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youve done so many of these and you repeat the same script instead of showing us interesting things like custom tools and using llama through ollama......

I would love to see automatic task delegation or rather agentic workflows here. I saw in your documentation that it says you are working on it. Is this presently available to play with? The scenario, from a one-sentence query, task a set of agents to complete the query.

Can we use any other model like Claude or Llama ?

Amazing! The possibility of building intangible AI tools is endless! 👏

snappy! love it!

I was able to get something similar running reliably using llama 3 8b quantized to 4 bits. Not quite as advanced, it doesn’t have any task delegation, but I don’t see a need for it so I doubt I’ll add it. But I’m really happy I was able to get it to run on such a relatively “weak” model that can run locally

This is great! Is there a way to add a lot of documents (hundreds or thousands) into the vector database so that we can have the agent query a large corpus?

Someone said it, I’ll say it again. How are these different from conversational agents which langchain showed how to build like a year ago… is it a nicer API? (Langchain at this point like bloatware) but what is the new pattern here? We’ve always had agents where agent = LLM+ knowledge + memory + tools. Otherwise, nice demo!!

Great video, right on money

Thanks for the video, extremely helpful to learn and build our own applications

What if I want to include RAG in this system? How can I do it?

Since this can see shell, can it also write or create files? Create or edit system settings if I chose to do so?

Very helpful and thanks for making it open source. Can you make a tutorial on implementing this in a Streamlit app? Also, can you explain how we can add descriptions to each PDF file, so when querying, the agent knows which PDF to look for the most accurate data?

Just wanted to let you know that the discord link doesn't work! i'd love to join!

Why are you even to make these tutorials short! There is so much to learn, please make them longer or make more videos :)

I'm very skeptical of all things around AI lately, but this is a really cool implementation/conceptualization of what a powerful LLM can do. I want to build one of these locally and see if I can make it an 'expert' at something niche and traditionally 'difficult' for a computer to do.

Thank you. This is so neat, I am dying to port our agent to this framework soon

Awesome, I could not find the Local RAG under cookbook ?

can you please comment on advantages of of phidata vs autogen vs crewai?

great. can you make customize assistants and customize tools with phidata? any example?

Thanks for the video. Very helpful. I have a question: You mention openDB at 6:55. What is the concept? Can you share a link, please?

This is amazing! Is a LLAMA-3 with Groq version in your roadmap? I'm going to attempt to convert it, but don´t know if I'm as skilled...

Phidata reminds me of early stages of Microsoft copilot... As a Self-taught Prompt Engineer, l can relate and join dots...

Had hands on Phidata AI Engineering framework, it simplifies the whole interconnected AI system structure

NotFoundError: Error code: 404 - {'error': {'message': 'The model `gpt-4o` does not exist or you do not have access to it.', 'type': 'invalid_request_error', 'param': None, 'code': 'model_not_found'}}

I was thinking same, to integrate LLM into Linux.

What software do you use for the zoom in/out effect? It looks great.

Can use local open source model instead of gpt4o?i mean how to code if can

If any individual agent needs to use memory, knowledge or internet how can it do so

Is there anyway we can create a team of agents with their own memory, knowledge and tools and allow them to communicate with each other to get a task done

does local models with function calling support work with it?

finally a good channel.

wow!

thanks can you make work with open LLM like ollama

Technically, LLM OS would work with a local Llama-3 model right? Since you do not need the "omni" multi-modal input.

your vibe is amazing! thx alot bro!

do you just repost the same video every day?

How does Phidata compare to other frameworks, like CrewAI, Autogen, Agency Swarm, etc.? What makes it standout compared to the competition (strengths/weaknesses)? It would be great if you made a video about this.

or just use the API this some a little pointless

Hello. This is a great tool. I’m new to coding but logically I completely follow you. Would you be able to assist me in where to find instructions for how to add ‘text’ as an option to go along with the correct ‘pdf’ option you mentioned? Thank you,

Brilliant, thank you ❤

Sorry to ask this, but what did you do after selecting the code (1:49) to get that new window running showing the reponse of the receipt? I have no clew what to do: did use a shortcut? And what did you do after switching from gpt-4o to llama3, (2:23) to get a new response in the new window? Another shortcut? Can assistants communicate with each others? Like ask python assistant script writer to use the websearch assistant to do research then write a code?

I think that a entire OS is to much for a LLM but I expect to use one as a linux shell in a near future.

oh I think this video answers my very recent question :D oh but ollama is an extra step I don't see the need for

holly cow... well done I assume this uses an online LLM? or does it load up a local gguf like I'd like? can you briefly explain were to make such a switch if needed?

Great job! I just test the framework and it's fantastic. Just curious why the 1k string length restriction on the tool instruction

Thank you sharing this amazing content!

Amazing job, thank you so much for sharing it!

Thank for creating great content

Where do you enter the API key? Apologies am pretty new to this.