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Pipe Chaining with LCEL

The | operator builds a RunnableSequence, as introduced in Runnables and LCEL. Each step's output becomes the next step's input, and the types have to line up — a prompt template's PromptValue output must be something the chat model's invoke accepts, and so on down the chain.

from langchain.chat_models import init_chat_model
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_template("Translate to French: {text}")
model = init_chat_model("gpt-4o-mini", model_provider="openai")
parser = StrOutputParser()

chain = prompt | model | parser
chain.invoke({"text": "Good morning"})

Coercion into Runnables​

| does not require every step to already be a Runnable. LCEL coerces two common shapes automatically:

  • A plain function def f(x): ... becomes a RunnableLambda.
  • A dict literal {"a": step_a, "b": step_b} becomes a RunnableParallel (covered next, in Parallel and Branching).
def format_output(text: str) -> str:
    return text.strip().upper()

chain = prompt | model | parser | format_output
tip

Coercion only happens inside a chain built with |. A bare function sitting outside a chain is not a Runnable and doesn't get invoke/batch/stream — wrap it explicitly with RunnableLambda if you need to call it that way on its own.

See also​