The common trend we've seen is that most of these other projects are okay for a true "just send messages to an AI and get responses" use case, but for most things beyond that they fall short / there a lot of paper cuts.
For an individual, this might show up when they try more complex tasks that require multiple tool calls in sequence or when they have a research task to accomplish. For an org, this might show up when trying to manage access to assistants / tools / connected sources.
Our goal is to make sure Onyx is the most advanced and mature option out there. I think we've accomplished that, so if there's anything missing I'd love to hear about it.
I have used vercel for several projects and I'm not tied to it, but would like to understand how onyx is comparable.
Benefits for my use cases for using vercel have been ease of installation, streaming support, model agnosticity, chat persistence and blob support. I definitely don't like the vendor lock in, though.
we have all of those!
> how onyx is comparable
For an AI-powered research assistant, Onyx might just work out of the box. We have ~45 connectors to common apps (https://github.com/onyx-dot-app/onyx/blob/main/backend/onyx/...), integrations with the most popular web search providers (https://github.com/onyx-dot-app/onyx/blob/main/backend/onyx/...), and a built in tool calling loop w/ deep research support (https://github.com/onyx-dot-app/onyx/blob/main/backend/onyx/...). If you wanted to customize, you could pretty easily tweak this / add additional tools (or even rip this out completely and build your own agent loop).
