This content originally appeared on DEV Community and was authored by ClickIT - DevOps and Software Development
When building with LLMs in 2025, one of the first questions devs ask is: Which framework should I use LangChain or LangGraph?
Both are popular in the AI dev ecosystem, but they serve different purposes. Let’s break it down.
🧩 LangChain in a nutshell
LangChain is one of the most widely used frameworks for LLM apps. It’s built for:
- Modularity: easy to connect prompts, memory, tools, and agents.
- Prototyping: fast iteration, experiment-heavy workflows.
- Ecosystem: integrations with APIs, vector DBs, embeddings, and more.
(LangChain with OpenAI):
from langchain_openai import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
llm = ChatOpenAI(model="gpt-4o-mini")
prompt = ChatPromptTemplate.from_template("Translate this text to French: {text}")
chain = prompt | llm
result = chain.invoke({"text": "Hello, how are you?"})
print(result.content) # Bonjour, comment ça va ?
LangChain makes it easy to link prompts and LLMs with minimal code perfect for experimentation.
🔄 LangGraph in a nutshell
LangGraph builds on top of LangChain, but adds more structure. It’s designed for:
- Stateful workflows: preserving context across complex tasks.
- Control flows: retry logic, loops, conditional branching.
- Multi-agent orchestration: multiple agents working together.
(LangGraph with two simple nodes):
from langgraph.graph import StateGraph, END
def step1(state):
return {"message": state["message"].upper()}
def step2(state):
return {"message": state["message"] + " ✅"}
graph = StateGraph(dict)
graph.add_node("step1", step1)
graph.add_node("step2", step2)
graph.set_entry_point("step1")
graph.add_edge("step1", "step2")
graph.add_edge("step2", END)
app = graph.compile()
result = app.invoke({"message": "langgraph is cool"})
print(result["message"]) # LANGGRAPH IS COOL ✅
Here, LangGraph behaves like a workflow engine explicit steps, state tracking, and orchestration.
So, which one should you use?
Start with LangChain if you’re exploring, prototyping, or just connecting tools quickly.
Move to LangGraph when you need production-ready structure, retry handling, and multi-agent setups.
Rather than competing, many devs use both:
- LangChain for building blocks.
- LangGraph for orchestration and reliability.
Watch our short breakdown here: LangChain vs LangGraph: Which LLM Should You Use?
- Have you tried LangGraph, or are you sticking with LangChain?
- For production apps, do you think LangGraph is a must-have?
- Would you recommend newcomers start directly with LangGraph or learn LangChain first?
Let’s share insights, because the “best” framework often depends on the use case.
Also, we usually share short-form content on AI engineering and dev tools on our YouTube channel.
This content originally appeared on DEV Community and was authored by ClickIT - DevOps and Software Development
ClickIT - DevOps and Software Development | Sciencx (2025-08-28T17:23:15+00:00) LangChain vs LangGraph: Which LLM Framework Should You Use?. Retrieved from https://www.scien.cx/2025/08/28/langchain-vs-langgraph-which-llm-framework-should-you-use/
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