Extract text content from model outputs as a string.
Converts model outputs (such as AIMessage or AIMessageChunk objects) into plain
text strings. It's the simplest output parser and is useful when you need string
responses for downstream processing, display, or storage.
Supports streaming, yielding text chunks as they're generated by the model.
Example:
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI
model = ChatOpenAI(model="gpt-4o")
parser = StrOutputParser()
# Get string output from a model
message = model.invoke("Tell me a joke")
result = parser.invoke(message)
print(result) # plain string
# With streaming - use transform() to process a stream
stream = model.stream("Tell me a story")
for chunk in parser.transform(stream):
print(chunk, end="", flush=True)Invoke the retriever to get relevant documents.
Asynchronously invoke the retriever to get relevant documents.
Parse the result of an LLM call to a JSON object.
Parse a list of candidate model Generation objects into a specific format.
Async parse a single string model output into some structure.
Parse the output of an LLM call with the input prompt for context.
Return the format instructions for the comma-separated list output.
Return dictionary representation of output parser.
Get a JSON schema that represents the input to the Runnable.
Get a JSON schema that represents the output of the Runnable.
The type of config this Runnable accepts specified as a Pydantic model.
Get a JSON schema that represents the config of the Runnable.
Return a list of prompts used by this Runnable.
Pipe Runnable objects.
Pick keys from the output dict of this Runnable.
Merge the Dict input with the output produced by the mapping argument.
Invoke the retriever to get relevant documents.
Asynchronously invoke the retriever to get relevant documents.
Run invoke in parallel on a list of inputs.
Run ainvoke in parallel on a list of inputs.
Stream all output from a Runnable, as reported to the callback system.
Generate a stream of events.
Bind arguments to a Runnable, returning a new Runnable.
Bind lifecycle listeners to a Runnable, returning a new Runnable.
Bind async lifecycle listeners to a Runnable.
Bind input and output types to a Runnable, returning a new Runnable.
Create a new Runnable that retries the original Runnable on exceptions.
Map a function to multiple iterables.
Add fallbacks to a Runnable, returning a new Runnable.
Create a BaseTool from a Runnable.