# CleanAgent: Automating Data Standardization with LLM-based Agents

arXiv: https://arxiv.org/abs/2403.08291 · HTML: https://mlsanity.com/paper/2403.08291/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 13 Mar 2024.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 2 of 2)

- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [ChatPipe: Orchestrating Data Preparation Program by Optimizing Human-ChatGPT Interactions](https://mlsanity.com/paper/2304.03540/) (2023)

## Built on by (most cited first, 22 of 22)

- [A Review of Large Language Models and Autonomous Agents in Chemistry](https://mlsanity.com/paper/2407.01603/) (2024)
- [DocETL: Agentic Query Rewriting and Evaluation for Complex Document Processing](https://mlsanity.com/paper/2410.12189/) (2024)
- [Code as Agent Harness](https://mlsanity.com/paper/2605.18747/) (2026)
- [A Survey of Data Agents: Emerging Paradigm or Overstated Hype?](https://mlsanity.com/paper/2510.23587/) (2025)
- [Kolb-Based Experiential Learning for Generalist Agents with Human-Level Kaggle Data Science Performance](https://mlsanity.com/paper/2411.03562/) (2024)
- [Large Language Model-based Data Science Agent: A Survey](https://mlsanity.com/paper/2508.02744/) (2025)
- [The FM Agent](https://mlsanity.com/paper/2510.26144/) (2025)
- [Exploring LLM Agents for Cleaning Tabular Machine Learning Datasets](https://mlsanity.com/paper/2503.06664/) (2025)
- [Demonstration of DB-GPT: Next Generation Data Interaction System Empowered by Large Language Models](https://mlsanity.com/paper/2404.10209/) (2024)
- [Can LLMs Clean Up Your Mess? A Survey of Application-Ready Data Preparation with LLMs](https://mlsanity.com/paper/2601.17058/) (2026)
- [Mitigating Gender Bias in Code Large Language Models via Model Editing](https://mlsanity.com/paper/2410.07820/) (2024)
- [What's the next frontier for Data-centric AI? Data Savvy Agents](https://mlsanity.com/paper/2511.01015/) (2025)
- [Cleaning Maintenance Logs with LLM Agents for Improved Predictive Maintenance](https://mlsanity.com/paper/2511.05311/) (2025)
- [InData: Towards Secure Multi-Step, Tool-Based Data Analysis](https://mlsanity.com/paper/2511.11933/) (2025)
- [Autonomous Data Processing using Meta-Agents](https://mlsanity.com/paper/2602.00307/) (2026)
- [FedPS: Federated Preprocessing for structured data via aggregated Statistics](https://mlsanity.com/paper/2602.10870/) (2026)
- [Lazy or Efficient? Towards Accessible Eye-Tracking Event Detection Using LLMs](https://mlsanity.com/paper/2604.13243/) (2026)
- [ProfiliTable: Profiling-Driven Tabular Data Processing via Agentic Workflows](https://mlsanity.com/paper/2605.12376/) (2026)
- [When Helping Hurts and How to Fix It: Multi-Agent Debate for Data Cleaning](https://mlsanity.com/paper/2606.02866/) (2026)
- [CITBench: A Comprehensive Benchmark for Interactive Tabular Data Processing with LLMs](https://mlsanity.com/paper/2608.00018/) (2026)
- [Agentic Data Cleaning Without a Clean Reference: An Experimental Study of Capabilities and Trade-offs](https://mlsanity.com/paper/2608.14765/) (2026)
- [Constraint-Guided Enterprise Data Mapping with Large Language Models](https://mlsanity.com/paper/2608.24218/) (2026)

## Abstract (arXiv)

Data standardization is a crucial part of the data science life cycle. While tools like Pandas offer robust functionalities, their complexity and the manual effort required for customizing code to diverse column types pose significant challenges. Although large language models (LLMs) like ChatGPT have shown promise in automating this process through natural language understanding and code generation, it still demands expert-level programming knowledge and continuous interaction for prompt refinement. To solve these challenges, our key idea is to propose a Python library with declarative, unified APIs for standardizing different column types, simplifying the LLM's code generation with concise API calls. We first propose Dataprep.Clean, a component of the Dataprep Python Library, significantly reduces the coding complexity by enabling the standardization of specific column types with a single line of code. Then, we introduce the CleanAgent framework integrating Dataprep.Clean and LLM-based agents to automate the data standardization process. With CleanAgent, data scientists only need to provide their requirements once, allowing for a hands-free process. To demonstrate the practical utility of CleanAgent, we developed a user-friendly web application, allowing users to interact with it using real-world datasets.