the scod again the performance

was greatly improved and that is because

when we execute our applications autogen

will first go to the cache folder and

then pick up the results from the

previous run from this cache that means

that it's not necessary for autogen to

call the model apis again which greatly

reduces performance and costs however

let's say you don't want autogen to pick

up on the scash you basically have two

options either you can delete this cash

folder or you can change the seed to

change the seed we can simply add a key

value to this dictionary in the llm

config the key is called seed and for

the value we can simply specify a

numeric value for example 42 now when we

execute this code we will see a new

folder created in the cache folder and

you will see a reduction in performance

because the apis are being called from

scratch let's execute this program let's

go to terminal and let's open up the

integrated terminal now in order to

execute this program we first need to

start up our virtual environment Again

by running V EnV scripts and then

activate now let's run our file by

typing py demo 1. piy now when we go to

the cache folder we can see this new

folder called 42 and we can also see

that it's taking way longer to get

responses from the Bots because

everything is being created from scratch

I'll go ahead and cancel this by typing

exit and enter so hopefully that makes

sense caching is a fantastic way to

improve performance ments on your

programs especially when you tend to

execute the same or similar instructions

now let's have a look at adding multiple

agents to our application for this demo

I'm actually going to make a copy of

this demo one file and let's call it

demo to so what I'm going to do here is

let's create another assistant agent but

this agent will be a product manager so

all we have to do is create a new

variable let's call it pm for product

manager which is equal to the assistant

agent class and for its arguments we

will pass in a name which we will call

product manager and then we also have to

pass in the llm config just like we did

for the coder but what we can also do

with these assistant agents is to

provide a system message and it is in

the system message but we can tell this

agent what its role is for instance I'll

say that you you are creative in

software product ideas if you want you

can do the same thing with the coder

agent so you could specify a system

message like you are good at writing

python code as an example but I'll just

leave it like this so now we need some

way for these three agents to talk to

each other in the previous video we had

a look at calling this initiate chat

method on the user proxy however the

limitation here is that we can only

specify one one recipient which was the

coder agent so now we need some way so

now we need some way for the user proxy

agent to talk to these two agents as

well and in autogen that is done by

adding these agents to a chat room or a

group chat let's have a look at how we

can do that I'm actually going to remove

this line like so and then from autogen

we will import the group chat class so

now in the code we can set up our group

chat by creating a new variable like

group chat which is equal to the group

chat class and this group chat can take

in a list of Agents so therefore we can

specify the argument agents which is

equal to a list and in this list we can

specify all the agents that we want to

include in this chat like the user proxy

agent the coder agent as well as the

product manager then as a second

argument we need to specify a list of

messages initially this list will simply

be empty then lastly we need to specify

an argument called Max rounds and this

is the maximum amount of rounds that the

agents are allowed to go for let's just

set this to 12 now that we have the

group chat set up whereby these three

agents can talk to each other we simply

need to add a group chat manager to this

group chat this agent is purely

responsible for coordinating the

conversation between the different

agents to create a group chat manager we

will simply import group chat manager

then to create this manager we will

simply create a new variable I will call

it manager which is equal to the group

chat manager class and this group chat

manager takes in two arguments the first

being the group chat itself which we

called group chat and secondly we need

to specify the LM config which is equal

to our llm config so all we have to do

now is to trigger our chat to initiate

the chat we will again call the user

proxy agent remember it is the user

proxy agent that we as the user interact

with on the user proxy agent we will

call the initiate chat method just like

we did in the first video but this time

for the recipient instead of calling the

coder agent directly we will pass pass

the message onto the group manager

instead then for the second argument we

will pass in our message and in this

example I'll just pass in this message

and that is all we need to do to add

additional agents to our application you

are more than welcome to create

additional agents perhaps you want to

also include a document writer a tester

or whatever and then all you have to do

is add those additional agents to this

list in the group chat before we execute

this code there is one last tip I do

want to show you and that is that on the

user proxy agent there is a very

important argument called human input

mode the default value for this argument

is always that means that after each

step in this process we will be asked to

provide feedback but it is possible to

change this behavior for instance we

could change the value to never which

means the program will execute without

stop copying to ask for our feedback and

will only terminate once the agents have

completed their task be careful setting

this to never as this could cause

infinite Loops I will just go ahead and

remove this value to leave it as always

we can now go ahead and execute this

code in the terminal I will run this

code by typing pi and then demo 2. Pi

here we can see the user proxy chatting

to the chat manager and then we can see

the coder passing its response back to

the chat manager as well this is

expected as a chat manager is

coordinating all of the agents tasks so

initially the coder is explaining to the

chat manager how the solution can be

built and the coder then provides the

python code for this solution because

the human input argument is said to

always we first need to provide feedback

I will simply press enter the user proxy

agent try to execute this code and the

execution of the code failed so the user

proxy is saying to the chat manager that

the execution of the code failed and

that is because this module feed parser

is not installed now the coder is

responding saying that this package is

missing so to solve this issue we need

to install this feed passer package so

I'll go ahead and copy this then I'll

open up a new terminal session I'll just

paste in that line and press enter to

install this package after installing

this I'll just switch back to this

terminal session and I'll just press

enter now the user proxy is telling the

chat manager that that package was

installed and now the coder is saying

that great now that this package is

installed we can go ahead and execute

this code so I'll just press enter to

let it continue and this time the user

proxy was able to execute that code and

the result from executing that code was

this list of Articles so what is very

cool is that now we can see the product

manager getting involved and now the

product manager is saying to the chat

manager that based on that result from

the python code here are a few potential

applications of gp4 and this is how we

can see these different agents working

together if you like this video then

please hit the like button and subscribe

to my channel I'll see you in the next

one bye-bye