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LangChain Expression Language (LCEL)

LangChain Expression Language (LCEL) is a declarative way to compose chains of components in LangChain. It is designed to facilitate the transition from prototypes to production-ready applications with minimal code changes.

Why LCEL?

LCEL pipelines automatically support streaming token delivery, async execution, parallel execution, and built-in observability tracking out of the box without changing code logic.

The Pipe Operator Syntax​

LCEL uses the pipe operator (|) to connect AI building blocks—such as prompts, models, retrievers, and parsers—allowing data to flow smoothly between them.

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_openai import ChatOpenAI

prompt = ChatPromptTemplate.from_template("Explain {topic} in 2 concise sentences.")
model = ChatOpenAI(model="gpt-4o")
output_parser = StrOutputParser()

# Construct the Runnable Chain using the Pipe Operator '|'
chain = prompt | model | output_parser

# Execute
result = chain.invoke({"topic": "Quantization in Edge AI"})
print(result)

Core Runnable Capabilities​

All LCEL objects implement the Runnable interface, which provides a consistent set of methods:

  • invoke(): Run the chain on a single input.
  • stream(): Real-time output streaming of chunks as they are generated.
  • batch(): Execute the chain across a list of inputs in parallel.
  • ainvoke(): Asynchronous execution for better performance.
# Streaming example
for chunk in chain.stream({"topic": "HNSW Vector Indexes"}):
    print(chunk, end="", flush=True)