An overview of AutoGen Studio 2.0 in under 10 minutes!
Summary
TLDRThis 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
🤖 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.
🛠 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.
Mindmap
Keywords
💡Autogen Studio 2.0
💡Agents
💡Skills
💡Large Language Models
💡Human Intervention
💡Environment Setup
💡OpenAI API Key
💡Workflows
💡Playground
💡Customization
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
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