Beyond Agents: Why Pydantic Works Well for Scientific Data

If you’ve come across Pydantic recently, it was probably in the context of LLM agents. Pydantic didn’t suddenly become useful because of agents. It's been solving data validation problems in Python for years. It can efficiently solve data problems that I was hacking around for years with if-else loops as a bioinformatician. It's one of the most powerful tools for handling the messy, complex data that dominates our field. 

At its core, Pydantic is a Python library for data validation. You describe what your data should look like and Pydantic enforces it. If the data matches your expectations, you get a clean Python object. If it doesn’t, you get a clear error immediately. It’s easy to underestimate how powerful that is. In bioinformatics, this matters because most of the hard problems aren’t algorithmic, they’re about data cleaning and formats. We deal with multiple file types like sequencing data, sample metadata spreadsheets, QC logs, configuration files, and the list goes on. Data comes from instruments, collaborators, and other sources, and it is rarely as clean as we want it to be.

Traditionally, we handle this with a mix of ad-hoc checks and hope that everything works without breaking. We check whether columns exist, whether values are in range, whether types look reasonable. Often those checks are scattered throughout the codebase. Sometimes they’re missing entirely. And sometimes the pipeline doesn’t even fail, it just produces the wrong result, which is far worse.

Pydantic can greatly influence this approach by systematically catching the errors before any computation even begins. Take sample metadata, for example. A small typo in a sample ID or an invalid tissue type can quietly propagate through an entire analysis.

from pydantic import BaseModel, Field, field_validator
from typing import Literal

class BiologicalSample(BaseModel):
    sample_id: str = Field(..., pattern=r'^[A-Z]{2}\d{6}$')
    tissue_type: Literal['tumor', 'normal', 'blood', 'saliva']
    age_at_collection: int = Field(..., ge=0, le=120)
    sequencing_depth: float = Field(..., gt=0)

    @field_validator('sequencing_depth')
    @classmethod
    def check_depth_quality(cls, v):
        if v < 30:
            raise ValueError("Error: Low Sequencing depth <30x")
        return v

The class BiologicalSample defines what a valid  sample looks like in your pipeline.

1. The sample_id  field uses a regular expression to enforce a specific format of two uppercase letters followed by six digits. If someone passes AB123 instead of AB000123, it fails immediately.

   
   

2. The tissue_type field uses a Literal, which means only a fixed set of values is allowed. Typos like "tumour" or "Tumor" are rejected.

   
   

3. For age_at_collection, the ge and le constraints enforce a biologically reasonable range. Negative ages or impossible values don’t make it into your analysis.

   
   

4. The sequencing_depth field does two things. First, it ensures the value is a positive number. Then, the custom validator adds domain-specific logic: if coverage is below 30×, the sample is rejected with a clear message.

With a model like this, every sample is validated against both technical and biological expectations. The same idea applies to biological sequences. Whether you’re working with DNA, RNA, or protein sequences, you can enforce valid characters and keep biological logic close to the data itself. Genomic coordinates are another classic source of subtle bugs where it is possible to encode these constraints directly.

Where Pydantic really starts to shine is when you compose these models together. Experiments made up of validated samples. Analysis runs that bundle configuration, inputs, and QC metrics. Clinical variants that combine genomic intervals, annotations, and patient metadata. Over time, your pipeline stops being a collection of loosely connected scripts and starts to look like a set of well-defined, validated data structures.

This is why Pydantic fits scientific workflows so naturally. It helps by making assumptions explicit and catching errors early. As a bonus, your IDE suddenly becomes much more helpful, with real autocomplete and type hints for your biological data.

Getting started doesn’t require a rewrite, just pick one messy boundary in your pipeline, maybe a simple sample sheet and write a model for it. Once you do, you’ll start noticing how many bugs simply disappear. Thus, Pydantic isn’t just for AI agents, it is a reliable validation tool for anyone working with complex, and messy data.

For coding examples, follow along this Tutorial and visit Pydantic website to learn more!

References

• Pydantic docs

• GPT 5 and Sonnet 4.5 for editing