Output Parser Types

This is a list of output parsers LangChain supports. The table below has various pieces of information:

Name: The name of the output parser

Supports Streaming: Whether the output parser supports streaming.

Has Format Instructions: Whether the output parser has format instructions. This is generally available except when (a) the desired schema is not specified in the prompt but rather in other parameters (like OpenAI function calling), or (b) when the OutputParser wraps another OutputParser.

Calls LLM: Whether this output parser itself calls an LLM. This is usually only done by output parsers that attempt to correct misformatted output.

Input Type: Expected input type. Most output parsers work on both strings and messages, but some (like OpenAI Functions) need a message with specific kwargs.

Output Type: The output type of the object returned by the parser.

Description: Our commentary on this output parser and when to use it.

Name	Supports Streaming	Has Format Instructions	Calls LLM	Input Type	Output Type	Description
OpenAIFunctions	✅	(Passes functions to model)		Message (with function_call)	JSON object	Uses OpenAI function calling to structure the return output. If you are using a model that supports function calling, this is generally the most reliable method.
JSON	✅	✅		str \| Message	JSON object	Returns a JSON object as specified. You can specify a Pydantic model and it will return JSON for that model. Probably the most reliable output parser for getting structured data that does NOT use function calling.
XML	✅	✅		str \| Message	dict	Returns a dictionary of tags. Use when XML output is needed. Use with models that are good at writing XML (like Anthropic's).
CSV	✅	✅		str \| Message	List[str]	Returns a list of comma separated values.
OutputFixing			✅	str \| Message		Wraps another output parser. If that output parser errors, then this will pass the error message and the bad output to an LLM and ask it to fix the output.
RetryWithError			✅	str \| Message		Wraps another output parser. If that output parser errors, then this will pass the original inputs, the bad output, and the error message to an LLM and ask it to fix it. Compared to OutputFixingParser, this one also sends the original instructions.
Pydantic		✅		str \| Message	pydantic.BaseModel	Takes a user defined Pydantic model and returns data in that format.
PandasDataFrame		✅		str \| Message	dict	Useful for doing operations with pandas DataFrames.
Enum		✅		str \| Message	Enum	Parses response into one of the provided enum values.
Datetime		✅		str \| Message	datetime.datetime	Parses response into a datetime string.
Structured		✅		str \| Message	Dict[str, str]	An output parser that returns structured information. It is less powerful than other output parsers since it only allows for fields to be strings. This can be useful when you are working with smaller LLMs.