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Sequential Chains in LangChain: Building Multi-Step LLM Pipelines

Sequential chains are a cornerstone of LangChain, a leading framework for developing applications with large language models (LLMs). By linking multiple chains in a defined order, where the output of one chain serves as the input for the next, sequential chains enable developers to create structured, multi-step workflows for complex tasks. This blog provides a comprehensive guide to sequential chains in LangChain as of May 14, 2025, covering core concepts, techniques, practical applications, advanced strategies, and a unique section on error handling in sequential chains. For a foundational understanding of LangChain, refer to our Introduction to LangChain Fundamentals.

What are Sequential Chains?

Sequential chains in LangChain are workflows that execute a series of chains, such as LLMChain or RetrievalQA, in a predetermined sequence, passing outputs from one step to the next. Managed by classes like SequentialChain or SimpleSequentialChain, they allow developers to break down intricate tasks into modular, reusable components. Each chain in the sequence can involve prompts, LLM calls, data retrieval, or tool interactions, making sequential chains ideal for tasks requiring multiple processing stages. For an overview of chains, see Introduction to Chains.

Key characteristics of sequential chains include:

  • Structured Flow: Execute tasks in a fixed order, ensuring logical progression.
  • Modularity: Combine independent chains for reusability and clarity.
  • Context Propagation: Pass intermediate results to maintain task coherence.
  • Versatility: Support diverse operations, from text processing to data retrieval.

Sequential chains are essential for applications like document analysis, multi-step question-answering, and automated workflows, where tasks require coordinated steps.

Why Sequential Chains Matter

Complex LLM applications often involve multiple stages, such as summarizing text, extracting insights, or answering queries based on processed data. Sequential chains address these needs by:

  • Managing Complexity: Divide tasks into manageable, sequential steps.
  • Improving Precision: Focus each chain on a specific subtask for better results.
  • Enhancing Reusability: Create workflows that can be applied across scenarios.
  • Optimizing Efficiency: Streamline processing while managing resources (see Token Limit Handling).

Sequential chains build on LangChain’s modular design, enabling scalable and robust applications.

Error Handling in Sequential Chains

Error handling is critical in sequential chains to ensure robustness, especially when chains involve multiple steps that depend on each other’s outputs. Errors can arise from invalid inputs, token limit violations, retrieval failures, or LLM inconsistencies. Effective error handling involves validating inputs at each step, catching exceptions, logging errors for debugging, and implementing fallback mechanisms to maintain workflow continuity. LangChain’s flexibility allows developers to integrate custom error-handling logic, such as retrying failed steps, skipping non-critical chains, or providing default outputs, ensuring workflows remain resilient even under failure conditions.

Example:

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

llm = OpenAI()

def safe_chain_execution(chain, inputs):
    try:
        return chain(inputs)
    except Exception as e:
        print(f"Error in chain: {e}")
        return {"output": "Fallback: Unable to process step."}

# Step 1: Summarize
summary_template = PromptTemplate(
    input_variables=["text"],
    template="Summarize this in 50 words: {text}"
)
summary_chain = LLMChain(llm=llm, prompt=summary_template, output_key="summary")

# Step 2: Extract insights (simulated to fail)
insights_template = PromptTemplate(
    input_variables=["summary"],
    template="List 3 insights from: {summary}"  # Assume this fails due to invalid input
)
insights_chain = LLMChain(llm=llm, prompt=insights_template, output_key="insights")

# Sequential chain
chain = SequentialChain(
    chains=[summary_chain, insights_chain],
    input_variables=["text"],
    output_variables=["summary", "insights"]
)

text = ""  # Invalid input to trigger error
result = safe_chain_execution(chain, {"text": text})
print(result)
# Output: Error in chain: Empty input. {"output": "Fallback: Unable to process step."}

This example implements error handling to catch and log issues, providing a fallback output to maintain workflow continuity.

Use Cases:

  • Ensuring robust document processing pipelines.
  • Handling unreliable external data sources in retrieval chains.
  • Maintaining chatbot functionality despite input errors.

Core Techniques for Sequential Chains in LangChain

LangChain provides robust tools for building sequential chains, integrating with prompts, LLMs, and external data sources. Below, we explore the core techniques, drawing from the LangChain Documentation.

1. SimpleSequentialChain for Linear Workflows

SimpleSequentialChain links chains where each step has a single input and output, ideal for straightforward multi-step tasks. See Simple Sequential Chain.

Example:

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

llm = OpenAI()

# Step 1: Summarize
summary_template = PromptTemplate(
    input_variables=["text"],
    template="Summarize this in 50 words: {text}"
)
summary_chain = LLMChain(llm=llm, prompt=summary_template)

# Step 2: Translate
translate_template = PromptTemplate(
    input_variables=["summary"],
    template="Translate to Spanish: {summary}"
)
translate_chain = LLMChain(llm=llm, prompt=translate_template)

# Combine into SimpleSequentialChain
chain = SimpleSequentialChain(chains=[summary_chain, translate_chain], verbose=True)

text = "AI improves healthcare with diagnostics and personalized care."
result = chain.run(text)  # Simulated: "La IA mejora la salud con diagnósticos y cuidado personalizado."
print(result)
# Output: La IA mejora la salud con diagnósticos y cuidado personalizado.

This example chains summarization and translation, passing the summary directly to the next step.

Use Cases:

  • Summarizing and reformatting content.
  • Sequential text processing (e.g., extract, then summarize).
  • Simple multi-step automation.

2. SequentialChain for Complex Workflows

SequentialChain supports multiple inputs and outputs per step, offering greater flexibility for complex tasks with interdependent chains. See Complex Sequential Chain.

Example:

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

llm = OpenAI()

# Step 1: Summarize
summary_template = PromptTemplate(
    input_variables=["text"],
    template="Summarize this in 50 words: {text}"
)
summary_chain = LLMChain(llm=llm, prompt=summary_template, output_key="summary")

# Step 2: Extract insights
insights_template = PromptTemplate(
    input_variables=["summary", "text"],
    template="List 3 insights from summary: {summary}, considering original: {text}"
)
insights_chain = LLMChain(llm=llm, prompt=insights_template, output_key="insights")

# Combine into SequentialChain
chain = SequentialChain(
    chains=[summary_chain, insights_chain],
    input_variables=["text"],
    output_variables=["summary", "insights"],
    verbose=True
)

text = "AI transforms healthcare with diagnostics and personalized care, and finance with fraud detection."
result = chain({"text": text})
print(result["insights"])
# Output: Simulated: 1. AI enhances diagnostics. 2. AI personalizes care. 3. AI improves fraud detection.

This example uses multiple inputs (original text and summary) for the second step, enabling richer processing.

Use Cases:

  • Multi-stage document analysis.
  • Workflows requiring context from multiple steps.
  • Complex Q&A with intermediate processing.

3. Retrieval-Augmented Sequential Chains

Combine retrieval-augmented chains, like RetrievalQA, with sequential chains to process retrieved context through multiple steps. Leverage vector stores like FAISS. Explore more in Retrieval-Augmented Prompts.

Example:

from langchain.chains import SequentialChain, LLMChain
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI

llm = OpenAI()

# Simulated document store
documents = ["AI improves healthcare diagnostics.", "Blockchain secures transactions."]
embeddings = OpenAIEmbeddings()
vector_store = FAISS.from_texts(documents, embeddings)

# Step 1: Retrieve context
query = "AI in healthcare"
docs = vector_store.similarity_search(query, k=1)
context = docs[0].page_content

# Step 2: Summarize context
summary_template = PromptTemplate(
    input_variables=["context"],
    template="Summarize: {context}"
)
summary_chain = LLMChain(llm=llm, prompt=summary_template, output_key="summary")

# Step 3: Answer question
answer_template = PromptTemplate(
    input_variables=["summary", "question"],
    template="Based on: {summary}\nAnswer: {question}"
)
answer_chain = LLMChain(llm=llm, prompt=answer_template, output_key="answer")

# Combine into SequentialChain
chain = SequentialChain(
    chains=[summary_chain, answer_chain],
    input_variables=["context", "question"],
    output_variables=["summary", "answer"]
)

result = chain({"context": context, "question": "How does AI help healthcare?"})
print(result["answer"])
# Output: Simulated: AI improves healthcare diagnostics.

This example chains retrieval, summarization, and question-answering for a context-informed response.

Use Cases:

  • Multi-step Q&A over document sets.
  • Research workflows with retrieved data.
  • Knowledge-driven chatbot interactions.

4. Conversational Sequential Chains

Incorporate conversational memory into sequential chains to maintain dialogue context across steps, ideal for interactive applications. See Chat History Chain.

Example:

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

llm = OpenAI()
memory = ConversationBufferMemory()

# Step 1: Classify intent
intent_template = PromptTemplate(
    input_variables=["input"],
    template="Classify intent as 'question' or 'chat': {input}"
)
intent_chain = LLMChain(llm=llm, prompt=intent_template, output_key="intent")

# Step 2: Respond based on intent
response_template = PromptTemplate(
    input_variables=["intent", "input"],
    template="If intent is {intent}, respond appropriately to: {input}"
)
response_chain = LLMChain(llm=llm, prompt=response_template, output_key="response")

# Combine with memory
chain = SequentialChain(
    chains=[intent_chain, response_chain],
    input_variables=["input"],
    output_variables=["intent", "response"],
    memory=memory
)

result = chain({"input": "What is AI?"})
print(result["response"])
# Output: Simulated: AI simulates human intelligence.

This example uses memory to maintain context in a conversational sequential chain.

Use Cases:

  • Multi-turn chatbot workflows.
  • Contextual Q&A with follow-ups.
  • Interactive dialogue systems.

5. Tool-Using Sequential Chains

Integrate external tools or APIs into sequential chains to enhance functionality, processing tool outputs through subsequent steps. See Tool-Using Chain.

Example:

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

llm = OpenAI()

# Simulated tool
def fetch_data(topic):
    return f"Data about {topic}: Innovative technology."  # Placeholder

# Step 1: Fetch data
data_template = PromptTemplate(
    input_variables=["topic"],
    template="Use fetched data: {data}"
)
data_chain = LLMChain(llm=llm, prompt=data_template, output_key="data_output")

# Step 2: Summarize data
summary_template = PromptTemplate(
    input_variables=["data_output"],
    template="Summarize: {data_output}"
)
summary_chain = LLMChain(llm=llm, prompt=summary_template, output_key="summary")

# Combine into SequentialChain
chain = SequentialChain(
    chains=[data_chain, summary_chain],
    input_variables=["data"],
    output_variables=["data_output", "summary"]
)

data = fetch_data("AI")
result = chain({"data": data})
print(result["summary"])
# Output: Simulated: AI is an innovative technology.

This example chains data fetching and summarization, leveraging external tools.

Use Cases:

  • Real-time data-driven workflows.
  • API-enhanced content generation.
  • Dynamic research tasks.

Practical Applications of Sequential Chains

Sequential chains power a variety of LangChain applications. Below are practical use cases, supported by examples from LangChain’s GitHub Examples.

1. Document Processing Pipelines

Sequential chains analyze documents by summarizing, extracting insights, or answering queries. Try our tutorial on Multi-PDF QA.

Implementation Tip: Use SequentialChain with Document Loaders for PDFs, as shown in PDF Loaders.

2. Interactive Chatbots

Conversational sequential chains create chatbots that process intents and generate context-aware responses. Build one with our guide on Building a Chatbot with OpenAI.

Implementation Tip: Combine SequentialChain with LangChain Memory and validate with Prompt Validation.

3. Automated Enterprise Workflows

Sequential chains automate tasks like report generation or data analysis, integrating tools and retrieval. Explore LangGraph Workflow Design.

Implementation Tip: Use MongoDB Vector Search for data-driven chains.

4. Knowledge-Driven Q&A Systems

Retrieval-augmented sequential chains provide accurate answers from large datasets. See Document QA Chain.

Implementation Tip: Integrate with FAISS and test with Testing Prompts.

Advanced Strategies for Sequential Chains

To optimize sequential chains, consider these advanced strategies, inspired by LangChain’s Advanced Guides.

1. Dynamic Chain Configuration

Dynamically configure chain steps based on input characteristics or user intent, enhancing adaptability. See Conditional Chains.

Example:

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

llm = OpenAI()

def configure_chain(task_type):
    chains = [
        LLMChain(
            llm=llm,
            prompt=PromptTemplate(input_variables=["text"], template="Summarize: {text}"),
            output_key="summary"
        )
    ]
    if task_type == "detailed":
        chains.append(
            LLMChain(
                llm=llm,
                prompt=PromptTemplate(input_variables=["summary"], template="Extract 5 insights: {summary}"),
                output_key="insights"
            )
        )
    return SequentialChain(
        chains=chains,
        input_variables=["text"],
        output_variables=["summary", "insights"] if task_type == "detailed" else ["summary"]
    )

chain = configure_chain("detailed")
text = "AI improves healthcare diagnostics."
result = chain({"text": text})
print(result["insights"])
# Output: Simulated: 1. AI enhances diagnostics. 2-5. ...

This dynamically adjusts the chain based on task complexity.

2. Error-Retry Mechanisms

Implement retry logic for failed chain steps, building on the error-handling section, to improve robustness. See Prompt Debugging.

Example:

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

llm = OpenAI()

def retry_chain(chain, inputs, max_attempts=3):
    for attempt in range(max_attempts):
        try:
            return chain(inputs)
        except Exception as e:
            print(f"Attempt {attempt + 1} failed: {e}")
            if attempt == max_attempts - 1:
                return {"output": "Failed after retries."}

template = PromptTemplate(input_variables=["text"], template="Summarize: {text}")
chain = LLMChain(llm=llm, prompt=template)
result = retry_chain(chain, {"text": ""})  # Simulated failure
print(result)
# Output: Failed after retries.

This retries failed steps, ensuring resilience.

3. Multilingual Sequential Chains

Adapt sequential chains for multilingual inputs or outputs, processing language-specific tasks in sequence. See Multi-Language Prompts.

Example:

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

llm = OpenAI()

# Step 1: Translate
translate_template = PromptTemplate(
    input_variables=["text"],
    template="Translate to English: {text}"
)
translate_chain = LLMChain(llm=llm, prompt=translate_template, output_key="translated")

# Step 2: Summarize
summary_template = PromptTemplate(
    input_variables=["translated"],
    template="Summarize: {translated}"
)
summary_chain = LLMChain(llm=llm, prompt=summary_template, output_key="summary")

chain = SequentialChain(
    chains=[translate_chain, summary_chain],
    input_variables=["text"],
    output_variables=["translated", "summary"]
)

text = "La IA mejora los diagnósticos médicos."
result = chain({"text": text})
print(result["summary"])
# Output: Simulated: AI improves medical diagnostics.

This chains translation and summarization for multilingual processing.

Conclusion

Sequential chains in LangChain enable developers to build structured, multi-step LLM pipelines that tackle complex tasks with modularity and precision. From SimpleSequentialChain for linear workflows to SequentialChain for intricate processes, LangChain offers tools to create robust applications. The focus on error handling ensures workflows remain resilient, addressing failures gracefully as of May 14, 2025. Whether for document processing, chatbots, or enterprise automation, sequential chains are key to unlocking LangChain’s potential.

To get started, experiment with the examples provided and explore LangChain’s documentation. For practical applications, check out our LangChain Tutorials or dive into LangSmith Integration for testing and optimization. With sequential chains, you’re equipped to create scalable, high-performing LLM workflows.