The RAG Framework Landscape

Retrieval-Augmented Generation (RAG) has become the standard pattern for building AI applications that need to access custom knowledge. Two frameworks dominate this space: LangChain and LlamaIndex.

LangChain: The Swiss Army Knife

LangChain excels at building complex AI agent workflows:

from langchain import ChatOpenAI, RetrievalQA
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
Simple RAG pipeline
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(documents, embeddings)
llm = ChatOpenAI(model="gpt-5-mini")
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=vectorstore.as_retriever(),
    chain_type="stuff"
)
answer = qa_chain.run("What are the latest AI trends?")

LlamaIndex: The Data Framework

LlamaIndex focuses on data ingestion and retrieval:

from llama_index import VectorStoreIndex, SimpleDirectoryReader
Even simpler RAG setup
documents = SimpleDirectoryReader("data/").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What are the latest AI trends?")

When to Use Which

• LangChain: Complex multi-step agents, tool use, chain composition
• LlamaIndex: Data-heavy applications, document Q&A, knowledge bases

Performance Comparison

Conclusion

Both frameworks are excellent. For most RAG applications, start with LlamaIndex for simplicity. For complex agent workflows, choose LangChain. And in 2026, both frameworks support hybrid approaches.