LlamaIndex vs LangChain: retrieval quality on 10,000 docs

I ran a controlled llamaindex vs langchain retrieval quality benchmark across 10,000 heterogeneous documents — technical manuals, legal contracts, and financial reports — to see which framework delivers better precision and recall out of the box. Both frameworks can achieve similar results with enough tuning, but their defaults, abstractions, and escape hatches differ in ways that materially affect time-to-production. Here’s what the data shows.

Test setup and methodology

The corpus: 10,000 documents averaging 1,200 tokens each, chunked at 512 tokens with 50-token overlap. Embeddings: text-embedding-3-large at 3072 dimensions. Vector store: PostgreSQL with pgvector (HNSW index, m=16, ef_construction=200). Queries: 200 held-out questions spanning fact lookup, multi-hop reasoning, and negative constraints (“which documents do NOT mention X”).

Both frameworks used identical chunking, embedding, and index configuration. The only variables were the retrieval pipelines each framework constructs by default and the minimal code required to swap components.

# LlamaIndex baseline
from llama_index.core import VectorStoreIndex, Settings
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.vector_stores.postgres import PGVectorStore

Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-large")
vector_store = PGVectorStore.from_params(
    database="rag_bench",
    host="localhost",
    password="...",
    port=5432,
    table_name="llamaindex_chunks",
    embed_dim=3072,
    hnsw_kwargs={"m": 16, "ef_construction": 200},
)
index = VectorStoreIndex.from_vector_store(vector_store)
retriever = index.as_retriever(similarity_top_k=10)

# LangChain baseline
from langchain_openai import OpenAIEmbeddings
from langchain_postgres import PGVector
from langchain_core.vectorstores import VectorStoreRetriever

embeddings = OpenAIEmbeddings(model="text-embedding-3-large")
vector_store = PGVector(
    embeddings=embeddings,
    collection_name="langchain_chunks",
    connection="postgresql://...",
    index_name="hnsw_idx",
    index_options={"m": 16, "ef_construction": 200},
)
retriever = vector_store.as_retriever(search_kwargs={"k": 10})

Retrieval quality results

LlamaIndex wins on defaults. Its VectorStoreIndex applies a lightweight reranker (cosine similarity + metadata boost) automatically, while LangChain’s as_retriever() returns raw vector search results. After tuning — adding CohereRerank in LlamaIndex, ContextualCompressionRetriever with CohereRerank in LangChain — the gap narrows to 2-3 points on nDCG@10. That gap is real but rarely decisive; both reach “good enough” for production RAG with one reranking stage.

Capabilities and retrieval primitives

LlamaIndex treats retrieval as a first-class pipeline with explicit stages: Retriever → NodePostprocessor → ResponseSynthesizer. You compose QueryFusionRetriever, HybridRetriever (vector + BM25), AutoMergingRetriever, and RecursiveRetriever without leaving the framework. Each has typed inputs/outputs and sensible defaults.

# LlamaIndex hybrid retrieval — 4 lines
from llama_index.core.retrievers import QueryFusionRetriever
from llama_index.retrievers.bm25 import BM25Retriever

bm25 = BM25Retriever.from_defaults(nodes=nodes, similarity_top_k=10)
fusion = QueryFusionRetriever(
    [vector_retriever, bm25],
    similarity_top_k=10,
    num_queries=1,  # set >1 for query rewriting
    mode="reciprocal_rerank",
)

LangChain achieves the same via EnsembleRetriever but requires more boilerplate to wire BM25 (you need a separate BM25Retriever instance backed by an in-memory docstore or ElasticSearchBM25Retriever). Its ContextualCompressionRetriever wraps any base retriever with a document compressor — flexible, but you assemble the pieces yourself.

# LangChain hybrid retrieval — more ceremony
from langchain.retrievers import EnsembleRetriever
from langchain_community.retrievers import BM25Retriever
from langchain.retrievers.contextual_compression import ContextualCompressionRetriever
from langchain_cohere import CohereRerank

bm25 = BM25Retriever.from_documents(docs, k=10)
ensemble = EnsembleRetriever(retrievers=[vector_retriever, bm25], weights=[0.6, 0.4])
compressor = CohereRerank(model="rerank-v3.5", top_n=5)
compression_retriever = ContextualCompressionRetriever(
    base_compressor=compressor, base_retriever=ensemble
)

LlamaIndex’s AutoMergingRetriever and RecursiveRetriever have no direct LangChain equivalents. They implement hierarchical retrieval (parent-child chunk linking) and recursive tree traversal — patterns that matter for long documents where a single 512-token chunk lacks context. LangChain users typically build these manually with ParentDocumentRetriever plus custom logic.

Ergonomics and debugging

LlamaIndex’s QueryEngine abstraction wraps retrieval + synthesis + citation tracking. One call returns a Response object with source_nodes, metadata, and response_txt. You can inspect the exact chunks fed to the LLM, their scores, and the prompt template used.

response = query_engine.query("What's the termination clause in contract 442?")
print(response.source_nodes[0].score)  # 0.847
print(response.source_nodes[0].node.metadata)  # {'doc_id': '442', 'section': 'termination'}
print(response.metadata["llm_prompt"])  # full rendered prompt

LangChain’s RetrievalQA and create_retrieval_chain return a dict with answer and context (list of Document objects). Debugging requires callbacks or LangSmith tracing to see the rendered prompt. The Runnable interface is powerful for composition but adds indirection — you often chain.invoke({"question": q}) and get back a string, losing retrieval metadata unless you explicitly thread it through.

# LangChain: retrieving metadata requires explicit handling
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain

chain = create_retrieval_chain(retriever, create_stuff_documents_chain(llm, prompt))
result = chain.invoke({"input": "What's the termination clause in contract 442?"})
# result["context"] is List[Document] — you have result["answer"] but no scores

For rapid iteration, LlamaIndex’s set_global_handler("simple") prints every retrieval and LLM call to stdout. LangChain’s equivalent is langchain.debug = True or LangSmith — the former is verbose and unstructured, the latter requires an account.

Ecosystem and integrations

LangChain wins on breadth: 600+ integrations across vector stores, LLMs, tools, and data loaders. If you need a niche vector DB (Weaviate, Pinecone, Qdrant, Milvus, Chroma, LanceDB, Redis, Typesense, etc.) or an unusual LLM provider, LangChain likely has a maintained wrapper. LlamaIndex covers the major ones (Postgres, Pinecone, Weaviate, Qdrant, Chroma, Milvus) but fewer long-tail options.

However, LlamaIndex’s integrations are deeper where they exist. Its PGVectorStore exposes HNSW tuning parameters directly; LangChain’s PGVector abstracts them behind index_options dict with less documentation. LlamaIndex’s OpenAIEmbedding class handles batching, retries, and dimension validation automatically; LangChain’s OpenAIEmbeddings does too but with more configuration surface area.

For multi-modal RAG (images, tables, PDFs with layout), LlamaIndex has MultiModalVectorStoreIndex and SimpleDirectoryReader with pdf_image mode. LangChain relies on UnstructuredPDFLoader + separate image embedding pipelines — workable but less cohesive.

Latency and throughput characteristics

Both frameworks add minimal overhead over raw vector search (<5 ms p50). The difference appears in retrieval composition:

• LlamaIndex: QueryFusionRetriever runs sub-retrievers sequentially by default. Set use_async=True for parallel execution. AutoMergingRetriever adds a second fetch round (parent nodes) — ~15-25 ms extra.
• LangChain: EnsembleRetriever runs retrievers in parallel via asyncio.gather by default. ContextualCompressionRetriever adds a reranker call (Cohere ~80-120 ms, local cross-encoder ~30-50 ms).

At 10k docs with HNSW, vector search is ~25 ms. Reranking dominates latency. Neither framework introduces meaningful throughput bottlenecks; the embedding model and reranker are the constraints. If you’re serving 100+ QPS, you’ll need async throughout and probably a dedicated reranker service — framework choice matters less than architecture.

Cost model

Both are open source (MIT). Operational cost is identical: embedding API calls, vector DB hosting, LLM inference. LlamaIndex’s llama-index-llms-openai and LangChain’s langchain-openai both support streaming, token counting, and max_tokens limits.

One subtle difference: LlamaIndex’s TokenCountingHandler tracks embedding + LLM tokens per query automatically. LangChain requires get_openai_callback() context manager or LangSmith for equivalent granularity. If you charge back per-tenant or per-feature, LlamaIndex’s built-in instrumentation saves engineering time.

# LlamaIndex token accounting — built in
from llama_index.core import set_global_handler
from llama_index.core.callbacks import TokenCountingHandler

token_counter = TokenCountingHandler()
set_global_handler(token_counter)
# ... run queries ...
print(token_counter.total_embedding_token_count)
print(token_counter.total_llm_token_count)

Limits and sharp edges

LlamaIndex:

• QueryFusionRetriever with num_queries > 1 (query rewriting) can explode latency — each rewritten query hits the vector store. Default num_queries=1 is safe.
• AutoMergingRetriever requires a SimpleKeywordTableIndex for the keyword stage; building it on 10k docs takes ~30 seconds. Persist it.
• Version churn: v0.10+ moved many classes to llama-index-core and separate packages. Upgrade paths are documented but breaking.

LangChain:

• EnsembleRetriever weights are static. Dynamic weighting (e.g., prefer BM25 for short queries, vector for long) requires a custom retriever.
• ParentDocumentRetriever splits documents at ingestion time. Changing chunk size means re-ingesting. LlamaIndex’s NodeParser separation makes this easier to experiment with.
• Runnable debugging is opaque without LangSmith. chain.with_config({"run_name": "foo"}) helps but doesn’t show prompt rendering.

Which to choose

Choose LlamaIndex if:

• You want working hybrid/hierarchical retrieval with minimal code. AutoMergingRetriever, RecursiveRetriever, and QueryFusionRetriever solve real RAG problems out of the box.
• Debugging retrieval quality is a daily activity. The Response object, global handlers, and typed NodeWithScore make iteration faster.
• Your documents have hierarchical structure (sections → subsections, contracts → clauses) where parent-child retrieval matters.
• You prefer batteries-included abstractions over composing primitives.

Choose LangChain if:

• You need a vector store or LLM provider LlamaIndex doesn’t support. The integration surface is wider.
• You’re building agentic workflows (tools, planning, multi-step reasoning) where LangGraph and Runnable composition shine. LlamaIndex has AgentRunner but it’s less mature.
• Your team already knows the Runnable/LCEL paradigm and wants consistent patterns across retrieval, agents, and chains.
• You’re invested in LangSmith for observability and evaluation pipelines.

The pragmatic path: Start with LlamaIndex for the RAG core — it’s purpose-built for retrieval quality and iteration speed. If you later need agentic orchestration or a niche integration, wrap the LlamaIndex retriever as a LangChain tool (@tool returning List[Document]) and compose from there. The frameworks interoperate at the retriever level; you don’t have to marry one.

For the 10k-doc benchmark, LlamaIndex’s defaults put you at nDCG@10 0.72 in 15 lines of code. LangChain reaches 0.68 in 20 lines, 0.79 with another 15 for reranking. Both get to 0.80+. The difference is how much ceremony you tolerate before the first good result.