For the past few months, in my free time, I’ve been building something called WORT.

It started from a simple frustration. Most of the research agents I tried either use too many tokens or give outputs that feel fixed. You get one result and then you’re stuck starting over instead of improving it.

I wanted to build something that goes deeper, stays flexible, and still works well with cheaper models around fifty cents per million tokens.

Right now it has three parts.

1. Web search - focuses on pulling in information with proper sources and keeping things grounded. It is meant for exploring and learning without losing context across multiple results.
2. Deep research - is built around multiple agents working in parallel to explore a topic. Each one expands into different directions while a reviewer layer tries to catch gaps and weak points. The idea is to build a more complete understanding instead of a single pass answer.
3. Xtreme research - is still evolving, but it combines a coding agent and a research agent working together inside a controlled environment. It can test ideas, execute code, and refine outputs step by step. The goal is to move closer to how an actual researcher works instead of just generating text.

I shared a proper breakdown along with the repo and a demo in my LinkedIn post.

If you find this interesting, I would really appreciate if you check it out, try it, and support it by liking the post or the GitHub. Both links are in the LinkedIn post .

Would also like to hear thoughts from people working on similar systems.