Research map / Multi-agent systems and games / LLM agents
CellAgent: LLM-Driven Multi-Agent Framework for Natural Language-Based Single-Cell Analysis
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Introduces CellAgent, an autonomous LLM-driven multi-agent framework that lets researchers perform scRNA-seq and spatial transcriptomics analysis through natural language.
Problem
Single-cell RNA-seq and spatial transcriptomics analysis requires extensive manual programming and complex tool integration, which is a barrier for researchers.
Approach
An autonomous LLM-driven multi-agent system that translates natural-language requests into single-cell analysis workflows, removing the need for manual coding and tool integration.
Closest papers by meaning
- CellVerse: Do Large Language Models Really Understand Cell Biology? (NeurIPS 2025 · similarity 0.89)
- scPilot: Large Language Model Reasoning Toward Automated Single-Cell Analysis and Discovery (NeurIPS 2025 · similarity 0.87)
- BioAgent Bench: An AI Agent Evaluation Suite for Bioinformatics (ICML 2026 · similarity 0.85)
- Cell2Sentence: Teaching Large Language Models the Language of Biology (ICML 2024 · similarity 0.85)
- CellPLM: Pre-training of Cell Language Model Beyond Single Cells (ICLR 2024 · similarity 0.84)
- A Simple and Comprehensive Benchmark for Single-Cell Transcriptomics (AAAI 2025 · similarity 0.83)
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