RAG Frameworks Compared: LangChain vs LlamaIndex
Compare RAG frameworks LangChain vs LlamaIndex on retrieval, indexing, and orchestration to know when a framework helps and when a raw embedding call wins.
Picking a RAG framework before you understand your retrieval shape is how teams end up maintaining 4,000 lines of pipeline code for a problem that needed 40. Most projects that "need LangChain" or "need LlamaIndex" actually need an embedding call, a similarity search, and a well-written prompt. The framework is a liability until proven otherwise.
Complex retrieval pipelines do justify the overhead, though. Hybrid search, parent-document retrieval, query decomposition, citation tracking: these are genuinely hard plumbing problems that both frameworks have already solved. The question isn't whether the tools are good. It's whether your specific retrieval shape actually demands them.
At Laxaar, we've built RAG systems across both frameworks and have shipped several that skipped frameworks entirely. Here's the honest breakdown.
What you'll learn
- What RAG frameworks actually do
- LangChain: strengths and where it struggles
- LlamaIndex: strengths and where it struggles
- Head-to-head comparison
- When to skip both and go raw
- Choosing by retrieval shape
- Frequently Asked Questions
What RAG frameworks actually do
A RAG framework is a library (or set of libraries) that provides pre-built components for the retrieval-augmented generation pipeline: document loading, chunking, embedding, vector store connectors, retrieval strategies, reranking, and prompt assembly. The goal is to let you wire those stages together without writing each one from scratch.
Both LangChain and LlamaIndex handle the full pipeline. Where they differ is emphasis. LangChain treats retrieval as one node in a larger graph of LLM calls, tool calls, and control flow. LlamaIndex treats documents and their indexes as the primary object model, with LLM calls sitting downstream.
That difference in emphasis turns into real API friction once your project grows.
LangChain: strengths and where it struggles
LangChain started as a chaining library for LLM calls and grew to absorb RAG, agents, and tool use. Its strength is breadth: hundreds of document loaders, dozens of vector store integrations, and a graph-based orchestration layer (LangGraph) that handles stateful multi-step flows.
If you're building a system that combines retrieval with tool use, conditional branching, or multi-agent coordination, LangChain's ecosystem is hard to beat on setup speed. LangSmith, its observability product, traces chain execution with good granularity out of the box.
The trade-offs are real. LangChain's abstraction layers are deep and sometimes leaky. Debugging a misbehaving chain often means stepping through three layers of base class inheritance to find where context is dropped. The library ships fast, which means breaking changes appear between minor versions. Teams that pin versions and audit upgrades don't feel this pain as much as teams that blindly update.
For purely retrieval-focused tasks without complex orchestration, LangChain's overhead doesn't justify itself. You're pulling in a large dependency graph for a RetrievalQA chain that wraps a vector store call you could write in ten lines.
# LangChain retrieval chain — explicit but verbose for simple cases
from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI
from langchain_community.vectorstores import Chroma
llm = ChatOpenAI(model="gpt-4o-mini")
vectorstore = Chroma(persist_directory="./chroma_db", embedding_function=embeddings)
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=vectorstore.as_retriever(search_kwargs={"k": 5}),
return_source_documents=True,
)
result = qa_chain.invoke({"query": "What is our refund policy?"})
LlamaIndex: strengths and where it struggles
LlamaIndex was built document-first. Its core model is the Index: a data structure over your documents that supports different query strategies. The VectorStoreIndex, SummaryIndex, KnowledgeGraphIndex, and PropertyGraphIndex each expose different retrieval semantics over the same underlying data.
This model pays off when your retrieval problem is inherently document-centric: enterprise knowledge bases, legal document search, technical documentation Q&A, or anything where you need to reason about document structure (sections, tables, parent-child relationships). LlamaIndex's node parser ecosystem handles PDF table extraction, markdown hierarchy, and multi-modal content better than LangChain's loaders.
The SubQuestionQueryEngine and RouterQueryEngine are genuinely useful when a single query needs to span multiple indexes with different semantics. That's where LlamaIndex's indexing model earns its keep.
Where LlamaIndex struggles: orchestration beyond retrieval. If your system needs an agent that calls external APIs, executes code, or manages stateful multi-turn conversation, LlamaIndex's agent layer feels bolted on compared to LangGraph. The community is smaller than LangChain's, so edge-case questions often go unanswered for weeks.
# LlamaIndex — cleaner for pure document Q&A
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.query_engine import SubQuestionQueryEngine
from llama_index.core.tools import QueryEngineTool
documents = SimpleDirectoryReader("./docs").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(
similarity_top_k=5,
response_mode="tree_summarize", # better for long docs
)
response = query_engine.query("Summarise the key findings in section 3.")
print(response.source_nodes) # built-in citation tracking
Head-to-head comparison
| Dimension | LangChain | LlamaIndex |
|---|---|---|
| Primary model | Chain / graph orchestration | Document indexes |
| Document loaders | Very broad (100+) | Deep quality on PDFs and structured docs |
| Retrieval strategies | Good, community-driven | Excellent, index-native |
| Agent support | Strong (LangGraph) | Limited |
| Observability | LangSmith (polished) | Basic, third-party needed |
| API stability | Moderate churn | More stable |
| Community size | Large | Medium |
| Best for | Multi-step LLM workflows | Document-heavy RAG |
| Raw SQL / hybrid search | Passable via integration | Stronger native support |
Our opinionated take: LlamaIndex writes better RAG than LangChain does, and LangChain orchestrates better agents than LlamaIndex does. If your project is genuinely both, that's where the ecosystem tension hurts.
When to skip both and go raw
This is the section most framework comparisons skip. Many RAG use cases don't need a framework at all.
If your retrieval shape is one collection of documents, a single embedding model, cosine similarity over a vector store, no reranking, and a static prompt template, you need about 30 lines of Python. Both LangChain and LlamaIndex will add hundreds of lines of transitive dependency, a version pin headache, and an abstraction you'll eventually want to remove.
The raw pattern is fast, debuggable, and dependency-light:
import openai
import numpy as np
client = openai.OpenAI()
def embed(text: str) -> list[float]:
return client.embeddings.create(
model="text-embedding-3-small", input=text
).data[0].embedding
def cosine_similarity(a: list[float], b: list[float]) -> float:
a, b = np.array(a), np.array(b)
return float(np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)))
def retrieve(query: str, corpus: list[dict], top_k: int = 5) -> list[dict]:
query_emb = embed(query)
scored = [
{**doc, "score": cosine_similarity(query_emb, doc["embedding"])}
for doc in corpus
]
return sorted(scored, key=lambda x: x["score"], reverse=True)[:top_k]
Swap the vector store for pgvector, add a reranker if recall suffers, and you have a production-grade system with no framework overhead. The Laxaar team has shipped several customer-facing RAG products on this pattern.
Where you should reach for a framework: when you genuinely need hybrid search, multi-index routing, multi-hop reasoning, query decomposition, or citation tracking across document hierarchies. Those are the retrieval shapes that frameworks actually solve, not just abstract.
Choosing by retrieval shape
The decision isn't about which framework is "better." It's about matching the framework's mental model to your data shape.
Use LlamaIndex when: your data is document-heavy and structured (PDFs, manuals, legal filings); you need parent-document retrieval or hierarchical summarisation; citation tracking per source node matters; or you're building an enterprise knowledge base where indexing quality is the whole product.
Use LangChain when: your RAG system is one step inside a larger multi-step agent; you need LangGraph's stateful graph execution; you want LangSmith tracing integrated from day one; or you're already inside the LangChain ecosystem with existing chains you don't want to rewrite.
Go raw when: your retrieval shape is simple (one vector store, one query, one prompt); your team is small and dependency surface matters; you need sub-10ms retrieval where framework overhead is measurable; or you're building a microservice that should stay portable.
The custom software development teams at Laxaar default to raw embeddings plus pgvector for greenfield projects and introduce framework components only when a specific retrieval problem exceeds what the raw approach handles cleanly. That keeps the codebase inspectable and the upgrade path predictable.
If you're evaluating AI tooling for a new product, our AI development practice covers the full stack from embedding choice through generation and evaluation. For teams hiring for this skill, the hire AI developers page covers what to look for in candidates and how we staff those roles.
For context on how RAG fits inside a broader agent architecture, see our comparison of generative AI development approaches across retrieval, fine-tuning, and agentic designs.
Frequently Asked Questions
Can you use LangChain and LlamaIndex together?
Yes, though it adds complexity. A common pattern is using LlamaIndex to build and query indexes while LangChain orchestrates the broader agent that calls those indexes as tools. The integration is possible, but maintaining two framework versions simultaneously increases the upgrade surface area. Only do it if you have a clear reason both are needed.
Is LangChain still worth using in 2026?
LangChain remains a strong choice for agent orchestration through LangGraph, which has stabilised considerably. For pure RAG without orchestration, the framework's overhead is harder to justify than it was in 2023 when the ecosystem was immature. Teams building chat interfaces with tool use still find it productive; teams building document search systems often outgrow it.
How does a raw embedding approach scale compared to a framework?
Both approaches hit the same architectural bottlenecks at scale: vector store throughput, reranking cost, and embedding latency. A framework doesn't solve those. It abstracts them. At scale, you end up configuring the same vector store, embedding model, and caching layers regardless of whether a framework sits on top. The raw approach has less indirection when performance debugging those layers.
What's the best way to evaluate retrieval quality for either approach?
Measure recall at K and mean reciprocal rank on a golden set of query-document pairs before choosing a framework. If your retrieval quality is poor, a framework won't fix it. You need better chunking, a stronger embedding model, or hybrid search. Frameworks help you implement the fix faster, not discover what the fix should be. We recommend building evals before touching any framework code.
Does LlamaIndex support agents?
LlamaIndex has an agent layer and supports ReAct-style tool use. For simple retrieval agents (query, retrieve, answer), it works well. For complex multi-step agents with conditional branching, persistent state, or parallel tool calls, LangGraph is more capable and better documented. LlamaIndex's agent support is improving but still trails LangChain for non-retrieval orchestration.
If you're building a RAG-based product and need help choosing the right architecture (framework, raw pipeline, or hybrid), the Laxaar team is happy to review your retrieval shape and give a concrete recommendation. Reach out through our quote page with a brief description of your document corpus and query patterns, and we'll tell you what we'd actually build.
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