LlamaIndex vs LangChain: When to Use Each Framework (with Code Comparisons)
You want to build a RAG pipeline over your company's documents. You search for "Python RAG framework" and two names dominate every result: LangChain and LlamaIndex. Both can do the job, both have massive ecosystems. But they were built with different goals, and picking the wrong one means fighting the framework instead of building your product.
LlamaIndex vs LangChain — What Is the Core Difference?
LlamaIndex is a data framework — it specializes in connecting LLMs to your data through indexing, retrieval, and query engines. LangChain is an orchestration framework — it provides a universal interface for chaining LLM calls, tools, memory, and agents together.
I think of it this way: if your main problem is "I have documents and I need an LLM to answer questions about them," LlamaIndex gets you there faster. If your main problem is "I need to coordinate multiple LLM calls, tool usage, and branching logic," LangChain gives you more control.
That said, both frameworks have expanded significantly. LangChain now has solid RAG support, and LlamaIndex added agent capabilities. The difference is which use case is the native strength versus an afterthought.
Quick Comparison Table
Before we look at code, here is a high-level comparison across the dimensions that matter most when choosing a framework.
What Is LlamaIndex? (Quick Refresher)
LlamaIndex (formerly GPT Index) was built to solve one problem: how do you give an LLM access to your private data? It abstracts the entire pipeline — loading documents, chunking them, creating embeddings, storing them in an index, and querying with natural language.
The core abstraction is the query engine. You feed it documents, it builds an index, and you ask questions. Three lines of code can give you a working RAG system.
That is genuinely five lines of functional code. LlamaIndex handles chunking, embedding model selection, and vector storage internally. This is its biggest selling point and also its biggest limitation. When the defaults work, it feels magical. When they do not, you need to understand the internals to customize them.
What Is LangChain? (Quick Refresher)
LangChain started as a way to chain LLM calls together — hence the name. Its core abstraction is the Runnable interface: every component (LLM, prompt template, output parser, retriever) implements .invoke(), .stream(), and .batch(). You compose them using the LCEL pipe operator (|).
Where LlamaIndex says "give me your documents, I will handle it," LangChain says "here are the building blocks, assemble them yourself." More work up front, but you control every piece.
More code, but every decision is explicit. You choose the chunk size, overlap, embedding model, vector store, and LLM independently. When I need to swap FAISS for Pinecone or change chunking for a specific document type, each piece is right there to modify.
Side-by-Side Code Comparisons
The best way to see the philosophical difference is to implement the same tasks in both frameworks. In each comparison, notice how LlamaIndex tends to hide complexity behind higher-level abstractions while LangChain exposes each step.
Document Loading
from llama_index.core import SimpleDirectoryReader
# Loads all supported file types automatically
# PDF, DOCX, TXT, CSV, etc.
documents = SimpleDirectoryReader(
input_dir="./data",
recursive=True
).load_data()
print(f"Loaded {len(documents)} documents")from langchain_community.document_loaders import (
DirectoryLoader,
PyPDFLoader,
TextLoader,
)
# You specify which loader handles which file type
loader = DirectoryLoader(
"./data",
glob="**/*.pdf",
loader_cls=PyPDFLoader
)
documents = loader.load()
print(f"Loaded {len(documents)} documents")LlamaIndex auto-detects file types and picks the right parser. LangChain requires you to specify the loader class — more explicit, but you know exactly what parser is running on your files.
Prompt Templates
Both frameworks let you customize the prompt sent to the LLM. The syntax differs, but the concept is identical: define a template with placeholders, then fill them at runtime. Since prompt templates are just string formatting, we can show this pattern with runnable code.
# LlamaIndex uses {variable} syntax
# (similar to Python str.format)
template = (
"Context: {context_str}\n\n"
"Question: {query_str}\n\n"
"Answer the question based only on "
"the context above."
)
# At runtime, LlamaIndex fills these automatically
# from the retrieved context and user query
print(template.format(
context_str="Python was created in 1991.",
query_str="When was Python created?"
))# LangChain uses {variable} syntax too,
# but wraps it in a PromptTemplate object
template = (
"Context: {context}\n\n"
"Question: {question}\n\n"
"Answer the question based only on "
"the context above."
)
# Simulating what PromptTemplate.format() does
print(template.format(
context="Python was created in 1991.",
question="When was Python created?"
))Chunking Strategy
from llama_index.core.node_parser import (
SentenceSplitter
)
# LlamaIndex calls chunks "nodes"
parser = SentenceSplitter(
chunk_size=1024,
chunk_overlap=20
)
# nodes = parser.get_nodes_from_documents(docs)
# Defaults work well for most use casesfrom langchain.text_splitter import (
RecursiveCharacterTextSplitter
)
# LangChain calls chunks "documents" (split docs)
splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
separators=["\n\n", "\n", " ", ""]
)
# chunks = splitter.split_documents(docs)
# You control the separator hierarchyThe naming reveals the philosophy. LlamaIndex calls chunks "nodes" because they sit inside a graph structure. LangChain calls them "documents" because each chunk is a standalone retrieval unit. LangChain's RecursiveCharacterTextSplitter lets you define the separator hierarchy explicitly.
Agent Implementation
from llama_index.core.tools import FunctionTool
from llama_index.core.agent import ReActAgent
from llama_index.llms.openai import OpenAI
def multiply(a: int, b: int) -> int:
"""Multiply two integers."""
return a * b
tool = FunctionTool.from_defaults(fn=multiply)
llm = OpenAI(model="gpt-4o-mini")
agent = ReActAgent.from_tools([tool], llm=llm)
response = agent.chat("What is 6 times 7?")from langchain_core.tools import tool
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
@tool
def multiply(a: int, b: int) -> int:
"""Multiply two integers."""
return a * b
llm = ChatOpenAI(model="gpt-4o-mini")
agent = create_react_agent(llm, [multiply])
result = agent.invoke(
{"messages": [("user", "What is 6 times 7?")]}
)Agent APIs look similar at first glance. The real difference shows up when you need complex workflows — multi-step reasoning, parallel tool calls, or human-in-the-loop approval. LangChain's LangGraph library provides a state machine for agent control flow. It is genuinely more powerful for complex orchestration. LlamaIndex's agent layer works well for simpler tool-calling patterns.
Write a function recommend_framework(requirements) that takes a dictionary of project requirements and returns either "LlamaIndex", "LangChain", or "Both" based on scoring rules.
The requirements dict has these boolean keys:
"rag" — project needs RAG over documents"agents" — project needs autonomous agents"multi_step" — project needs multi-step orchestration"quick_prototype" — need a working prototype fast"custom_pipeline" — need fine-grained pipeline controlScoring rules:
rag=True adds 2 points to LlamaIndex, 1 to LangChainagents=True adds 1 to LlamaIndex, 2 to LangChainmulti_step=True adds 0 to LlamaIndex, 2 to LangChainquick_prototype=True adds 2 to LlamaIndex, 0 to LangChaincustom_pipeline=True adds 0 to LlamaIndex, 2 to LangChainReturn "LlamaIndex" if its score is higher, "LangChain" if its score is higher, or "Both" if tied.
Feature-by-Feature Comparison
RAG Pipeline Complexity
This is where the difference is starkest. In LlamaIndex, a basic RAG pipeline is genuinely three lines: load, index, query. In LangChain, you make each decision yourself: chunk size, embedding model, vector store, retrieval strategy, chain type.
Which is better? It depends on your stage. When I am prototyping with a new dataset, I use LlamaIndex to see if RAG even works for that data before investing in optimization. Once I know the approach works, I sometimes rebuild it in LangChain for production because I want explicit control over every parameter.
Vector Store Integration
Both frameworks integrate with the same major vector databases — Pinecone, Weaviate, Chroma, Qdrant, FAISS, Milvus. The integration depth varies though.
LlamaIndex has an edge here: its VectorStoreIndex abstraction means switching from in-memory to Pinecone is often a one-line change. In LangChain, the vector store is a standalone object with its own API, so switching requires a bit more refactoring.
Agent Capabilities
LangChain's agent ecosystem is significantly more mature. With LangGraph, you can build stateful multi-agent workflows with branching, loops, and human-in-the-loop steps. This is the area where the "orchestration framework" identity shines.
LlamaIndex agents work well for tool-calling and query-plan generation, but complex multi-agent coordination is harder. If your project is primarily about agents that use RAG as one of several tools, LangChain plus LangGraph is the stronger choice.
Memory and Conversation
LangChain offers multiple memory types: ConversationBufferMemory (keep everything), ConversationSummaryMemory (summarize older messages), ConversationBufferWindowMemory (keep last N turns), and entity memory. LlamaIndex has conversation memory through its ChatEngine, but with fewer options.
Ecosystem and Community
Both projects are open source and actively developed. LangChain has a larger community — more GitHub stars, more third-party tutorials, more Stack Overflow answers. LlamaIndex has a more focused community centered on RAG use cases. For beginners, LangChain's ecosystem means more examples to learn from. For RAG specialists, LlamaIndex's focused documentation may be more useful.
When to Use LlamaIndex
Picture this: your team has 500 internal wiki pages, and everyone keeps asking the same questions that are buried somewhere in those docs. You need an LLM that can search and answer from that content. This is the exact problem LlamaIndex was designed for.
Document Q&A is the clearest win. Load your PDFs, build an index, query it. Five lines and you have a working prototype. Knowledge base search follows the same pattern but at larger scale, often backed by Pinecone or Weaviate.
Research assistants that query academic papers benefit from LlamaIndex's structured node graph. Each paper becomes a set of nodes with metadata (title, authors, date), and the query engine can filter by metadata before doing semantic search.
The common thread: your LLM needs data, and LlamaIndex is the shortest path from raw documents to a working retrieval system.
When to Use LangChain
LangChain is the better choice when your application involves complex orchestration, multiple tools, or workflows that go beyond simple retrieval. Here are the scenarios where LangChain's flexibility pays off.
I once built a customer support bot that needed to: check a knowledge base, look up order status via API, and escalate to a human if confidence was low. That kind of branching, multi-tool workflow is where LangChain + LangGraph excels. LlamaIndex could handle the knowledge base part, but orchestrating the full flow would have been fighting the framework.
Write a function classify_project(description) that takes a project description string and returns the recommended framework.
Rules:
"rag", "document", "index", "knowledge base", "search" — return "LlamaIndex""agent", "chain", "orchestrat", "multi-step", "workflow" — return "LangChain""Both""Either"The keyword matching should be case-insensitive.
Can You Use Both Together?
Yes, and this is actually a common production pattern. The idea: use LlamaIndex for what it does best (indexing and retrieval) and LangChain for what it does best (orchestration and agents). They are complementary, not competing.
This pattern gives you the best of both worlds: LlamaIndex's effortless document indexing wrapped in LangChain's agent orchestration. The agent can decide when to search documents versus when to use other tools.
Common Mistakes When Choosing a Framework
I have seen teams waste weeks because they picked a framework based on a blog post rather than their actual requirements. Here are the mistakes that come up most often.
Mistake 1: Using LangChain for a simple RAG project. If all you need is document Q&A, LangChain's explicit setup adds complexity without benefit. You write 20 lines where LlamaIndex needs 5, and the result is functionally identical. Start with LlamaIndex; migrate to LangChain only if you outgrow it.
Mistake 2: Using LlamaIndex for complex agent orchestration. LlamaIndex's agent capabilities are improving, but LangGraph's state machine model is purpose-built for multi-step workflows with branching and persistence. Trying to build that in LlamaIndex means working against the framework.
Mistake 3: Assuming framework = production-ready. Neither framework handles deployment, monitoring, or scaling for you. Both produce prototypes that need engineering work before they can serve real traffic. A common pitfall is demoing a working prototype and promising production delivery on the same timeline.
Verdict: Which Should You Choose?
There is no single right answer. But there is a decision tree that works for most projects.
My personal rule of thumb: default to LlamaIndex for data-centric projects and LangChain for agent-centric projects. When in doubt, prototype in LlamaIndex (less boilerplate) and migrate if you hit its limits.
Write a function decide_with_budget(primary_task, team_size) that factors in team expertise.
Rules:
primary_task is "rag" and team_size is 1 or 2, return "LlamaIndex" (less code to maintain)primary_task is "rag" and team_size is 3 or more, return "LangChain" (team can handle the complexity)primary_task is "agents", always return "LangChain" (regardless of team size)primary_task is "both" and team_size is 1, return "LlamaIndex" (solo dev needs simplicity)primary_task is "both" and team_size >= 2, return "Both" (team can manage two frameworks)primary_task, return "LlamaIndex"Frequently Asked Questions
Is LlamaIndex easier to learn than LangChain?
For RAG use cases, yes. LlamaIndex's default pipeline handles chunking, embedding, and retrieval with minimal configuration. LangChain requires understanding more abstractions (Runnables, LCEL, chain types) before building the same pipeline. For non-RAG tasks, the learning curves are comparable.
Can I migrate from LlamaIndex to LangChain later?
Yes, but it is not a simple swap. The core concepts (documents, embeddings, vector stores) transfer, but the APIs are completely different. Plan for a partial rewrite, not a find-and-replace. The integration pattern (using both together) is often a better migration path than a full replacement.
Which framework has better documentation?
Both have improved significantly. LangChain's documentation is more comprehensive because the framework covers more surface area. LlamaIndex's documentation is more focused and easier to navigate for RAG-specific tasks. Both have searchable API references and example notebooks.
Do I even need a framework? Can I build RAG from scratch?
Absolutely. A basic RAG pipeline is just: split text, embed chunks, store in a vector database, retrieve relevant chunks, pass them to an LLM. You can build this with openai + chromadb in about 30 lines. Frameworks save time when you need production features like hybrid search, reranking, streaming, or tool calling.