Announcing the BioAgents elizaOS Plugin: A New Era of Agentic Science in Biotech
BioAgents elizaOS Plugin, an open-source tool, automates scientific research by monitoring papers, extracting knowledge into a decentralized graph, and generating hypotheses. It accelerates drug discovery and more. Join the Bio x AI Hackathon (April-May, $125K prizes) to build with it.
BioAgents elizaOS Plugin is a new open-source tool that brings autonomous AI agents into the heart of scientific research.
Built on the elizaOS framework, this plugin can read and reason over scientific literature automatically, pointing toward a future of agentic science where AI models transform into scientific task-runners.
Imagine if scientists could run 100 experiments while they sleep.
BioAgents are designed to accelerate fields like drug discovery, longevity research, and synthetic biology - from research paper to knowledge graph, hypothesis generation, experiment design, lab submission and beyond.
Automated Literature Monitoring: The BioAgents plugin watches a designated Google Drive folder for newly added scientific papers (such as PDFs or preprints). Whenever you drop in a new paper, the agent detects it and gets to work.
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Knowledge Extraction: The plugin parses each paper and converts key information into structured knowledge assets. In practice, that means extracting facts, relationships, and findings and representing them as machine-readable data (e.g. as RDF triples, a format for knowledge graphs). This turns unstructured text into an organized knowledge base that an AI can query and build upon.
Decentralized Knowledge Integration: Extracted knowledge isn’t kept in a silo – it’s integrated into a shared RDF triple store, essentially a decentralized knowledge graph of scientific information. Using OriginTrail’s DKG (Decentralized Knowledge Graph) plugin, the agent automatically adds new nodes and edges based on insights from each paper. Over time, this builds a growing, searchable “BioGraph” of facts that anyone can access and verify, rather than a private database.
Autonomous Hypothesis Generation: With an up-to-date knowledge graph at its fingertips, the AI agent can begin to generate and evaluate new hypotheses. It can brainstorm research questions or theoretical connections and then scour the literature (and the knowledge graph) to see if evidence supports or contradicts those ideas. The agent effectively becomes a tireless research assistant, proposing experiments or analyses and checking them against existing data.
Toward Agentic Science: Open-Source and Decentralized by Design
The BioAgents plugin is more than a convenience – it’s a stepping stone toward agentic science, an emerging paradigm where AI agents actively participate in the scientific process.
Consider that modern biology produces over one million new publications each year, far beyond what any human can read. An army of AI “BioAgents” can work in parallel to digest this avalanche of knowledge, ensuring no crucial insight is missed. These agents don’t just read; they write to a collective memory. By logging discoveries into a decentralized knowledge network, each agent contributes to a commons that all agents (and humans) can draw from.
BioAgents operate as a user-run, decentralized fleet of researchers, continuously running at home or in the cloud. This decentralization means the knowledge graph isn’t controlled by any single company or lab, but by the community – an approach in line with the ethos of decentralized science (DeSci) and open collaboration.
Crucially, the BioAgents elizaOS plugin is open-source, so anyone can inspect how it works, modify it, and extend it. Open-source AI tools ensure that the latest advances are accessible to all – from a startup biotech lab to a university student – rather than locked behind closed platforms. Likewise, integrating with a decentralized infrastructure (like the OriginTrail DKG) means the data and insights your BioAgent produces are globally addressable and verifiable, not confined to one server. By combining open-source AI with decentralized knowledge sharing, the BioAgents plugin shows how we can accelerate biotech innovation in a transparent and inclusive way.
We’re just getting started here. The connection between Agents with scientific Tools and the traditional research world is only beginning, and we have a lot more to add to both this plugin and more to come. Community-driven tools like BioAgents can accelerate fields like drug discovery, longevity research, and synthetic biology.
Join the Bio x AI BioAgentHack Hackathon
To spur innovation with this new plugin, we’re thrilled to invite developers, researchers and bioscientists to apply to the Bio x AI “BioAgentHack” Hackathon – a 2-month online event where you can build the next generation of BioAgents and win big.
AI researchers, biotech entrepreneurs, and students alike are welcome. Over April and May (with a kick-off in Berlin and online on April 8-10), participants will form teams and develop projects leveraging AI and knowledge graphs to advance science. It’s a unique opportunity to work with cutting-edge tools like the BioAgents Eliza plugin, with mentorship from leaders in AI, biotech, and decentralized science.
Why participate? You’ll get access to resources and mentorship to help turn your ideas into reality, collaborate with a vibrant community at the intersection of AI and biotech, and compete for over $125,000 in prizes across multiple categories.
Whether you’re interested in building new Eliza plugins (e.g. integrating lab datasets or designing experiment-planning agents) or applying the BioAgents plugin to a real research problem, this hackathon will provide support every step of the way.
Ready to get involved? The BioAgents plugin is available now on GitHub for you to explore, and we can’t wait to see what you build with it. Check out the code on our GitHub repository, and head over to the Hackathon Devfolio page to secure your spot in the hackathon.
Apply now to the Bio x AI Hackathon BioAgentHack and become part of a community that’s pushing the boundaries of what’s possible in biotech with open-source AI agents.
