AI Agents EXPLAINED: Unbiased Review of Langraph, AutoGen, and Crew AI Frameworks

if you like me you probably heard the term a agent being used to describe just about everything this days customer agents Gaia generative AI agent I don't think we've kind of nailed the the right way to interact with these agent applications it seems like every company is claiming to have their own unique AA agent that's going to revolutionize the world but what exactly are these agents and how do they defer from one another let's focus on three major products there are making waves in the AI agent langra autogen crei will take a deep dive into their approaches to creating a agents and will break down the technical jargon to give you a clear understanding of what sets them apart my opinions only my opinions I'm sure there'll be lots of discussion whether I'm right or wrong in my assessment I welcome you to the comment section to provide your point of view we'll also explore the potential real world applications of this AA agents how could businesses use langra a to improve their operations what industries could benefit from autogen approach and how might Qi agents change the game when it comes to customer service so if you're thinking about integrating a engines 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 teag gig 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 get into the specifics of each framework let us take a step back and understand why multi-agent collaboration is so important Andrew Ang pointed out at the recent Ascent conference given a complex task like writing software a multi-agent approach would break down the task into subtask to be executed by different roles such as software engineer product manager designer and quality engineer with different agents accomplishing different sof tasks 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 Ang explains there are several compelling reasons first it works way better than just using llm calls many teams are getting good results with this method and abl 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 two even though some llms can very long input context the ability to truly understand long complex inputs is mixed and a gentic workflow in which Z llm is prompted to focus on one thing at a time can give much better performance and number three perhaps most importantly the multi-agent design pattern gives us as developers a framework for breaking down complex task into subtask which allows us to create a much more complex task and being able to debug it much easier with with that context in mind let's dive into our first framework autogen the oldest and most mature framework of the three autogen supports multi-agent systems and streaming output making a versatile choice for complex projects when it comes to customization aogen allows you to update agent system messages giving you the flexibility to tell your agents to your needs one of the standout features of autogen is a containerized code execution which provides an extra layer of safety basically you can protect your system from any potentially harmful code that llm can accidentally develop kinded has become self-aware additionally auten's feedback cycle enables agents to solve issues autonomously saving your time and effort in the long run when it comes to Randomness probably one of the biggest weaknesses of autogen is hard to fine-tune the outcome of the autogen multi-agent application also there's still problem is infinite Loops where unless you put fairly conservative number of Max iterations uh you will run out of your open AI budget very fast now let's talk about the user experience autogen Studio offers a UI layer but it can be a bit slow not very intuitive kind of like navigating a maze blindfolded the documentation is pretty decent but the studio might leave newcomers scratching their heads it's a little bit confusing qu 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 L graph but before we move on if you're finding this information valuable let us know in the comments we love hearing about your experiences with this AI agents next up we have lra the newest framework of the bunch this trendy new technology uses directa cylic graphs as a foundation for its agent applications L graph provides a good mental model for users but 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 langra has a good documentation 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 ler of examples focus on web browsing scraping customer service info Gathering code assistance also the systems that you could build with lcraft are not only that basically you could have multiple agents working in collaborative approach or supervision approach and you could actually have different level systems like you have a manager you have director you have CEO it's Innovative because with Lang graph you could build anything that autogen can do but also you could build much more so we're a big fan of lra at ASM we think it's probably one of the best technologies that came out in 2024 and we excited to use it for our own projects now get ready for the crew eii they're doing something completely different last but not least we have crei the middle child of the group it's like the galux of a gentic Frameworks not to Old not to new but just right crei both an intermediate level of maturity and hierarchical agent structure although it lacks native support for dynamic planning customization CI is a Brea thanks to support for agent and task definitions also crew AI is built on L chain which makes it compatible with lsmith which as we saw with langra is invaluable tool when it comes to debugging and optimizing your agents the documentation is stop NCH 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 one of the creators I forgot his name he has a very cool video about how to use Lama 3 with crei 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 Lang graph and works with both local and Global llms making it a versatile choice for AI engineers and entrepreneurs whether you want to host your own solution within the company company or you want to 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 Ang 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 a 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 Ang in description below until next time happy coding and may your AI agents be ever in your favor

Channel: AI SIMP

Views: 11,310

Keywords: AI agents, Agentic frameworks, AutoGen, Crew AI, Multi-agent collaboration, Customization, Integration, Business applications, AI technology, Andrew Ng, landgraph, ai revolution

Id: NjOj5mkytvw

Length: 8min 29sec (509 seconds)

Published: Wed May 15 2024