langflow VS orange

Hey folks I'm the Head of DevRel @ DataStax here and just wanted to share to the HN community that in conjunction with this big acquisition news, the LF team has shipped 1.0-alpha of Langflow.

It's a simple `pip install` and the team would love any and all feedback!

https://github.com/logspace-ai/langflow/

You can also check langflow, a node UI for langchain https://github.com/logspace-ai/langflow

There's a few tools out there like AgentGPT (https://github.com/reworkd/AgentGPT, although it's a more conversational interface), and (https://github.com/logspace-ai/langflow) and others. I think most developers definitely prefer a code-first interface though like a library but haven't found one that's great yet. We've used them in the past but didn't have the best experience so would love to hear if anyone has worked with a library they found really flexible.

I found this thread talking about it here: https://github.com/logspace-ai/langflow/issues/263

also check https://github.com/logspace-ai/langflow

AutoGPT is basically a demo of what you can do with Langchain. If you want to play with Langchain in a drag and drop blueprint environment I suggest Langflow

Hi, I like this! I'm curious what drove the decision to use the vertical block builder style you chose. I'm partial to node-based editors and have been building things with React Flow recently. LangFlow [1] is a good example, but there's lots of UIs that use a similar interface (e.g. Blender [2] and Unity [3]).

[1] https://github.com/logspace-ai/langflow

[2] https://docs.blender.org/manual/en/3.5/interface/controls/no...

[3] https://unity.com/features/unity-visual-scripting

Here's an open source version that's very similar LangFlow

I know I've tooted its horn before, but Orange3 is a pretty neat Python-based GUI platform that makes this and a metric buttload of other statistical/ML techniques available to non-programmer types.

Just watch out for null character `x00` in the corpus. That always seems to kill it stone dead.

https://orangedatamining.com/

https://orange3.readthedocs.io/projects/orange-visual-progra...

For all you folks who aren't ace programmer types, the Orange3[1] platform gives you a very miniaturized[2] ability to turn out these sorts of visualizations very rapidly. It's not the most stable thing in the world, but the node-based ML workflow designer is worth the price of admission all by itself.

[1] https://orangedatamining.com/

[2] The Wikipedia extension in Text limits each search result to 25 articles, so sucking all of Wikipedia is . . well, Orange text analytics crashes when I look at it sideways with a null character, so let's not think about what would happen.

First is identifying the "similar" things in a corpus. Best way I know to do that, for non-programmer audiences, is the Orange Data Mining tool, which gives you a node-based text mining interface to perform statistical analysis on text. Hierarchical Clustering shows - very rapidly - how similar your "modules" are, which ones are most similar. There's many other techniques (semantic viewer, similarity hash, etc) as well - the right one will depend on how your content is laying about.

Both tools are ripoffs of a data mining framework named Orange 3

You should also check out Orange Data Mining, it allows to create a lot of charts, filter data from a chart to another, build ML models, predictions and a lot more. And you can do it with zero code.

You can start with a free GUI based tool Orange. It is a component based data science workflow tool, which you can use to handle 60-75% of the traditional data science tasks from classification, regression, to basic neural networks.