I don't understand pipe operator. :(

"very scientific diagram" lol

Dude I love your agent vids. Of all the stuff out here they have been the most helpful because your breakdowns are complete and articulated well. Thanks, man. PLEASE do more! Ha. I would love some case study ones or different sort of mash up ones that go outside the norm and use idk different kinds of tools and models and trigger all sorts of weird actions. I think these things are legit the first step to robot brains so if that sparks any creativity…. Also brother what did you use to make that 3D graph? And do you have any opinions on graphrag? (not to confuse since the name is similar but subject is not related)

Thank you Adam! You have a unique style of presenting those concepts, making them very clear, understandable, and complete. I think LangGraph offers one of the best ways to set up those multi-agent workflows compared with other frameworks (AutoGen, CrewAI, AgentScope, Phidata, Langroid, etc.). The only missing piece is the low-code GUI/IDE option to reliably build such workflows for LangGraph - existing low-code tools (like Flowise, Langflow, etc.) do not yet have good support for LangGraph. Maybe this could be another video idea for you? :)

EXCITED

Hell yeah Adam you’re killing it (although I have no clue what you’re talking about)

Loved this video. Great stuff.

dude, you next video should be comparing the multi agent collbaration framework or patterns

Does it do Tibetan ?

Thanks! You deserve more views.

Can you integrate your routing node to perplexica the clone of perplexity?

do anybody else get "No good DuckDuckGo Search Result was found" from from DuckDuckGo_Search? i have tried with different versions of ddg..i'm running it from the terminal and not Jupyter Notebook. *edit: Brilliant videos btw!

on kaggle pls

Which would you say is more crucial to analyzing the "correctness" of the language agent tree search result: "blah blah blah" or "yada yada yada"?

Thanks for this video! Several smaller details you emphasized were things I had missed in other tutorials, and really helped me.

absolutely fantastic. I've been trying to do something similar today and experienced much of the 'going rogue' - with incorrect special tokens, until I followed your example.

I wonder if anyone has went through the thought exercise of how an AI model could benefit from having anyone "fold at home" in order to build it. In other words... instead of the owner (OpenAI, Llama, whatever..) dedicating servers to build a LLM, could it not be distributed such that video cards in PC's around the world contribute idle cycles towards the training? Seems like a good way to have an open sourced model to get off the ground, or build using more tokens. Could even have a reward system (minor) allowing X privileged API access per contribution node or whatever.

Hi Adam, thank you very much for your video, it's very helpful. I have one doubt, I thought that Langchain would take care of those special tokens for us when we are using the ChatOllama class, am I wrong?

And it can be used to communicate in discord?

Thank you for the wonderful lecture and example of source code. The example source code worked nicely in the local environment, and the test code inserted conveniently in the middle helped me understand the example a lot

Very well done, subbed. A perfect complexity project to get into LangGraph.

You are an awesome teacher. I am already running almost the same setup, with agents created automatically by another PraisonAI but didn’t fully follow what was going on and many times not working and I didn’t know why. I loved the way you broke down and explained everything. Looking forward to see more videos from you. Best wishes

Very good video, I'm impressed, you got yourself a new sub. I'm not a massive fan of LangChain but your video style is very easy to follow so I'm looking forward to watching your others too. Great work.

Hey can you please share the miro board link? Or drop it into a high res pdf? AWESOME work btw 👍👍👍

I like seeing these. Something to consider is having the web search differentiate between sponsored and non sponsored results. I have not seen anyone tackle that yet. It seems to me that search results and LLM outputs would be more accurate when steering away from sponsored data.

Interesting concept. I try to force my llms to produce valid json and this would be easier.

Excellent video thank you!

Awesome project! Is it possible to use another service as translation rather than Chatgpt that doesn't require a subscription?

loving your content, thank you for sharing this. learning a lot! would you be interested in making a video to show how to deploy these models and have access to them from inputs like whatsapp chat, email? would be super helpful

Thanks for the really interesting and well done clip. I get an error at the end, when testing the whole thing: It says, it has a ratelimit exception in the duckduckgo_search module: 202 Ratelimit.

I really enjoyed this video. I've seen lots of "how to" vids WRT programming using local LLMs but this is by far the best one I've viewed. I often get lost and have to re read some of the steps however you moved along at exactly the right pace for me and explained pretty much all of the questions I was dreaming up during the view. Thank you! Ollama does have function calling but this method seems to be more logical and easier to understand.

What's the point, SORA is not released yet. Just a waste of time!

Great job. Subscribing. Any chance you would make some video on long term memory in vector db? greetings

Nice work

Kalakitta nanba!

what is used for scoring?

Really impressive that combination of these 3. But to have a perfect loop how to deal with an input audio (voice) in real time before start speaking to respond ? And another question the generating audio at last could be an emulation of your microphone ?

awesome dude, I've been working with llama3 and langgraph to see if you can use tools with llama3 but you did it, you are great, cheers.

Awesome stuff!

Question: If I have a data preprocessing agent that has access to around 20 preprocessing tools, what is the best way to go about executing them on a pandas data frame? Do I have the data frame in the State and then pass that input in the function? Does the agent need to have access to that data frame or can we abstract that?

Hi Adam, great work. I've been struggling trying to evaluate the different agent frameworks, autogen, crewai VRSEN and on and on. langchain etc. seems to be more logical as we can see what's happening and is more predictable. Would it be possible to get the Miro you built for this presentation? Greetings from France.

¿Puedes compartir con nosotros tu presentación de Miro?, Great Job

Great work, thanks for this🙏. There is another agentic approach which is called self discovery. Would be cool if you cover that as well 😊.

Good explanation!

Well done! Thank you, sir

This breakdown is insanely helpful 👏 I’ve been working as a Web Engineer for > 10 yrs and recently started learning about AI/ML. I began my career as a self-taught dev in the good ol’ jQuery days, but my lack of CS fundamentals is starting to come back an bite me. These architectural diagrams are incredibly useful for breaking down high-level concepts.

Thank you for sharing! Great summary.

Great video! Could you share the code?

Your channel has been very valuable today to get me situated on how to get the hang of LLM use. I can now start thinking about project ideas to get some practice. Thank you very much !

Very nicely done, thank you for such a good preseentation.