LangGraph vs LangChain: What Is the Difference?

A developer comparison explains that LangChain provides high-level components and a ready-made agent loop, while LangGraph is the low-level orchestration runtime for stateful, controllable workflows, with LangChain's agent running on LangGraph underneath. The writeup contrasts a simple tool-calling assistant with a claims workflow that uses typed state, conditional routing, checkpointers and an interrupt() call to pause for human approval on claims over ₹50,000. It recommends learning LangChain first, then LangGraph, and promotes the TechSimPlus Vector 2.0 cohort taught by Prateek Mishra.

Short answer: LangChain gives you high-level building blocks and a ready-made agent. LangGraph is the low-level orchestration runtime for stateful, controllable workflows. LangChain's agent runs on LangGraph under the hood. Let's see the difference in code. python from langchain.agents import create agent from langchain.tools import tool @tool def get policy limit policy id: str - str: """Return the claim limit for a policy.""" return "Limit for " + policy id + " is ₹5,00,000" agent = create agent model="openai:gpt-4.1-mini", tools= get policy limit , system prompt="You are a helpful insurance assistant.", result = agent.invoke {"messages": {"role": "user", "content": "What is the limit on policy P-1029?"} } print result "messages" -1 .content The model decides which tools to call and when to stop. Perfect for assistants and simple tool use. Now a claims workflow: classify the claim, route it, and pause for human approval on large amounts. python from typing import TypedDict from langgraph.graph import StateGraph, START, END from langgraph.checkpoint.memory import InMemorySaver from langgraph.types import interrupt, Command class ClaimState TypedDict : claim text: str amount: float approved: bool def assess state: ClaimState : call an LLM or a decision model here to extract the amount return {"amount": 75000.0} def route state: ClaimState - str: return "human review" if state "amount" 50000 else "auto approve" def human review state: ClaimState : decision = interrupt {"question": "Approve this claim?", "amount": state "amount" } return {"approved": decision == "yes"} def auto approve state: ClaimState : return {"approved": True} builder = StateGraph ClaimState builder.add node "assess", assess builder.add node "human review", human review builder.add node "auto approve", auto approve builder.add edge START, "assess" builder.add conditional edges "assess", route, "human review", "auto approve" builder.add edge "human review", END builder.add edge "auto approve", END graph = builder.compile checkpointer=InMemorySaver config = {"configurable": {"thread id": "claim-42"}} graph.invoke {"claim text": "Hospital bill..."}, config pauses at human review graph.invoke Command resume="yes" , config resumes after approval In production, swap InMemorySaver for a Postgres checkpointer so the workflow survives restarts. | | LangChain | LangGraph | |---|---|---| | Level | High-level components and agent | Low-level orchestration runtime | | Control flow | Model decides agent loop | You define nodes, edges, branches, loops | | State | Messages | Any typed state you design | | Durability | Via LangGraph underneath | Built-in checkpointers | | Human-in-the-loop | Via middleware | interrupt anywhere in the graph | | Best for | Assistants, RAG chains, simple tool use | Business workflows, multi-agent systems, long-running jobs | LangChain first: models, prompts, structured output, tools. Then LangGraph, because every serious agentic system you build will need state and control. That is exactly the order in Vector 2.0 , the live Gen-AI developer cohort by TechSimPlus and Prateek Mishra : Sprint 1 covers LangChain, Sprint 3 covers LangGraph with ClaimSense, and Sprint 4 adds multi-agent systems with MCP and A2A. Check the Complete Content: vector.techsimplus.com https://vector.techsimplus.com/?utm source=devto&utm medium=blog&utm campaign=vector2 langgraph