LangChain vs LangGraph

Back LangChain vs LangGraph 05 Apr, 2025

ABHISHEK AGNIHOTRI

Let’s dive into the difference between LangChain and LangGraph — and when to use which. 🚀

🔍 TL;DR: LangChain vs LangGraph

Feature/Aspect	LangChain	LangGraph
What it is	A Python (and JS) framework to build LLM-powered apps	A sub-library of LangChain to define stateful workflows as graphs
Main Concept	Chains, tools, agents, memory	Graphs of nodes (steps) with states and control flow
Focus	Abstraction for LLMs (prompting, chaining, memory)	Stateful, multi-step, and multi-path workflows
Control Flow	Linear, branching via chains or tools	Full control with nodes, edges, conditions, loops
Memory Handling	Conversation memory, token-based memory	Graph state memory + deterministic flow
Best for	Fast prototyping, RAG, agents, chatbots	Complex apps with conditionals, retries, state-aware paths
Underlying Framework	LangChain core	Built on top of LangChain

📦 LangChain: The Toolbox

Think of LangChain as the core framework to:

Chain prompts and LLMs
Add tools, retrievers, memory
Manage conversations
Build agents (like ReAct, MRKL)

It's modular, very composable, and great for building:

RAG pipelines
Chatbots
Agentic apps
Tool-using assistants

Example:

from langchain.chains import LLMChain
chain = LLMChain(llm=ChatOpenAI(), prompt=PromptTemplate(...))
chain.run("Tell me about India")

🔁 LangGraph: The Workflow Brain

LangGraph is a more advanced way to build stateful workflows — like a flowchart or FSM (finite state machine).

It’s part of LangChain but gives you:

Graphs of nodes (each node is a function or step)
Custom control flow (if/else, loops, retries)
Persistent state passed across steps
Concurrency and retries for robustness

Great for:

Multi-step agents
Tool-using workflows
RAG with fallback paths
Custom chatbot pipelines with memory

Example:

from langgraph.graph import StateGraph

def node1(state): return {"x": state["input"] + " processed"}
def node2(state): return {"output": state["x"].upper()}

graph = StateGraph()
graph.add_node("step1", node1)
graph.add_node("step2", node2)
graph.set_entry_point("step1")
graph.add_edge("step1", "step2")
graph.set_finish_point("step2")

executor = graph.compile()
result = executor.invoke({"input": "hello"})

🎯 When to Use What?

Use Case	Use LangChain	Use LangGraph
Simple LLM chains	✅	❌ Overkill
Chatbot with memory and tools	✅	Optional (LangChain works)
RAG with simple flow	✅	Optional
RAG with fallback logic, loops, retries	⚠️ Hard to manage	✅ Use LangGraph
Complex workflows or agents	⚠️ Complex code	✅ Clean and scalable
State tracking across steps	Manual	✅ Built-in

🔄 Combine Both

LangGraph uses LangChain under the hood, so most LangChain tools (chains, LLMs, retrievers, tools) can be used as nodes in LangGraph.

🧠 Analogy:

LangChain = LEGO bricks (build anything, step by step)
LangGraph = LEGO instruction manual (organize those steps into a smart, guided flow)