# CellAgent: An LLM-driven Multi-Agent Framework for Automated Single-cell Data Analysis arXiv: https://arxiv.org/abs/2407.09811 ยท HTML: https://mlsanity.com/paper/2407.09811/ ## Key facts - Cited by 39 papers in the map (data through 14 Oct 2026). - Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. - Builds on 6 of its 37 references that are in the map. - First posted on arXiv 13 Jul 2024. ## Placed in - LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/) ## Builds on (most cited first, 6 of 6) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023) - [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023) - [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023) - [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023) - [Agents: An Open-source Framework for Autonomous Language Agents](https://mlsanity.com/paper/2309.07870/) (2023) ## Built on by (most cited first, 30 of 39) - [ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2503.09501/) (2025) - [Agentic AI for Scientific Discovery: A Survey of Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2503.08979/) (2025) - [A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures](https://mlsanity.com/paper/2506.19676/) (2025) - [A Survey of Agentic Reasoning for Large Language Models: Towards Recursively Self-Improving and Collective Agents](https://mlsanity.com/paper/2601.12538/) (2026) - [A Survey on the Optimization of Large Language Model-based Agents](https://mlsanity.com/paper/2503.12434/) (2025) - [A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval](https://mlsanity.com/paper/2503.05659/) (2025) - [PiFlow: Principle-Aware Scientific Discovery with Multi-Agent Collaboration](https://mlsanity.com/paper/2505.15047/) (2025) - [DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering](https://mlsanity.com/paper/2504.18243/) (2025) - [Coarse-to-Fine Grounded Memory for LLM Agent Planning](https://mlsanity.com/paper/2508.15305/) (2025) - [LLM/Agent-as-Data-Analyst: A Survey](https://mlsanity.com/paper/2509.23988/) (2025) - [AI Meets Brain: Memory Systems from Cognitive Neuroscience to Autonomous Agents](https://mlsanity.com/paper/2512.23343/) (2025) - [HeurekaBench: A Benchmarking Framework for AI Co-scientist](https://mlsanity.com/paper/2601.01678/) (2026) - [Automating Exploratory Proteomics Research via Language Models](https://mlsanity.com/paper/2411.03743/) (2024) - [Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics](https://mlsanity.com/paper/2510.09901/) (2025) - [Prompt Stability Matters: Evaluating and Optimizing Auto-Generated Prompt in General-Purpose Systems](https://mlsanity.com/paper/2505.13546/) (2025) - [Automating High Energy Physics Data Analysis with LLM-Powered Agents](https://mlsanity.com/paper/2512.07785/) (2025) - [How to Interpret Agent Behavior](https://mlsanity.com/paper/2605.13625/) (2026) - [Autonomous Research Agents: A Survey of AI Scientists and the Verification Gap](https://mlsanity.com/paper/2608.05179/) (2026) - [PIORS: Personalized Intelligent Outpatient Reception based on Large Language Model with Multi-Agents Medical Scenario Simulation](https://mlsanity.com/paper/2411.13902/) (2024) - [BioAgents: Democratizing Bioinformatics Analysis with Multi-Agent Systems](https://mlsanity.com/paper/2501.06314/) (2025) - [GenoMAS: A Multi-Agent Framework for Scientific Discovery via Code-Driven Gene Expression Analysis](https://mlsanity.com/paper/2507.21035/) (2025) - [HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts](https://mlsanity.com/paper/2603.01396/) (2026) - [GenCellAgent: Generalizable, Training-Free Cellular Image Segmentation via Large Language Model Agents](https://mlsanity.com/paper/2510.13896/) (2025) - [BrainAgent: A Large Language Model-Driven Multi-Agent Framework for Autonomous Brain Signal Understanding](https://mlsanity.com/paper/2606.25400/) (2026) - [A Survey of Large Language Models in Discipline-specific Research: Challenges, Methods and Opportunities](https://mlsanity.com/paper/2507.08425/) (2025) - [Automating Exploratory Multiomics Research via Language Models](https://mlsanity.com/paper/2506.07591/) (2025) - [Active Inference AI Systems for Scientific Discovery](https://mlsanity.com/paper/2506.21329/) (2025) - [An AI Co-Data-Scientist for Prioritizing Candidate Biomarkers from Wearable Sensor Data](https://mlsanity.com/paper/2604.14615/) (2026) - [SpaCellAgent: A Self-Evolving LLM-Based Multi-Agent Framework for Trajectory Analysis](https://mlsanity.com/paper/2607.07467/) (2026) - [Biomedical reasoning in action: Multi-agent System for Auditable Biomedical Evidence Synthesis](https://mlsanity.com/paper/2510.05335/) (2025) ## Abstract (arXiv) Single-cell RNA sequencing (scRNA-seq) data analysis is crucial for biological research, as it enables the precise characterization of cellular heterogeneity. However, manual manipulation of various tools to achieve desired outcomes can be labor-intensive for researchers. To address this, we introduce CellAgent (http://cell.agent4science.cn/), an LLM-driven multi-agent framework, specifically designed for the automatic processing and execution of scRNA-seq data analysis tasks, providing high-quality results with no human intervention. Firstly, to adapt general LLMs to the biological field, CellAgent constructs LLM-driven biological expert roles - planner, executor, and evaluator - each with specific responsibilities. Then, CellAgent introduces a hierarchical decision-making mechanism to coordinate these biological experts, effectively driving the planning and step-by-step execution of complex data analysis tasks. Furthermore, we propose a self-iterative optimization mechanism, enabling CellAgent to autonomously evaluate and optimize solutions, thereby guaranteeing output quality. We evaluate CellAgent on a comprehensive benchmark dataset encompassing dozens of tissues and hundreds of distinct cell types. Evaluation results consistently show that CellAgent effectively identifies the most suitable tools and hyperparameters for single-cell analysis tasks, achieving optimal performance. This automated framework dramatically reduces the workload for science data analyses, bringing us into the "Agent for Science" era.