What is the difference between sequential and parallel chains?

In a sequential chain, components run one after another: the output of step N becomes the input of step N+1. This is the default LCEL pipe behaviour — chain = step1 | step2 | step3 means step2 cannot start until step1 finishes.

In a parallel chain, multiple branches run concurrently on the same input, and their results are merged into a single dict. LangChain implements this with RunnableParallel:

from langchain_core.runnables import RunnableParallel, RunnablePassthrough
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser

llm = ChatOpenAI()

parallel_chain = RunnableParallel(
    summary=summary_prompt | llm | StrOutputParser(),
    sentiment=sentiment_prompt | llm | StrOutputParser(),
    keywords=keywords_prompt | llm | StrOutputParser(),
)

# Runs all three LLM calls concurrently, then returns:
# {"summary": "...", "sentiment": "...", "keywords": "..."}
result = parallel_chain.invoke({"text": "LangChain is amazing..."})

Use sequential chains when each step depends on the previous result. Use parallel chains when steps are independent of each other — this reduces wall-clock time to the slowest branch's latency rather than the sum of all branches.

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