An overview of AutoGen Studio 2.0 in under 10 minutes!

Summary

This tutorial introduces Autogen Studio 2.0, a web app for managing multi-agent AI applications. It showcases how agents with Python skills and large language models collaborate to complete tasks efficiently. The video guides viewers through setting up the environment, installing Autogen Studio, and integrating OpenAI's GPT-3.5. It explores key concepts like agents, skills, models, and workflows, and demonstrates testing in the playground. The tutorial promises more detailed guides on customization and advanced features in upcoming videos.

Takeaways

• 🤖 Autogen and Autogen Studio 2.0 enable the creation of AI Bot teams that can communicate and assign tasks to each other.
• 📈 The script demonstrates how AI agents can generate a plot of stock prices through collaboration, which would take humans hours to do manually.
• 🌐 Autogen Studio is a web app built on top of the Autogen framework for managing and prototyping multi-agent applications.
• 💡 Key concepts introduced include 'agents' with 'skills' (Python scripts), 'models' for text interpretation and generation, and 'workflows' defining agent interactions.
• 🛠️ The tutorial guides users on setting up their environment, installing Autogen and Autogen Studio, and adding an OpenAI API key for model access.
• 🔧 Skills in Autogen Studio are represented as Python functions that agents can use to perform tasks like image generation.
• 🧩 Models in Autogen Studio can be pre-configured or added by users, with the example of adding GPT-3.5 Turbo and testing its functionality.
• 🤝 Agents are configured with properties like name, description, and models, with the 'user proxy' and 'primary assistant' being default agents.
• 🔄 Workflows define how agents communicate, with examples given like a two-agent setup and a more complex group chat workflow.
• 🎮 The 'playground' in Autogen Studio allows users to test workflows, agents, and skills to see how they interact in real-time.

Q & A

What is the main feature of Autogen Studio 2.0 mentioned in the script?

Autogen Studio 2.0 facilitates the creation and management of multi-agent applications where AI bots can communicate with each other, assign tasks, and complete requests.

How does Autogen Studio 2.0 save time in completing tasks as shown in the script?

In the script, Autogen Studio 2.0 is demonstrated to generate a plot of two stock prices in a few seconds, a task that would typically take hours to complete manually.

What are the key components of an agent in Autogen Studio 2.0?

An agent in Autogen Studio 2.0 can have skills, which are Python scripts that extend its capabilities, and uses one or more large language models to interpret and generate text.

How can one set up Autogen Studio 2.0 as described in the script?

To set up Autogen Studio 2.0, one needs to create a working directory, set up a virtual environment, install Autogen and Autogen Studio 2.0 using pip, and add an OpenAI API key for using models like GPT-3.5 Turbo.

What is the role of the 'user proxy' agent in Autogen Studio 2.0?

The 'user proxy' agent in Autogen Studio 2.0 acts on behalf of the user, relaying user requests to other agents and executing code within the environment.

What is meant by 'skills' in the context of Autogen Studio 2.0?

In Autogen Studio 2.0, 'skills' refer to Python functions that define the capabilities an agent can have, such as web scraping or sending emails.

How can one add a new model to Autogen Studio 2.0?

To add a new model in Autogen Studio 2.0, one can click on 'new model', provide a name and API key, and test the model to ensure it works properly before saving it.

What is the purpose of the 'playground' in Autogen Studio 2.0?

The 'playground' in Autogen Studio 2.0 is a testing area where users can test workflows, agents, and skills to see how everything comes together to create multi-agent applications.

How does Autogen Studio 2.0 handle human intervention in agent interactions?

Autogen Studio 2.0 allows users to define when human intervention is necessary in agent interactions, providing control over the autonomy and decision-making process of the AI agents.

What future tutorials are hinted at in the script regarding Autogen Studio 2.0?

Future tutorials will cover details on customizing skills, creating new ones, adding new models, discussing local models using Olama or LM Studio, and exploring custom agents and more workflow information.

Outlines

00:00

🤖 Introduction to Autogen Studio 2.0

The video script introduces Autogen Studio 2.0, a web application that simplifies the creation and management of multi-agent applications. It demonstrates how AI agents can collaborate to complete tasks efficiently. The script showcases a scenario where agents generate a stock price plot, which would typically take hours, but is accomplished in seconds using Autogen. The speaker guides viewers on setting up the environment, installing Autogen and Autogen Studio 2.0, and integrating the OpenAI API key. The video promises future tutorials on advanced features and customizations.

05:01

🛠 Setting Up Autogen Studio and Exploring Its Features

The second paragraph delves into the setup process of Autogen Studio, starting with creating a working directory and setting up a virtual environment. It details the installation of Autogen Studio and the integration of the OpenAI API key. The script then transitions into an exploration of the platform's features, including skills, models, agents, and workflows. Skills are Python scripts that enhance agent capabilities, while models are large language models used by agents. The configuration of agents is discussed, highlighting their autonomous nature and the ability to assign skills. Workflows define agent interactions, with examples provided of simple and complex setups. The paragraph concludes with a demonstration of the 'playground' feature, where users can test their configurations and observe agent collaboration in real-time.

Keywords

💡Autogen Studio 2.0

Autogen Studio 2.0 is a web application built on top of the Autogen framework. It is designed to facilitate the management and prototyping of multi-agent applications. In the video, the presenter demonstrates how to set up and use Autogen Studio 2.0 to create a team of AI bots capable of communicating and assigning tasks to each other. The application is central to the video's theme of showcasing how complex tasks can be automated and expedited through AI collaboration.

💡Agents

In the context of the video, 'agents' refer to AI entities that can perform specific tasks, such as generating plots or executing code. Agents are equipped with skills, which are Python scripts that extend their capabilities. The video illustrates how agents interact with each other to complete a request, such as generating a stock price plot, showcasing the concept of distributed AI problem-solving.

💡Skills

Skills, as mentioned in the video, are Python scripts that agents can utilize to perform tasks. They define the abilities of an agent, such as web scraping or sending emails. In the script, the presenter shows how skills are implemented within Autogen Studio, allowing agents to carry out actions like generating images using OpenAI's DALL-E 3 model.

💡Large Language Models

Large language models, such as GPT-3.5 Turbo, are AI systems that generate human-like text based on input prompts. In the video, these models are used by agents to interpret requests and generate responses or code. The script explains how agents use these models to autonomously create content, reducing the time required for tasks that would typically take humans hours to complete.

💡Human Intervention

Human intervention refers to the instances where a human must step in to guide or correct the AI's actions. The video script discusses how the workflow can be defined to specify when an agent requires human input, ensuring that there is a balance between automation and human oversight. This concept is crucial for managing complex tasks and ensuring the accuracy and appropriateness of the AI's actions.

💡Environment Setup

Environment setup in the video involves creating a working directory, setting up a virtual environment, and installing necessary packages like Autogen and Autogen Studio. This process is essential for preparing the system to run multi-agent applications and is demonstrated as a foundational step in the tutorial.

💡OpenAI API Key

The OpenAI API key is a credential required to access OpenAI's services, such as GPT-3.5 Turbo. In the script, the presenter guides viewers on how to obtain and use this key to enable their agents to utilize OpenAI's models. This is an important aspect of integrating third-party AI services into the Autogen Studio environment.

💡Workflows

Workflows in the video are the predefined sequences that dictate how agents communicate and collaborate. They define the interactions between agents, such as which agent initiates a conversation and when the interaction should stop. The script introduces viewers to different workflow setups, like the general agent workflow and a travel agent group chat, emphasizing the customizable nature of agent interactions.

💡Playground

The playground in Autogen Studio is a testing area where users can experiment with their workflows, agents, and skills. As described in the script, it allows users to create new sessions and test various functionalities, such as requesting a stock price plot. The playground serves as a sandbox for users to see their AI configurations in action and ensure they work as intended.

💡Customization

Customization is a recurring theme in the video, emphasizing the ability to tailor agents, skills, and workflows to specific needs. The script mentions how users can create their own skills, add new models, and define agent interactions, highlighting the flexibility of Autogen Studio for creating bespoke AI solutions.

Highlights

Autogen and Autogen Studio 2.0 enable the creation of AI Bot teams that can communicate and assign tasks to each other.

A demonstration of two agents generating a stock price plot through collaboration.

Autogen Studio 2.0 is a web app for managing and prototyping multi-agent applications.

Agents can have skills, which are Python scripts that extend their capabilities.

Agents use large language models to interpret and generate text.

Communication protocols between agents can be defined, including when human intervention is needed.

A tutorial on setting up the environment for Autogen Studio 2.0.

Instructions on installing Autogen and Autogen Studio 2.0 using pip.

Adding an OpenAI API key to utilize models like GPT-3.5 Turbo.

A walkthrough of the Autogen Studio interface, including skills, models, agents, and workflows.

Skills in Autogen Studio are represented as Python functions that agents can execute.

Preconfigured models in Autogen Studio include GPT-4 and local LLMs.

Agents are autonomous and can be configured to not require human input.

Workflows define how agents interact with each other in完成任务.

The playground in Autogen Studio allows for testing workflows, agents, and skills.

A live example of agents generating a stock price plot in the playground.

Behind-the-scenes look at agent communication and problem-solving to execute tasks.

Future tutorials will cover customizing skills, adding new models, and creating custom agents.

Encouragement for viewers to subscribe for upcoming detailed tutorials on Autogen Studio.

Transcripts

hey how's it going it's never been

easier to create a team of AI Bots that

are capable of talking with each other

and assigning tasks to one another to

complete a request thanks to autogen and

autogen Studio

2.0 now check this out I asked two

agents to generate a plot of two stock

prices and after some back and forth one

of the agents generated some code the

other one tried to execute it it ran

into some issues but finally it was able

to generate the plot as requested and it

did it in a few seconds versus what

would take us hours to complete in this

video I'm going to show you how you can

set up autogen Studio 2.0 a web app

built on top of autogen that facilitates

managing and prototyping multi-agent

apps so if you're interested in learning

how to use autogen Studio stick around

all right it's important to go over some

key Concepts first an agent can have

skills now skills are just Python

scripts that extend what an agent is

capable of doing for example scraping

the contents of of a web page or sending

an email second an agent uses one or

more large language models to interpret

and generate text third we can Define

how agents talk to each other for

example if we have three agents as part

of a group we can determine which one

initiates a conversation with the others

and when the interaction stops you can

also specify when there is need for a

human intervention now that's just

scratching the surface I'm going to go

into more details in future videos so

make sure you subscribe to the channel

right now so that you don't miss

upcoming tutorials all right so now

we're going to set up our environment

I'm going to open up the terminal window

here and we're going to install autogen

and autogen Studio 2.0 first let's

create our working directory and I'm

going to call it AG Studio demo and then

we're going to CD now we're going to

create an environment you can choose to

use cond for this example I'm going to

be using VMV just because it's simple to

use but if you're familiar with Gonda

you can do exactly the same thing that

we're going to do with VM let's do

this now you you can call the

environment whatever you want I'm just

going to call it AG studio and then

we're going to activate it then activate

we know it's activated because we can

see the name here in the terminal now

we're going to install autogen Studio

we're going to use pip autogen studio

now this installs autogen Studio of

course and it's going to install autogen

the framework as well all right now

we're going to be adding our open AI API

key because we're going to be using GPT

3.5 turbo as our model for this tutorial

and to do that you're going to go ahead

and grab the key from the open AI

console and then we're going to add it

in our terminal so we're going to do

export and then you're just going to add

your key here all right I've added my

open AI key and now I'm going to run the

autogen studio so to do that we're going

to type autogen Studio UI now that's

going to take a few seconds but you're

going to see that you can access the

website on this URL so we're going to

take it and we're going to paste it in

the browser window so let's do

that and as you can see that's our

autogen studio running in the browser

now for this tutorial I'm going to keep

things very simple and we're just going

to go over the main sections within

autogen studio so we're going to take a

look at what skills are what models are

agents and workflows and finally I'm

going to show you how you can test

everything in the playground now in the

future there's going to be more detailed

tutorials for each of the sections so

make sure you're subscribed to the

channel so you get notified when I

publish these videos first we're going

to go to skills you can think of them as

the capabilities of your agents so

basically an agent can have one or more

skills skills are represented as python

functions so if if we click on one of

the functions that are available by

default within autogen studio like

generate images we can see the python

code that the skill uses in this case to

generate an image and we can see here on

line 19 that it's uh calling the model

doll E3 from open AI to generate an

image based on a query and obviously

within the studio you can create your

own skill here you're going to need to

remove this function which is a

placeholder and you're going to need to

give your skill a name and then you can

write your python script now let's take

a look at models and if we go to models

here we can see that autogen studio

comes with preconfigured models we have

GPT 4 two of them one is using the open

AI platform and one that's hosted on AIA

and we have a local llm preconfigured

here now if you want to take a closer

look let's click on the GPT 41 and we

can see that we have a name and a key

here and other configuration that we're

not going to touch on right now but I'm

going to show you how you can add your

own model if you want to so for example

I'm going to be using gbt 3.5 for future

videos so I'm going to go ahead and

create it right now I click on new model

and then say GPT 3.5 turbo and I'm going

to paste in my open AI key this button

here test model just to make sure that

everything is working properly perfect

it says model tested successfully save

it and now this model that we added

right now is accessible for our agents

and we're going to see how we can add

the models later within the agents tab

all right now let's see what we have in

the agents tab autogen comes

preconfigured with two agents uh one is

called user proxy and another one

primary assistant this one here the user

proxy acts on your behalf so whenever

you send a prompt the user proxy agent

is going to take your request and it's

going to relay it to other agents and

then if there is code that needs to be

executed within the environment user

proxy is going to do it for you primary

assistant uses a large language model

like GPT and if we click here we can see

the properties and its configuration we

have the name we have a description uh

Max consecutive auto reply this is the

number of replies that this agent can do

before it requires human intervention

agent default auto reply so in case the

agent does not execute any code what

would the reply be and the human input

mode never so it's never going to ask

for a human input it's just going to be

autonomous and work by itself system

message is essentially uh a message that

is used by the model so that it can

understand how it's going to behave so

these are kind of instructions that you

give to the model when you set up your

agent now we have the models that we're

going to be using so we have GPT 3.5

turbo which is the one we just created

we have the temperature so this kind of

controls the randomness of the response

from the model uh just going back to the

model briefly we can add other ones so

we can add GPT 4 this agent is going to

be using 3.5 by default but if it fails

for some reason it's going to fall back

to GPT 4 you can add many models here or

you can go with just one now these are

the skills that you can assign to this

agent by default this agent can find

papers on archive and generate images

now if you create other skills like

sending an email you can click this

button you would call your skill maybe

send email then you can choose it and

add

scill now workflows lets us Define how

these agents are going to be talking

with each other for example we have the

general agent workflow a two agent setup

where we have the user proxy and this

agent is going to be like I said

executing tasks on our behalf and we

have a primary assistant which is the

other agent that's going to be using the

large language model to generate text

and to generate code and that code is

going to be executed by this user proxy

now this is a two agent setup but we

have more complicated setups that we're

not going to really dive into in this

tutorial but I'm just going to show you

this travel agent group chat workflow

that includes a user proxy and a group

chat manager which inside of it we have

many agents so we have a travel planner

an assistant language assistant Etc now

it's important to note that you're in

the driver's seat so you can Define how

these agents talk to each other and when

human intervention is necessary now I'm

going to dive deeper into these Topics

in future videos so make sure you're

subscribed to the Channel just so you

don't miss any updates as soon as they

become available all right now the last

thing that I want to show you is the

playground and that's where you can test

your workflows and your agents and your

skills basically everything comes

together within the playground to create

a new session we're going to click on

either of the buttons here and I'm going

to do new and we're going to get the

option to choose the workflow I'm going

to select General agent workflow for

this tutorial I'm going to hit create

now I'm going to show you a couple of

examples as you can see we have stock

price and other options let's go with

travel and whenever we send in a request

like we mentioned before our user proxy

agent is going to take our message or

our prompt and send it to the primary

assistant which going to use the model

that it has access to to generate the

request now it can also generate code

and to demo this we're going to do stock

price now let's take a look at what the

agents are doing behind the scenes to

understand how it came up with this

chart if we expand this agent messages

tab we can see the instruction that we

sent which is to plot a chart and then

to save it as a PNG file the primary

assistant came up with the steps to do

this task it generated some python code

then the user proxy attempted to execute

it but ran into some issues and then the

primary assistant looked at the issues

and gave the user proxy some

instructions on how to resolve it there

was some back and forth between the two

agents until the code executed properly

as you can see here so the code executed

successfully and we can see see the

result file here with the stock prices

and the plot as we requested now that's

something that would usually take us a

few hours to do but you know having the

assistant of AI agents and autogen

Studio this was done in basically no

time so that's everything for this

tutorial but there's going to be many

more coming soon so make sure to

subscribe as I've mentioned a couple of

times before so you don't miss any new

videos as soon as I release them

especially that we're going to go into

details on how to customize skills and

create new ones add new models we're

also going to discuss how we can add

local models using o Lama or LM studio

and we're going to take a look at custom

agents and more workflow information so

I'm looking forward to seeing you soon

thank you for watching and I'll see you

soon