I'm making some updates to the LangGraph WebSearch Agent. Here's a summary: - Changes to the agent graph - Refactor of the agent script turning agents into classes. - Small bug fix where the scraped content isn't currently being passed to the reporter agent. Only the chosen page and a high level description is being passed. - Supporting Groq models (and maybe Claude if I have time). I'll be putting a video out on this tomorrow.
I would definitely like to see more videos on integrating Ollama, Gemini, and Claude into a mixed agent workflow. I think using the big context of Gemini for the reviewer, Claude for the reporter, and GPT-3.5/Llama mix as the researcher/planner could be a fun project.
That's the spirit! The best explanatory video I have ever seen. Thank you for your work. I will be waiting for videos explaining the comparison with other frameworks, whether Crew or Agency swarm or etc, with different examples.
knowing what you know now about Langgraph, does your position change re: your suggestion/preference to build custom workflows from scratch? I recall you mentioning that preference in your CrewAI tutorial & you don't mention that at the end of your video here. Has Langgraph converted you to frameworks?
Good explanation, than you. But information that AutoGen state management is just to share everything in a Group Chat is outdated: AutoGen already has Memory Enabled Agent and Memory Manager Agent that are likely even more advanced and automated than LangGraph state management, even though that feature is not 100% production ready most likely. AutoGen also allows to use sequential and programmatic workflows to manage what parts of message history are shared with what agents. - These agent frameworks progress very fast and it is hard to keep up with it for sure.
Great explanation of LangGraph.. But the coding part is kind of overwhelming.. I wish there's GUI for LangGraph soon, so that, nodes can be defined, and then connected visually..
How can we build human-in-the-loop workflows where the agent-workers prompt the human for input, such as helping the person expand on an idea or clarify their goals before proceeding to subsequent steps in the graph?
So there is RU-vidrs with 1.5mil subs that do AI and you are 100% better! I can follow you and you make sense. Can we use AI to make predictions based on csv data?
Definitely the most comprehensive explanation of Langgraph. I've used it, even made a video, but I learned a lot from how you broke down the state. Going to implement a DevOps type of workflow with this, see how well it does. Thanks!
Wow Amazing work with clear explanation. Thank you. I want to try it with Ollama using LLama3 model. Will appreciate if you could create another video on same request. I don't want to give my savings to OpenAI.
I know right, then all the main ones with millions of views are crap and videos are unfollowable, gonna stick links everywhere for this to help the guy out.
Language-based AI agents are transformative. While exploring Slang AI, consider alternative tools that might better suit your specific needs in communication and automation.
Amazing, @Data-Centric. I appreciate this tutorial a lot. You really break down the code in a way that is easily digestible, and it is simply articulate and beautiful code, IMO. The VLLM integration tutorial is highly desired for lame ducks like moi.
If the answer of the complex query was wrong. how would you make sure its correct? how can we increase the accuracy of the result? (I want people to trust AI responses)
Fantastic job explaining State :: Read/Write :: Graph - Most Langgraph tutorials out there skip this crucial step which leaves a major hole in truly grasping this concept. I also appreciate the way you organize your scripts. The tip of having a model that outputs JSON only is helpful, too. Great job!
Would it be possible to actually intelligently detect which model would be sufficient at answering the query? Would tou have any ideas for handling that logic? Thanks for posting, nice and tidy!
It's possible. I think if you took that approach you would need a model intelligent enough to decide which model would be best for answering the query.
Thank you so much for taking the time to share your thoughts (during the weekend!) breaking it all down so thoroughly. I've been looking forward to spend some quality time searching for a good starting point on agents until, weeks ago, I found your channel and easily became confortable with your syle. It's sunday morning and, - yesss! Let's see what he has to say. - Got yourself a new subscriber! Keep up the good work!👏👏👏
Thanks for sharing your view on Langgraph. As far as your efforts towards an open source solution, this is very exciting with Ollama. Not sure I have any others I could suggest. With the structure you have established in your custom agent, it seems to me you could have a more dynamic approach via a control sheet along with a Flask framework to establiah a RAD build out of the code. Any thoughts on this DRY methodology since there is a Template layout? Thanks again.
Hey John, thank you for this awesome video/resource! I have a question for us that work with a dB aswell. How would you structure the file system and how would you implement handling dB requests and writes, for example, if you want to store the planner's agent thoughts in a dB, how would you achieve that with your modular approach? Thank you so much I learned a lot from this video
top notch work this is excellent stuff thanks for the video! I'm about to ditch crewai. I want to build agents though with a prompt template so that I can apply llama3 specific prompt template so that it works properly and then I can use it on my own hardware. I see you make your own template within the prompt itself. If you use a template then you can abstract that part from the text part of the prompt and use the same format throughout the conversation.
I don't think the disadvantage of agency swarm is OpenAI Assistants API, the beta thing will go away soon. The Open Assistants API also will develop quickly. However, it seems to me that LangGraph lets us really define how are agentic workflow moves and how we provide prompts and context to agents in the chain. I don't like relying on a "CEO" or manager agent to decide on the agentic workflow, it seems likely to get this incorrect from time to time. I have had trouble getting the agents I wanted to be used to get selected were the controlling agent at the top of the hierarchy has a choices. Agency Swarm has some configuration possible on this, but it seems LangGraph is much more in this area.
First thanks for the great video and visualisation of the current topic…my question is if it’s possible with this scheme to build an agent graph where you have an agent node that decides which of three tools it should use in the next step to call a specific function from this decided tool could be called parallel tool decision making
What a coincidence. I was playing today with communication between 3 “experts” by going back and forth manually. I was wondering if i could have automated the workflow. I came across this video by chance, and I am very pleased the way you presented. Thank you!
I will also love to see the adaptation to ollama ran locally. Can you do a chat application in langgraph using ollama as well so we can see how memory works. Thanks great video
thanks man for explaining these concepts in a very simple terms. I had kinda hard time understanding the state in langgraph, your video really helped me understanding it. Keep em coming!
@@free_thinker4958 the most this program does is assemble ready-made imports that query a database on the cloud. Show me architecture that you built without using a single third party package, and you can count me impressed.
Twice now my really cool value added comments got deleted that took a lot of time and effort. Done with that now, sees all ypuu want is people to say "Excellent, clear, concise explanation. Thank you.". Which is true i wont like. Ill just do that from now on, no added effort, apologies
I have not deleted any comments. RU-vid has it's own comment moderation in place, your comments must have triggered it. I am open to constructive feedback, but let's keep things respectful. Thanks for watching.
@@Data-Centric channels can select for comments to be removed from particular users. My own comment was deleted and the criticism was light as can be, talking about the number of dependencies.
I don't know what makes the Langraph a impressive framework. Nobody wants to see this big response and it keeps giving the same response until the Recursion limit and then it stops deh.
