Build an AI Workforce 🤖💬🤖 Multiple Conversable Agents
Summary
TLDRThis video tutorial explores enhancing an Autogen application by integrating multiple assistant agents, each with a distinct role, such as a coder or product manager. It delves into the concept of caching, or 'seeding', which significantly boosts performance by storing previous results. The video demonstrates how to create a new cache by altering the seed value and discusses the impact on performance. It also guides viewers on setting up a group chat for agents to communicate, introducing the Group Chat Manager to coordinate interactions. Lastly, it touches on the 'human input mode' setting, which controls user feedback frequency, and concludes with a live demo of agents collaborating to solve a coding task and propose product applications.
Takeaways
- 😀 The video discusses enhancing an autogen application by adding multiple assistant agents, each with a distinct role.
- 🔧 Autogen's caching mechanism, facilitated by a 'cache' folder, significantly improves performance by storing results from previous runs.
- ⏱️ The first execution of an autogen application is slower due to model API calls, but subsequent executions are faster thanks to caching.
- 🗂️ To bypass caching, one can either delete the cache folder or change the 'seed' value in the llm config, which generates a new cache folder.
- 💻 The script demonstrates how to create a new assistant agent, such as a 'product manager', and specify its role through a system message.
- 🗣️ Communication between multiple agents is managed through a 'group chat' setup, allowing for coordinated interactions.
- 🔄 The 'group chat manager' is crucial for coordinating conversations between different agents within the group chat.
- 🛠️ The 'human input mode' on the user proxy agent determines whether the program pauses for user feedback after each step or runs continuously.
- 🔄 The video shows a practical example where the coder agent writes code, the user proxy agent executes it, and the product manager suggests applications based on the output.
- 🎯 The video concludes with a demonstration of how the agents collaborate, from coding to problem-solving and application suggestion, showcasing the power of multi-agent coordination.
Q & A
What is Autogen and what was discussed in the previous video?
-Autogen is a system that allows the creation of applications with multiple assistant agents, each serving a different purpose. In the previous video, the setup of Autogen was discussed, and the creation of a first application using a user proxy agent and a single assistant agent was demonstrated.
What is the benefit of having multiple assistant agents in an Autogen application?
-Multiple assistant agents allow for specialized roles within an application, such as a coder, project manager, or tester, which can enhance the functionality and efficiency of the application.
What is caching in the context of Autogen, and why is it important?
-Caching in Autogen refers to the storage of previous run results in a 'cache' folder. It is important because it significantly improves the performance and reduces costs by allowing Autogen to retrieve results from the cache instead of calling model APIs every time.
How can the caching mechanism in Autogen be controlled?
-The caching mechanism can be controlled by either deleting the cache folder or changing the seed value in the 'llm config' dictionary. Changing the seed creates a new cache folder and forces the APIs to be called from scratch.
What is the purpose of the 'seed' in Autogen's caching system?
-The 'seed' in Autogen's caching system determines which cache folder is used. Changing the seed value creates a new cache folder, effectively resetting the cache and causing the application to call APIs from scratch.
How can additional agents be added to an Autogen application?
-Additional agents can be added to an Autogen application by creating new variables for each agent, specifying their roles, and then including them in a group chat managed by a group chat manager.
What is a group chat in Autogen, and how does it facilitate communication between agents?
-A group chat in Autogen is a setup that allows multiple agents to communicate with each other. It is facilitated by a group chat manager, which coordinates the conversation between different agents.
What is the role of the group chat manager in an Autogen application?
-The group chat manager in an Autogen application is responsible for coordinating the conversation between different agents, ensuring that messages are passed between them effectively.
How does the 'human input mode' argument affect the execution of an Autogen application?
-The 'human input mode' argument determines whether the application will ask for user feedback after each step. Setting it to 'always' means the application will prompt for feedback, while setting it to 'never' will run without stopping for feedback, potentially leading to infinite loops if not managed properly.
How can an Autogen application handle the installation of missing packages during execution?
-An Autogen application can handle the installation of missing packages by having the coder agent recognize the missing package and instruct the user proxy agent to install it. Once installed, the application can continue with the execution.
What is the significance of the product manager agent's involvement in the Autogen application demo?
-The product manager agent's involvement in the demo signifies the collaborative nature of Autogen applications, where different agents can work together. The product manager agent suggests potential applications based on the results provided by the coder agent, showcasing the agents' coordinated efforts.
Outlines
😀 Enhancing Autogen Applications with Multiple Assistant Agents
This paragraph introduces the concept of adding multiple assistant agents to an Autogen application, each with a distinct role. The video demonstrates how to integrate a product manager agent alongside a coder agent. It also explains the importance of caching in Autogen, which significantly improves performance by reusing results from previous runs. The process of changing the 'seed' to generate new cache folders and the impact on performance is discussed. The paragraph concludes with a demonstration of executing the application and the effects of modifying the cache.
🔧 Setting Up Group Chats for Agent Interaction in Autogen
The second paragraph delves into the setup of a group chat within Autogen, allowing multiple agents to communicate. It details the creation of a 'group chat' class instance that includes a list of agents such as the user proxy, coder, and product manager. The paragraph explains the role of a 'group chat manager' in coordinating conversations between agents. It also touches on the 'human input mode' setting, which can be adjusted to control the level of user interaction during the execution of the application. The paragraph concludes with a live demonstration of the agents interacting in a group chat, handling tasks, and responding to issues such as missing packages.
📈 Exploring the Collaborative Potential of Autogen Agents
The final paragraph showcases the collaborative capabilities of different agents working together within an Autogen application. It highlights how the product manager agent can suggest potential applications based on the results from the coder agent's execution. The paragraph emphasizes the synergy between agents and encourages viewers to engage with the content by liking and subscribing. It ends with a farewell note, signaling the end of the video.
Mindmap
Keywords
💡Autogen
💡User Proxy Agent
💡Assistant Agent
💡Caching
💡Seeding
💡Group Chat
💡Group Chat Manager
💡Human Input Mode
💡Product Manager
💡Coder Agent
Highlights
Introduction to adding multiple assistant agents in Autogen applications for diverse roles like coder, project manager, and tester.
Explanation of caching or seeding in Autogen, which improves performance by reusing results from previous runs.
Demonstration of how to create a cache folder in Autogen and its role in enhancing application performance.
The impact of changing the seed value on cache performance and how it affects the execution of Autogen applications.
Step-by-step guide on executing Autogen applications and the importance of virtual environments.
How to add a product manager agent to an Autogen application and set its role through a system message.
The process of enabling communication between multiple agents using a group chat setup in Autogen.
Introduction to the Group Chat Manager in Autogen and its role in coordinating conversations between agents.
A practical example of how to initiate a chat with multiple agents in a group chat environment.
The significance of the 'human input mode' argument in the User Proxy Agent and its effect on feedback loops.
A demonstration of how agents collaborate to solve a problem, including code execution and error handling.
How the Product Manager agent gets involved in the conversation to suggest potential applications based on code results.
The importance of feedback in the Autogen process and how it affects the flow of the conversation between agents.
A summary of how different agents work together in an Autogen application to achieve a common goal.
Encouragement for viewers to like and subscribe to the channel for more content on Autogen and its applications.
Transcripts
hello and welcome back in the previous
video we had a look at setting up
autogen on our machines and we created
our first autogen application using a
user proxy agent and a single assistant
agent one of the strengths of using
autogen is the ability to add multiple
assistant agents in our applications and
each of these assistants can have a
different purpose for instance we could
have a coder agent as well as a project
manager a tester Etc so in this video we
will have a look at adding additional
assistant agents to our application and
we will also have a look at some other
key important features of these classes
before we add an additional agent I
first want to explain caching or seeding
when you executed autogen you would have
noticed this cach folder was created in
your project if I open up this folder I
can see a subfolder with a numeric value
in my example 41 caching plays an
extremely important role in autogene you
might have noticed that the first time
we executed our autogen application it
took quite a long time to get results
back from the models however when we
executed the scod again the performance
was greatly improved and that is because
when we execute our applications autogen
will first go to the cache folder and
then pick up the results from the
previous run from this cache that means
that it's not necessary for autogen to
call the model apis again which greatly
reduces performance and costs however
let's say you don't want autogen to pick
up on the scash you basically have two
options either you can delete this cash
folder or you can change the seed to
change the seed we can simply add a key
value to this dictionary in the llm
config the key is called seed and for
the value we can simply specify a
numeric value for example 42 now when we
execute this code we will see a new
folder created in the cache folder and
you will see a reduction in performance
because the apis are being called from
scratch let's execute this program let's
go to terminal and let's open up the
integrated terminal now in order to
execute this program we first need to
start up our virtual environment Again
by running V EnV scripts and then
activate now let's run our file by
typing py demo 1. piy now when we go to
the cache folder we can see this new
folder called 42 and we can also see
that it's taking way longer to get
responses from the Bots because
everything is being created from scratch
I'll go ahead and cancel this by typing
exit and enter so hopefully that makes
sense caching is a fantastic way to
improve performance ments on your
programs especially when you tend to
execute the same or similar instructions
now let's have a look at adding multiple
agents to our application for this demo
I'm actually going to make a copy of
this demo one file and let's call it
demo to so what I'm going to do here is
let's create another assistant agent but
this agent will be a product manager so
all we have to do is create a new
variable let's call it pm for product
manager which is equal to the assistant
agent class and for its arguments we
will pass in a name which we will call
product manager and then we also have to
pass in the llm config just like we did
for the coder but what we can also do
with these assistant agents is to
provide a system message and it is in
the system message but we can tell this
agent what its role is for instance I'll
say that you you are creative in
software product ideas if you want you
can do the same thing with the coder
agent so you could specify a system
message like you are good at writing
python code as an example but I'll just
leave it like this so now we need some
way for these three agents to talk to
each other in the previous video we had
a look at calling this initiate chat
method on the user proxy however the
limitation here is that we can only
specify one one recipient which was the
coder agent so now we need some way so
now we need some way for the user proxy
agent to talk to these two agents as
well and in autogen that is done by
adding these agents to a chat room or a
group chat let's have a look at how we
can do that I'm actually going to remove
this line like so and then from autogen
we will import the group chat class so
now in the code we can set up our group
chat by creating a new variable like
group chat which is equal to the group
chat class and this group chat can take
in a list of Agents so therefore we can
specify the argument agents which is
equal to a list and in this list we can
specify all the agents that we want to
include in this chat like the user proxy
agent the coder agent as well as the
product manager then as a second
argument we need to specify a list of
messages initially this list will simply
be empty then lastly we need to specify
an argument called Max rounds and this
is the maximum amount of rounds that the
agents are allowed to go for let's just
set this to 12 now that we have the
group chat set up whereby these three
agents can talk to each other we simply
need to add a group chat manager to this
group chat this agent is purely
responsible for coordinating the
conversation between the different
agents to create a group chat manager we
will simply import group chat manager
then to create this manager we will
simply create a new variable I will call
it manager which is equal to the group
chat manager class and this group chat
manager takes in two arguments the first
being the group chat itself which we
called group chat and secondly we need
to specify the LM config which is equal
to our llm config so all we have to do
now is to trigger our chat to initiate
the chat we will again call the user
proxy agent remember it is the user
proxy agent that we as the user interact
with on the user proxy agent we will
call the initiate chat method just like
we did in the first video but this time
for the recipient instead of calling the
coder agent directly we will pass pass
the message onto the group manager
instead then for the second argument we
will pass in our message and in this
example I'll just pass in this message
and that is all we need to do to add
additional agents to our application you
are more than welcome to create
additional agents perhaps you want to
also include a document writer a tester
or whatever and then all you have to do
is add those additional agents to this
list in the group chat before we execute
this code there is one last tip I do
want to show you and that is that on the
user proxy agent there is a very
important argument called human input
mode the default value for this argument
is always that means that after each
step in this process we will be asked to
provide feedback but it is possible to
change this behavior for instance we
could change the value to never which
means the program will execute without
stop copying to ask for our feedback and
will only terminate once the agents have
completed their task be careful setting
this to never as this could cause
infinite Loops I will just go ahead and
remove this value to leave it as always
we can now go ahead and execute this
code in the terminal I will run this
code by typing pi and then demo 2. Pi
here we can see the user proxy chatting
to the chat manager and then we can see
the coder passing its response back to
the chat manager as well this is
expected as a chat manager is
coordinating all of the agents tasks so
initially the coder is explaining to the
chat manager how the solution can be
built and the coder then provides the
python code for this solution because
the human input argument is said to
always we first need to provide feedback
I will simply press enter the user proxy
agent try to execute this code and the
execution of the code failed so the user
proxy is saying to the chat manager that
the execution of the code failed and
that is because this module feed parser
is not installed now the coder is
responding saying that this package is
missing so to solve this issue we need
to install this feed passer package so
I'll go ahead and copy this then I'll
open up a new terminal session I'll just
paste in that line and press enter to
install this package after installing
this I'll just switch back to this
terminal session and I'll just press
enter now the user proxy is telling the
chat manager that that package was
installed and now the coder is saying
that great now that this package is
installed we can go ahead and execute
this code so I'll just press enter to
let it continue and this time the user
proxy was able to execute that code and
the result from executing that code was
this list of Articles so what is very
cool is that now we can see the product
manager getting involved and now the
product manager is saying to the chat
manager that based on that result from
the python code here are a few potential
applications of gp4 and this is how we
can see these different agents working
together if you like this video then
please hit the like button and subscribe
to my channel I'll see you in the next
one bye-bye
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