While there are other TS LLM frameworks, I think RΞASON fills a unique space in the market: it's laser-focused on only three areas and, most importantly, actively stays away from pre-made prompting & retrieval.

I've been in the LLM space since GPT-3 originally came out, and I've always had problems with other frameworks, such as LangChain. I dislike that they focus a ton on out-of-the-box prompting & pre-made agents — I, as the dev, should be the one in charge of it.

My belief is that LLMs are a new primitive that programmers can use — not a new way to program; it's still up to the programmer to do the right thing & create the right abstractions. Therefore, it's the developer's job to learn the new concepts that come from this new primitive, such as prompting & retrieval. I see a similar analogy here with ORMs & SQL.

What RΞASON helps with is in areas that don't differentiate your app: getting structured outputs, handling streaming, and observability.

The goal of RΞASON is to make creating great LLM experiences easier. We try to accomplish this by simplifying the hard stuff & maximizing performance — decreasing as much as possible the TTUB.

RΞASON is OpenTelemetry compatible — which allows observability in almost any tool (Zipkin, Jaeger, paid solutions, etc.).

I'd really love to hear feedback about RΞASON! It has been a hobby project for the last months and I'm super curious to what y'all will think.

By the way, contributions welcome!