ScienceClaw A hackathon for building decentralized collectives of AI agents that solve real scientific and technical problems.

ScienceClaw explores when decentralized collective intelligence can produce capabilities beyond a single agent or conventional workflow.

Teams build working collectives of specialized agents, models, simulations, sensors, and laboratory tools. Different members of the collective can hold distinct knowledge and capabilities, coordinate and adapt, and leave traceable scientific artifacts, provenance, findings, and unmet needs that the collective can build upon—without requiring one central planner to prescribe the entire process.

Teams identify a difficult scientific or technical problem and build a functioning agentic system to address it. Each project should demonstrate:

The goal is not simply to propose an idea or agent architecture, but to demonstrate a working system and what it can accomplish.

ScienceClaw is model- and framework-agnostic. Teams can combine frontier models, open models, their own models, existing agent frameworks, scientific datasets, compute, sensors, and laboratory infrastructure.

We will provide access to a shared pool of resources, including compute, models, tools, and other infrastructure, where available. Resources are limited and will be allocated based on project needs, technical fit, and availability.

The goal is not to prove that one model is best. Teams can use, reuse, modify, and combine available technology—including capabilities developed during the event—to build a functioning decentralized system that accomplishes something scientifically meaningful.

Scientific domains are intentionally open. We provide challenge seeds, problems, datasets, resources, or sponsor-led challenges to provide entry points without constraining what teams build.

Teams can formulate their own problems and work across challenge areas. There is one overall competition, rather than separate tracks.

The hackathon operates across multiple levels:

The competition therefore becomes a live experiment in decentralized collective intelligence: teams compete to build strong systems while also having an incentive to make the broader ecosystem stronger.

Science is the benchmark. Success is not simply whether an agent completed a task, but whether the system produced a scientifically meaningful result supported by evidence, validation, or measurable progress.

The judging rubric also asks whether organizing capabilities as a decentralized collective adds something beyond a single agent, isolated agents, or a conventional workflow.