AI Agent

A comprehensive review from Cell envisioning 'AI scientists' as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents integrating AI models and biomedical tools with expert human oversight. Covers the landscape of AI agents for biomedical discovery, including virtual laboratories, autonomous experimentation, and human-AI collaboration frameworks.

We envision 'AI scientists' as systems capable of skeptical learning and reasoning that empower biomedical research through collaborative agents that integrate AI models and biomedical tools with expert human oversight. This review covers the landscape of AI agents for biomedical discovery, including virtual laboratories, autonomous experimentation, and human-AI collaboration frameworks.

A comprehensive review from Nature Reviews Cancer examining how AI agents (beyond traditional ML classifiers) are transforming cancer research and oncology. Covers LLM-powered agents for clinical decision support, drug discovery, treatment planning, and patient care, including agentic systems capable of logical reasoning, multi-step planning, and tool use in oncology contexts.

Since 2022, artificial intelligence (AI) methods have progressed far beyond their established capabilities of data classification and prediction. Large language models (LLMs) can perform logical reasoning, multi-step planning, and tool use, enabling a new paradigm of AI agents for cancer research and oncology. This review examines how AI agents are transforming clinical decision support, drug discovery, treatment planning, and patient care in oncology.

AI-MARRVEL (AIM) is a knowledge-driven AI system for diagnosing Mendelian disorders that uses a random-forest machine-learning classifier trained on over 3.5 million variants from thousands of diagnosed cases. It incorporates expert-engineered features to recapitulate the complex decision-making processes in molecular diagnosis. AIM doubled the rate of accurate genetic diagnosis across three independent real-world cohorts compared to benchmarked methods. Its confidence metric achieved 98% precision and identified 57% of diagnosable cases from 871 unsolved cases. AIM also demonstrated potential for novel disease gene discovery.

Diagnosing genetic disorders requires extensive manual curation and interpretation of candidate variants, a labor-intensive task even for trained geneticists. Although artificial intelligence (AI) shows promise in aiding these diagnoses, existing AI tools have only achieved moderate success for primary diagnosis. AI-MARRVEL (AIM) uses a random-forest machine-learning classifier trained on over 3.5 million variants from thousands of diagnosed cases. AIM additionally incorporates expert-engineered features into training to recapitulate the intricate decision-making processes in molecular diagnosis. AIM improved the rate of accurate genetic diagnosis, doubling the number of solved cases as compared with benchmarked methods, across three distinct real-world cohorts. AIM achieved a precision rate of 98% and identified 57% of diagnosable cases out of a collection of 871 cases. AIM demonstrated potential for novel disease gene discovery by correctly predicting two newly reported disease genes from the Undiagnosed Diseases Network.

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.

CRISPR-GPT is an LLM-based agent system for automating gene-editing experiments. It leverages large language models to guide researchers through the entire CRISPR experiment workflow, including guide RNA design, off-target prediction, experimental protocol generation, and result interpretation, making gene-editing more accessible to non-expert researchers.

Performing effective gene-editing experiments requires a deep understanding of both the CRISPR technology and the biological system involved. Meanwhile, despite their versatility and promise, large language models have not been fully leveraged for automated experimental design in molecular biology. Here we present CRISPR-GPT, an LLM-based agent system for automating gene-editing experiments across the entire CRISPR workflow, including guide RNA design, off-target prediction, experimental protocol generation, and result interpretation.

ChemCrow is an LLM-based agent system that augments large language models with 13 expert-designed chemistry tools, enabling autonomous chemical reasoning and experiment design. It integrates tools for organic synthesis, drug discovery, and materials design, allowing the agent to plan syntheses, analyze chemical properties, and execute complex chemical tasks through natural language interaction.

Large language models (LLMs) have shown strong performance in tasks across domains but struggle with chemistry-related problems. These models also lack access to external knowledge sources, limiting their usefulness in scientific applications. Here we introduce ChemCrow, an LLM-based agent that integrates 13 expert-designed chemistry tools, enabling autonomous chemical reasoning and experiment design. ChemCrow successfully plans syntheses, analyzes chemical properties, and executes complex chemical tasks through natural language interaction.

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.

Hermes Agent is an open-source autonomous AI agent built by Nous Research with a built-in closed learning loop — it creates skills from experience, improves them during use, persists knowledge, searches past conversations, and builds a user model across sessions. Supports 14 messaging platforms (Telegram, Discord, Slack, WhatsApp, Signal, etc.) and 6 execution backends (local, Docker, SSH, Modal, Singularity). Features include persistent memory (FTS5 + Honcho), automated skill creation, cron scheduling, parallel sub-agent delegation, full browser automation, MCP integration, and model-agnostic provider switching via CLI. MIT licensed, first released February 2026.

A natural-language interface that enables large language models (LLMs) to perform end-to-end variant interpretation for Mendelian diseases via structured tool access (MCP). MARRVEL-MCP equips LLMs with 44 tools spanning gene and variant utilities, pathogenicity databases, phenotype resources, expression atlases, ortholog data, and literature APIs. Without hard-coded workflows, LLMs infer which tools to invoke and in what sequence, performing named-entity recognition, identifier normalization, and multi-database synthesis from clinical queries. A 20B-parameter model achieved 94% pass rate on 100 expert-curated questions (vs 41% without MARRVEL-MCP), approaching state-of-the-art proprietary performance. Establishes context engineering as a core principle for biomedical AI.

Variant interpretation in rare diseases requires navigating multiple genomic databases, each with strict input formats, while synthesizing heterogeneous evidence. To address these usability barriers, we developed MARRVEL-MCP, a natural-language interface that enables large language models (LLMs) to perform end-to-end variant interpretation via structured tool access. MARRVEL-MCP equips LLMs with 44 tools spanning gene and variant utilities, pathogenicity databases, phenotype resources, expression atlases, ortholog data, and literature APIs. Without hard-coded workflows, LLMs infer which tools to invoke and in what sequence, performing named-entity recognition, identifier normalization, and multi-database synthesis from clinical queries. Using 100 expert-curated questions, lightweight models (3B-20B parameters) with MARRVEL-MCP matched or outperformed larger models without tool access. A 20B-parameter model achieved a 94% pass rate, versus 41% without MARRVEL-MCP, approaching state-of-the-art proprietary performance. These findings establish context engineering as a core principle for biomedical AI and support scalable integration of LLMs with curated genomic resources.

MIRA is an autonomous AI agent powered by GPT-4o (T=0.01) with o1-preview for structured reasoning, operating within a sandboxed HL7 FHIR-based EHR environment. It navigates 85,000+ clinical decision options across 8 emergency department diagnoses, using 11 FHIR-compliant tools (PatientHistory, PhysicalExam, Lab/Urine/Microbiology/Radiology requests, Medication/Procedure ordering, Plan, Admission). Evaluated on 574 real MIMIC-IV patient cases, MIRA outperformed two independent physician cohorts in diagnostic accuracy, guideline-concordant treatment, medication safety, and appropriate admission decisions. All tool parameter validity is enforced through token masking, making hallucination of non-existent options programmatically impossible.

Large language models (LLMs) show great potential for clinical decision-making, yet most applications remain narrow, task-specific chat tools rather than systems integrated into clinical workflows. However, building physician copilots will require models that operate within the electronic health record (EHR), with governed access to patient data and the ability to initiate permitted EHR actions within defined safety constraints. Here we show that MIRA (Medical Intelligence for Reasoning and Action), an autonomous artificial intelligence agent operating in a sandboxed EHR environment, can navigate a large clinical action space to obtain patient histories; order and interpret laboratory, imaging and microbiology tests; generate differential diagnoses; and formulate treatment plans such as prescribing medications, scheduling surgical procedures and planning admissions. In simulations on real patient cases spanning multiple diagnoses, MIRA outperformed physicians in diagnostic accuracy and made guideline-concordant, medication-safe and appropriate admission decisions.

Open-source, local-first autonomous AI assistant: file/shell access, browser automation, skills/plugins, and optional chat-app bridges; model-agnostic (e.g. Claude, OpenAI, local/Ollama) with your own API keys.

SciSciGPT is an open-source, prototype AI collaborator that uses the domain of science of science as a testbed to explore LLM-powered scientific collaboration. It assists researchers in analyzing scientific literature, identifying research trends, generating hypotheses about scientific dynamics, and facilitating human-AI collaborative research in metascience and scientometrics.

We introduce SciSciGPT, an open-source, prototype artificial intelligence (AI) collaborator that uses the domain of science of science as a testbed to explore the potential of large language model-powered scientific collaboration. SciSciGPT assists researchers in analyzing scientific literature, identifying research trends, generating hypotheses about scientific dynamics, and facilitating human-AI collaborative research in metascience and scientometrics.

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.