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LangGraph vs LangChain

Trust Score comparison · March 2026

LangGraph
88
Trust
Good
View profile
VS
Trust Score Δ
8
🏆 LangChain wins
LangChain
96
Trust
Excellent
View profile

Signal Comparison

200k / wknpm downloads8.2M / wk
250 commitsCommits (90d)201 commits
11k ★GitHub stars95k ★
2k q'sStack Overflow7.8k q's
Growing fastCommunityHigh
LangGraphLangChain

Key Differences

FactorLangGraphLangChain
LicenseMITMIT
LanguageTypeScript / PythonPython / TypeScript
HostedSelf-hostedSelf-hosted
Free tier
Open Source✓ Yes
TypeScript

Pick LangGraph if…

  • Building complex multi-step agent workflows
  • You need stateful agents with memory across steps
  • Human-in-the-loop approval flows

Pick LangChain if…

  • You need the largest pre-built tool ecosystem for agents
  • You're building complex multi-step workflows
  • Your team already has LangChain experience

Side-by-side Quick Start

LangGraph
import { StateGraph, END } from '@langchain/langgraph';

const workflow = new StateGraph({ channels: { messages: { value: (x, y) => x.concat(y) } } });

workflow.addNode('agent', async (state) => {
  const response = await llm.invoke(state.messages);
  return { messages: [response] };
});

workflow.setEntryPoint('agent');
workflow.addEdge('agent', END);

const app = workflow.compile();
const result = await app.invoke({ messages: [{ role: 'user', content: 'Hello' }] });
LangChain
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

llm = ChatOpenAI(model="gpt-4o")

prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("user", "{input}"),
])

chain = prompt | llm
response = chain.invoke({"input": "Hello!"})
print(response.content)

Community Verdict

Based on upvoted notes
🏆
LangChain wins this comparison
Trust Score 96 vs 88 · 8-point difference

LangChain leads on Trust Score with stronger signal data across downloads and community health. That said, the other tool is worth considering if your use case matches its specific strengths above.