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I think for tasks , react is a general purpose worker agent .. but function calling is only a tool . So it cannot be compared . We should understand the difference between tools and function calls ! As tools generally take a single string input amd give a string output and functions can take args and return args ! So : ReAct ! The correct way ! To do a react chain ! You need to define a self-query tool , which takes a query input . So you should create a tool which uses this tool , ie : each tool now is a mini agent , so the only thing different is the prompt : So your think tool ! First we need a planner tool, ie uses the think tool and a planner preset prompt ! This is the thoughts ! Then you need your action tool : This is a mini toolbox , framed as actions : so the model would call the action with the parameters , and the reponse would be formatted as a observation ! Also your final answer tool: This takes your final answer and formats it into something lovely 😍 So each step is a tool: Now we can male a executable toolchain ! So if we create a tool which is basically a graph ! From the thought to the action to the final answer with a loop for thought (do i meed another tool call) 🤙.. we have made the react process into a toolchain ! So we force the process with chain /graph. Or we can just give the model the tools instead ! But three tools and one tools ? I think by giving the model just the single chain we optimise its workflow : Now we can also make selfquery-agents for other agents such as refiner and coder , So we can add these as tools or create another toolchain from the self-query Tools .. based on the graph set up in the langgraph docs ! So now we have two tool chains or a collection of self-query-agents ? We can give thr model the agents to choose ots own destiny or , we can create an intent detector tool ! A conditional node ! ... Or tool: So now we create a new process which goes for the intent node first and fron the output of the intent node the correct chain can be selected , ao we can have multiple chains fixed processes . And our intent detector detect of we need to use a agentchain or graph , or construct a graph with some tools ! So : the advanced model would alao have a rag ( or local memory tool) So we create a new set of tools which are based on the rag : So our search tools and file loader tools etc can manage local memory , perform querys etc . Agan we should deploy the same set up ! ... Tools to perform tasks and chain whicj perform multi turn tasks , such as application devlopment , ETL, finetuning , information gathering .. large sumary writing .. We can note also that some of these intesive multi turn tasks such as producing a dissertation etc . Would take a research step , (which utilizes websercher chains etc) and a sumary writing stage , whichw oudl also have its owm subchains , so these combined intesive tasks would take days or hrs! .. bit the output produced would be of the highest calibur ! It has been said that the work produced by gpt is not great quality especaily for scientific . But its only about how to implement a methodolgy . As seen above we implented a reAct . But this cohld have been a chain of thoughts , or forrest of thoughts . Or a sema or Crisp datamining methodolgy ! So by segmenting tasks into mini tools or agents or processes and chains in fact the output would have been produced multiple models or agents prompted differently to spexific role and purpose , even with personality injection and self crtique .. the output ia totally I recognizable and infact even Unique !

I didn’t get why with ReAct we have more control. Isn’t LLM still responsible to selecting the tool?

Great video!! Thank you for taking the time! My confusion is…How would I create a multi agent graph where the initial agent asks the user a few questions to determine intent -> based on that it determines what agent to send the user to - this 2nd agent has its own LLM prompt logic -> when this 2nd agent requires feedback from the user … does it communicate with the user directly ? Or does the initial agent only communicate with the user That is where I’m really confused - any guidance would be great! Thank you again!!

Could have added eamples where either was a better choice.

Like your none beginner course.

Bro, I have your course and I must say it's amazing. Can you add a section to explain a SQL Agent? Honestly I understand you better than the langgraph guide itself. Thank you very much in advance

Hello Eden, Langgraph is a wonderful tool to create workflows. I was trying to work with payment workflows and came across several challenges. I was working on the the verification example and it seemed like it could not handle failure and exit strategy very well. Could you shed some light on it in your upcoming videos. Would love to see an example workflow for making payments for services based on customer needs.

What about open souls? Seems to be very good at steering.

I'm thrilled with your Udemy course-it's truly impressive! We're dedicated to boosting enrollments, cultivating glowing reviews, and maximizing revenue. I'm eager to brainstorm customized strategies to take your course to even greater heights.

Thanks(: great as always

Hello, I was looking at your course on Udemy, you mentioned that you will build apps using python and langchain, however, the student needs to have significant experience in python programming and concepts like "classes". I am disappointed that you mention this because, you can build any app with python without the use of classes for mega projects in ML/DL etc.. That discouraged me from getting your course. You can use Langchain without the need to be an expert python programmer.

drop that bullshit thumbnail. Be better!

Thank you very much. It's really cool <3

hello Eden, pls can you make a tutorial for us on how to use Langgraph Cloud from beginning to end, for example, create a simple AI LangGraph agent and deploy it on LangGraph Cloud or you can just put it in your new course. I have already subscribed.

Thank you for clearly explaining the system architecture, helps everyone understand.

Bought the course bro! what are your thoughts on GraphRAG's compared to "standard" (but advanced) RAG systems ?

Thanks for covering this!

thanks for this! for production graded SaaS what infrastructure would you suggest ? were looking at DataStax <> Amazon , or possible Azure/Google. Keep it up. ps. is your name the same on linkedin? ps. what is your take on RAGGraphs ?

hey bro, does LangFlow play a part in your picture or is it more an "'abstraction" programmers should avoid ? great channel btw.

I study the langchain codebase quite a bit to understand the lessons they're learned and how they've solved them. However, I find langchain to be quite wild and unwieldy and find myself opting to use less and less langchain and more my own abstractions. Langgraph _seems_ be, to me, the approach that Langchain could/should have gone with and I'm finding LG not-too-much-framework.

Very cool

I am looking for something like

Until a few years ago, the AI Engineer was supposed to actually train a model (and know how to train and evaluate it in a correct manner and put in production + evaluate while the model is running over time). But today both Software Engineers and Data Scientists need to embrace the advent of the pre-trained models and Gen AI (otherwise they will be useless in 5 years and loose their jobs). So i still think that today's Gen AI engineers are just Software Engineers that know how to put all of the AI components together and just use an API call to the trained AI. Likely they do not know 80% of AI literature amd how to train and build a model from scratch. Unfortunately this will be the direction in this field in the near future (until the AI will take over and these jobs will be useless)

After hours of mix and matching function calling with anthropic, the way you just demonstrated it made click, thank you so much.

As an example of something you DON'T need a framework to do: if you want to use multiple models you can do that using any routing service, such as OpenRouter, Martian, or BrainTrust. Not only do they handle the model abstraction (generally making every model look like GPT), but they also handle the billing so you don't need N accounts to support N models. If you start development with GPT but want to try out Claude, Gemini, Mistral, etc., this is the easiest way to go.

🎯 Key points for quick navigation: 00:13 *📁 The speaker has been working on a public GitHub repository that implements advanced RAG workflows using LangGraph.* 00:40 *💡 The speaker felt that the existing notebook was missing a software engineering perspective on how to structure an advanced LangGraph application and write maintainable code.* 01:07 *🔩 The speaker refactored the notebook to make it more maintainable, splitting it into sub-modules and writing tests for each chain.* 01:47 *📊 The speaker emphasizes the importance of writing unit tests for code.* 02:44 *🚀 The Advanced RAG workflow involves choosing whether to retrieve documents from a vector store or use a web search, grading documents, and generating an answer while checking for hallucinations and relevance.* 04:23 *💡 The implementation is a combination of three papers on Advanced RAG, corrective RAG, adaptive RAG, and self-RAG.* Made with HARPA AI

To summarize: Don’t build your own software because you’re a moron. Just use this super smart framework from these super smart people. Why reinvent the wheel when someone else is literally reinventing the wheel for you? If LangChain doesn’t do what you need it to do, DONT try to develop something custom or test other frameworks. Instead, just add those features to LangChain using their poorly designed api. Concerned about privacy and vulnerabilities? Fear not, LangChain has explicitly labeled the massive amount of components that are dangerous. Also who do you think you are expecting an open source project to care about your safety. The nerve. This was a great anti-LangChain video. I think I’ll continue to use anything else. Maybe I’ll start with something wild like designing multi-modal apps in python and attaching these revolutionary things called databases so I can integrate my own parsed and formatted data. If I get lucky I might even be able to figure out how to host it all on my own secure servers that don’t expose every console log. Although it might feel a little lonely knowing trackers aren’t watching over me. Who knows though, I’m just a fucking idiot. I should just stick with ChatGPT. I’m sure my company won’t mind if I force feed all of our user data and internal ip into a black box owned by Elon clones.

Why‽ Lang Chain isn't needed if you know how to work with templating, JSON, retrieval, and storage. To be clear, I'm not saying don't use LangCHain. I am saying don't confuse opinionated frameworks for what is right for you. If you like the lying chain approach, go with it for those who have different ideas that are not in line with LangChain or strong opinions. Don't use it; roll your own and share with the community.

Langchain is moving at such breakneck speed with complete disregard to backwards compatibility that the code you wrote couple months ago is obsolete and is not working anymore... Yes it saves you time when you do a quick test but for production, especially if you care about the ability to upgrade your libraries, I'd always build from scratch.

Great videos! keep them coming please.

gen ai is in too early for frameworks to be opinionated... learn by experimenting with prompts and Python.. don't use black boxes.. if you're a technical developer, these frameworks won't help you anyway I take exception with Llamaindex pdf reader...

How about llamaindex?

LangChain could use some serious library refactoring/organizing. Importing libraries shouldn’t take 40 lines of code.

thank you!, one idea I saw and think is a good improvement to the architecture is adding a search into a knwoledge graph module, like dbpedia or similar KGdatabase with the posibilty of adding triplets extracted from the RAG documents itself. The result of the semantic and keyword queries to vectorDb and KGDb will enrich the context provided to the LLM

Really nice work, Eden. Thank you for such a great content.

תגיד אח יקר אני מדמיין או שאתה מדבר כמוני באנגלית? 😂

Nice Video. I subscribed!

Completely agree with your assessment Eden. Looking forward to seeing more informative videos from you.

can't you just combine them both to get the best of both worlds? i guess you could also bind the tools when invoking the react prompt, so that the model would call a necessary tool based on the final result decision?

couldn't agree more. I was having issues using frameworks like crewAI to actual do anything slightly useful. Having more control and giving the LLMs more 'binary' choices seems the way to go at the moment.

Excellent piece, and completely agree.

Is CrewAi also included in your Autonomous Agents Frameworks list?

💯 This is spot on Eden - LLMs need boundaries to thrive! Langchain/Langraph's elegance is giving devs control to leverage the LLM superpowers safely. 2024 is gonna be the year of the *working* agents thanks to this approach! Great stuff as always, Eden! 🙏

Instantiated?

Just a quick question for open weather map langchain agent which one will be good Thank for your comments

Eden, lang graph doesn't have any good checkpoint libraries apart from sqlite for production use cases like you have for langchain. Do you know anything about that?

i am taking your courses on Udemy i must say those are thought provoking....LLM+LangGraph

Can you make a video going through at a high level each branch in order? Also could you cover LangGraph workflows involving tool use / function calling? Thank you!

please can you share the website sources,papers of what you explained?

amazing! but i'm struggling to understand when RAG should be used and when it should not be used