Yes, I think Rivet, langflow, and flowise all came to the idea of visual programming for LLMs in parallel. I like to think it's a good sign that visual programming is a powerful paradigm here :)
I do think Rivet hits a pretty different use-case. Beyond being in the TypeScript ecosystem, Rivet's remote debugging and embedability are pretty unique, and super critical!
Thanks! Really glad to hear it, and excited to hear what you think!
Rivet doesn't have a built-in way of choosing between different ChatGPT plugins, so you have to explicitly build out the graph and choose via prompting. We published an example app that actually does this, although the "plugin choosing" part is intentionally simplistic: https://github.com/Ironclad/rivet-example
And yes... the docs and a decent amount of the code was heavily assisted by LLMs (all Andy, not me). Apparently if you look in the commit history, you can kind of see how Andy used Rivet to build Rivet!
Unfortunately nothing beyond the example apps. We wanted to get an example with a full agentic loop together, but settled for the simpler chatbot.
We'd love suggestions here, actually. Can you think of a use-case that might be a good example to open-source? Preferably something that interacts with multiple APIs towards a specific purpose?
That's at least what a lot of our early design partners and collaborators have been. Rivet slots in neatly to most modern TypeScript applications, and is pretty easy to adopt progressively.
(Also depends on your definition... I'd consider Ironclad a "growth-stage start-up," but I imagine others would say otherwise!)
I don't think it's a good idea to give an agent tools "just in case."
We've opted for giving our agents access to a few distinct, but powerful tools, and then trusting them to combine these tools in a strategic way. It's going well so far, but we are pretty careful to clearly explain the tools.
However, I've spoken to other teams that have opted for giving their agents tons of tools, and it sounds like that can work pretty well. But everything seems to take work and experimentation.
Actually, when running locally, you can open it in a web app. There's one catch, though: saving project files is pretty janky (it downloads the project file). I think basically everything else works pretty well, though!
I think it's people building tool-using agent applications.
We've been collaborating with several amazing teams over the past few months, who have been pushing Rivet in various ways. We used it for a chat interface at Ironclad, but we've seen companies like Bento and Willow integrate it with different UX paradigms.
The commonality seems to be that we are all integrating LLMs into an application, and want the LLM to somehow interact with that application (set up search filters, build a guide based on documentation).
And yes, we'd love to support OSS LLMs. And we welcome contributions ;)
Rivet also has a plug-in system, and our friends at AssemblyAI were actually able to develop a plug-in for using their audio transcription and understanding AI models in Rivet!