johnicassere/lab-rat-race

LabRat describes itself as an autonomous multi-agent research orchestrator, a system where multiple AI assistants work in parallel to explore a research question you define. The concept is that instead of asking a single AI for answers, you deploy a "swarm" of specialized agents: one focused on reviewing existing literature, one generating hypotheses, one running simulations, and one validating results statistically. These agents are coordinated by a central orchestrator that treats the research as a resource allocation problem, where agents bid for compute time and API calls based on their confidence in a given direction. You would theoretically use this if you wanted to automate scientific research workflows, for instance, exploring a hypothesis about drug binding properties or materials discovery, by providing a configuration file that describes how many agents to run, which AI models to use, time budgets, and consensus thresholds. The system integrates with external AI APIs and claims to output PDF reports with citations. It runs from a command line on Linux, macOS, or Windows. Note that this repository appears to be primarily illustrative or promotional in nature, with a README that is heavier on architecture diagrams and marketing language than on actual runnable code evidence.