AutoGen: The Open-Source Framework for Building Powerful Chatbots and Conversational AI Systems

Discover AutoGen: The Revolutionary Open-Source Framework for Building Chatbots and AI Systems

In today’s rapidly expanding digital world, the development of chatbots and AI systems has become integral to improving user interaction and automating responses. AutoGen, an innovative open-source framework introduced by Microsoft, emerges as a powerful tool in this domain. Designed to manage complex workflows and integrate various learning models (LMs), AutoGen is redefining how businesses and developers create efficient, intelligent conversational agents.

What is AutoGen?

AutoGen is an open-source framework that facilitates the creation of sophisticated conversational agents, commonly known as chatbots. These agents are capable of executing a wide range of tasks by leveraging multiple LMs and code executors. The versatility of AutoGen allows it to serve numerous applications, from language translation to monitoring system security.

Key Features of AutoGen

1. Integration with Multiple Learning Models

AutoGen supports integration with various learning models, allowing the agents within the framework to perform diverse and complex tasks efficiently.

2. Flexible Agent Interactions

The framework enables different agents to interact within an environment, exchange messages, and collaborate to complete tasks. This interaction mimics a real-world process where multiple entities work together to solve problems.

3. Easy Installation and Setup

Getting started with AutoGen is straightforward. It can be installed within a Python environment using simple pip install commands, and it also supports Docker, making deployment versatile and scalable.

4. User-Friendly Interface

AutoGen offers both CLI and GUI interfaces, accommodating different user preferences and making it accessible to a broader audience, from beginners to advanced developers.

Applications of AutoGen

AutoGen can be employed in various scenarios, such as:

• Automated Language Translation: Simplifying the process of translating documents or text from one language to another without human intervention.
• System Security Monitoring: Providing real-time surveillance of systems to detect and respond to security threats automatically.
• Financial Analysis: Analyzing and predicting market trends by processing large volumes of data from financial markets.

Getting Started with AutoGen

Installation Process

To begin with AutoGen, users need to set up a suitable Python environment and execute a simple installation command:

pip install autogen

Following installation, developers can start exploring AutoGen’s capabilities by setting agents and defining workflows.

Building Your First Chatbot

Creating a chatbot with AutoGen involves defining agents and their roles. For instance, one agent could handle user inquiries while another processes the data and generates responses. Here’s a basic example of setting up a chatbot with AutoGen:

from autogen import Agent, Environment
env = Environment()
agent_user = Agent(role='user')
agent_assistant = Agent(role='assistant')
env.add_agents([agent_user, agent_assistant])

Conclusion: Why Choose AutoGen?

AutoGen stands out as a framework due to its robustness, flexibility, and the backing of an active community and industry leader Microsoft. It continuously evolves with contributions from developers worldwide, ensuring it remains cutting-edge.

Whether you’re a novice looking to explore the realm of AI and chatbots or an experienced developer needing a powerful tool to build complex automated systems, AutoGen offers the resources and capabilities to transform ideas into real-world applications.

Stay tuned for further parts of our series where we delve deeper into tutorials, advanced features, and real-life case studies of AutoGen at work. Join us in harnessing the power of open-source AI to create dynamic and impactful solutions.

[h3]Watch this video for the full details:[/h3]


Discover AutoGen, a revolutionary open-source framework that empowers you to create sophisticated chatbots and conversational AI systems. Unleash the potential of large language models, code executors, and even human experts to craft interactive agents capable of engaging conversations and completing complex workflows. Explore real-world examples and dive deep into the world of AutoGen in this comprehensive tutorial!

[h3]Transcript[/h3]
as soon as I show this video now you could have found what we are going to speak today it’s none other than autogen hi hello everyone Welcome to our Channel today we are going to talk about autogen an open source framework from Microsoft that allows complex workflows using agent okay let’s get started okay let’s get started Auto autogen is somewhat the Hot Topic in AI where we can try to accomplish complex workflows using multiple LMS autogen is nothing but uh we are setting an environment inside that there will be multiple LMS which could accomplish our task a complex task also okay in the world of computers an agent is something like a special kind of software uh that can interact with the environment and other agents they can also send messages and receive uh messages to complete complex task or have conversations within each other to understand what it is doing action autogen agents can be powered by different things like uh uh llms or code executors um or even humans can also interact with the environment this makes them very flexible and allows us uh to create a wide variety of workflows a most complex use cases can be solved using this okay uh for example uh we could use autogen to create a workflow that automatically translates the document from one language to another or we could also use to create a workflows that monitors the system security for any threats uh currently going on the the use case which I have mentioned now uh maybe simpler uh but we could see in future and near future um uh how complex it could solve everything okay uh this is the part one uh soon we will see multiple parts and cover all the topics which is available in autogen we could cover the parts like getting started installation and uh a tutorial of interactions uh on autogen uh in getting started itself uh we could see some uh images uh basically getting started with already what I mentioned like what autogen can do it is something like an in an environment there are multiple LMS which could do more things that is in getting started and uh in this uh in this image right we could see that here uh there are two agents like user proxy agent and an assistant agent uh where uh as like users uh ask to plot meta and Tesla stock price in YTD uh basically it starts interacting with each other and trying to figure out the figure out and solve the errors and as soon as sometime uh users again input has also given to these two agents and then at last it uh gives a output which the user expects or user diss output okay then after this uh let’s move with the installation part uh installation when it comes to python right we just need to create an environment uh within the environment we need to just install pip install by Auto that’s it everything will be done over that and uh now currently we uh we have GOI version also using uh python script also we could write um when it comes to UI version that we will see in later Parts how to start the UI version and execute what is required that we can see again we could do with the docker installation also Docker image is also available and optional dependencies they mention over here but uh when I tried it out uh it is somewhat autogen should be in some older versions some of the dependencies need autogen P autogen older version but the later version is more more enough for us to get started okay uh let’s start with the tutorials uh let’s try to cover uh other tutorials pretty soon uh let’s start today with uh uh introduction right uh introduction to autogen right okay uh let’s start with why autogen uh think of an agent as a small computer program uh that can chat with other agents uh they can send messages or receive messages and they can even come up with the response using different tools uh these tools can be anything from fancy language models uh to a simple computer program or even like uh humans what we give as an input uh this lets agents to handle uh all sort of task like uh Translate languages or keeping eye on uh computer security issues like already I mentioned right uh then when it comes to uh building a complex workflow like uh worklow right uh autens are like Lego toys we have known about Lego toys right uh for building computer workflows or uh you can take up a simple agent and make a special feature to them uh making them more more powerful and giving uh access using we can even try to run python script or uh we can give human valuable inputs in between their execution um then we can connect these agents together uh to tackle big tasks uh imagine a team of Agents working together uh to Pro solve a problem right that will be a massive massive uh which will uh give us more power on this uh more understanding on different different llms even one llm makes a mistake other LMS can figure it out that why autogen are special right uh there are two main things to make autogen show unique first is consistently being improved by a a bunch of clever engineers and uh researchers around this backed by Microsoft actually uh they’re always adding a new features to making uh make sure that it stay in stateoftheart uh Frameworks this is version two uh version one was released before 6 months I I guess the second one is being uh it is being used by tons of different things uh more open source developers open source Community is contributing to this open source Community try to use this uh in a very great Manner and try to solve their own use cases Let’s uh go with uh agents part okay what are autogen agents we have already talked what is Agent everything but still when it comes to agents right uh it is uh something like which able to Tiny robots which is able to uh receive messages and respond accordingly uh this tool can be like uh anything uh like a large language model like gp4 or any open source model or any model which has an APA and AP key right uh that will allow as an agent uh this flexib this flexibility allows agents to handle all types of task um okay U let’s start uh thinking about uh uh building a teamate autogen is like a workshop for building uh the super squats actually I mentioning here super squats as uh uh these multiple agents uh let’s start with the basic agent and then uh we can add up special features to it like different tools and more powerful tools uh here uh the cool part is right you can connect the agents together and imagine the teams uh agent working side by side to solve a problem which we uh seen in nvidias and meta stock comparison right that is something more powerful in this uh let’s when we are saying super squads right uh let’s talk about uh let’s take some examples and uh explain that uh let’s think of a superb brain uh these fancy language models can uh understand and generate text uh these super brains can be like gbd4 uh then let’s keep uh another agent called code ninjas right uh these can be run run special programs and perform complex calculations then human experts that’s where we come uh we can even bring our own inputs to the models uh we could give uh suggest new things so that new task can be described everything can be intered well the execution okay at last who can use the autogen right autogen is a great tool for anyone who wants to build a complex computer program using these teams are very helpful for them uh whether you are a beginner or an expert autogen is something to offer you anything what you need in uh as a completed to be completed as a complex task uh now now we can see an example how to execute a single agent and we can also uh use multiple agents to set roles and make them con conversate within each other okay uh here we can see an example of uh setting up a single agent which is a gp4 agent here we need to give a open AP key for the gp4 to execute and and uh do we need anything now at least for now uh code execution config is seted as false and function map is something like I already mentioned even a single simple python code uh can be executed that is known as the function map now we are not going to give any function map uh does this uh does this agent need any uh human inputs in between that is also set up to never and uh basically they are trying to ask agent or generate reply like message is something like content tell me a joke role who are who the who this asking is something you that is us right then the replay is something like sure let me tell a light harded joke and it it spit outs a joke I think this joke is something more common joke which is generated by uh gp4 or llms uh then when it comes to roles and conversation here comes the multiple agents One agent is known as Kathy and the system prop is your name your name is Kathy and you are a part of do in a comedi um then the here comes the config we are using same gbd4 and the temperature is 0.9 and we are also passing the AP ke human input is neighbor then again here is another Joe and Joe is system promp is your name is Joe and you are a part of uh uh of a do of a comedian comedians okay here also same LM config and human input mode uh basically we are starting with joe. initiate chat of Kathy also passed as input over here messages Kathy tell me a joke and Max turn is is equal to zero Max turn means uh how much each agent should respond uh that is known as the Max uh as soon as this we start executing it right first the Jo Joe to Kathy something like Kathy tell me a joke Kathy starts to say a joke Joe uh replies to I mean Joe replies to the joke which is regenerated by the Kathy and uh it also asks some questions and within some answers then Kathy to Joe it again responds uh this is one uh Max turn this this is first turn and this is the second turn this is first turn uh by Kathy and this is second turn by Kathy uh both Joe and Kathy takes only two turns that that’s why we have mentioned uh Max turns is equal to two Okay uh let’s see this in collab when it comes to collab right uh main thing we need to do is we need to install py autogen okay let’s just give P Auto install p autogen uh let’s just run anyway P within that P autogen is running right here also we are going to mention this here the example is also like the same Kathy and Jo’s example um the main thing is something like uh uh we need to give uh open a key as an input uh and we also uh need to configure uh these things that are all the main things uh as a conversation uh this is a single agent uh running we we already said right using only one gp4 uh to generate jokes uh we could see the response of 1G pt4 okay why don’t as not trust uh atems uh because they make up everything uh this is very very standard joke from uh alms what they always generate let’s see about roles and conversations here we are setting Kathy and Joe and uh if he started running we started running this out right uh we could see um the it has been run the temperature 0.9 and 0.7 uh let’s uh if we start to run the results right first Joe to Kathy Kathy tell me a joke Kathy to joke it tells some joke and again Joe to Kathy and Kathy this I I already mentioned that Max turns is equal to two what if we turn to three okay uh 1 2 3 and 1 2 3 it makes up three conversation within each other uh done right uh this is something very fantastic and uh very exciting to us uh uh now let’s try out uh changing the system messages uh your your name is Kathy and you are a part of of no let’s change this to you are a part of uh rocket science team uh let’s copy paste this to Joe Also let’s set this system prompt for both of them and Kathy tell Mead me Waters rocket signs right let’s start executing this will be going to be interesting right okay the wat rocket science rocket science is a branch in Aero aerospace engineering and it starts explaining long very long sentences and Joe says thank you Kathy as rocket scientist my job involves a lot of complex calculations then Kathy explain says to Joe absolutely uh the immense level of details uh then Joe says to yes yes you right Kathy everything a single detail matters in rocket science and again Kathy says to Jo uh as you should be it’s um wonderful to hear to passion and dedications they both each other talk right this is something interesting uh we are now just seeing out uh what is uh just text explanations but in near future or there will be multiple use cases understanding the whole code base one will be uh handling like uh one will be like thinking how to generate codes one one agent will be thinking how to solve errors uh one agent will be thinking how to uh generate unit test for this code uh everything will be done with agents right uh at last uh this uh this autogen is something like uh uh as already mention multiple agents multiple en llms getting involved uh to complete complex task now today we have seen a very simple very very simple and introduction task uh in pretty soon we will see uh other use cases for autogen and how it can be Ed for uh our our own day-to-day lives uh thank you thank you guys uh thank you