Production RAG in 2026: LangChain vs LlamaIndex

Title: LangChain vs. LlamaIndex: Which Framework is Better for Building Production RAG Apps?

Last updated: January 2026

Retrieval-Augmented Generation (RAG) has matured from “cool demo” to “default architecture” for enterprise-grade LLM apps.
But shipping RAG to production still means solving real engineering problems: ingestion pipelines, chunking strategy, vector storage,
retrieval quality, latency, evaluation, and observability.

Two open-source frameworks dominate this space:
LangChain and LlamaIndex.
They overlap, they integrate, and they both can ship production RAG.
The real question is: which one matches how you want to build?

Table of Contents

• TL;DR: Which should you choose?
• What matters for production RAG in 2026
• The mental models: orchestration vs. data framework
• Feature comparison table
• Build the same minimal production-style RAG app (both frameworks)
• Production checklist: what to harden after your first working prototype
• The underrated answer: use both together
• Practical recommendations by use case
• References (verified up to Jan 2026)

TL;DR: Which should you choose?

Pick LangChain if:

• You care most about orchestration: multi-step flows, tools, agents, guardrails, and “how work moves through the system”.
• You want a strong story for observability and evaluation in production via LangSmith.
• You expect to evolve from “RAG chatbot” into agentic RAG or complex workflows (often via LangGraph).

Pick LlamaIndex if:

• You are deeply data-centric: loaders, transformations, indexing, retrieval tuning, post-processing, and query engines.
• You want “RAG batteries included” with strong built-in primitives for indexing + retrieval + evaluation.
• You want a clean separation between core and integrations and you like the “data framework” abstraction.

If you want the most reliable production outcome:

Consider using LlamaIndex for ingestion/indexing and LangChain (plus LangGraph) for orchestration.
This is not a compromise. It is often the fastest route to a robust system when requirements grow.

What matters for production RAG in 2026

By now, most teams can get “hello world RAG” working in a day. Production RAG is different because you must make it:
correct enough, fast enough, observable enough, and maintainable enough.

Production RAG tends to fail for predictable reasons

• Retrieval quality drift: your corpus changes, your chunking is wrong, embeddings change, or metadata gets messy.
• Latency spikes: too many chunks, slow vector DB calls, rerankers, or multiple LLM calls per question.
• Hallucinations: the model answers despite weak evidence or the prompt fails to enforce groundedness.
• Observability gaps: you cannot trace “why this answer happened” from user query to retrieved sources to final output.
• No evaluation loop: you ship without a benchmark and then you guess when things break.

LangChain and LlamaIndex both address these issues, but they do so with different “center of gravity”.
LangChain’s docs explicitly describe multiple RAG architectures (2-step, agentic, hybrid) and the tradeoffs you must manage.

The mental models: orchestration vs. data framework

LangChain: composable building blocks + orchestration

LangChain is designed to help you assemble LLM apps from interoperable components and integrations. Its ecosystem has expanded into
workflow and agent orchestration (LangGraph) and production-grade tracing and evaluation (LangSmith). LangChain’s OSS install docs also
emphasize provider integrations being separate packages (for example, OpenAI as langchain-openai).

LlamaIndex: the data layer for LLM apps

LlamaIndex positions itself as a “data framework” focused on connecting your LLM to your data sources, structuring that data (indexes),
retrieving effectively, and synthesizing responses. Its packaging model is very explicit: core plus many integration packages, with a clear
import convention that differentiates core modules from integrations.

In practice:
LangChain feels like “build an app”,
while LlamaIndex feels like “build a retrieval system”.
Both can do both. But their defaults shape your build.

Feature comparison table

About versions: as of late Dec 2025 and early Jan 2026, common production setups often pin around
langchain-openai 1.1.6 and langchain-chroma 1.1.0, while LlamaIndex commonly pins around
llama-index 0.14.12 and llama-index-vector-stores-chroma 0.5.5.

Build the same minimal production-style RAG app (both frameworks)

Below are two minimal, copy-paste friendly examples that do the same thing:

1. Load local documents from ./data
2. Chunk them
3. Embed and store in Chroma (persisted on disk)
4. Retrieve top-k passages for a user question
5. Generate a grounded answer

Notes:

• These snippets assume you have OPENAI_API_KEY set.
• Model availability differs by account. Use env vars to pick models you have access to.
• Use pinned versions in production and upgrade intentionally after running evals.

A) LangChain minimal 2-step RAG with persisted Chroma

Install

pip install -U langchain langchain-openai langchain-text-splitters langchain-community langchain-chroma chromadb

LangChain requires Python 3.10+ per official install docs.

Code

import os

from langchain_community.document_loaders import DirectoryLoader, TextLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_chroma import Chroma

from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough


# ----------------------------
# Config
# ----------------------------
DATA_DIR = "./data"
CHROMA_DIR = "./chroma_langchain_db"
COLLECTION = "docs"

OPENAI_CHAT_MODEL = os.getenv("OPENAI_CHAT_MODEL", "gpt-4o-mini")
OPENAI_EMBED_MODEL = os.getenv("OPENAI_EMBED_MODEL", "text-embedding-3-small")

TOP_K = int(os.getenv("RAG_TOP_K", "4"))


def format_docs(docs) -> str:
    # Keep formatting simple and predictable
    return "\n\n".join(f"[source={d.metadata.get('source', 'unknown')}]\n{d.page_content}" for d in docs)


# ----------------------------
# 1) Load documents
# ----------------------------
loader = DirectoryLoader(
    DATA_DIR,
    glob="**/*.*",
    loader_cls=TextLoader,  # good default for .txt/.md; use richer loaders for PDF/HTML/etc
    show_progress=True,
)
documents = loader.load()

# ----------------------------
# 2) Chunk
# ----------------------------
splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)
chunks = splitter.split_documents(documents)

# ----------------------------
# 3) Embed + store (persisted)
# ----------------------------
embeddings = OpenAIEmbeddings(model=OPENAI_EMBED_MODEL)

vector_store = Chroma(
    collection_name=COLLECTION,
    embedding_function=embeddings,
    persist_directory=CHROMA_DIR,
)

# For a real ingestion pipeline, you typically upsert new/changed docs only.
# Here we add everything for a simple example.
vector_store.add_documents(chunks)

retriever = vector_store.as_retriever(search_kwargs={"k": TOP_K})

# ----------------------------
# 4) RAG prompt + chain (LCEL)
# ----------------------------
prompt = ChatPromptTemplate.from_messages(
    [
        (
            "system",
            "You are a careful assistant. Answer using ONLY the provided context. "
            "If the answer is not in the context, say you don't know. "
            "Cite sources using the [source=...] tags when relevant.",
        ),
        ("human", "Question: {question}\n\nContext:\n{context}"),
    ]
)

llm = ChatOpenAI(model=OPENAI_CHAT_MODEL, temperature=0)

rag_chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

# ----------------------------
# 5) Ask
# ----------------------------
question = "What does this documentation say about rate limits?"
answer = rag_chain.invoke(question)
print(answer)

The langchain-chroma docs show persisted mode via persist_directory, and also reference optional LangSmith tracing env vars.

Optional: turn on tracing (LangSmith)

export LANGSMITH_API_KEY="..."
export LANGSMITH_TRACING="true"
export LANGSMITH_PROJECT="rag-prod"

LangChain docs and ecosystem highlight LangSmith as the production platform for tracing, testing, and monitoring LLM apps.

B) LlamaIndex minimal RAG with persisted Chroma

Install

pip install -U llama-index llama-index-llms-openai llama-index-embeddings-openai llama-index-vector-stores-chroma chromadb

LlamaIndex’s packaging model is explicitly “core plus integrations” and encourages installing only the integrations you need.

Code

import os

import chromadb
from llama_index.core import SimpleDirectoryReader, StorageContext, VectorStoreIndex
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.vector_stores.chroma import ChromaVectorStore


# ----------------------------
# Config
# ----------------------------
DATA_DIR = "./data"
CHROMA_DIR = "./chroma_llamaindex_db"
COLLECTION = "docs"

OPENAI_CHAT_MODEL = os.getenv("OPENAI_CHAT_MODEL", "gpt-4o-mini")
OPENAI_EMBED_MODEL = os.getenv("OPENAI_EMBED_MODEL", "text-embedding-3-small")

TOP_K = int(os.getenv("RAG_TOP_K", "4"))

# ----------------------------
# 1) Load documents
# ----------------------------
documents = SimpleDirectoryReader(DATA_DIR, recursive=True).load_data()

# ----------------------------
# 2) Chroma client (persisted) + vector store wrapper
# ----------------------------
chroma_client = chromadb.PersistentClient(path=CHROMA_DIR)
chroma_collection = chroma_client.get_or_create_collection(COLLECTION)

vector_store = ChromaVectorStore(chroma_collection=chroma_collection)

# ----------------------------
# 3) Build index with explicit storage context
# ----------------------------
storage_context = StorageContext.from_defaults(vector_store=vector_store)

embed_model = OpenAIEmbedding(model_name=OPENAI_EMBED_MODEL)
llm = OpenAI(model=OPENAI_CHAT_MODEL, temperature=0)

index = VectorStoreIndex.from_documents(
    documents,
    storage_context=storage_context,
    embed_model=embed_model,
)

# ----------------------------
# 4) Query engine
# ----------------------------
query_engine = index.as_query_engine(llm=llm, similarity_top_k=TOP_K)

question = "What does this documentation say about rate limits?"
response = query_engine.query(question)

print(str(response))

The official LlamaIndex Chroma vector store reference describes how embeddings live in Chroma collections and notes support for MMR search mode.

Optional: persist and reload (LlamaIndex storage)

If you are not persisting in an external vector DB, LlamaIndex also supports saving/loading index data to disk via its storage context utilities.

Production checklist: what to harden after your first working prototype

The framework choice matters, but production success is usually determined by a handful of engineering decisions.
Use this checklist no matter which framework you pick.

1) Ingestion and chunking

• Pick chunking intentionally: chunk size, overlap, and separators should match your domain.
• Track document identity: stable IDs, source URLs, timestamps, and version hashes.
• Incremental updates: avoid re-embedding the world. Upsert changed docs only.
• Structured parsing: PDFs and HTML need better loaders than “plain text”.

2) Retrieval quality

• Metadata filters: enforce tenant boundaries and time ranges early.
• Hybrid retrieval: semantic + keyword can beat either alone for many corpora.
• Reranking: use cross-encoders or LLM rerankers for top-k refinement when quality matters.
• Query rewriting: rewrite vague user prompts into retrieval-friendly queries.

3) Answer grounding and safety

• Make “I don’t know” easy: enforce refusal when context is insufficient.
• Source citations: return links or document references with the answer.
• Post-generation checks: basic hallucination detection, policy filters, and PII rules.

4) Latency and cost

• Cap retrieval: keep k small, then rerank if needed.
• Cache aggressively: cache embeddings, retrieved results, and even final answers for identical queries.
• Prefer 2-step when you can: agentic RAG is powerful but can be unpredictable in cost and latency.

5) Observability and evaluation

• Tracing: you need end-to-end traces that show retrieval inputs/outputs and model prompts.
• Golden datasets: create a benchmark set of questions that reflect real user traffic.
• Regression testing: every embedding/model/chunking change should run against evals before shipping.

LlamaIndex provides an observability guide centered on OpenTelemetry instrumentation for tracing LLM and RAG events.

LlamaIndex also provides evaluation module guides and examples (for example, faithfulness evaluation) to measure groundedness and response quality.

LangChain’s ecosystem pushes strongly toward LangSmith for tracing, testing, and monitoring, and even the Chroma integration docs mention optional LangSmith tracing setup.

The underrated answer: use both together

There is a surprisingly common production pattern:

• LlamaIndex handles ingestion, transformations, indexing, retrieval tuning, and evaluation primitives.
• LangChain handles orchestration: multi-step flows, tool use, long-running agents, routing, and app wiring.

This is not theoretical. LlamaIndex’s own project description explicitly calls out integrating with “outer application frameworks” and mentions LangChain as an example.

If you expect your RAG system to grow into a broader assistant with tools, workflows, and human-in-the-loop steps,
combining them often yields a cleaner architecture than forcing one framework to do everything.

Practical recommendations by use case

Use case 1: Internal knowledge base Q&A with strict latency targets

Start with a 2-step RAG pipeline. Prefer the framework your team can maintain.
If the biggest risk is retrieval quality and data ingestion complexity, start with LlamaIndex.
If the biggest risk is workflow complexity and production tracing, start with LangChain + LangSmith.

Use case 2: Customer support assistant with tools (tickets, CRM, order lookup)

You will end up orchestrating tool calls, state, and guardrails. LangChain plus LangGraph tends to shine here,
with RAG as one of several tools in the agent’s toolkit.

Use case 3: Data platform team building a reusable retrieval layer

LlamaIndex is often a strong fit: connectors, indexing patterns, and retrieval-focused modules are the core value proposition.
Pair it with OpenTelemetry if you want traces to flow into existing observability stacks.

Use case 4: Rapid experimentation with frequent iteration

Pick one, move fast, and add an evaluation loop early.
If you are constantly changing chunking, reranking, and retrieval components, LlamaIndex’s retrieval-first design can speed iteration.
If you are constantly changing workflows and tool routing, LangChain’s orchestration patterns may feel more natural.

References (verified up to Jan 2026)

• LangChain install docs (Python): provider integrations as separate packages, Python requirement.
• LangChain retrieval conceptual guide (2-step vs agentic vs hybrid RAG).
• LangChain Chroma integration docs (persisted mode via persist_directory, tracing note).
• langchain-openai PyPI page (version and release date context).
• langchain-chroma PyPI page (version context).
• LlamaIndex PyPI page (core positioning, packaging, version context).
• LlamaIndex Chroma vector store API reference (MMR support note).
• LlamaIndex save/load storage guide.
• LlamaIndex evaluation module guide + faithfulness example.
• LlamaIndex observability guide (OpenTelemetry integration).

Conclusion:

There is no universal winner. The better framework is the one that matches what you are optimizing for:
workflow complexity and production tracing (often LangChain),
or data ingestion and retrieval depth (often LlamaIndex).

If you are building a serious production RAG system, treat the first prototype as the beginning.
The real work is evaluation, observability, incremental indexing, and retrieval tuning.
Choose the framework that makes those steps easiest for your team, and do not be afraid to combine both.