Agents

Kosmos

Your autonomous AI scientist for end-to-end research.

Kosmos conducts complete research workflows — from reading scientific literature and analyzing data to forming and testing hypotheses and producing comprehensive, citable reports.

Key Capabilities

Kosmos can digest over 1,500 research papers and execute more than 42,000 lines of analysis code in a single run. It operates through multiple AI agents working in parallel, sharing information through structured "world models." Every conclusion is fully auditable, so you can trace any finding back to its original code or scientific source. Kosmos also generates publication-ready figures and data visualizations alongside its written analysis.

Example Discoveries

• Suggested that high circulating levels of SOD2 protein may reduce myocardial fibrosis in humans

• Identified a possible mechanism linking a specific genetic variant (SNP) to reduced risk of Type 2 diabetes

When to Use Kosmos

Kosmos is ideal for complex, high-dimensional datasets (such as scRNA-seq, proteomics, or environmental parameters), comprehensive research projects that require both literature synthesis and data analysis, generating publication-ready reports with validated findings, and discovering novel patterns across multiple datasets.

For more information on how to interact with Kosmos, please see our guide for best practices.

To learn more about Kosmos, please read our blog post Kosmos: An AI Scientist for Autonomous Discovery.

Literature

Fast, accurate, and deeply cited answers to your scientific questions.

Literature handles search and synthesis tasks with high accuracy, drawing from over 175 million papers, trials, and patents. It natively understands citation graphs, journal quality, clinical trial data, and structured sources.

When to Use Literature

Literature is a great fit when you need quick, high-quality answers to complex scientific questions, deep literature reviews and comprehensive synthesis, analysis of conflicting evidence across hundreds of papers, complete reports with abstracts and conclusions, or a guided exploration of a new research domain.

Example Use Cases

• "What are the known genetic associations with pancreatic beta cell dysfunction?" — Get a quick, precise, and fully cited response.

• "Analyze contradictory findings on COBLL1's role in adipocyte differentiation" — Receive a comprehensive synthesis of conflicting evidence.

• Preparing journal club presentations with dozens of synthesized papers.

Literature also supports diagram creation and figure pullouts.

Analysis

Turn your experimental data into detailed, actionable insights.

Analysis specializes in processing complex experimental data — from lab results and RNA-sequencing data to other biological datasets — and transforms them into thorough analyses that answer your research questions.

When to Use Analysis

Analysis works well for processing flow cytometry data, RNA-seq analysis, statistical analysis of experimental results, data visualization and figure generation, and multi-modal dataset integration.

Example Use Case

After running flow cytometry experiments on GFP-transfected cells with intracellular markers (such as Insulin, NKX6.1, and PDX1), Analysis can process your data, generate visualizations, and identify statistically significant populations or expression patterns.

Analysis also includes protein tools and additional figure generation capabilities.

Precedent

Find out if your research idea has been tried before.

Precedent (formerly known as "Has Anyone") searches across fields to determine whether your hypotheses are truly novel, helping you focus your efforts where they'll have the most impact.

When to Use Precedent

Precedent is perfect for answering "Has anyone done this before?", identifying research gaps, avoiding duplication of existing work, and finding innovative approaches in the literature.

Example Use Case

Before starting SSR1 knock-in experiments for pseudoislets, Precedent can search the literature to confirm whether similar lentiviral transduction approaches with this specific genetic variant have been attempted, helping you pinpoint what's truly novel about your approach.

Molecules

A chemistry-focused agent for molecular design and analysis.

Given a molecule (via SMILES, CAS number, or IUPAC name), it can predict ADMET properties, plan retrosynthesis routes, perform safety assessments, search chemical databases like ChEMBL, and propose optimized lead candidates.

When to Use Molecules

Molecules is a good fit for ADMET property prediction, retrosynthesis planning, molecular property calculation (logP, logS, QED, PSA), safety and toxicity assessments, database searches for similar compounds, and iterative lead optimization.

Example Use Case

You might ask Molecules to calculate ADMET properties for a given SMILES string, search ChEMBL for similar anti-inflammatory compounds, run safety assessments on the top candidates, and propose modifications to improve solubility.