All right, welcome back to the deep dive.
We all know you're here
because you wanna be ahead of the curve, right?
You wanna understand the big things
and you wanna understand them fast.
And well, today, we're gonna dive into something
that is seriously shaping the future of AI.
How we make sense of how these AIs act
when there's a whole lot of them.
Yeah, that's it.
It's like, you know, we're moving beyond
just looking at a single AI doing its thing.
We're talking about like frameworks.
Think of them like these advanced labs
where researchers can observe how tons of AIs behave
when they're all together.
So it's like instead of studying a single ant,
we're looking at the entire ant colony.
Exactly.
And you know what we're using
to look at these AI colonies today?
Wait on me.
Camel.
Yeah.
And it's not just another fancy AI term, you know,
right from their GitHub page,
which is where we're getting most of our info from today.
Camel, which stands for, get this,
communicative agents for mind exploration
of large language model society.
It says, and I quote, the first and the best
multi-agent framework.
Bold statement.
I know, right?
But what's really cool is it's got this huge
open source community around it.
Like there are over a hundred researchers
all working together, trying to figure out the rules,
what they call scaling laws of how these AIs behave
when you have so many of them.
And you can check out all their work at camel-ai.org.
Okay, so to sum it up for our listeners,
we're going deep on camel today.
We're gonna figure out what it is,
why all these researchers are so hyped about it,
and what kind of doors it opens to study these AI agents.
You got it.
All from their GitHub.
All right, so first things first,
what exactly is camel, like in plain English?
So basically think of camel as a toolkit,
a super well-organized set of tools and instructions
that researchers can use to really dig deep
into how AIs behave, what they can do,
and you know, any potential problems they might cause,
especially when there's a lot of them.
Gotcha, so the whole point is to study them
on a large scale, not just individual AIs.
That's exactly it.
It's not just about making individual AIs
a little bit better, it's about looking at what happens
when you have a whole bunch of them working together,
what new things emerge.
And this isn't just some like academic thing, right?
Their GitHub makes it clear that it's open source
and it's all about communities.
Oh yeah, totally.
The fact that there are over a hundred researchers
working on it, that says a lot.
They really believe in this approach.
The core idea is that you can't really understand
how AIs act by just looking at one at a time.
You've got to see how they act in a group.
So it's like, less about one AI solving a problem
more about how they work together,
or maybe even like compete with each other.
Exactly, and they're not just talking about it.
The GitHub page shows they've made actual tools,
different types of agents, like AIs with special skills,
different tasks they can do,
different ways to give them instructions,
different models they can use,
and even simulated worlds for them to live in.
It's amazing.
Wow, so they basically built a whole research lab
for these multi-agent AIs.
Yeah, pretty much.
And you know how on GitHub
you can see those stars and forks?
Well, it has 8.8 thousand stars and 921 forks.
And the open source world, that's a lot.
So people are definitely paying attention.
Big time.
Plus, it's under the Apache 2.0 license,
which basically means anyone can use it and build on it.
Okay, so Camel's this open, community-driven framework
to study lots of AIs together.
But what are the like ground rules?
How do they design it?
What were the main ideas?
So there are four big design principles
they talk about on GitHub.
The first one is evolvability.
It means these multi-agent systems aren't fixed.
They can actually get better over time.
Either from the data they generate themselves
or by interacting with these simulated environments.
It's like they can learn using either reinforcement learning
where they learn by getting rewards,
or supervised learning where they learn from labeled examples.
And it's not just like getting a little better.
The exciting part is that totally new strategies
or even like new types of intelligence could just pop up.
Whoa, so the system can like evolve.
Like the AIs can learn and change.
Yeah, totally.
The next principle is scalability.
Since the whole point is to study huge groups of AIs,
the framework is built to handle systems
with millions of agents.
And it's not just about the numbers,
it's about making sure they can coordinate,
communicate, and share resources efficiently.
Imagine like millions of AIs all working at the same time.
Millions.
That's just mind-boggling.
It's like managing a whole digital world.
I know, right.
Okay, next up is statefulness.
This is all about memory.
The agents in CAML,
they don't just react to what's happening right now.
They can remember past interactions and experiences.
This helps them have longer, more complex conversations
and tackle harder tasks
that need them to remember what happened before.
So they can learn from their mistakes
and build on what worked before.
Exactly.
And that's super important
for any kind of collaboration
or complex problem solving.
And the last principle is, this is a cool one,
code as prompt.
So in CAML, the actual code that's written for the agents,
along with any comments in the code,
is used as a way to guide them.
The idea is that good, clear code
isn't just for humans to understand.
It can also help the AI agents themselves
figure out what they're supposed to do.
So like the code itself is a set of instructions for the AI.
Yeah.
So there's an agent that's supposed to write summaries,
and in the code, there's a comment that says,
like, focus on the main financial numbers.
That comment becomes part of the instructions for the AI.
That's a really neat connection between coding and AI.
Okay, so we've got the design principles down,
but why are all these researchers choosing CAML?
What are the big reasons they list on their GitHub page?
Okay, so first, the ability to create
a large scale agent system is huge,
simulating up to a million agents.
That lets researchers look at emergent behavior,
those unexpected patterns that only pop up in big groups,
and see how these AI societies scale.
Emergent behavior.
So like, how individual neurons firing
can lead to consciousness, but for AIs?
Yeah, exactly.
It's about understanding how complexity arises
from these interactions.
Next, CAML allows for dynamic communication.
The agents can interact and work together in real time,
so they can communicate, coordinate their actions,
and really collaborate to achieve a shared goal.
So they're not just working independently.
They can chat and negotiate with each other.
Exactly.
It's all about teamwork for AIs.
Then of course, there's stateful memory,
which we talked about, lets them remember stuff
and act more intelligently over time.
Right, right.
What else is there?
Oh, this is important.
Support for multiple benchmarks.
This means researchers can test their agents
on standard tasks and compare the results
to others in a fair way.
It's like standardized tests, but for AIs.
Helps make sure everyone is measuring progress
in the same way.
So it's like standardized testing for AI societies.
Makes sense.
What other benefits does CAML have for researchers?
So there's support for different agent types.
CAML is super flexible.
Researchers can use all sorts of agents, tasks, models,
and environments.
It can be used for all kinds of research questions
in all kinds of fields.
And lastly, there's data generation and tool integration.
CAML can automatically create huge data sets,
which is super helpful for training and testing AI models.
It can also easily connect with other specialized tools,
which makes the whole research process
from start to finish way smoother.
Sounds like CAML could really speed up AI research.
Okay, so with all these features,
what can you actually build with it?
What are some applications they mentioned?
They highlight three main areas.
First, data generation, which we already talked about.
You can use CAML to create tons of data
to train and test AI models in all sorts of areas.
Second is task automation.
You can build systems with multiple AIs
that can work together to automate really complex tasks.
So imagine a team of AI assistants all working on a project,
each with their own specialty.
Exactly, they can each handle a different part of the task
and work together to get it done.
And third, world simulation.
They can create simulated environments
with AIs interacting,
which allows them to study all sorts of things,
like how markets work, how information spreads,
or even how social rules develop.
That's so cool.
You can basically create virtual societies of AIs
and see how they evolve.
Yep, now let's get a bit more technical.
They have a text stack section
that describes all the different parts of CAML
that make all this possible.
Okay, let's hear it.
First, there are the agents themselves,
which is basically the code that defines how each AI acts.
Then there's agent societies,
which is the framework that helps manage all these AIs
and their interactions.
We already talked about the data generation module.
Then there's the models module,
which lets researchers customize the AI models
that power the agents.
Tool integration is really important,
and they have a module for that,
so the agents can use external tools for specific jobs.
There's a memory module to handle the AI's memory.
Oh, and they also have modules for storage, benchmarks,
interpreters, data loaders, retrievers,
which are used for this thing called
retrieval augmented generation, a runtime environment,
and even human in the loop components
for when you need a human to step in and oversee things.
Well, that's a lot.
They've thought of everything.
Right, it's a really well-designed system.
Like we talked about, it's all community-driven.
Yeah, that was really impressive.
So what are they focused on researching with Camel,
and how can someone join the party?
So their main goal, as they say on their GitHub,
is to learn more about how AI agents behave,
what they can do, and any risks they might pose,
all by studying them at a large scale.
And they really want other people to use Camel
for their own research and join their projects.
They even give an email address
for people who want to collaborate.
It's all about working together.
It's great that they're so open.
Oh, and I saw they have a bunch of data sets on Huggingface.
That's awesome.
Oh, yeah, that's a great resource.
They have data on AI interactions, code generation,
math, physics, chemistry, even biology.
And the data sets are in different formats,
like chat logs, instructions, even translated conversations.
They even have visualizations to help people understand
the data.
So researchers don't just get the tools,
they get a bunch of data to play with, too.
That's a great way to get people started.
OK, now for the people who want to actually get their hands
dirty, they have these cookbooks, right?
Yeah, the cookbooks are like tutorials and guides
for using Camel.
They've got a bunch of different categories.
There's basic concepts, which covers the basics,
like how to create an agent, a group of agents,
how they talk to each other.
Then there's advanced features, which
goes into more complex stuff, like integrating external tools,
using memory, and implementing this thing
called retrieval augmented generation.
That involves using knowledge graphs
to make the agents even smarter.
So these advanced features are like the next level
of multi-agent systems.
Exactly.
And they also have cookbooks on model training and data
generation, where they show you how to create data with Camel
and then fine-tune models using tools like Unsloth.
They also show you how to work with specific data formats,
generate data for this chain of thought, reasoning,
and even how to upload your data sets to HuggingFace
so everyone can use them.
And then there's a section on multi-agent systems
and applications with examples of how to build real-world things.
They show you how to make web scrapers that
can role play to get you information,
build AI committees to judge hackathons,
and even how to create customer service bots on Discord
using both agentic retrieval augmented generation
and local AI models.
It's really amazing what you can do with it.
Those are some cool examples.
I like that they're showing people how to build practical stuff.
What about that data processing section?
What's that about?
Oh, yeah.
So that section is all about using agents
to analyze video data, different ways
to get and process data from websites using tools like FireCrawl,
and even how to work with PDFs using tools like Chunker
and these really powerful models for Mistral AI.
They want to give people the skills to use
Camel for real data problems.
So they're really making Camel easy to use
and showing its potential.
What if someone wants to contribute to Camel itself?
How do they do that?
Well, on their GitHub page, they
have a contributing to Camel section.
They really encourage people to contribute code,
and they have guidelines for how to do it.
But you don't have to be a coder.
They also say that just spreading the word about Camel,
like talking about it on social media or ad events,
is super helpful.
So anyone can help coder or not.
What about staying in touch with the Camel team?
How can people reach out?
They have a bunch of ways.
You can use GitHub issues to report bugs, suggest features,
and just see what's going on with the project.
They have a Discord server for real-time help, discussions,
and connecting with other people who are using Camel.
They're also active on X, what used to be Twitter,
where they share updates and announcements.
And for the super passionate Camel fans,
they even have an ambassador project.
Wow, so many ways to connect.
OK, so like any good research project,
giving credit where credit is due is really important.
Do they have info on how to cite Camel?
Yes, they give a specific citation
for their research paper, which was
presented at this big AI conference called NURY PS in 2023.
And they have a whole section on acknowledgments
where they thank NOMIC AI for giving them access
to their Atlas tool and Haya Hemud
for creating the Camel logo.
They also mentioned that they use ideas from other research,
like Baby, AGI, Persona Hub, and Self-Instruct.
And they ask people to cite those original papers
if they use those specific parts of Camel in their own work.
It's all about good research ethics.
That's great to see.
OK, so we've learned a lot about Camel today.
What do you think is the most important thing about it?
I think the biggest thing about Camel
is that it gives us a powerful way
to explore this whole new world of large-scale, multi-agent AI
systems.
It gives researchers the tools they
need to understand how AIs behave when there are lots of them,
how they work together, and how they interact
in these shared spaces.
It's not just about making AI more powerful.
It's about understanding the complex dynamics that
emerge when you have AI societies.
And I think the fact that it's open source and community
driven is super important.
It makes this kind of cutting edge research accessible
to everyone.
Absolutely.
It makes AI research more democratic and collaborative.
The data sets, the documentation, the cookbooks,
it all shows that they want to make Camel useful
and have a real impact on AI research.
OK, so for our listeners who are totally intrigued,
where should they start if they want to learn more?
Definitely check out the Camel GitHub page.
Just search for Camel iCaml.
You'll find the code, all the documentation,
and links to all the community stuff we talked about.
And if you want to see Camel in action,
look for their demo of a conversation between two chat
GPT agents.
It's a really cool example of how these multi-agent
interactions work.
That sounds awesome.
Thanks for all this info.
And for our listeners, here's a final thought.
As AI's get smarter and more connected
and they start forming these complex digital societies,
what kind of unexpected things will we discover about them
using frameworks like Camel?
And how will understanding all this
change the way we develop AI and how it impacts our lives?
That's something to think about.
Thanks for joining us on the deep dive.