semantifly VS webwright

My company (actually our two amazing interns) was working on this over the summer, we abandoned it but it’s 85% of the way to doing what you want it to do: https://github.com/accretional/semantifly

We stopped working on it mostly because we had higher priorities and because I became pretty disillusioned with top-K rag. We had to build out a better workflow system anyway, and with that we could instead just have models write and run specific queries (eg list all .ts files containing the word “DatabaseClient”), and otherwise have their context set by users explicitly.

The problem with RAG is that simplistic implementations distract and slow down models. You probably need an implementation that makes multiple passes to prune the context down to what you need to get good results, but that’s complicated enough that you might want to build something else that gives you more bang for your buck.

https://github.com/MittaAI/webwright

Let me know if you want to go over the code or want to discuss what works and what doesn’t. We had a loop on the action/function call “pipeline” but I changed it to just test if there was a function call or not and then just keep calling.

Webwright, like some other similar tools, will build and run code in a terminal or container. Just ask it to do a Mandelbrot set. We're currently working on adding ChromaDB to it for history and abstracting the LLM class to support Ollama.

https://github.com/MittaAI/webwright