Articles → LANGCHAIN → Sequential Chaining In Langchain

Sequential Chaining In Langchain






Purpose







Example


from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI

# LLM
llm = ChatOpenAI(
    api_key="your_api_key",
    model="gpt-4.1-mini",
    temperature=0.7
)

# Prompt 1
prompt1 = PromptTemplate.from_template(
    "Generate a blog topic about {idea}"
)

# Prompt 2
prompt2 = PromptTemplate.from_template(
    "Write an outline for this topic: {topic}"
)

# Chain 1
chain1 = prompt1 | llm

# Chain 2
chain2 = prompt2 | llm

# Combine chains manually
topic_result = chain1.invoke({
    "idea": "Artificial Intelligence"
})

topic = topic_result.content

outline_result = chain2.invoke({
    "topic": topic
})

print(outline_result.content)



Output


Picture showing the output of sequential chaining in langchain





Posted By  -  Karan Gupta
 
Posted On  -  Thursday, May 14, 2026

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