AutoGen Quickstart 🤖 Build POWERFUL AI Applications in MINUTES

Summary

This video tutorial introduces Autogen, an open-source framework by Microsoft for creating AI applications with multiple agents. It guides viewers through setting up Autogen locally, including installing Python 3.8, creating an OpenAI API key, and using a code editor like VS Code. The process involves creating a virtual environment, installing the Autogen package, and configuring AI models and API keys. The tutorial demonstrates creating a user proxy agent for interaction and an assistant agent for tasks, culminating in a demo where the agents collaborate to plot stock price changes for Nvidia and Tesla, showcasing Autogen's potential for complex problem-solving.

Takeaways

• 🌐 Autogen is an open-source framework by Microsoft for creating AI applications with multiple agents.
• 💻 To set up Autogen locally, you need Python 3.8 or higher, an OpenAI API key, and a code editor like VS Code.
• 🛠️ It's recommended to create a virtual environment for Autogen to manage dependencies specific to the project.
• 🔑 The OpenAI API key is stored in a file named 'oaioreconfig.json' in the project's root folder.
• 📝 The 'oaioreconfig.json' file specifies AI models available to the application, with 'GPT-4' used as an example.
• 🤖 Autogen applications require at least one user proxy agent for user interaction and one assistant agent to perform tasks.
• 💬 The user proxy agent serves as a middleman, receiving user input and directing it to other agents, and can execute code.
• 🔧 In the demo, the 'coder' assistant agent is tasked with generating code to plot stock price changes for Nvidia and Tesla.
• 📊 The coder agent proposes using Python libraries to generate the required code, which is then executed by the user proxy agent.
• 🔄 The process involves iterative communication between agents, with opportunities for user feedback or termination of the session.

Q & A

What is Autogen and who created it?

Autogen is an open-source framework created by Microsoft that simplifies the creation of AI applications using multiple agents.

How does Autogen facilitate problem-solving?

Autogen allows adding agents to a chat room where they collaborate to solve complex problems, either with or without user input, by assigning unique roles to the agents.

What are the minimum requirements to set up Autogen locally?

To set up Autogen locally, you need Python 3.8 or greater, an OpenAI API key, and a code editor like VS Code.

Why is it recommended to create a virtual environment before installing Autogen?

Creating a virtual environment ensures that all dependencies are installed only for the project and not globally on the machine, avoiding conflicts.

How do you create a virtual environment for Autogen in VS Code?

In the integrated terminal of VS Code, you type 'python -m venv .venv' on Mac or Linux, then activate it by running '.venv/bin/activate'.

What is the purpose of the 'oaoreconfig.json' file in an Autogen project?

The 'oaoreconfig.json' file is used to store a list of AI models that will be made available to the application, along with their API keys.

What is a user proxy agent in the context of Autogen?

A user proxy agent is a middleman between the user and other agents, receiving user input and passing it to the agents, and possibly asking for additional feedback based on agent responses.

How does the user proxy agent execute code on behalf of the user?

The user proxy agent receives coding instructions from the user, hands them over to an assistant agent to generate the code, and then executes the code in a designated folder.

What is the role of the 'initiate chat' method in Autogen applications?

The 'initiate chat' method is used to start a conversation with the application, specifying the recipient (an assistant agent) and the message containing the task instruction.

What happens if the user proxy agent encounters an error during code execution?

If an error occurs, such as a missing module, the assistant agent will inform the user proxy agent and suggest the necessary dependencies to install to proceed.

How can users provide feedback or make changes to the solution in an Autogen application?

Users can provide feedback or request changes by typing new instructions, which the user proxy agent passes to the assistant agent for adjustments.

Outlines

00:00

💻 Setting Up Autogen for AI Applications

The video introduces Autogen, an open-source framework by Microsoft, designed to simplify the creation of AI applications using multiple agents. It allows users to integrate agents into a chat room to collaboratively solve complex problems, either autonomously or with user input. The setup process involves installing Python 3.8 or higher, creating an OpenAI API key, and using a code editor like VS Code. The tutorial guides viewers through creating a virtual environment, installing the Autogen package, and storing the API key in a project file. It also covers the creation of a configuration file for AI models and the fundamental concepts of user proxy and assistant agents in Autogen applications.

05:00

👨‍💻 Creating User Proxy and Assistant Agents

This segment of the video script details the creation of a user proxy agent and an assistant agent named 'coder' within the Autogen framework. The user proxy agent serves as an intermediary between the user and other agents, capable of executing code on the user's behalf. The assistant agent, in this case, is tasked with generating code. The video demonstrates how to import these agents, set their attributes, and specify the AI models they can access. It also explains how to set up the environment for code execution and how the agents interact to fulfill a user's request to plot a chart of stock price changes.

10:02

📈 Testing the Autogen Application and Iterating on Agent Responses

The final paragraph of the script describes the process of testing the Autogen application. It outlines how the user proxy agent communicates with the assistant agent to execute a task, such as plotting a stock price chart. The video shows the interaction between agents, the user's opportunity to provide feedback or terminate the session, and the handling of errors like missing dependencies. It also demonstrates how to install necessary modules and rerun the code successfully. The script concludes with a user request to modify the task, which the assistant agent accommodates, and a teaser for the next video, which will explore adding multiple agents to create an AI workforce.

Keywords

💡Autogen

Autogen is an open-source framework developed by Microsoft that simplifies the creation of AI applications using multiple agents. It allows for the addition of agents to a chat room where they collaborate to solve complex problems, either with or without user input. In the video, Autogen is the central theme, as the tutorial demonstrates how to set it up locally and create the first application using this framework.

💡Agents

In the context of Autogen, agents are the individual components that perform specific tasks within the AI application. They can be assigned unique roles and work together to achieve a common goal. The video explains how to set up agents like the user proxy agent and assistant agents, which are crucial for creating a digital workforce.

💡User Proxy Agent

The user proxy agent in Autogen serves as an intermediary between the user and other agents within the application. It receives user input and passes it along to the appropriate agents. The video script provides an example of creating a user proxy agent named 'user proxy', which is set to execute code on behalf of the user.

💡Assistant Agent

Assistant agents in Autogen are designed to perform tasks such as generating code or processing data. In the script, an assistant agent named 'coder' is created to write code for visualizing stock price changes. This demonstrates how assistant agents can be specialized for different functions within an AI application.

💡Open AI API Key

An Open AI API key is a unique identifier required to access OpenAI's services, such as their AI models. In the video, the script instructs viewers to create an Open AI API key and store it in a configuration file within the project. This key is essential for integrating Autogen with AI models like GPT-4.

💡Virtual Environment

A virtual environment in Python is a self-contained directory tree that includes a Python installation for a particular version of Python, plus a number of additional packages. The video script recommends creating a virtual environment for the Autogen project to manage dependencies and avoid conflicts with system-wide packages.

💡Dependencies

Dependencies in the context of the video refer to external libraries or packages that the Autogen application needs to function correctly. The script mentions installing dependencies like 'why Finance' for the coder agent to execute the stock price visualization code successfully.

💡Code Execution

Code execution in the video refers to the process where the user proxy agent runs the code generated by the assistant agent. The script illustrates a scenario where the user proxy agent attempts to execute the code but encounters a failure due to missing dependencies, which are then installed to enable successful execution.

💡Digital Workforce

The term 'digital workforce' is used in the video to describe a collection of AI agents working together to perform tasks. The video outlines the process of setting up Autogen to create a digital workforce by adding multiple agents, each with a specific role, to solve complex problems.

💡Configuration File

A configuration file in the video is a text file that stores settings for the Autogen application, such as the list of AI models and their corresponding API keys. The script guides viewers through creating a 'oaore config uncore list' file where the model 'GPT-4' and its API key are specified.

Highlights

Autogen is an open-source framework by Microsoft for creating AI applications with multiple agents.

Agents in Autogen can solve complex problems with or without user input by assuming unique roles.

Setting up Autogen locally requires Python 3.8 or greater, an OpenAI API key, and a code editor like VS Code.

Creating a virtual environment for Autogen installation is recommended to manage dependencies.

Autogen can be installed using pip by typing 'pip install py autogen'.

An OpenAI API key should be stored in a project file named 'oaioreconfig.json' for secure access.

The 'oaioreconfig.json' file specifies AI models available to the application.

Autogen applications require at least one user proxy agent for user interaction.

User proxy agents can execute code on behalf of the user, facilitating tasks like writing code.

Assistant agents in Autogen perform tasks assigned by the user proxy agent, such as generating code.

The 'initiate chat' method allows the user proxy agent to interact with assistant agents.

Autogen applications can dynamically adjust solutions based on user feedback during interactions.

Dependencies for code execution generated by Autogen can be installed using provided instructions.

Autogen allows for the execution of generated code, showcasing its capability to perform practical tasks.

The coder agent in Autogen can modify its solution based on user requests, like plotting percentage changes.

Autogen supports the creation of a digital workforce by integrating multiple agents with distinct roles.

Future videos will explore adding multiple agents to Autogen applications for complex problem-solving.

Transcripts

autogen is an open- Source framework

created by Microsoft that simplifies the

creation of AI applications using

multiple agents basically autogen allows

you to add agents to a chat room where

they will then work together to solve

complex problems this can be done with

or without the user's input by assigning

unique roles to the agents you can

easily create your very own digital

Workforce in this video we will set up

autogen locally and you will learn the

fundamentals of creating your very first

autogen applications setting up autogen

locally is quite straightforward all you

need is to install python 3.8 or greater

you also need to create an open AI API

key and you will need a code editor like

vs code open up a new folder in VSS code

and then open the integrated terminal by

clicking on Terminal and then new

terminal before installing the autogen

package it is recommended to First

create a virtual environment this will

ensure that all dependencies are

installed for this project only and not

globally on our machines creating a

virtual environment is quite easy simply

type python if you're using Mac or Linux

type Python 3 followed by dasm then

VV space

VV again press enter this will now

create this VV folder in order to

activate our virtual environment we can

simply type V EnV tab scripts and within

scripts activate and then press enter if

everything was done correctly you should

see the virtual environment name at the

start of this command we can now go

ahead and install autogen by typing pep

install py autogen gen and this will now

install the auto genen package we are

nearly done with the setup there's only

one more step now we need to store our

open AI API key somewhere in the project

so after copying your open AI API key go

back to the project and in the root

folder create a new file and call it o

aore config uncore list in this file we

will specify a list of AI models that we

will make available to our application

in this example we will only make use of

one model but if you want you can add

multiple models to this list to add a

model we will create a dictionary using

curly braces and this dictionary will

accept two key value pairs the first

value is model followed by a value which

is the name of the model that we'd like

to use in our example that model will be

GPT 4 if you want you can also replace

this with something like GPT 3.5 turbo

but for this example I will use GPT 4 as

this will provide better results the

second value is API key with the value

of the open AI API key if you want you

can add additional models to this list

simply by typing comma and then adding

another dictionary but we will only use

one model this is all the setup we need

to create our autogen application let's

close this file and then in the root of

this folder let's create our first demo

let's create a new file and let's call

it demo 1. piy the first thing we need

to do is to pull in this list of AI

models into our application to do that

we'll type the following from Auto Jen

import config list and in the auto

complete we can see the different

functions that are available for

importing this config we will use the

fromjson function then we will create a

new variable called config list which we

will set equal to the config list from

Json function and this function takes an

argument called EnV or file which we

will set equal to the name of our config

list file which was O aior config uncore

list now in order to create autogen

applications we need at least the

following first we need a user proxy

agent and then second we need at least

one assistant agent let me explain what

these are the user proxy agent allows us

the users to interact with our

application this agent is sort of the

middleman between the user and the

agents and it plays quite an important

role the user proxy agent will receive

input from the user and then pass that

input to the agents and depending on the

responses from the agents this proxy

agent might ask the user for additional

feedback another important note about

proxy agents is that they are able to

execute code on the user's behalf so

let's go ahead and create this proxy

agent from autogen we will now also

import the user proxy agent agent and

now we can create a new variable called

user proxy but you are welcome to call

this whatever you want we will set this

equal to the user proxy agent class and

this class takes in a couple of

attributes the first one being the name

of this agent which will call user proxy

but you are welcome to call this agent

whatever you want then we will specify

another attribute called code uncore

execution underscore config and this

argument takes a dictionary as input

with a key value of work uncore dur

which we can give any value that we want

I'll just call it coding as I mentioned

earlier these user proxy agents are able

to execute code on the user's behalf so

in this demo we'll actually ask our

agent to write code for us and the proxy

agent will hand that coding instruction

over to an assistant agent which will

then generate the code that code will

then be saved in a folder called coding

and the user proxy agent will then

execute the code in that coding folder

so with that said let's go ahead and

create our coding agent we can do that

by creating an assistant agent so from

autogen let's also import an assistant

agent then under assistant agent let's

create a new variable called coder you

are welcome to call this whatever you

want if this was a tester or a document

writer or a project manager you will

call this variable whatever you want we

can set this equal to the assistant

agent class which also takes a couple of

attributes like the name of the agent

which will simply call coder for these

assistant agents we need to specify the

llm models that it has access to so as a

second argument we need to specify the

llm config argument which will set equal

to a dictionary with a key value of

config list with a value of the config

list variable that we created up here so

now we have an assistant with a name

coder which has access to the models

specified in this file we will look at

this later in this video but you are

also able to specify other attributes

like the system message which you can

use to Prime this agent but we will have

a look at that a bit later one in

announcement I do recommend you do is to

remove this dictionary and instead

create a new variable called llm config

which is equal to that dictionary so

when we create a new agent we simply

pass in the llm config variable and this

makes sense for autogen applications

since the whole purpose of using autogen

is to have multiple assistant agents in

your application and the just simplif

things if you can reuse the same

variable so now that we have a proxy

agent that we as the users can interact

with and we have one assistant agent

that will perform a task we can now

finally test out our application in

order to chat with our application we

can call user proxy and on user proxy

there's a method called initiate chat

the initiate chat method takes in an

argument of recipient and recipient is

this assistant agent that it needs to

interact with so let's simply add coder

to this argument so now that the proxy

agent knows which agent it's talking to

we now need to tell it what the message

is by specifying an argument called

message and the string value containing

our task instruction let's go with this

example where we say Plott a chart of

Nvidia and Tesla's stock price change

year to date let's say this file and

then in the terminal let's just expand

this a bit so in the terminal we can go

ahead and run this file by typing py

demo 1. py in the terminal we can see a

few important things first you will

notice this message is from the user

proxy and in Brackets it's saying which

agent it's talking to and in this

example it's talking to the coder agent

and it's passing our instruction to the

the coder agent after a few seconds we

can now see a message from the coder

agent and it is passing the message to

the user proxy and this is the message

from the coder basically it's saying

that it will use these python libraries

to write the code to visualize this

output and we can also see the code that

it's written also at the bottom of the

terminal we now have an opportunity to

change the solution or to provide

feedback or we can type X it to just

terminate this session alternatively we

can just press enter and that will allow

the agents to continue by themselves so

after pressing enter we can see this

message saying that no human input was

received and therefore the agents just

kept going the user proxy agent tried to

execute the code generated by the coder

agent we can also see that the user

proxy agent tried to execute the code

but the execution of the code failed and

the reason reason for the failure was no

module named why Finance so the coder

then replied back to user proxy saying

that in order for this code to work we

need to install a couple of dependencies

and it also tells us how to install

those dependencies so let's do that we

can simply copy this code I'll open up a

new terminal I'll paste in that code and

just run this after installing these

dependencies I'll just go back to this

terminal and let's let just press enter

so now the user proxy is telling the

coder that those packages have already

been installed because we just installed

them ourselves and we can now see the

coder responding to the user proxy

saying great now that you have these

modules installed we can now proceed to

execute this code and it's giving us the

code again so what I'll do now is just

press enter and the user proxy agent

just executed that code and now we can

see this graph being returned what we

can also see in our project folder is

that within the coding folder we can see

the code that was generated by the coder

agent and if we expand this we can see

all the code and this is the code that

our user proxy agent executed after

closing the popup we can see the user

proxy telling the coder that the

execution was successful and we can see

the coder respond back to the user proxy

saying it's glad the code execution was

successful and now we have another

opportunity to make changes to the

solution or to terminate the session

let's try making a simple change let's

type something like plot the percentage

change instead we can see the user proxy

agent passing our instruction back to

the coder the coder is now politely

apologizing to the user proxy agent and

it's explaining the changes that it will

make to the solution let just press

enter and now we are seeing a different

graph for the percentage change in the

stock price for in video and Tesla in

the next video we will have a look at

adding multiple agents to our

application in order to create our very

own AI Workforce if you enjoyed this

video please hit the like button and

consider subscribing to my channel I'll

see you in the next one bye-bye