“AI Agents Explained: Unbiased Reviews of Langraph, AutoGen, and Crew AI Frameworks”

Unbiased Review Langraph

I’m sure there’ll   be lots of discussion whether I’m right or wrong  in my assessment. I welcome you to the comments   section to provide your point of view. We’ll also  explore the potential real world applications   of this a a agents.
How could businesses use  language of agents to improve their operations?   What industries could benefit from Autogen’s  approach? And how might Kuya agents change   the game when it comes to customer service? So  if you’re thinking about integrating AI agents   into your business, this video will give you the  knowledge you need to make an informed decision   even if you’re not a developer.

So, whether  you’re a tech geek like me, a business owner   looking to stay ahead of the curve or you’re  someone who is curious about the future of AI,   you wouldn’t want to miss this deep dive into  the world of AI agents. But before we get into   the specifics of each framework, let  us take a step back and understand why   multi agent collaboration is so important.
Andrew Ng pointed out at the recent Ascent   Conference. Given a complex task like writing  software, a multi agent approach would break down   the task into subtasks to be executed by different  roles. Such such as software engineer, product   manager, designer, and quality engineer. With  different agents accomplishing different subtasks.   Now, you might be thinking, wait a minute, if  we’re just making multiple calls to the same large   language model, why bother with multiple agents? Well, as Andrew Ng explains, there are several   compelling reasons. 1st, it works way better than  just using LLM calls.

Many teams are getting good  
results with this method. And ablation studies  shown here on the screen shows that multiple   agents give superior performance to a single  agent no matter what LLM you’re using. Number 2.   Even though some LLMs can accept very long input  contacts, their ability to truly understand long,   complex inputs is mixed.
An agentic workflow  in which the LLM is prompted to focus on one   thing at a time can give much better performance.  And number 3, perhaps most importantly, the multi   agent design pattern gives us as developers  a framework for breaking down complex tasks   into subtasks, which allows us to create a much  more complex task and being able to debug it much   easier. With that context in mind, let’s dive into  our first framework, AutoGen.
The oldest and most   mature framework of the 3, AutoGen supports multi  agent systems and streaming output, making it a   versatile choice for complex projects. When it comes to customization, Origin   allows you to update agent system messages, giving  you the flexibility to tailor your agents to your   needs.
One of the standout features of Origin is  a containerized code execution, which provides an   extra layer of safety. Basically, you can protect  your system from any potentially harmful code that   l l m can accidentally develop. Skynet has become  self aware. Additionally, Autogen’s feedback cycle   enables agents to solve issues autonomously,  saving you time and effort in the long run.  

Mastering Agents: LangGraph Vs Autogen Vs Crew AI – Galileo

When it comes to randomness, probably one of the  biggest weaknesses of AutoGen is hard to fine tune   the outcome of the AutoGen MultiGen application.  Also, there’s still problems with infinite loops   where unless you put fairly conservative number  of max iterations, you will run out of your OpenAI   budget very fast. Now, let’s talk about the user  experience.
AutoGen Studio offers a UI layer,   but it can be a bit slow and not very intuitive.  Kind of like navigating a maze blindfolded.   The computation is pretty decent, but the  studio might leave newcomers scratching   their heads. It’s a little bit confusing.  Coil quality is another area where AutoGen   could improve with some verbosity and self  recursive functions that might make your   code look like a tangled mess of spaghetti. But  wait, there is more. Let’s check out Lang Graph.  

But before we move on, if you’re finding this  information valuable, let us know in the comments.   Love hearing about your experiences with this  AI agents. Next up, we have a Langraph, the   newest framework of the bunch. This training  new technology uses direct to cachely graphs   as a foundation for its agent applications.  Langraph provides a good mental model for users.  
By defining notes and specifying agents can be  a bit verbose, like filling out a text form for   every little thing. On the bright side though,  Langraph has a good documentation with with   clear examples, so you won’t feel like you lost  in a foreign country without a map. What is also   amazing is that the code quality is also cleaner  and better organized compared to AutoGen, making   it easier to navigate and maintain.
Language  of examples focus on web browsing and scraping,   customer service, info gathering, code  assistance. Also, the systems that you   could build with Langraph are not only that. Basically, you could have multiple agents working   in collaborative approach or a supervisional  approach, and you could actually have different   level systems. Like, you have a manager,  you have a director, you have CEO.

It’s   innovative because with Langraph, you could  build anything that AutoGen can do, but, also,   you could build much more. So we’re a big fan of  Langraph at AiSiMP. We think it’s probably one of   the best technologies that came out in 2024, and  we’re excited to use it for our own projects.  
Now get ready for the CrewAI. They’re  doing something completely different.   Last but not least, we have a krui AI, the  middle child of the group. It’s like the   Goldilocks of agentic frameworks. Not  too old, not too new, but just right.   CrewAI boasts an intermediate level of  maturity and hierarchical agent structure.  
Although it lacks native support for dynamic  planning. Customization, Crew AI is a breeze,   thanks to support for agent and task definitions.  Also, Crew AI is built on Langchain, which makes   it compatible with Langsmith, which as we  saw with Langraph, is invaluable tool when   it comes to debugging and optimizing your  agents. The documentation is stop touch.  
It was clear examples, core concepts, nicely  explained. We have how to guides from installing,   getting started, creating custom tools, trip  planner, create Instagram post. This one is   was very interesting example, which I will link  the video of 1 of the creators. I forgot his   name. He has a very cool video about how to use  llama 3 with crew AI to create Instagram posts.  
Very advanced, to be honest with you. As  I mentioned before, integration is another   area where crew AI shines. It can be integrated  with other systems like Langraph and works with   both local and global LLMs, making it a versatile  choice for AI engineers and entrepreneurs whether   you wanna host your own solution within the  company or you wanna have it available via   API to the rest of the world.
In conclusion,  choosing the right agentic framework depends   on your specific needs and priorities. Whether you  value maturity, ease of use, or unique features,   there is a framework out there for you. As Andrew Ng notes, the output quality   of multi agent collaboration can  be hard to predict. Especially,   when allowing agents to interact freely and  providing them with multiple tools.
However,   the more mature patterns of reflection and  tool use are much more production ready   systems. If this video has helped you understand  AI agents better, give it a thumbs up and let us   know which approach you’re most excited to try  out. And if you’re interested in learning more,   be sure to check out the papers recommended  by Andrew Ng in the description below.  
Until next time, happy coding, and may  your AI agents be ever in your favor.

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