LangGraph vs. Chains: Choosing the Right LangChain Tool for Your AI Workflow

Building AI applications is like choosing the perfect tool for a job—sometimes you need a simple hammer, and other times you need a Swiss Army knife. In the LangChain ecosystem, Chains and LangGraph are two powerful tools for creating AI workflows, but they serve different purposes. Chains are great for straightforward, linear tasks, while LangGraph shines in complex, dynamic scenarios that require looping, branching, and state management. In this beginner-friendly guide, we’ll compare LangGraph and Chains, explore their strengths, and show you when to use each with practical examples. With a conversational tone and clear explanations, you’ll be ready to pick the right tool for your AI project, even if you’re new to coding!

What Are Chains and LangGraph?

Before diving into the comparison, let’s clarify what each tool does.

Chains

Chains in LangChain are linear sequences of tasks that combine language models (like those from OpenAI) with prompts, tools, or data processing steps. They’re designed for straightforward workflows where tasks follow a fixed order, like generating text, fetching data, and formatting the output.

• Structure: A chain is a pipeline of components (e.g., prompt → model → output parser).
• Use Cases: Simple Q&A, text summarization, or data extraction.
• Example: A chain that takes a question, queries a database, and returns an answer.

To learn more about Chains, see Chains Introduction.

LangGraph

LangGraph is a more advanced library that builds on LangChain to create stateful, graph-based workflows. Instead of a linear sequence, LangGraph organizes tasks as a graph with nodes (tasks), edges (connections), and a state (shared data). It supports looping, branching, and dynamic decision-making, making it ideal for complex, interactive applications.

• Structure: A graph with nodes, direct/conditional edges, and a persistent state.
• Use Cases: Multi-step chatbots, iterative agents, or workflows with decision points.
• Example: A bot that retries generating a poem until it meets criteria or branches to different tasks based on user input.

For LangGraph basics, check Introduction to LangGraph.

Comparing LangGraph and Chains

Let’s break down the key differences to help you choose the right tool.

When to Use Chains

• You need a simple, linear workflow (e.g., question → search → answer).
• The task is one-shot or has minimal context (e.g., summarizing a document).
• You want quick setup with minimal code complexity.

When to Use LangGraph

• You need dynamic workflows with loops, branches, or decisions (e.g., retrying tasks, choosing paths).
• The application requires stateful context (e.g., tracking conversation history or task progress).
• You’re building complex, interactive systems (e.g., multi-step chatbots or agents).

For a deeper dive into LangGraph’s structure, see Core Concepts.

Practical Example: Chains vs. LangGraph

Let’s compare Chains and LangGraph by building a question-answering bot in two ways: a simple Chain for a linear Q&A and a LangGraph workflow for a dynamic, retry-capable bot.

Scenario

The bot answers a question about recent AI research. If the answer isn’t clear, the LangGraph version retries; the Chain version gives a one-shot response.

Using a Chain

Goal: Take a question, search the web, and return a summarized answer in a linear flow.

Code:

from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain_community.tools import SerpAPI
from langchain.chains import LLMChain, SequentialChain

# Initialize tools and model
search_tool = SerpAPI()
llm = ChatOpenAI(model="gpt-3.5-turbo")

# Chain 1: Search web
search_prompt = PromptTemplate(
    input_variables=["question"],
    template="Search for: {question}"
)
search_chain = LLMChain(
    llm=llm,
    prompt=search_prompt,
    output_key="search_results",
    verbose=True
)

# Chain 2: Summarize results
summary_prompt = PromptTemplate(
    input_variables=["question", "search_results"],
    template="Summarize the answer to: {question}\nBased on: {search_results}"
)
summary_chain = LLMChain(
    llm=llm,
    prompt=summary_prompt,
    output_key="response",
    verbose=True
)

# Combine chains
overall_chain = SequentialChain(
    chains=[search_chain, summary_chain],
    input_variables=["question"],
    output_variables=["response"],
    verbose=True
)

# Run
result = overall_chain({"question": "What’s new in AI research?"})
print(result["response"])

What’s Happening?

• Structure: Two chains (search_chain, summary_chain) run sequentially.
• Flow: Linear—question → search → summarize → output.
• Limitations: No retries if the summary is unclear; no dynamic decisions.
• Pros: Simple setup, quick for one-shot tasks.

See Sequential Chains for more.

Using LangGraph

Goal: Take a question, search the web, summarize, and retry if the summary isn’t clear, up to three attempts.

Code:

from langgraph.graph import StateGraph, END
from typing import TypedDict
from langchain_openai import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain_community.tools import SerpAPI
import logging

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# State
class State(TypedDict):
    question: str
    search_results: str
    response: str
    is_clear: bool
    attempt_count: int

# Initialize tools and model
search_tool = SerpAPI()
llm = ChatOpenAI(model="gpt-3.5-turbo")

# Nodes
def process_input(state: State) -> State:
    logger.info(f"Processing: {state['question']}")
    state["attempt_count"] = 0
    return state

def search_web(state: State) -> State:
    try:
        results = search_tool.run(state["question"])
        state["search_results"] = results if results else "No results found"
    except Exception as e:
        logger.error(f"Search error: {str(e)}")
        state["search_results"] = f"Error: {str(e)}"
    return state

def generate_response(state: State) -> State:
    template = PromptTemplate(
        input_variables=["question", "search_results"],
        template="Summarize: {question}\nBased on: {search_results}"
    )
    chain = template | llm
    response = chain.invoke({
        "question": state["question"],
        "search_results": state["search_results"]
    }).content
    state["response"] = response
    state["attempt_count"] += 1
    return state

def check_clarity(state: State) -> State:
    state["is_clear"] = len(state["response"]) > 50 and "." in state["response"]
    logger.info(f"Clarity: {state['is_clear']}, Attempts: {state['attempt_count']}")
    return state

def decide_next(state: State) -> str:
    return "end" if state["is_clear"] or state["attempt_count"] >= 3 else "generate_response"

# Build the graph
graph = StateGraph(State)
graph.add_node("process_input", process_input)
graph.add_node("search_web", search_web)
graph.add_node("generate_response", generate_response)
graph.add_node("check_clarity", check_clarity)
graph.add_edge("process_input", "search_web")
graph.add_edge("search_web", "generate_response")
graph.add_edge("generate_response", "check_clarity")
graph.add_conditional_edges("check_clarity", decide_next, {
    "end": END,
    "generate_response": "generate_response"
})
graph.set_entry_point("process_input")

# Run
app = graph.compile()
result = app.invoke({
    "question": "What’s new in AI research?",
    "search_results": "",
    "response": "",
    "is_clear": False,
    "attempt_count": 0
})
print(result["response"])

What’s Happening?

• Structure: A graph with nodes for input, search, response generation, and clarity checking.
• Flow: Dynamic—searches once, then loops to retry summarization if unclear, up to three attempts.
• Strengths: Handles retries, maintains state, and supports conditional logic.
• Pros: Flexible for complex, iterative tasks.

For more on dynamic flows, see Looping and Branching.

When to Choose Chains vs. LangGraph

Use Chains When:

• Task is Linear: You need a simple sequence (e.g., prompt → tool → output).
• Context is Minimal: The workflow doesn’t require persistent state or history.
• Speed is Key: Quick setup for prototyping or one-off tasks.
• Examples:
• Summarizing a document with Document QA Chain.
• Generating SQL queries from text with Generate SQL Tutorial.

Chain Example: A chain for summarizing a webpage:

from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI
from langchain_community.document_loaders import WebBaseLoader

loader = WebBaseLoader("https://example.com")
docs = loader.load()
llm = ChatOpenAI(model="gpt-3.5-turbo")
chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff")
result = chain.run("Summarize the webpage")
print(result)

This is fast and simple but lacks retries or dynamic decisions.

Use LangGraph When:

• Task is Complex: Requires loops, branches, or multiple decision points.
• State is Critical: Needs to track context, history, or progress across steps.
• Interactivity is Needed: For conversational or iterative processes.
• Examples:
• A chatbot with memory and retries, as in Customer Support Example.
• An agent that chooses tools dynamically with Agent Integration.

LangGraph Example: The research bot above, which retries unclear summaries, showing its strength in dynamic workflows.

Combining Chains and LangGraph

You don’t always have to choose one! LangGraph can incorporate Chains as nodes for specific tasks, blending simplicity with flexibility. For example, use a Chain for a linear sub-task (like summarization) within a LangGraph workflow that handles looping or branching.

Example: Modify the LangGraph research bot to use a Chain for summarization:

# Replace generate_response with a Chain
summary_chain = LLMChain(
    llm=llm,
    prompt=PromptTemplate(
        input_variables=["question", "search_results"],
        template="Summarize: {question}\nBased on: {search_results}"
    ),
    output_key="response"
)

def generate_response(state: State) -> State:
    result = summary_chain({
        "question": state["question"],
        "search_results": state["search_results"]
    })
    state["response"] = result["response"]
    state["attempt_count"] += 1
    return state

This keeps the summarization simple while leveraging LangGraph’s dynamic flow. See Workflow Design for more on structuring workflows.

Best Practices for Choosing Between Chains and LangGraph

• Start Simple: Use Chains for quick prototypes or linear tasks. Graduate to LangGraph for complex needs. Check Best Practices.
• Plan State Needs: If you need persistent context, LangGraph’s state management is superior. See State Management.
• Test Both: Experiment with a Chain for simplicity, then switch to LangGraph if you need loops or branches. Explore Graph Debugging.
• Leverage Tools: Both support tools, but LangGraph’s dynamic decisions shine for tool selection. See Tool Usage.
• Use Memory: LangGraph’s memory integration is more robust for conversational apps. Check Memory Integration.

Enhancing Workflows with LangChain Features

Both Chains and LangGraph benefit from LangChain’s ecosystem:

• Tools: Add external data with SerpAPI Integration or SQL Database Chains.
• Prompts: Craft effective prompts with Prompt Templates.
• Memory: Enhance Chains or LangGraph with LangChain Memory.
• Agents: Use LangGraph for advanced agent logic with Agent Integration.

For example, add a node or chain to fetch real-time data with Web Research Chain.

Conclusion

Choosing between LangGraph and Chains depends on your AI workflow’s needs. Chains are your go-to for simple, linear tasks like one-shot Q&A or text processing, offering quick setup and ease of use. LangGraph is the powerhouse for complex, dynamic workflows, with looping, branching, and stateful memory for interactive or iterative applications. By understanding their strengths, you can pick the right tool—or combine them—for your project, whether it’s a basic summarizer or a sophisticated chatbot.

To start, follow Install and Setup and try Simple Chatbot Example. For more, explore Core Concepts or real-world applications at Best LangGraph Uses. With LangGraph and Chains, you’re equipped to build AI workflows that fit any challenge!

External Resources:

• LangGraph Documentation
• LangChain GitHub
• SerpAPI Documentation