Most important take away form this comparison: Langflow only supports the OpenAI embeddings whereas Flowise supports OpenaI, Azure OpenAI, Hugingface and Cohere embeddings. Therefore, only with Flowise one can use zero cost embeddings as well as enterprise level embeddings (Azure OpenAI). Additionally, Flowise has much better choices for Vector Stores and LLM parameters.

Gpt4 video summary: Langflow pros and cons: Pros: Langflow has a wider selection of loaders, which can be more specific and in-depth compared to Flowise. It provides a more intuitive interface with tiles that offer more explanation and detail. Langflow has a cleaner and nicer interface for saving and exporting flows. It offers more possibilities in terms of tools and functionalities. Langflow provides a Python code tab in addition to the Python API, which can be useful for developers. Cons: Langflow has a limited zoom out feature, which can be restrictive when working with large canvases. It has fewer options for embeddings and language models compared to Flowise. Langflow lacks some of the Vector stores available in Flowise, such as Pinecone. The code part in Langflow is less detailed compared to Flowise. Flowise pros and cons: Pros: Flowise allows for a far zoom out, which can be beneficial when working with large canvases. It provides more options for embeddings and language models, including Hugging Face, Cohere, and Azure. Flowise offers more Vector stores, including Pinecone, which can be a preferred option for some users. The code part in Flowise is more detailed and intuitive, offering more options for embedding the website or using the API. Cons: Flowise has a smaller selection of loaders compared to Langflow. The interface for saving and exporting flows in Flowise is less clean compared to Langflow. Flowise's component bar disappears after selecting a component, which can be inconvenient for users. It has fewer tools and functionalities compared to Langflow. Evaluation of both solutions: Both Langflow and Flowise have their strengths and weaknesses. Langflow seems to offer a more intuitive interface and a wider selection of loaders, which can be beneficial for users who require more specific functionalities. On the other hand, Flowise provides more options for embeddings and language models, as well as a more detailed code part, which can be advantageous for developers. However, the choice between Langflow and Flowise would largely depend on the specific needs and preferences of the user. It's recommended for users to try out both tools and see which one fits their requirements better.

Thanks for the video. Very helpful.

Congrats with the video choice. Getting close to what people really really want, getting AI agents become specialist assistants doing tasks autonomously on our behalf. If Flowise and Langflow can help achieve that agnosticly is the advantage to explore!? Ideally at zero cost using the known LLMs.

Thanks for this video and all your past episodes - SO HELPFUL! One area I'm interested in is how would one take what they've done in Flowise or ChainFlow and deploy them as an app on a password protected hosting service like Render? Epsiode "How to LangChain + Flowise + Render = Chat With ANY Site!" mentioned part of this process but creating a fine-tuned chat app on a secured pdf (or something else) all the way through to deployment on Render would be cool.

That was an awesome video. Thanks for sharing about React Flow and also ruining my weekend plans😂😀

Thank you!

'Best' is a point in Time. Both will evolve .... for me, I go with LangFlow because I am Python Bias.

does langflow have html endpoint like flowise for the chatbot integration to a website?

I want to know if you can use a open ai plug in at the same time ?

This is a great video, thank you for taking us through these two amazing repositories. Do you know whether any of the two already supports LlamaIndex workflows and/or allows to add custom workflows? Best

🎉I haven’t watched this video yet, but I’m sure everybody has been waiting for it! ❤ I don’t mean to be greedy, but I wish you could do a video on how to do GUI with these tools…😊

I really like your videos! Clear, straightforward, positive. Have subscribed and looking forward for a new videos. Have an unrelated but probably quite an important for many AI enthusiasts question - generally speaking, unless a solution solves a real life problem and provides a real value for it's users, it's kind of ....useless. So what, in your opinion, are indeed valuable AI solutions for general businesses, maybe sort of SMEs, freelancers etc, and probably their owners/employees that are sort of turned on by the idea of implementing AI powered solutions, but can not afford services of well established AI agencies and service providers and do not have their own IT or AI departments? I mean that a lot of examples, such as chatbots etc are great, but their value to potential users/subscribers is not as clear as it should be. What are your thoughts on this topic?

Wonder if it's possible to connect your own LLM?

🎯 Key Takeaways for quick navigation: 00:00 📌 Introduction and overview of Flowwise and LangFlow 00:45 🧩 Flowwise installation and setup 03:02 🐍 LangFlow installation and setup (Render) 08:22 🌐 Overview of LangFlow and React Flow 09:29 ☁️ Other deployment options and Gina AI Cloud 12:05 🗒️ Comparison of Flowwise and LangFlow interfaces and features 16:25 🧩 Deep dive into the tiles and component differences in LangFlow and Flowwise 19:39 📚 Comparison of document loaders and embeddings in LangFlow and Flowwise 21:12 🧠 Comparison of memory components in LangFlow and Flowwise 22:12 🧠 Comparison of Tiles and Code Features in LangFlow and Flowwise 25:52 🧩 Comparison of Tools and Vector Stores in LangFlow and Flowwise 26:58 📚 Overview of LangFlow Interface, Exporting, and Code Generation Features Made with HARPA AI

Please share an example for flowiswe and langflow as comparison thanks

how to use PrivateGPT + langflow pls

great, amathing👍👍👍👍👍

Will langflow remain free or open-source.

Summary ?

For me, this is like various brands of kombucha, and not Coke versus Pepsi. Likely, people will navigate between both. There’s too much awareness and developer circles in too much awareness of people within the entire product ecosystem and trying to get rich for anyone platform to totally standardize. I’m using both equally.

Could you send the thumbnail image without the words on it because I love parrots

Now I can see why Langflow is less popular than Flowise

so langflow have not github loader so its useless

Can you go over local embedding in flowise? I can get local ai to run with open AI embedding but not localai embedding. Trying to not use api calls.