I've tried some of these agent frameworks like autogpt, babyagi and all earlier.

They didn't seem to be very useful TBH. How is this one different?

Goal of SuperAGI is to build useful autonomous agents and to do that there are bunch of things I have included in the project which is not there in autogpt etc, like agent trajectory fine tuning, running concurrent agents or agent clusters, configurable workflows for each iteration of agent

This project came out of building an autonomous marketing app, so faced some of the challenges of using autogpt , babyagi etc in the prod

Configurable workflows for what?

Very interesting! Keen to know what all models it supports currently and what’s in the roadmap?

Currently, there is support for GPT3.5, GPT3.5 16k and GPT4, but there are some open prs for opensource models like GPT4All and Vicuna. Going forward idea is to integrate with as many models as possible and philosophically it is model agnostic

I'm currently using langchain for a project. What will be the trade-offs of using this framework instead of just continuing with langchain agents?

So langchain's agents are lightweight implementation of existing liberaries. Superagi on the other hand is focussed on real world production deployment

At the risk of sounding stupid: How is it different from autoGPT or baby agi