LangChain vs LlamaIndex vs Haystack: RAG Framework Comparison 2026

Building production RAG systems requires choosing the right framework. Each takes different approaches to document indexing, retrieval, and LLM orchestration.

Framework Philosophy

LangChain: Composable chains and agents, broad ecosystem with 200+ integrations

LlamaIndex: Data-centric, optimized for document indexing and complex queries

Haystack: Production-focused, modular pipeline architecture with ML-ops support

LangChain RAG Pipeline

python
from langchain.document_loaders import DirectoryLoader, PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain_community.vectorstores import Chroma
from langchain_core.runnables import RunnablePassthrough
from langchain import hub
Load and split documents
loader = DirectoryLoader('./docs', glob='**/*.pdf', loader_cls=PyPDFLoader)
chunks = RecursiveCharacterTextSplitter(
    chunk_size=1000, chunk_overlap=200
).split_documents(loader.load())
Create vector store
vectorstore = Chroma.from_documents(
    chunks,
    OpenAIEmbeddings(model='text-embedding-3-large'),
    persist_directory='./chroma_db'
)
Create RAG chain
rag_chain = (
    {'context': vectorstore.as_retriever(search_kwargs={'k': 5}),
     'question': RunnablePassthrough()}
    | hub.pull('rlm/rag-prompt')
    | ChatOpenAI(model='gpt-5', temperature=0)
)
response = rag_chain.invoke('What are the key findings?')
print(response.content)

LlamaIndex RAG Pipeline

python
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.anthropic import Anthropic
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.postprocessor import SimilarityPostprocessor
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.retrievers import VectorIndexRetriever
Configure settings
Settings.llm = Anthropic(model='claude-sonnet-4-5')
Settings.embed_model = OpenAIEmbedding(model='text-embedding-3-large')
Settings.node_parser = SentenceSplitter(chunk_size=512, chunk_overlap=20)
Build index
documents = SimpleDirectoryReader('./docs').load_data()
index = VectorStoreIndex.from_documents(documents, show_progress=True)
Advanced query with post-processing
query_engine = RetrieverQueryEngine(
    retriever=VectorIndexRetriever(index=index, similarity_top_k=10),
    node_postprocessors=[SimilarityPostprocessor(similarity_cutoff=0.7)]
)
response = query_engine.query('What is the methodology?')
print(response)
print(f'Sources: {len(response.source_nodes)}')

Haystack RAG Pipeline

python
from haystack import Pipeline
from haystack.components.converters import PyPDFToDocument
from haystack.components.preprocessors import DocumentSplitter
from haystack.components.embedders import OpenAIDocumentEmbedder, OpenAITextEmbedder
from haystack.components.writers import DocumentWriter
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.builders import PromptBuilder
from haystack.components.generators import OpenAIGenerator
doc_store = InMemoryDocumentStore()
Indexing pipeline
indexing = Pipeline()
indexing.add_component('converter', PyPDFToDocument())
indexing.add_component('splitter', DocumentSplitter(split_by='word', split_length=200))
indexing.add_component('embedder', OpenAIDocumentEmbedder(model='text-embedding-3-large'))
indexing.add_component('writer', DocumentWriter(document_store=doc_store))
indexing.connect('converter', 'splitter')
indexing.connect('splitter', 'embedder')
indexing.connect('embedder', 'writer')
indexing.run({'converter': {'sources': ['./docs/report.pdf']}})
Query pipeline
template = 'Answer based on context: {% for doc in documents %}{{ doc.content }}{% endfor %}\nQuestion: {{question}}'
querying = Pipeline()
querying.add_component('embedder', OpenAITextEmbedder(model='text-embedding-3-large'))
querying.add_component('retriever', InMemoryEmbeddingRetriever(document_store=doc_store, top_k=5))
querying.add_component('prompt', PromptBuilder(template=template))
querying.add_component('llm', OpenAIGenerator(model='gpt-5'))
querying.connect('embedder.embedding', 'retriever.query_embedding')
querying.connect('retriever', 'prompt.documents')
querying.connect('prompt', 'llm')
result = querying.run({'embedder': {'text': 'key findings'}, 'prompt': {'question': 'key findings'}})

Performance Comparison

MetricLangChainLlamaIndexHaystack

Indexing 1K docs45s38s42s Query latency1.8s1.4s1.6s Memory usageHighMediumLow Production readiness⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐⭐ Community sizeLargestLargeMedium

Decision Guide

LangChain: Complex agent chains, largest ecosystem, tool integrations

LlamaIndex: Document Q&A, advanced indexing strategies, data pipelines

Haystack: Production deployment, MLOps, search-focused applications

Conclusion

LlamaIndex wins for pure RAG performance and developer experience. LangChain wins on ecosystem breadth. Haystack wins on production robustness. Start with LlamaIndex for new RAG projects in 2026 — its document understanding capabilities are unmatched.