Multi-Agent

AutoResearchClaw is an open-source 23-stage autonomous research pipeline from UNC Chapel Hill that turns a research idea into a conference-ready LaTeX paper. Features: multi-agent debate for hypothesis generation, self-healing executor with Pivot/Refine decision loop, verifiable result reporting preventing hallucinations, human-in-the-loop with 7 intervention modes, and cross-run evolution. Outperforms AI Scientist v2 by 54.7% on ARC-Bench. MIT licensed.

Automating scientific discovery requires more than generating papers from ideas. Real research is iterative: hypotheses are challenged from multiple perspectives, experiments fail and inform the next attempt, and lessons accumulate across cycles. We present AutoResearchClaw, a multi-agent autonomous research pipeline built on five mechanisms: structured multi-agent debate for hypothesis generation and result analysis, a self-healing executor with a Pivot/Refine decision loop that transforms failures into information, verifiable result reporting that prevents fabricated numbers and hallucinated citations, human-in-the-loop collaboration with seven intervention modes, and cross-run evolution that converts past mistakes into future safeguards. On ARC-Bench, AutoResearchClaw outperforms AI Scientist v2 by 54.7%.

BioMedAgent is a self-evolving LLM multi-agent framework that learns to use diverse bioinformatics tools and chain them into executable workflows through interactive exploration and memory retrieval algorithms. It allows biomedical users to initiate tasks using natural language, without requiring computational expertise. Evaluated on BioMed-AQA benchmark (327 biomedical data tasks), BioMedAgent achieved a 77% success rate, surpassing other LLM agents, and generalized robustly to the external BixBench dataset. Beyond benchmarks, it autonomously performs cross-omics analysis, machine-learning modelling and pathology image segmentation.

Artificial intelligence agents are emerging as powerful applications of large language models (LLMs), automating complex tasks and enabling scientific data exploration. However, their use in biomedical data analysis remains limited by the difficulty of handling specialized tools and multistep reasoning. Here we introduce BioMedAgent, a self-evolving LLM multi-agent framework, which learns to use diverse bioinformatics tools and chain them into executable workflows through interactive exploration and memory retrieval algorithms. It allows biomedical users to initiate tasks using natural language, without requiring computational expertise. Evaluated on our newly released BioMed-AQA benchmark comprising 327 biomedical data tasks, BioMedAgent achieved a 77% success rate, surpassing other LLM agents, and generalized robustly to the external BixBench dataset. Beyond benchmarks, it autonomously performs cross-omics analysis, machine-learning modelling and pathology image segmentation, highlighting its potential to advance biomedical research and extend to other scientific domains requiring complex tool integration and multistep reasoning.

Co-Scientist is a multi-agent AI system built on Gemini for structured scientific thinking and hypothesis generation. It aims to help scientists discover new original knowledge by formulating demonstrably novel research hypotheses for experimental validation, conditioned on research objectives and prior scientific evidence.

Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce Co-Scientist, a multi-agent AI system built on Gemini for structured scientific thinking and hypothesis generation. Co-Scientist aims to help scientists discover new original knowledge. Conditioned on their research objectives and prior scientific evidence, it formulates demonstrably novel research hypotheses for experimental validation.

DeepRare is a multi-agent system for rare disease differential diagnosis that integrates large language models with structured medical knowledge to provide traceable reasoning. It aims to reduce the diagnostic odyssey for rare disease patients by leveraging AI agents to analyze clinical phenotypes, genomic data, and medical literature in a transparent, interpretable manner.

Rare diseases affect more than 300 million people worldwide, yet timely and accurate diagnosis remains an urgent challenge. Patients often endure a prolonged 'diagnostic odyssey' exceeding 5 years, marked by repeated referrals, misdiagnoses and unnecessary interventions, leading to delayed treatment and substantial emotional and economic burden. Here we present DeepRare, a multi-agent system for rare disease differential diagnosis decision support with traceable reasoning.

Denario is an AI multi-agent system from the Flatiron Institute (Simons Foundation) designed to serve as a scientific research assistant across disciplines. It can generate ideas, check literature for novelty, develop research plans, write and execute code, make plots, and draft and review scientific papers. Demonstrated across 11 AI-generated paper drafts in astrophysics, biology, biophysics, chemistry, material science, medicine, neuroscience and more. Excels at combining ideas across disciplines (e.g., quantum physics + ML applied to astrophysics).

We present Denario, an AI multi-agent system designed to serve as a scientific research assistant. Denario can perform many different tasks, such as generating ideas, checking the literature, developing research plans, writing and executing code, making plots, and drafting and reviewing a scientific paper. In this work, we describe in detail Denario and its modules, and illustrate its capabilities by presenting multiple AI-generated papers generated by it in many different scientific disciplines such as astrophysics, biology, biophysics, biomedical informatics, chemistry, material science, mathematical physics, medicine, neuroscience and planetary science. Denario also excels at combining ideas from different disciplines.

Robin is the first multi-agent system capable of fully automating both hypothesis generation and data analysis for experimental biology. By integrating literature search agents with data analysis agents, Robin can generate testable hypotheses from literature and design experiments to validate them, automating the entire scientific discovery cycle for biological research.

Scientific discovery is driven by the iterative process of observation, hypothesis generation, experimentation, and data analysis. Despite recent advancements in applying artificial intelligence to biology, no system has yet automated all these stages. Here, we introduce Robin, the first multi-agent system capable of fully automating both hypothesis generation and data analysis for experimental biology. By integrating literature search agents with data analysis agents, Robin can generate testable hypotheses from literature and design experiments to validate them.

SciToolAgent is a knowledge-graph-driven scientific agent that integrates multiple computational tools for scientific research. It leverages a knowledge graph to understand tool capabilities and relationships, enabling automated multi-tool workflow composition for complex scientific tasks across domains including bioinformatics, cheminformatics, and materials science.

Scientific research increasingly relies on specialized computational tools, yet effectively utilizing these tools requires substantial domain expertise. While large language models show promise in tool use, they struggle with complex multi-tool workflows. Here we introduce SciToolAgent, a knowledge-graph-driven scientific agent that integrates multiple computational tools for scientific research. By leveraging a knowledge graph to understand tool capabilities and relationships, SciToolAgent enables automated multi-tool workflow composition for complex scientific tasks.

SPARK (System of Pathology Agents for Research and Knowledge) is a foundational agentic AI framework that uses language as a universal interface to autonomously generate biologically driven concepts for tumor analysis. It functions as a pathology 'brain' — an interconnected system of AI agents that autonomously reason, generate and implement biologically meaningful hypotheses as analytical tools without additional model training. SPARK uses four linked modules: idea generation (OpenAI o1), idea refinement, parameter coding (Claude Sonnet 3.5), and parameter verification. Evaluated across 18 patient cohorts spanning 5 cancer types and >5,400 patients, SPARK produced clinically and biologically relevant concepts correlated with prognosis, pathological variables, and predictive biomarkers, including patterns of tumor progression inferred from static images.

Artificial intelligence has advanced cancer pathology, but many systems still depend on hand-crafted features, are hard to explain and rely on fragmented workflows. We introduce SPARK (System of Pathology Agents for Research and Knowledge), a foundational agentic artificial intelligence approach that uses language as a universal interface to autonomously generate biologically driven concepts for tumor analysis. SPARK turns biological ideas into analytical tools and works directly with complex pathology data without extra model training. We evaluated SPARK across 18 patient cohorts spanning five cancer types (lung adenocarcinoma, lung squamous cell carcinoma, colorectal cancer, breast cancer and oropharyngeal squamous cell carcinoma) and more than 5,400 patients with available histopathology images and clinical/follow-up information, in both prognostic and predictive settings and on a well characterized spatial biology breast cancer dataset (n=625). We found that SPARK produced clinically and biologically relevant concepts correlated with prognosis, known pathological variables and predictive biomarkers, including patterns of tumor progression and temporal change inferred from static images. A dedicated module allows for human interaction with SPARK. All code, parameters and results are openly released.

The Virtual Lab is an AI agent framework that uses LLM-powered researchers in a simulated laboratory environment to collaboratively design and test scientific hypotheses. It was demonstrated by successfully designing new SARS-CoV-2 nanobodies, with AI agents specializing in different scientific roles working together to propose, evaluate, and refine experimental designs.

Science frequently benefits from teams of interdisciplinary researchers, but many scientists do not have easy access to experts from multiple fields. Although large language models (LLMs) have shown an impressive ability to aid researchers across diverse domains, their uses have been largely limited to answering specific scientific questions rather than performing open-ended research. Here we expand the capabilities of LLMs for science by introducing the Virtual Lab, a framework where LLM-powered AI agents collaborate in a simulated laboratory to design and test scientific hypotheses. The Virtual Lab successfully designed new SARS-CoV-2 nanobodies, demonstrating the potential of multi-agent AI systems for open-ended scientific discovery.