LangChain vs LlamaIndex: Which AI Framework Should You Use? (2025)
Both LangChain and LlamaIndex help you build LLM-powered applications — but they're optimized for different things. LangChain is the go-to for agents, chains, and complex multi-step workflows with 600+ integrations. LlamaIndex is the specialist: best-in-class RAG, simpler document Q&A setup, and deeper retrieval defaults. Here's how to choose — and when to use both together.
Key differences at a glance
| Feature | LangChain | LlamaIndex |
|---|---|---|
| Primary focus | Agents + general LLM apps | RAG + document retrieval |
| RAG quality | Good | ✓ Best (specialized) |
| Agent capabilities | ✓ Best (LangGraph) | Basic agents |
| Integrations | 600+ (broader) | 160+ (deeper for RAG) |
| Learning curve | Steeper | Gentler for RAG use cases |
| Streaming support | ✓ | ✓ |
| TypeScript support | ✓ LangChain.js | ✓ TypeScript SDK |
| Observability | LangSmith ($) | Llamatrace (free) |
| Use with Claude | ✓ | ✓ |
| License | MIT | MIT |
What is LangChain?
LangChain is a Python/TypeScript framework for building LLM-powered applications. It's designed around composable building blocks that let you wire together models, tools, memory, and retrieval in complex sequences.
Best for
Chains (sequence of LLM calls), agents (autonomous AI that uses tools), memory management, and applications that integrate many components across many providers.
Core concepts
- Chain — a sequence of steps (prompt → LLM → output parser → next step)
- Agent — an LLM that decides which tools to call and in what order
- Tools — functions the agent can invoke (search, code runner, database query)
- Memory — conversation state that persists across turns
LangGraph
LangChain's newer framework for building stateful, multi-agent workflows with graph-based execution — multiple agents collaborating with conditional branching, cycles, and human-in-the-loop steps. This is where LangChain has the clearest lead over LlamaIndex.
GitHub: 90k+ stars — one of the most-starred AI frameworks on GitHub.
What is LlamaIndex?
LlamaIndex (formerly GPT Index) is a Python/TypeScript framework specialized for RAG (Retrieval-Augmented Generation) — connecting LLMs to your own data. Where LangChain is a general-purpose orchestration framework, LlamaIndex is purpose-built for the retrieval layer.
Best for
Building search over documents, question-answering from your data, building knowledge bases, and indexing and retrieving private documents at scale.
Core concepts
- Document — what you index (PDF, text file, database row, web page)
- Index — the searchable structure built from your documents
- QueryEngine — retrieve relevant chunks and answer with an LLM
- VectorStore — the semantic search backend (Pinecone, Chroma, Weaviate, etc.)
GitHub: 36k+ stars — the dominant RAG-focused framework in the Python ecosystem.
RAG: code comparison
Both frameworks support RAG. LlamaIndex's API is simpler and has better defaults out of the box. LangChain gives you more control but requires more boilerplate.
LlamaIndex RAG — simpler, better defaults:
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What are the main findings?")LangChain RAG — more verbose, more control:
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI
embeddings = OpenAIEmbeddings()
vectorstore = Chroma(embedding_function=embeddings)
retriever = vectorstore.as_retriever()
qa_chain = RetrievalQA.from_chain_type(llm=ChatOpenAI(), retriever=retriever)
result = qa_chain.invoke({"query": "What are the main findings?"})
LlamaIndex's SimpleDirectoryReader → VectorStoreIndex → as_query_engine() is 4 lines. LangChain's equivalent is 8 lines with more manual wiring. For RAG specifically, LlamaIndex is faster to build and retrieves better by default.
Agents: where LangChain leads
LangChain's agent story is stronger, especially with LangGraph:
- Multi-agent workflows — multiple agents collaborating, each with different roles and tool sets
- Stateful execution — graph-based flow with conditional branching, loops, and human approval gates
- LangSmith observability — traces every step, evaluates outputs, catches failures before they hit users
- 600+ integrations — tools for web search, code execution, databases, APIs, and more
LlamaIndex agents: simpler tool-calling interface, well-suited for single-agent RAG use cases where the agent retrieves documents and answers questions. Not optimized for complex multi-agent workflows.
Integration breadth
LangChain: 600+ integrations
- Every major LLM (OpenAI, Anthropic, Gemini, Mistral, Llama)
- 30+ vector stores
- 50+ document loaders
- Tools: search, code execution, databases, APIs
- LangSmith observability (paid)
LlamaIndex: 160+ integrations
- All major LLMs supported
- Deep vector store integrations (optimized for retrieval)
- Rich document loaders (PDFs, Notion, Confluence, SQL)
- LlamaParse (advanced document parsing)
- Llamatrace observability (free)
When to choose each
Choose LangChain if…
- You're building an AI agent that uses tools (search, code execution, database queries)
- Your app has a complex multi-step workflow with conditional logic — use LangGraph
- You need the widest integration ecosystem (600+ sources)
- You want built-in observability and debugging via LangSmith
- You're building a production chatbot with memory and conversation history
Choose LlamaIndex if…
- Your core use case is RAG — connecting an LLM to your own documents
- You want faster setup for document Q&A with better retrieval defaults
- Simplicity matters — LlamaIndex has less boilerplate for RAG workflows
- You're building semantic search or a knowledge base over enterprise data
Use both together (production pattern)
The recommended production-ready approach: LlamaIndex handles the retrieval layer (better RAG defaults), LangChain/LangGraph handles the agent and orchestration layer (more powerful agents).
- LlamaIndex manages your document store and vector index
- LlamaIndex query engines are wrapped as LangChain tools
- LangGraph orchestrates the agent logic, branching, and state
- Best of both: LlamaIndex retrieval quality + LangChain agent power
Monitor LLM API status at prismix.dev
LangChain and LlamaIndex both depend on LLM APIs. When OpenAI, Anthropic, or Groq is having issues, your app breaks — monitor status at prismix.dev and get alerts before your users notice.
FAQ
Is LangChain better than LlamaIndex?
For agents and complex multi-step workflows: LangChain (especially LangGraph) is better. For RAG and document retrieval: LlamaIndex has better defaults and simpler setup. The question is your use case — not which framework is universally better.
What is LlamaIndex used for?
LlamaIndex is primarily used for RAG (Retrieval-Augmented Generation): indexing your documents (PDFs, text, databases), building semantic search over them, and connecting an LLM to your private data to answer questions. Common use cases: enterprise document Q&A, knowledge base search, customer support over product documentation.
Can I use LangChain and LlamaIndex together?
Yes. A common production pattern: use LlamaIndex for the retrieval layer (it has better RAG defaults) and LangChain/LangGraph for the agent and orchestration layer. LlamaIndex's query engines can be wrapped as LangChain tools. This combines LlamaIndex's retrieval quality with LangChain's richer agent framework.
Is LangChain still relevant in 2025?
Yes. LangChain remains widely used for production AI applications. LangGraph (LangChain's graph-based agent framework) has gained significant adoption for multi-agent workflows. Criticism that LangChain adds too much abstraction led to a leaner v0.3 with better ergonomics. LangSmith (the observability product) is a key differentiator.