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LangChain vs LlamaIndex
Trust Score comparison · March 2026
VS
Trust Score Δ
14
🏆 LangChain wins
Signal Comparison
8.2M / wkPyPI downloads300k / wk
201 commitsCommits (90d)300 commits
95k ★GitHub stars38k ★
7.8k q'sStack Overflow5k q's
HighCommunityLarge & active
LangChainLlamaIndex
Key Differences
| Factor | LangChain | LlamaIndex |
|---|---|---|
| License | MIT | MIT |
| Language | Python / TypeScript | TypeScript / Python |
| Hosted | Self-hosted | Self-hosted |
| Free tier | — | — |
| Open Source | ✓ Yes | — |
| TypeScript | ✓ | ✓ |
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
Pick LlamaIndex if…
- Building RAG over documents, PDFs, or databases
- You want a data-centric approach vs LangChain's agent-centric
- Complex ingestion pipelines with multiple data sources
Side-by-side Quick Start
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)LlamaIndex
import { VectorStoreIndex, SimpleDirectoryReader } from 'llamaindex';
const documents = await new SimpleDirectoryReader().loadData({ directoryPath: './data' });
const index = await VectorStoreIndex.fromDocuments(documents);
const queryEngine = index.asQueryEngine();
const response = await queryEngine.query({ query: 'What is the main topic?' });
console.log(response.toString());Community Verdict
Based on upvoted notes🏆
LangChain wins this comparison
Trust Score 96 vs 82 · 14-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.