Sequential chains in LangChain

Introduction

Sequential chains help you run multiple steps one after another, passing results from one step to the next. This makes complex tasks easier by breaking them into simple parts.

When you want to process data step-by-step, like cleaning text then summarizing it.

When you need to combine different language models or tools in order.

When you want to build a workflow that depends on previous answers.

When you want to keep your code organized by separating tasks.

When you want to reuse small chains to build bigger ones.

Syntax

LangChain

from langchain.chains import SequentialChain

chain = SequentialChain(
    chains=[chain1, chain2, ...],
    input_variables=["input1", "input2"],
    output_variables=["output1", "output2"]
)
result = chain.run({"input1": value1, "input2": value2})

You create a SequentialChain by giving it a list of smaller chains.

Input variables are the starting data you provide, output variables are what you want back at the end.

Examples

This example runs chain1 then chain2 in order, passing the output of chain1 to chain2.

LangChain

from langchain.chains import SequentialChain

# Define two simple chains
chain1 = SomeChain()
chain2 = AnotherChain()

# Create a sequential chain
seq_chain = SequentialChain(
    chains=[chain1, chain2],
    input_variables=["text"],
    output_variables=["summary"]
)

result = seq_chain.run({"text": "Hello world"})

Here, three chains run one after another to answer a question step-by-step.

LangChain

seq_chain = SequentialChain(
    chains=[chainA, chainB, chainC],
    input_variables=["question"],
    output_variables=["answer"]
)

answer = seq_chain.run({"question": "What is AI?"})

Sample Program

This program first summarizes the input text, then translates the summary to French using two chains connected sequentially.

LangChain

from langchain.llms import OpenAI
from langchain.chains import LLMChain, SequentialChain
from langchain.prompts import PromptTemplate

# Create a prompt template for first chain
template1 = "Summarize this text: {text}"
prompt1 = PromptTemplate(input_variables=["text"], template=template1)

# Create first chain
llm = OpenAI(temperature=0)
chain1 = LLMChain(llm=llm, prompt=prompt1, output_key="summary")

# Create a prompt template for second chain
template2 = "Translate this summary to French: {summary}"
prompt2 = PromptTemplate(input_variables=["summary"], template=template2)

# Create second chain
chain2 = LLMChain(llm=llm, prompt=prompt2, output_key="french_translation")

# Create sequential chain
seq_chain = SequentialChain(
    chains=[chain1, chain2],
    input_variables=["text"],
    output_variables=["summary", "french_translation"]
)

# Run the chain
result = seq_chain.run({"text": "Langchain helps you build chains of language models."})
print(result)

Important Notes

Make sure each chain's output keys match the next chain's input variables.

Sequential chains keep your workflow clear and easy to debug.

You can nest sequential chains inside other chains for more complex flows.

Summary

Sequential chains run multiple chains one after another.

They pass outputs from one chain as inputs to the next.

This helps build clear, step-by-step workflows.