Structured Output Parser

What it is A Structured Output Parser is a tool in LangChain that ensures LLM outputs follow a specific structured format, often defined by a schema or template. It generalizes JSON, Pydantic, or list parsers to enforce predictable, machine-readable outputs.

Why it exists LLM responses are naturally free-form, which makes automation or further processing difficult. Structured parsers provide a consistent format, reducing errors and making integration with databases, APIs, or workflows reliable.

Real-world analogy It’s like filling out a standardized tax form: every field has a designated place and format. Even if different people write it, the form’s structure ensures it can be processed automatically.

Minimal beginner example

import os
from dotenv import load_dotenv
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_core.output_parsers import StructuredOutputParser
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 structured schema
schema = {
    "name": "string",
    "age": "integer",
    "hobbies": "list[string]"
}

parser = StructuredOutputParser(schema=schema)

# Step 2: Prepare prompt
prompt = PromptTemplate(
    template="Provide a person’s info in the structured format:\n{name, age, hobbies}",
    input_variables=[]
)

# Step 3: Run LLM and parse output
response = llm.invoke(prompt.format())
parsed_output = parser.parse(response.content)

print(parsed_output)
# Example: {'name': 'Alice', 'age': 30, 'hobbies': ['reading', 'hiking']}

Small LangChain workflow

1. Define a schema for the output.

2. Prepare a prompt specifying the structured format.

3. Send it to the LLM.

4. Parse the raw text with StructuredOutputParser.

5. Use the structured data in your chains, APIs, or database.

Common beginner mistakes

• Schema doesn’t match the prompt instructions → parser errors.

• LLM output contains extra text or formatting → parsing may fail.

• Using structured parser for very simple outputs where a comma-separated or JSON parser suffices.

When to use this vs alternatives

• Use StructuredOutputParser for complex, multi-field outputs that need validation.

• Use JSONOutputParser for standard JSON responses.

• Use PydanticOutputParser if you need Python object validation alongside structure.

• Use CommaSeparatedParser for simple flat lists only.