The Intricacies of LangChain Chains: Unleashing the Power of Modern AI Workflows

Name

Akira Sakamoto

Updated on 11/14/2025

LangChain continues to evolve rapidly, and chains remain one of its most essential concepts. In this updated guide, we explore what LangChain Chains actually mean in 2025, how they work using the new LCEL (LangChain Expression Language), and how they help developers build reliable, multi-step AI applications.

This article updates all examples to modern LangChain standards, replacing deprecated classes like LLMChain with Runnables, PromptTemplate, ChatModels, and sequential pipelines.

What is LangChain? (2025 Overview)

LangChain is a powerful Python/JavaScript framework that helps developers build AI applications using:

• Large language models (LLMs)
• Prompt templates
• Memory
• Tools & agents
• Retrieval (RAG)
• Workflow graphs (LangGraph)

LangChain’s most iconic concept is the Chain:

a reusable, composable pipeline that passes data step-by-step through prompts, models, tools, transformations, and output parsers.

📘 Want a basic introduction? Read our LangChain Intro Article.

What Are LangChain Chains?

In 2023, chains were commonly created using LLMChain and custom Python classes.
In 2025, the recommended way is LCEL, which uses a clean, functional pipeline syntax:

chain = prompt | model | output_parser

A Chain is simply:

• A sequence of steps
• Connected into a single callable unit
• Where the output of one step becomes the input of the next

LangChain now provides:

• Simple Chains (single model + prompt)
• Sequential Chains (multiple steps)
• Parallel Chains (branching workflows)
• Agents (dynamic tool-choosing chains)

Let’s walk through modern examples.

Basic Chains (Updated for 2025)

A basic chain triggers one model with one prompt.

from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
 
prompt = PromptTemplate.from_template(
    "Describe a perfect day in {city}."
)
 
model = ChatOpenAI(model="gpt-4o-mini")
 
chain = prompt | model
 
chain.invoke({"city": "Paris"})

What’s Happening?

1. PromptTemplate formats "Describe a perfect day in Paris."
2. The LLM generates the description
3. The output is returned as a ChatMessage or string, depending on configuration

This is the simplest possible chain in modern LangChain.

Advanced Chains: Multi-Step Pipelines

Multi-step chains combine several operations.

Example: Summarize → Translate using LCEL:

from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
 
summarize_prompt = PromptTemplate.from_template(
    "Summarize this text:\n\n{text}"
)
 
translate_prompt = PromptTemplate.from_template(
    "Translate this into Spanish:\n\n{summary}"
)
 
model = ChatOpenAI(model="gpt-4o-mini")
 
summarize_chain = summarize_prompt | model
translate_chain = translate_prompt | model
 
full_chain = summarize_chain | (lambda x: {"summary": x.content}) | translate_chain
 
full_chain.invoke({"text": "Climate change is causing rapid ocean warming..."})

Why this matters

This reflects modern LangChain best practices:

• Runnables instead of old chain classes
• Functional composition using |
• Intermediate data mapping with Python functions

Chains Without LLMs (Transform + Clean Pipelines)

Not every chain needs an LLM. You can create pre-processing or post-processing pipelines.

Example: trim whitespace → convert to lowercase → feed to LLM:

def cleanup(text):
    return text.strip().lower()
 
chain = cleanup | model
chain("   WHAT is the    WEATHER   LIKE?   ")

Any Python function can be inserted into a chain as long as it accepts and returns serializable data.

Sequential Chains (Replaces SimpleSequentialChain)

SimpleSequentialChain and SequentialChain are deprecated. The LCEL equivalent is simply:

chain = step1 | step2 | step3

Example: Clean the prompt → rewrite → answer

from langchain_openai import ChatOpenAI
 
clean = lambda x: x.strip()
rewrite_prompt = PromptTemplate.from_template(
    "Rewrite this more clearly: {text}"
)
answer_prompt = PromptTemplate.from_template(
    "Answer this question: {question}"
)
 
model = ChatOpenAI(model="gpt-4o-mini")
 
rewrite_chain = rewrite_prompt | model
answer_chain = answer_prompt | model
 
chain = clean | (lambda x: {"text": x}) | rewrite_chain | (lambda x: {"question": x.content}) | answer_chain
 
chain.invoke("   What is LangChain used for?  ")

This is the modern replacement for SimpleSequentialChain.

The Role of Agents in LangChain (2025 Edition)

Agents allow LLMs to choose tools dynamically:

• Search engines
• Code execution
• Calculators
• APIs
• Retrievers
• Custom functions

Example (modern syntax):

from langchain.agents import AgentExecutor, load_tools, create_react_agent
from langchain_openai import ChatOpenAI
 
llm = ChatOpenAI(model="gpt-4o-mini")
tools = load_tools(["llm-math"], llm=llm)
 
agent = create_react_agent(llm, tools)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
 
executor.invoke({"input": "If I'm half my dad's age and he'll be 60 next year, how old am I?"})

Agents are best used when the task requires:

• Reasoning
• Choosing correct tools
• Multiple decision steps

For fixed workflows, a simple chain is often better.

Practical Example: A Movie Recommendation Workflow (Updated)

We modernize your chain-of-two-LLMs example using LCEL.

Step 1 — Ask user about genres

from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
 
ask_prompt = PromptTemplate.from_template(
    "Ask the user about their favorite movie genres."
)
 
ask_chain = ask_prompt | ChatOpenAI(model="gpt-4o-mini")

Step 2 — Recommend movies

recommend_prompt = PromptTemplate.from_template(
    "Based on these genres: {genres}, recommend 5 movies."
)
 
recommend_chain = recommend_prompt | ChatOpenAI(model="gpt-4o-mini")

Combine them

chain = ask_chain | (lambda x: {"genres": x.content}) | recommend_chain
chain.invoke({})

Adding memory (modern)

from langchain.memory import ConversationBufferMemory
 
memory = ConversationBufferMemory(return_messages=True)
 
# pipelines can load memory before passing to next step

Conclusion

Modern LangChain (2024–2025) makes it easier than ever to build scalable AI workflows using LCEL pipelines, Runnables, and agents. Whether you're:

• Preprocessing text
• Building multi-step workflows
• Integrating tools
• Creating dynamic agents

LangChain provides the building blocks to connect everything together cleanly and reliably.

Experiment freely—simple chains can quickly evolve into powerful AI applications.

FAQ (Updated)

1. What is LangChain? LangChain is a framework for building AI applications using prompts, models, tools, memory, and workflow graphs.

2. What replaces SimpleSequentialChain? LCEL pipelines using the | operator for sequential processing.

3. How do I add memory? Use ConversationBufferMemory, SummaryMemory, or custom memory injected into your chain.

4. Are agents still useful? Yes—when dynamic tool selection is required. For fixed steps, chains are cleaner.

5. Where can I learn more? Visit LangChain documentation or explore LangGraph for workflow-based agent orchestration.