Every time we start a new AI project, someone on the team asks whether to use LangChain or LlamaIndex. We have shipped production systems with both. Here is an honest comparison based on that experience — not a feature matrix copied from the docs.
Quick context
Both libraries have matured significantly since their early versions. LangChain went through a messy period of API churn around v0.1/v0.2 that burned a lot of teams. LlamaIndex (formerly GPT Index) went deep on the retrieval and indexing use case. Both are now stable enough for production use, but they still have different strengths.
What LangChain is actually good at
Chains and agents with complex logic. LangChain's expression language (LCEL) is well-suited for building multi-step pipelines where you need explicit control over how data flows between components. If you are building an agent that needs to decide between multiple tools, maintain conversation state, and handle branching logic — LangChain's abstractions are a reasonable fit.
Broad ecosystem of integrations. LangChain has integrations with almost everything: every major LLM, every vector store, most common APIs. If you need to wire together a lot of different services quickly, the breadth of LangChain's integration library saves time.
Streaming support. LangChain's streaming support is solid and straightforward to implement, which matters for user-facing applications where you want tokens to appear as they are generated.
A typical LangChain chain for a simple RAG setup:
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_community.vectorstores import PGVector
from langchain.chains import RetrievalQA
from langchain.prompts import PromptTemplate
llm = ChatOpenAI(model="gpt-4o", temperature=0.2)
embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
vectorstore = PGVector(
connection_string="postgresql://user:pass@localhost/db",
embedding_function=embeddings,
collection_name="documents"
)
prompt = PromptTemplate(
template="""Answer based only on the context below.
If the answer isn't in the context, say so.
Context: {context}
Question: {question}
Answer:""",
input_variables=["context", "question"]
)
chain = RetrievalQA.from_chain_type(
llm=llm,
retriever=vectorstore.as_retriever(search_kwargs={"k": 4}),
chain_type_kwargs={"prompt": prompt},
return_source_documents=True
)
result = chain.invoke({"query": "What is the refund policy?"})
print(result["result"])
What LlamaIndex is actually good at
Document ingestion and indexing. LlamaIndex's abstractions around loading, chunking, and indexing documents are more thought-through than LangChain's. If your primary use case involves ingesting varied document types — PDFs, Word docs, Notion pages, web pages, database rows — and querying across them, LlamaIndex handles this more cleanly.
Query engines and advanced retrieval. LlamaIndex has better out-of-the-box support for more sophisticated retrieval patterns: hybrid search, recursive retrieval, query decomposition, and sub-question generation. If retrieval quality is your main concern, LlamaIndex gives you more levers to pull.
Structured outputs from documents. Extracting structured data from unstructured documents is a use case where LlamaIndex's PropertyGraphIndex and structured extraction tools outperform what you'd put together yourself with LangChain.
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.postprocessor import SimilarityPostprocessor
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
# Load and index documents
documents = SimpleDirectoryReader("./docs").load_data()
index = VectorStoreIndex.from_documents(
documents,
embed_model=OpenAIEmbedding(model="text-embedding-3-small")
)
# Build query engine with similarity threshold
retriever = VectorIndexRetriever(index=index, similarity_top_k=4)
postprocessor = SimilarityPostprocessor(similarity_cutoff=0.7)
query_engine = RetrieverQueryEngine(
retriever=retriever,
node_postprocessors=[postprocessor],
llm=OpenAI(model="gpt-4o", temperature=0.2)
)
response = query_engine.query("What is the refund policy?")
print(response.response)
print([node.metadata for node in response.source_nodes])
Where each falls short
LangChain:
The abstraction layers can work against you. When something breaks in a LangChain chain, the stack traces are deep and the error messages are not always helpful. Debugging a complex agent that is misbehaving takes longer than it should because you are reasoning through multiple layers of abstraction rather than your own code.
API stability has improved but LangChain still moves fast. Pinning your versions carefully and reading changelogs before upgrading is not optional.
The breadth of integrations is also a maintenance burden — many community integrations are not well-maintained and break quietly when upstream services change.
LlamaIndex:
Steeper initial learning curve. LlamaIndex's concepts (nodes, indices, query engines, postprocessors) take longer to internalise than LangChain's chain/agent model.
Less suited for complex agentic workflows. If you need an agent that takes sequential actions across multiple external tools — not just retrieval — LangChain or building directly on the OpenAI/Anthropic function calling API is usually cleaner.
The documentation has improved a lot but some of the advanced features are still better understood by reading source code than docs.
What we actually reach for
For RAG on existing Django/Python apps: We often skip both and build directly with the OpenAI API and pgvector, as described in our RAG guide. For straightforward retrieval use cases the framework overhead is not worth it.
For document-heavy use cases with varied inputs: LlamaIndex. The document loaders and chunking pipeline alone justify it.
For multi-step agents or complex chains: LangChain, specifically LCEL. The composability is genuinely useful for orchestrating multi-step logic.
For anything client-facing where we need fine control: Neither. We build directly on the provider APIs (OpenAI, Anthropic) with our own thin wrappers. This gives us complete control over prompts, error handling, costs, and debugging.
The honest take
Both are tools, not commitments. The mistake most teams make is picking one and trying to use it for everything. LangChain and LlamaIndex have different strengths; the right one depends on your specific use case.
If you are building your first AI feature: start without a framework. Call the API directly. Add a framework when the complexity justifies it, not before.
Lycore builds production AI systems for businesses — RAG pipelines, agents, LLM integrations, and custom AI applications. We work with both LangChain and LlamaIndex depending on the project requirements. Get in touch if you want to talk through your use case.
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