LangChain vs LlamaIndex: Which Framework Should You Use?

Two frameworks dominate the AI development landscape. Here’s when to use each.

Quick Summary

Use Case	Winner
General AI apps	LangChain
Document Q&A	LlamaIndex
Agents & Tools	LangChain
Complex RAG	LlamaIndex
Production scale	Both work

LangChain Overview

LangChain is a general-purpose framework for building LLM applications.

Strengths

• 🔗 Chains: Combine multiple LLM calls
• 🤖 Agents: Autonomous decision-making
• 🔧 Tools: 100+ integrations
• 📚 Memory: Conversation history

Basic Example

from langchain.chat_models import ChatOpenAI
from langchain.chains import LLMChain
from langchain.prompts import ChatPromptTemplate

llm = ChatOpenAI(model="gpt-4")

prompt = ChatPromptTemplate.from_template(
    "Write a {tone} email about {topic}"
)

chain = LLMChain(llm=llm, prompt=prompt)

result = chain.run(tone="professional", topic="project update")

Agent Example

from langchain.agents import initialize_agent, Tool
from langchain.tools import DuckDuckGoSearchRun

search = DuckDuckGoSearchRun()

tools = [
    Tool(
        name="Search",
        func=search.run,
        description="Search the internet for current information"
    )
]

agent = initialize_agent(
    tools,
    llm,
    agent="zero-shot-react-description",
    verbose=True
)

agent.run("What's the latest news about AI agents?")

LlamaIndex Overview

LlamaIndex (formerly GPT Index) is specialized for RAG and document-based applications.

Strengths

• 📄 Document handling: Best-in-class
• 🔍 Advanced retrieval: Multiple strategies
• 📊 Structured data: Tables, databases
• ⚡ Optimized: Built for production

Basic Example

from llama_index import VectorStoreIndex, SimpleDirectoryReader

# Load documents
documents = SimpleDirectoryReader("./data").load_data()

# Create index
index = VectorStoreIndex.from_documents(documents)

# Query
query_engine = index.as_query_engine()
response = query_engine.query("What is the refund policy?")

Advanced RAG Example

from llama_index import (
    VectorStoreIndex,
    ServiceContext,
    StorageContext
)
from llama_index.node_parser import SentenceSplitter
from llama_index.retrievers import VectorIndexRetriever
from llama_index.query_engine import RetrieverQueryEngine
from llama_index.postprocessor import SimilarityPostprocessor

# Custom chunking
parser = SentenceSplitter(chunk_size=512, chunk_overlap=50)

# Custom retrieval
retriever = VectorIndexRetriever(
    index=index,
    similarity_top_k=10
)

# Re-ranking
postprocessor = SimilarityPostprocessor(similarity_cutoff=0.7)

# Assemble query engine
query_engine = RetrieverQueryEngine(
    retriever=retriever,
    node_postprocessors=[postprocessor]
)

Head-to-Head Comparison

Document Processing

LlamaIndex wins here:

• Native support for 100+ file types
• Smart chunking strategies
• Metadata preservation

Agent Building

LangChain wins here:

• ReAct, Plan-and-Execute, etc.
• Tool creation is simpler
• Better debugging

Production Readiness

Tie - Both are production-ready:

• LangChain has LangSmith for monitoring
• LlamaIndex has built-in observability

Learning Curve

LlamaIndex is easier to start:

• Fewer concepts
• Better defaults
• Clearer documentation

When to Use Both

Many production apps use both frameworks:

# LlamaIndex for document processing
from llama_index import VectorStoreIndex
index = VectorStoreIndex.from_documents(docs)
retriever = index.as_retriever()

# LangChain for agents
from langchain.agents import Tool, initialize_agent

tools = [
    Tool(
        name="DocumentSearch",
        func=lambda q: retriever.retrieve(q),
        description="Search internal documents"
    )
]

agent = initialize_agent(tools, llm, agent="conversational-react")

My Recommendation

1. Starting a new project? → Start with LlamaIndex
2. Need agents? → Add LangChain
3. Complex workflows? → Use both
4. Maximum control? → Build custom (skip frameworks)

Need Help With Your AI Project?

I’ve built production systems with both frameworks. Get in touch to discuss your project!