LangChain vs LlamaIndex: Best Framework for AI Apps in 2026

When you’re building an LLM application — whether a RAG pipeline, an AI agent, or a document Q&A system — you’ll quickly encounter two dominant frameworks: LangChain and LlamaIndex. Both solve similar problems but with different philosophies and strengths.

Quick answer: LangChain has the broader ecosystem and is better for general LLM applications and agents. LlamaIndex is the better choice for complex document retrieval and RAG pipelines.

What Is LangChain?

LangChain is the most widely used framework for building LLM applications. It provides abstractions for chains, agents, memory, and retrieval — a composable toolkit for building applications that use language models.

LangChain’s philosophy: provide building blocks for any LLM-powered application.

What Is LlamaIndex?

LlamaIndex (formerly GPT Index) is a data framework for building RAG applications. It provides specialized tools for ingesting, indexing, and querying documents with LLMs.

LlamaIndex’s philosophy: make it easy to build knowledge-grounded AI applications over your data.

Core Philosophy Difference

This is the most important distinction:

LangChain is a general-purpose toolkit. It handles:

Chains and pipelines
Agent frameworks (planning, tool use, memory)
RAG (as one of many use cases)
Prompt management
100+ integrations

LlamaIndex is specialized for data retrieval and RAG. It excels at:

Advanced document parsing and chunking
Diverse index types (vector, keyword, graph)
Query engines with sophisticated retrieval strategies
Multi-document reasoning
Knowledge graph construction

Feature Comparison

Feature	LangChain	LlamaIndex
RAG pipeline	Good	Excellent
Agent framework	Excellent	Good
Document parsing	Basic (via loaders)	Advanced (built-in)
Index types	Vector primarily	Vector, keyword, graph, custom
Query routing	Manual	Automatic
Re-ranking	Via custom code	Built-in
Memory management	Excellent	Limited
Tool/function calling	Excellent	Good
LLM integrations	100+	50+
Vector store integrations	50+	40+
Observability	LangSmith (paid)	Basic logging

RAG: Where LlamaIndex Has the Edge

If you’re building a document retrieval or Q&A system, LlamaIndex’s specialized architecture provides capabilities that LangChain’s general-purpose RAG can’t match.

LlamaIndex Advanced RAG Features

SubQuestionQueryEngine: Automatically breaks complex questions into sub-questions, queries different parts of your index, and synthesizes answers:

from llama_index.core.tools import QueryEngineTool
from llama_index.core.query_engine import SubQuestionQueryEngine

# Automatically splits "Compare Q1 and Q2 results" into two sub-questions
engine = SubQuestionQueryEngine.from_defaults(query_engine_tools=[...])
response = engine.query("How did Q1 2026 compare to Q2 2026 in revenue?")

Multiple Index Types:

from llama_index.core import VectorStoreIndex, KeywordTableIndex

# Vector index for semantic search
vector_idx = VectorStoreIndex.from_documents(docs)

# Keyword index for exact term matching
keyword_idx = KeywordTableIndex.from_documents(docs)

# Combine both in a routing query engine

Re-ranking: LlamaIndex makes it trivial to add re-ranking to improve retrieval quality:

from llama_index.core.postprocessor import CohereRerank

reranker = CohereRerank(api_key="key", top_n=3)
query_engine = index.as_query_engine(
    node_postprocessors=[reranker]
)

Recursive Retrieval: Query summaries first, then dive into details — useful for large document collections:

from llama_index.core.retrievers import RecursiveRetriever

LangChain RAG

LangChain’s RAG is simpler:

from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI

qa_chain = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(model="gpt-4o-mini"),
    retriever=vectorstore.as_retriever(search_kwargs={"k": 5})
)
result = qa_chain.invoke({"query": "What is the refund policy?"})

Simple, effective, but limited for complex retrieval scenarios. For basic RAG, LangChain’s simplicity is appropriate.

Agents: LangChain’s Advantage

For building AI agents — systems that plan actions, use tools, and complete multi-step tasks — LangChain is more mature:

LangChain Agent

from langchain.agents import create_react_agent, AgentExecutor
from langchain_community.tools import DuckDuckGoSearchRun, WikipediaQueryRun

tools = [DuckDuckGoSearchRun(), WikipediaQueryRun(...)]
agent = create_react_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

result = executor.invoke({"input": "Research the latest AI developments and write a summary"})

LangChain supports ReAct agents, Plan-and-Execute agents, OpenAI Functions agents, and more frameworks.

LlamaIndex has agent capabilities (via OpenAIAgent and ReActAgent) but they’re less central to the framework’s design.

Ecosystem and Integrations

Category	LangChain	LlamaIndex
LLM providers	50+	30+
Vector databases	50+	40+
Document loaders	100+	80+
Community size	Larger	Growing
Documentation	Extensive	Good
GitHub stars	~100K	~40K

LangChain has the larger ecosystem. Most popular tools (OpenAI, Anthropic, Pinecone, Supabase, HuggingFace) have first-class integrations in both frameworks.

Ease of Use

LangChain has a steeper learning curve. The LCEL (LangChain Expression Language) pattern is powerful but takes time to learn. Abstractions can feel over-engineered for simple tasks.

LlamaIndex is more opinionated about document-based use cases, which makes it simpler for RAG applications: load documents, create an index, query it.

# LlamaIndex — simple RAG in 5 lines
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("./docs").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What is the cancellation policy?")

This simplicity makes LlamaIndex the faster starting point for document Q&A.

Production Observability

LangChain → LangSmith: LangChain’s observability platform provides tracing, evaluation, and debugging. Free tier (5,000 traces/month) is sufficient for development. Paid tier at $39/month for teams.

LlamaIndex: Basic logging and tracing via Arize Phoenix integration or custom callbacks. Less polished than LangSmith.

For production observability, LangChain + LangSmith has a clear advantage.

When to Choose LangChain

Building AI agents that use tools, search the web, or interact with APIs
Memory-intensive conversational apps where conversation history management matters
General-purpose LLM pipelines beyond document retrieval
Teams that want production observability via LangSmith
Applications with many different LLM tasks requiring orchestration

When to Choose LlamaIndex

Complex document Q&A over large document collections
Multi-document reasoning that needs sub-question decomposition
Applications requiring multiple index types (vector + keyword + graph)
Re-ranking-heavy pipelines where retrieval quality is critical
Building knowledge graphs from documents
Simple, clean RAG with less boilerplate

Can You Use Both?

Yes — and some teams do. LlamaIndex as the retrieval layer for document indexing and querying, with LangChain for agent orchestration and chaining that retrieval into broader workflows.

LlamaIndex integrates directly into LangChain:

from langchain.retrievers import LlamaIndexRetriever

retriever = LlamaIndexRetriever(index=llama_index)
qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)

Conclusion

For most LLM applications and agents: Start with LangChain. The ecosystem, documentation, and LangSmith observability are best-in-class.

For complex document retrieval and RAG systems: LlamaIndex’s specialized architecture delivers better results with less custom code.

If you’re unsure: implement the same RAG pipeline in both frameworks for your specific data. The one that produces better retrieval results with less code complexity wins.

Both are open source, free to use, and integrate with OpenAI, Pinecone, and Supabase.

Get started with LangChain →

LangChain vs LlamaIndex: Best Framework for AI Apps in 2026