Pydantic Output Parser

What it is A Pydantic Output Parser takes the raw text output from a language model and converts it into a structured Python object (like a dictionary or model). It ensures the AI response is predictable and easy to work with.

Why it exists LLMs often return free-form text, which can be inconsistent or hard to process. Using a Pydantic parser enforces a schema, catches errors, and makes downstream code more reliable.

Real-world analogy Think of it like a form scanner that reads handwritten forms and converts them into a standardized digital spreadsheet. Even if people write differently, the parser enforces the correct fields and types.

Minimal beginner example

import os
from dotenv import load_dotenv
from pydantic import BaseModel
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.output_parsers import PydanticOutputParser
from langchain_core.prompts import PromptTemplate

load_dotenv()
api_key = os.getenv("GEMINI_API_KEY")

llm = ChatGoogleGenerativeAI(model="gemini-2.5-flash", api_key=api_key)

# Step 1: Define a Pydantic model
class PersonInfo(BaseModel):
    name: str
    age: int
    country: str

# Step 2: Create a parser using the model
parser = PydanticOutputParser(pydantic_object=PersonInfo)

# Step 3: Prepare prompt
prompt = PromptTemplate(
    template="Provide a JSON with name, age, and country for Elon Musk.",
    input_variables=[]
)

# Step 4: Run LLM and parse output
response = llm.invoke(prompt.format())
parsed_output = parser.parse(response.content)

print(parsed_output)
print(parsed_output.name, parsed_output.age, parsed_output.country)

This ensures the output is structured and ready for your program to use.

Small LangChain workflow

1. Prepare a PromptTemplate.

2. Send it to the LLM.

3. Pass the raw text to PydanticOutputParser.

4. Receive a validated Python object.

5. Use it in chains, databases, or APIs.

Common beginner mistakes

• Model outputs text that doesn’t match the schema → parser errors.

• Forgetting to define all required fields in the Pydantic model.

• Assuming parsing fixes incorrect LLM answers—it only enforces structure, not correctness.

When to use this vs alternatives

• Use PydanticOutputParser when you need structured, validated output.

• Use RegexParser for simple pattern extraction.

• Use JSONOutputParser if the output is already a JSON object but doesn’t need strict schema enforcement.