LLM Agents for Automated Data Cleaning

LLM Agents for Automated Data Cleaning

Large language model agents can automatically detect, explain, and fix common data quality problems such as missing values, type mismatches, and free-text inconsistencies, letting analysts spend more time on insights instead of janitorial work.

Why do we need automated data cleaning?

Data engineers report that 60-80 % of any analytics project is spent massaging raw data into a trustworthy, analysis-ready state. Conventional rule-based pipelines are brittle: they require up-front specification of every edge case, and the moment the source schema or business logic changes they silently break. Large language models (LLMs) bring two superpowers that make them uniquely suited to this problem:

• Natural-language reasoning — They can understand column names, documentation, and even plain-English requirements.
• Code synthesis — They can generate valid SQL, Python, or Spark transformations tailored to the identified issues.

What exactly is an LLM agent?

An agent is a loop around an LLM that chooses actions, observes the environment, and iterates until a goal is reached. In data cleaning, the goal might be \"produce a dataframe that passes a set of quality tests\". The agent typically chains together these steps:

1. Profile the dataset with tools such as pandas-profiling or Great Expectations.
2. Summarise the profile for the LLM.
3. Ask the LLM to propose cleaning operations.
4. Execute the code with a sandboxed Python interpreter or SQL engine.
5. Re-profile; if tests fail, loop back to step 3.

Common tasks automated by LLM agents

1. Missing-value imputation

Agents can infer statistically plausible fill values based on column semantics (\"age\" → median, \"country\" → mode).

2. Type inference and casting

Free-text \"2023-04-01\" strings are converted into proper DATE types with timezone awareness.

3. Standardisation and normalisation

Company names such as \"ACME Inc.\" and \"Acme Incorporated\" are mapped to a unified canonical form.

4. Deduplication and entity resolution

Embedding-based similarity search combined with LLM reasoning can detect near-duplicate records.

5. Schema mapping

Automatically align disparate source schemas (e.g., \"cust_id\" → \"customer_identifier\") when joining data.

Architectural patterns

Prompt-only agents

The simplest pattern feeds raw samples and asks the LLM to return cleaned rows directly. While quick to prototype, it struggles with large datasets due to context-window limits.

Plan-and-code agents

The LLM first outputs a plan (for human auditability) and then synthesises executable code (SQL or Python) that is run outside the model. This scales to millions of rows because the heavy lifting happens in the database or Spark cluster.

Validator-in-the-loop agents

Tools like Great Expectations or Pandera provide automated tests. The agent receives the failing assertions and generates incremental fixes until all expectations pass, yielding measurable data quality.

Best practices

• Ground the model with metadata: supply table comments, schema diagrams, and sample queries to reduce hallucination.
• Always run deterministic tests: LLMs should suggest fixes, but rule-based tests decide pass/fail.
• Sandbox execution: Run generated code in an isolated environment to prevent harmful operations.
• Log every step: Persist prompts, responses, and dataset versions for reproducibility and auditing.
• Human-in-the-loop overrides: Allow engineers to approve or tweak the plan before execution in production.

Practical example

The snippet below shows a minimal LangChain agent that inspects a customers.csv file and outputs a cleaned parquet file. The agent is tool-augmented: it can run Python code, re-run validation, and loop until success.


from langchain.agents import initialize_agent, Tool
from langchain.chat_models import ChatOpenAI
from langchain.utilities import PythonREPL
import pandas as pd, great_expectations as ge

# Load raw data
df = pd.read_csv('customers.csv')

# Profiling helper
def profile(frame):
ge_df = ge.from_pandas(frame)
report = ge_df.profile() # simple summarisation
return report.to_markdown()

# Register tools the agent is allowed to call
tools = [
Tool(
name='run_python',
func=PythonREPL().run,
description='Execute python code to transform the dataframe "df"'
),
Tool(
name='profile',
func=lambda _: profile(df),
description='Return a textual profile of the dataframe "df"'
)
]

llm = ChatOpenAI(model='gpt-4', temperature=0)

agent = initialize_agent(
tools=tools,
llm=llm,
agent='zero-shot-react-description',
verbose=True
)

agent.run('Clean the dataframe so that there are no null emails and the column "signup_date" is a timezone-aware datetime. When finished, save as cleaned_customers.parquet')


Galaxy integration

Although Galaxy is primarily a SQL editor, you can orchestrate the above flow directly from its AI copilot. For example, paste a failing SELECT with messy data into Galaxy, highlight it, and ask the copilot to propose a CTE that trims whitespace or casts types. Because the copilot is context-aware, it can leverage table comments and prior queries stored in Galaxy Collections to generate precise fixes. For heavier lifts, you can call an external Python agent from Galaxy's forthcoming workflow runner and store the cleaned results in a staging schema.

Common misconceptions

• \"LLMs replace all validation\" — In reality they augment rule-based checks; deterministic tests remain the source of truth.
• \"Send the entire table to the model\" — Use sampling or summarisation; context windows are finite and cost scales linearly with tokens.
• \"One prompt fits all datasets\" — Prompts must be customised with business rules and domain vocabulary.

Conclusion

LLM agents are not magic, but they are a powerful new tool in the data engineers toolbox. By combining natural-language reasoning with traditional validation frameworks, teams can slash the time spent on data cleaning and build more adaptive pipelines that evolve with the business.