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LangGraph vs LangChain
Trust Score comparison · March 2026
VS
Trust Score Δ
8
🏆 LangChain wins
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
| Factor | LangGraph | LangChain |
|---|---|---|
| License | MIT | MIT |
| Language | TypeScript / Python | Python / TypeScript |
| Hosted | Self-hosted | Self-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.