Output Parsers

💻 Practice Notebook

Master the concepts from this page with hands-on practice: 💻 VS Code | 🚀 Colab | 📥 Download Notebook Large Language Models output plain text. However, applications often require structured data to feed into APIs, databases, or frontend components. Output Parsers bridge this gap.

1. Introduction to Output Parsers

LangChain provides several output parsers to structure model outputs:

Parser	Output Type	Example Use Case
`StrOutputParser`	`str` (String)	Extracting clean text response (bypassing `AIMessage` wrappers).
`JsonOutputParser`	`dict` (Dictionary)	Extracting structured JSON keys and values.
`PydanticOutputParser`	`BaseModel` (Python Object)	Parsing and validating outputs against a strict data schema.
`CommaSeparatedListOutputParser`	`list` (List of strings)	Splitting comma-separated words into a Python list.

2. Using Output Parsers

2.1 StrOutputParser

Converts the output of a chat model (AIMessage) into a clean, raw string.

from langchain_core.output_parsers import StrOutputParser

parser = StrOutputParser()
response = parser.invoke("Hello World")

print(response)       # "Hello World"
print(type(response))  # <class 'str'>

2.2 JsonOutputParser

Parses JSON-formatted strings generated by LLMs into a native Python dictionary.

from langchain_core.output_parsers import JsonOutputParser

parser = JsonOutputParser()
response = parser.invoke("""
{
   "name": "John Doe",
   "age": 30,
   "occupation": "Software Engineer"
}
""")

print(response)       # {'name': 'John Doe', 'age': 30, 'occupation': 'Software Engineer'}
print(type(response))  # <class 'dict'>

2.3 PydanticOutputParser

Validates the output against a Pydantic model definition. This ensures type safety and field presence.

from pydantic import BaseModel
from langchain_core.output_parsers import PydanticOutputParser

# Define the schema
class Person(BaseModel):
    name: str
    age: int
    occupation: str

parser = PydanticOutputParser(pydantic_object=Person)
response = parser.invoke("""
{
   "name": "John Doe",
   "age": 30,
   "occupation": "Software Engineer"
}
""")

print(response)            # Person(name='John Doe', age=30, occupation='Software Engineer')
print(type(response))      # <class 'Person'>
print(response.name)       # "John Doe"

2.4 CommaSeparatedListOutputParser

Splits comma-separated lists generated by the model into a Python list of strings.

from langchain_core.output_parsers import CommaSeparatedListOutputParser

parser = CommaSeparatedListOutputParser()
response = parser.invoke("Python, Java, JavaScript, Go, Rust")

print(response)       # ['Python', 'Java', 'JavaScript', 'Go', 'Rust']
print(type(response))  # <class 'list'>

3. Practice Exercises

Practice 1: Comma Separated List Parsing

Create a prompt template that requests the model to list the top 3 programming languages for web development, and chain it with the CommaSeparatedListOutputParser to obtain a Python list.

Solution

from langchain_core.prompts import PromptTemplate
from langchain_core.output_parsers import CommaSeparatedListOutputParser
from langchain.chat_models import init_chat_model

llm = init_chat_model("groq:llama-3.3-70b-versatile")
parser = CommaSeparatedListOutputParser()

prompt = PromptTemplate(
    template="List 3 top programming languages for {use_case} as a comma-separated list.",
    input_variables=["use_case"]
)

chain = prompt | llm | parser
result = chain.invoke({"use_case": "web development"})

print(result)
print(type(result)) # <class 'list'>

Foundations

LangChain Prompts

💻 Practice Notebook

1. Introduction to Output Parsers

2. Using Output Parsers

2.1 StrOutputParser

2.2 JsonOutputParser

2.3 PydanticOutputParser

2.4 CommaSeparatedListOutputParser

3. Practice Exercises

Practice 1: Comma Separated List Parsing

​💻 Practice Notebook

​1. Introduction to Output Parsers

​2. Using Output Parsers

​2.1 StrOutputParser

​2.2 JsonOutputParser

​2.3 PydanticOutputParser

​2.4 CommaSeparatedListOutputParser

​3. Practice Exercises

​Practice 1: Comma Separated List Parsing

💻 Practice Notebook

1. Introduction to Output Parsers

2. Using Output Parsers

2.1 StrOutputParser

2.2 JsonOutputParser

2.3 PydanticOutputParser

2.4 CommaSeparatedListOutputParser

3. Practice Exercises

Practice 1: Comma Separated List Parsing