AutoGen Studio Tutorial - NO CODE AI Agent Builder (100% Local)

Summary

Autogen Studio, a Microsoft Research-backed project, has launched an open-source tool enabling users to create AI agent teams effortlessly. It supports integration with external models like GPT and local models, facilitating tasks from stock chart plotting to trip planning and coding. This video tutorial guides viewers through the installation, setup, and utilization of Autogen Studio, demonstrating how to harness its capabilities with GPT 4 and local models. It covers creating environments, setting up API keys, defining agents and skills, and constructing workflows. The video also showcases real-time agent interactions and task completion, highlighting the platform's flexibility and potential for complex, multi-agent operations.

Takeaways

• 🚀 Autogen Studio is a new tool released by Microsoft Research, enabling users to create AI agent teams with ease.
• 💡 It's an open-source project that can be run locally and supports integration with both GPT and local models.
• 🛠️ Users can perform a variety of tasks with Autogen Studio, such as plotting stock charts, planning trips, and writing code.
• 💻 To use Autogen Studio with GPT, you need to install Conda for managing Python environments and create a new Conda environment.
• 🔑 An OpenAI account and API key are required to power Autogen Studio with GPT.
• 📦 Autogen Studio includes a user interface that simplifies the process of setting up and managing AI agents and their tasks.
• 🛠️ Skills in Autogen Studio are tools or pieces of code that AI agents can use to accomplish tasks, such as generating images or finding papers.
• 🤖 Agents are individual AI entities with roles and tools; they can be set up to use different models, including local models.
• 🔄 Workflows in Autogen Studio combine agents and tasks, allowing for complex interactions and the creation of agent teams.
• 🌐 The platform supports local model usage, which can be set up using tools like Ollama and Light LLM for on-premise AI model execution.
• 🔍 Autogen Studio also allows for the creation of custom skills and the ability to assign different tools to different agents for specialized tasks.

Q & A

What is Autogen Studio?

Autogen Studio is a tool developed by Microsoft Research that allows users to create sophisticated AI agent teams with ease. It is fully open source and can be run locally, powered by models like GPT or local models.

How can Autogen Studio be installed and set up?

To install Autogen Studio, you need to create a new conda environment with Python 3.11, then install Autogen Studio using pip. After setting up the environment and installing the necessary packages, you can start using it.

What is the role of conda in setting up Autogen Studio?

Conda is used to manage Python environments, which simplifies the process of setting up the required environment for Autogen Studio. It allows users to create a new environment and install the necessary packages without affecting the system's global Python installation.

How do you integrate Autogen Studio with GPT-4?

To integrate Autogen Studio with GPT-4, you need to create an API key from your OpenAI account and export it in your environment. This allows Autogen Studio to access the GPT-4 model for its AI agent teams.

What are skills in the context of Autogen Studio?

Skills in Autogen Studio are tools that AI agents can use. They are usually written in code and can be anything from generating images to fetching data. Skills allow AI agents to perform specific tasks.

What is an agent in Autogen Studio?

An agent in Autogen Studio is an individual AI with a role, tools, and the capability to perform tasks. It can be configured to use different models and can be part of an AI agent team.

How can you create a new skill in Autogen Studio?

To create a new skill, you go to the 'Build' tab, click 'New Skill', give it a name, and write out the code for the skill. This code defines the functionality that the AI agents can use.

What is a workflow in Autogen Studio and how is it used?

A workflow in Autogen Studio puts everything together, including the team and the task to be accomplished. It defines the interaction between agents, the summary method for conversations, and the sequence of tasks to be executed.

How can Autogen Studio be used with local models instead of online models like GPT-4?

To use Autogen Studio with local models, you can use tools like Olama to download and run models locally, and Light LLM to expose an API for these models. You then configure Autogen Studio to use these local APIs instead of online models.

What is the Playground in Autogen Studio and how is it used?

The Playground in Autogen Studio is where you test different agent teams. You can create a session, assign a task, and see the agents interact to accomplish the task. It also allows you to publish sessions to the web for further analysis.

How can you switch between different models for different agents in Autogen Studio?

You can switch between different models for different agents by creating separate agents for each model and configuring their respective workflows. This allows for a flexible setup where each agent can be optimized for specific tasks using the most suitable model.

Outlines

00:00

🚀 Introduction to Autogen Studio

Autogen Studio, developed by the Microsoft Research team, is a platform for creating AI agent teams. It's open-source and can be run locally with support for both Chat GPT and local models. The video will guide viewers through installation, setup, and usage with both GPT 4 and local models. The presenter emphasizes the need for 'conda' for managing Python environments and demonstrates creating a new conda environment and installing Autogen Studio. It also covers setting up an OpenAI API key for Autogen to access.

05:00

🛠 Setting Up Autogen Studio with Chat GPT

The video provides a step-by-step guide on setting up Autogen Studio with Chat GPT. It starts with activating the conda environment and installing Autogen Studio. The process involves creating an API key from the OpenAI account, setting it in the environment, and starting Autogen Studio with a specified port. The presenter then introduces the user interface, explaining the terminology like 'skills', 'agents', and 'workflows'. Skills are tools for AI agents, agents are individual AIs with roles and tools, and workflows combine agents and tasks.

10:02

🎨 Using Autogen Studio with Local Models

The presenter demonstrates how to set up Autogen Studio with local models, specifically using 'olama' and 'light llm'. The process includes installing olama, downloading a local model, and setting up a server with light llm. The video shows how to create a new agent powered by a local model and configure a workflow to use this agent. It also highlights the ability to have multiple agents with different local models running simultaneously, showcasing the flexibility of Autogen Studio.

15:04

🔧 Advanced Configuration and Testing in Autogen Studio

The video concludes with advanced configuration tips and testing the setup. It shows how to create different agents with various local models and incorporate them into workflows. The presenter also discusses the ability to publish sessions to a gallery for review and the potential for custom authentication logic within Autogen Studio. The video ends with a call to action for feedback and suggestions for future content.

Keywords

💡Autogen Studio

Autogen Studio is an open-source project developed by Microsoft Research, which enables users to create sophisticated AI agent teams with ease. It is a platform that allows for the integration of different AI models and tools to perform a variety of tasks. In the video, the presenter demonstrates how to install and use Autogen Studio, highlighting its user-friendly interface and the ability to connect with models like GPT-4 or local models.

💡AI Agent Teams

AI Agent Teams refer to a collection of individual AI agents that work together to accomplish tasks. These agents can be programmed with specific roles and tools, allowing them to perform complex operations autonomously or with user input. The video script discusses setting up AI agent teams in Autogen Studio, showing how they can be used for tasks like plotting stock charts or planning trips.

💡OpenAI API Key

An OpenAI API Key is a unique identifier used to access OpenAI's services programmatically. In the context of the video, the presenter guides viewers on how to create an API key for Autogen Studio to interact with OpenAI's services, such as generating images or accessing GPT models. The API key is essential for enabling Autogen Studio to perform tasks that require access to external AI services.

💡Local Models

Local models refer to AI models that are hosted and run on the user's own infrastructure, as opposed to cloud-based services. The video explains how to set up Autogen Studio to use local models like Mistral, which can provide faster and more private interactions. The presenter demonstrates the process of downloading and integrating local models into Autogen Studio, allowing for greater control and customization.

💡Skills

In Autogen Studio, 'skills' are tools or functionalities that AI agents can utilize to perform specific tasks. These skills are often coded scripts that agents can execute when needed. The video script includes an example where the presenter creates a skill for generating images, showcasing how skills can be added to agents to expand their capabilities.

💡Workflows

Workflows in Autogen Studio are the sequences of actions that define how a task is to be accomplished by the AI agent team. They include the agents involved, the order of operations, and the tools or skills to be used. The video demonstrates setting up workflows for different tasks, such as creating visualizations or group chats, to show how tasks can be automated through the platform.

💡GPT-4

GPT-4 is a reference to a hypothetical next-generation model from OpenAI, which is expected to be more advanced than its predecessors. In the video, the presenter mentions using GPT-4 as the powering model for Autogen Studio, indicating the potential for leveraging cutting-edge AI capabilities within the platform.

💡Conda

Conda is a package manager and environment management system used for installing and managing software packages and their dependencies. In the video, the presenter uses Conda to create a new environment for Autogen Studio, ensuring that all required packages are installed and isolated from other projects.

💡Group Chat Manager

The Group Chat Manager is a component within Autogen Studio workflows that handles communication between multiple AI agents. It is especially useful when more than two agents are involved in a task, facilitating the coordination and exchange of information. The video script describes how to set up a group chat manager and add agents to it, which is crucial for complex agent interactions.

💡Playground

The Playground in Autogen Studio is an area where users can test and experiment with different agent teams and workflows. It allows for the creation of sessions where tasks are assigned to AI agents, and their execution can be monitored. The video demonstrates using the Playground to create sessions and assign tasks like plotting stock charts or generating images.

💡Mistal

Mistal, in the context of the video, refers to a local AI model that can be used with Autogen Studio. The presenter shows how to download and set up Mistal using tools like Ollama and Light LLM, which allow for the local hosting of AI models. Mistal is used as an example of how Autogen Studio can be powered by different local models, providing flexibility in choosing the right AI for the task.

Highlights

Autogen Studio, a project by Microsoft Research, allows users to create AI agent teams with ease.

It's a fully open-source project that can be run locally and powered by various models.

Autogen Studio can be used for a wide range of tasks, from plotting stock charts to planning trips and writing code.

The video tutorial guides viewers on how to install, set up, and use Autogen Studio with both GPT 4 and local models.

Cond is required to manage Python environments for integration with Chat GPT as the powering model.

Autogen Studio comes with a user-friendly interface that simplifies the creation and management of AI agents.

Skills in Autogen Studio are tools that AI agents can use, often written in code and accessible to any agent.

Agents are individual AIs with roles, tools, and the ability to perform tasks, and can be powered by different models.

Workflows in Autogen Studio combine agents and tasks to accomplish specific objectives.

The Playground feature allows users to test different agent teams and their performance on given tasks.

Autogen Studio enables the creation of custom skills through coding, expanding the capabilities of AI agents.

The video demonstrates how to use Autogen Studio with local models, showcasing its flexibility.

Olama and Light LLM are tools used to power local models, providing an alternative to cloud-based AI services.

Autogen Studio supports the use of different models for different agents, allowing for tailored AI solutions.

The video includes a walkthrough of setting up authentication within Autogen Studio for team sharing.

The tutorial concludes with a demonstration of Autogen Studio's ability to handle complex tasks like generating images and plotting stock data.

Transcripts

autogen studio is here the Microsoft

research team behind autogen the

Revolutionary AI agent project has

finally released autogen Studio which

allows you to create sophisticated AI

agent teams with ease it's a fully open

source project you can run it locally

you could power it with chat GPT and you

can also power it with local models

everything from plotting stock charts to

planning trips to writing code this is

what chaty PT's cust gpts were supposed

to be so in this video I'm going to show

you how to install it how to set it up

and how to use it both with GPT 4 and

local models so let's go first the only

thing you're going to need to get this

to work with chat GPT as the powering

model is cond so if you don't already

have cond installed it's a super easy

way to manage python environments which

is always a headache otherwise so if you

don't already have it installed go ahead

and install it now it's very easy so the

First Command we're going to run is to

create a new cond in environment and I'm

going to say condac create DN AG for

autogen and then we're going to use

Python equals 3.11 and then just hit

enter it's going to ask you if you want

to proceed just hit enter again and it's

going to install all the packages that

we need all right once you're done there

you're going to highlight this code

right here to activate the new

environment so go ahead and highlight it

paste it and it's cond to activate Ag

and then hit enter and you could tell

it's activated now because it says AG

right there next and this really

couldn't be easier we're going to

install autogen studio and by installing

autogen Studio we get everything with

autogen as well as the user interface so

it's pip install autogen studio and

remember this installs to the

environment that we just created so if

you deactivate this environment or you

switch environments it's not going to be

available there so hit enter and it

installs everything we need next open up

your open AI account go to the API key

section and we're going to create a new

key and I'm going to call it AG for

autogen and then create secret key I am

going to revoke this key before

publishing the video go ahead and click

copy switch back to your terminal and

now we're going to export the open AI

key setting it in our environment which

allows autogen to access it and to do

that we're going to type export open

aore API key all capitalized equals and

then you're going to paste in your newly

created key then just hit enter next

we're going to spin up autogen studio

now we're pretty much done so just type

autogen Studio space ui-- port 8081 and

then hit enter and then it's going to

spin up autogen studio for us and

provide us with a URL and here we go

it's Local Host 8081 so we're going to

copy this URL right here switch over to

your browser and now this is autogen

Studio it is absolutely gorgeous and

it's super easy to use and I'm going to

show you how to do all of it now that's

really all you need to do to get autogen

Studio working with chat GPT a little

bit later in this video I'm going to

show you how to set it up with local

models including powering individual

agents with different models it's pretty

amazing and the first tab we're going to

start on is built just so I can tell you

about all the different terminology so

first let's talk about skills skills are

tools that that you can give your AI

agents and AI agent teams they can be

anything but they're usually written in

code so there are three by default here

generate images and if I click into this

we can see this is actually just the

code for generating images and what it

does is it sets up a method that hits

the open aai Dolly endpoint and

generates an image and then it Returns

the image and that's it and now any

agent can use this generate images tool

we also have find papers on archives so

if I click into there it's exactly what

it sounds like it's going to accept a

query and it's going to return papers

that are found on the archive website

now you can probably imagine how

amazingly powerful this really is you

can give it tools for anything any API

that you can connect with you can give

it tools for and not only apis you can

give it instructions to accomplish

pretty much any task and where my mind

starts going is connecting it to a

service like zapier and then all of a

sudden you have Integrations into so

many different applic appliations and

you can mix and match them and

accomplish incredibly sophisticated

tasks through giving zappier

Integrations as tools to your agents so

this is what you do so let's click new

skill and it's as simple as that you

give it a name and then you write out

the code for your actual skill so we're

not going to do that but that's how you

would accomplish it next are agents and

this is the most obvious one an agent is

just an individual AI that has a role

tools and can perform tasks so by

default it comes with two primary

assistant and user proxy this mimics the

autogen framework for when we didn't

have a user interface so user proxy you

can think of as you the user and the

user can actually jump in and give input

or not give input at all and let the

autogen team accomplish the task

completely autonomously the primary

assistant is also exactly what it sounds

like it's another AI agent that doesn't

represent the actual user and it's

completely autonomous it can write and

run code it can use tools it takes on a

roll a description and everything and

I'll show you how to create a new agent

in a little bit and by the way this is

also where you specify which model the

agent is going to use whether you want

to use chat GPT 4 or you want to use a

local model next is workflows and a

workflow puts everything together

including the team and the task you want

to accomplish so here's a travel agent

group workflow let's click that so we

name it a group chat workflow so we have

some options for summary method which is

just defining the method to summarize

the conversation and we can just say

last none or llm and then we have the

sender and the receiver and this is

really important the sender is usually

going to be user proxy although it can

change if you get more complex teams and

then the receiver is going to be the

group chat manager so whenever you have

more than two agents more than just a

user and an assistant agent that's when

you start to use the group chat manager

and I can even click into the group chat

manager and here is where I can add all

of the agents into this group chat so I

have the primary assistant the local

assistant and the language assistant I

give the group chat manager a name I can

give it a description I can give it Max

consecutive auto replies and if a lot of

this stuff seems forign to you check out

my video where I break down autogen in

detail and Define and show you how to

use all of these different settings cuz

they are important I'll drop that link

in the description below human input

mode so we have never only on terminate

or always on every step here we have our

system message where we can actually

just say group chat manager or we can

define a more complex system message

which helps control the agent Behavior

then here is where we can Define our

model we can add multiple models and

it's going to daisy chain them together

so it's going to start with GPT 4 here

and if I added another one it would fall

back to that if for whatever reason GPT

4 didn't work so remember whatever the

first model is here in the list that's

going to be your default model and

unfortunately I couldn't figure out a

way to drag and reorder the models so

you'll actually have to just delete it

and add it in order then down here is

where you can add skills and remember

the skills are pieces is a code that the

agents can run so go ahead and click

that and we have for example this

generate images and we can just add the

skill like that and now this agent the

group chat manager has the skill of

generate images and then down here it

says or replace with an existing agent

so we can just say that and it'll fill

out everything for us so then when we're

done with that we click okay but I'm not

going to do that because I don't want to

save it next we have the playground and

this is actually where you're going to

be testing out the different agent teams

so you can think of a session as a fixed

amount of time where an a agent team

goes to accomplish a task and the cool

thing is I believe this is asynchronous

so let's go ahead and click and create a

new session I'll show you how to do the

mistal workflow in a bit cuz that's a

local model but let's do visualization

agent workflow and we can see all agent

workflows here if we want but let's go

back and let's choose the visualization

agent workflow then we click create and

there we go now from here we can publish

it to the web which is really cool we

can delete it and this is where we give

it the task we want it to complete so

let's say stock price plot h chart of

Nvidia and Tesla stock price for 2023

save the result to a file named Nvidia

Tesla PNG so now it's pinging gp4 to do

that and you can see it's working by

this little waiting icon right here now

one thing I would have liked is if it

streamed the results to this window but

it seems like it waits till it's

completely done before showing the

result all right there so that is again

one thing that I'd like to see a little

bit different is I want to see each step

being output because the only way to

really tell anything is happening is if

I switch over to my terminal and I

actually watch the output so you can see

all the output here this is what autogen

typically looks like and then the UI

just puts it in a really pretty

interface so let's scroll to the top

sure here's the result of your request

okay so we can see the different agent

messages going back and forth so the

user proxy says so this is the user

agent representing me so plot the chart

of Nvidia and Tesla then we have the

visualization assistant which creates

the plan to actually do that writes the

code so here here's the code that I just

wrote so the visualization assistant

says please run the above script to

fetch the stock data once the data is

fetched save it to stock data.csv so it

does that the user proxy says okay done

and fetched and saved then the

visualization assistant says great now

we can run the visualization of it and

then the user proxy runs that code and

saves it to Nvidia Tesla PNG and then

here's the results so we have the stock

data.csv file we have the PNG which is

the actual visualization of the stock

price over time we have the plot stock

chart. py file which is the code to do

it and we have the fetch stock data. py

also and the nice thing is you can

easily turn these into tools so that it

doesn't have to recreate these tools

next time and So currently it doesn't

look like you can do it in a one-click

way what you would do is you would go

back to build go to skills create a new

skill and essentially copy paste what's

in here back into a skill on this page

right here but that's it that shows how

to do it and it is incredible I find

that autogen studio makes it a lot

easier to manage your tools most of all

I always found that tool usage from the

autogen code was a little bit difficult

and let's try one more thing let's

create new and I'm going to do travel

agent group workflow create and I'm

going to click paint here let's see what

it does paint a picture of a glass of

Ethiopian coffee freshly brewed and a

tall glass cup so obviously this is

going to be using Dolly I'm going to

switch over to terminal and we're going

to watch it actually work so here it is

the user proxy agent saying that's what

it's going to do okay so it's saying I'm

unable to physically paint a picture so

what that is telling me is that my agent

team doesn't actually have the right

tool to do that so let's give it that

tool so to fix this problem what we're

going to do is we're actually going to

use a different agent team that actually

has the paint skill so let's go back to

build and let's look at the general

agent workflow now we can see it has a

sender of user proxy and a primary

assistant receiver with two skills if I

click into there I can see one of those

skills is generate images so that should

be good to actually generate a picture

and we can see the daisy chain of

different models that it's using so

first it's using GPT 4 so let's go ahead

and try it now so go back to playground

I'm going to create a new session I'm

going to use the general agent workflow

create and then I'm going to say paint

and now hopefully this works all right

switching over to the terminal it does

look like it generated the image and

let's see what happened there it is

perfect that's exactly what I asked for

wonderful so you can see it is important

you think about which tools are assigned

to which agents which agent teams if

you're asking it to do specific things

that using that tool and we can also

look at the pi file and we can actually

see the code that it wrote to generate

that image and so now I like that one if

I just click publish it says session

successfully published I go over to

gallery and now I can find that session

I just click here and open it up and I

can actually see what just happened all

right now now I want to show you how to

use this completely locally and what

you're going to need for that is two

things olama and light llm oama is a

wonderful tool that allows you to power

models locally super easily and light

llm is a wrapper to expose an API even

if you don't understand what any of that

means it doesn't matter I'm going to

show you how to use it it's dead simple

so we're going to switch back to our

terminal we're going to create a new tab

right here we're going to use the same

cond environment so cond activate AG hit

enter now it's activated and the first

thing you're going to do is install oama

and it really could not be easier go to

ol. click download and go through the

installation process I've already done

it so I'm not going to do it right now

but it's dead simple when you're done

you should see a little llama icon in

your task tray and that's it now to

download a model what we're going to do

is type O Lama run mistol and we're

going to download the mistal model now I

already have it downloaded so it's not

going to download it again for me but

when you run this it will download it

and it's about 4 gigabytes so I hit

enter just to make sure it's working and

there it is so I can just test it with

tell me a joke okay perfect so there it

is now we know this is mistal running

completely locally okay now we're going

to open up another tab again we're going

to use the same cond environment and by

the way you don't actually need to keep

this oama instance open anymore but it

doesn't matter if you do or don't so

over here we're going to switch back to

cond activate AG perfect and now we're

going to install Light llm so pip

install Light llm Das Das up upgrade

just in case you already have it and you

need to upgrade it hit enter now one

issue that I ran into that I already

fixed and I probably won't run into it

again but I want to show you it is it

said that it was missing a module and

the module that was missing is called

gunicorn and I don't know why it wasn't

installed as part of the light llm

package but it wasn't so all I had to do

to fix that was pip install gunicorn so

if you get an error where G unicorn

can't be found that's how to fix it now

to set up a server with mistal running

powered by oama this is all you do light

l m-- model oama SL mistol and then hit

enter and there we go we're all ready to

go and we can see that the server spun

up right here it's Local Host 8000 so

we're going to go ahead copy that URL

now switch back to autogen now we're

going to go back to the build tab then

I'm going to go to agents and I already

have the mistal assistant so I'm going

to delete that as well and now I'm going

to create a new agent first and it's

going to be a mistal agent so I'm going

to say mistal assistant agent

description helpful assistant power by

mistal locally Max consecutive auto

replies I'm going to leave that there

human input mode never and a system

message and I'm just going to keep it

simple you are a helpful assistant now

right here you can see it's defaulting

to gp4 so go ahead and get rid of that

we're going to click add and this is

where we actually tell it to be powered

by the local mistal model so here I'm

going to call it mistel you don't need

an API key the base URL we're going to

click paste this is that local URL that

we just copied everything else we do not

need and then we're going to click add

model we're not going to give it any

skills now but feel free to do that when

you're testing it then click okay now we

have a mistal assistant powered by

mistal and the cool thing is I can also

have a Mixel assistant and I can also

have a nous Hermes assistant and they

can all run at the same time it is truly

incredible now let's go to workflows

we're going to create a new workflow and

we're going to say this is a mistal

workflow workflow description we'll

leave that the same summary method

that's fine user proxy that's fine and

this is going to be gp4 powered so if

you did want to have the user proxy

locally powered go to the user proxy

agent and switch out GPT 4 for mistol

then as the receiver we're going to

change that so we're going to change the

receiver to be the mistal assistant and

so for the agent name we're going to

call it mistal assistant agent

description I'm going to leave it blank

for now again feel free to customize

this as much as you want human input

mode never this is the system message

that is used used for autogen so I'm not

going to touch this then we're going to

delete gp4 here we're going to add a new

model again we're going to say it's

mistol same thing Local Host 8,000 right

there and then we're going to click add

model we're not going to give it any

skills and we're just going to click

okay then okay again now we have a

mistel agent we have a mistel workflow

we should be able to use it powered by

mistal so let's go to the playground

we're going to click new we select

mistal workflow and then create and then

let's say tell me a joke just to see if

it works hit enter and there we can

actually see that it worked so there's

the post two chat completions and this

is the light llm so it should have

worked and it did although it did not

tell me anything good that's fine let's

see it accomplished something a little

bit more difficult write code to Output

numbers 1 to 100 okay there it is so it

was extremely fast switching back over

to the terminal we can see that it

actually did post to chat completion so

it worked and there's the code and

here's the termination messages so user

proxy says write code to Output numbers

1 to 100 the mistal assistant writes the

code and sends terminate which

terminates everything and so that's it

now you know how to power autogen studio

with a local model and what if you did

want to have different models for

different agents to do that we come back

here over to ol llama exit out of there

and so what you would do is AMA run

llama 2 that'll initiate the download

and once it's done downloading with

olama we can leave this light llm that's

running mistl up then we create a new

tab then we cond activate AG light llm

-- model o lama lama 2 hit enter it'll

give you a new URL and you do the same

exact thing you come in here go to build

set up a new agent as llama assistant

and then you input the URL as normal

then you set up the same workflow as

normal and you're done now you have

different assistants powered by

different local models and you can plug

and play as you see fit the best part is

you can find the right fine-tuned model

for the right task and one last thing I

want to mention is it actually has this

sign out functionality but when you

click it it says please Implement your

own logout logic which means you can set

up your own authentication within

autogen studio so if you wanted to share

this amongst your team you could set it

up to do that I am so impressed by

autogen Studio let me know what you

think in the comments if you want me to

do any kind of followup or deeper dive

into autogen Studio let me know what you

want to see in the comments if you liked

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