LangChain vs LlamaIndex: Choosing the Right AI Framework

The Framework Decision

When building AI applications, two frameworks dominate: LangChain and LlamaIndex. While they overlap in some areas, each has distinct strengths that make them better suited for different use cases.

Quick Comparison

LangChain Deep Dive

Overview

LangChain is a comprehensive framework for building applications powered by language models. It provides tools for everything from simple chatbots to complex multi-agent systems.

Core Components

LangChain Architecture:
├── Models
│   ├── LLMs (GPT-4, Claude, etc.)
│   ├── Chat Models
│   └── Embeddings
├── Prompts
│   ├── Templates
│   ├── Few-shot examples
│   └── Output parsers
├── Memory
│   ├── Conversation buffer
│   ├── Summary memory
│   └── Entity memory
├── Chains
│   ├── Sequential
│   ├── Router
│   └── Custom
├── Agents
│   ├── Tools
│   ├── Toolkits
│   └── Agent executors
└── Retrieval
    ├── Document loaders
    ├── Text splitters
    └── Vector stores

Strengths

Weaknesses

Use Cases

Best for:

• Multi-step AI workflows
• Agent-based systems
• Complex orchestration
• Research/prototyping

LlamaIndex Deep Dive

Overview

LlamaIndex (formerly GPT Index) specializes in connecting LLMs with external data. It excels at building RAG (Retrieval-Augmented Generation) systems.

Core Components

LlamaIndex Architecture:
├── Data Connectors
│   ├── File loaders
│   ├── API connectors
│   └── Database readers
├── Indices
│   ├── Vector index
│   ├── Summary index
│   ├── Tree index
│   └── Keyword table
├── Query Engine
│   ├── Retrievers
│   ├── Response synthesizers
│   └── Query transformations
├── Agents
│   ├── OpenAI agents
│   ├── ReAct agent
│   └── Custom agents
└── Evaluation
    ├── Metrics
    └── Batch evaluation

Strengths

Weaknesses

Use Cases

Best for:

• Document Q&A systems
• Knowledge bases
• Search applications
• Data-heavy applications

Head-to-Head Comparison

RAG Implementation

LlamaIndex Approach:

from llama_index import VectorStoreIndex, SimpleDirectoryReader

# Load and index documents
documents = SimpleDirectoryReader('data').load_data()
index = VectorStoreIndex.from_documents(documents)

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

LangChain Approach:

from langchain.document_loaders import DirectoryLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA

# Load documents
loader = DirectoryLoader('data')
documents = loader.load()

# Create vector store
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(documents, embeddings)

# Create chain
qa_chain = RetrievalQA.from_chain_type(
    llm=ChatOpenAI(),
    retriever=vectorstore.as_retriever()
)
response = qa_chain.run("What is the main topic?")

Lines of Code Comparison

When to Choose Each

Choose LangChain When

1. Building complex multi-step workflows
2. Need sophisticated agents
3. Require maximum flexibility
4. Using many different tools
5. Building production chatbots

Choose LlamaIndex When

1. Building document Q&A systems
2. Focus is on data retrieval
3. Want simpler implementation
4. Need built-in evaluation
5. Working primarily with structured data

Use Both When

Some teams use both:

• LlamaIndex for data indexing
• LangChain for orchestration

# LlamaIndex for indexing
from llama_index import VectorStoreIndex
index = VectorStoreIndex.from_documents(documents)

# LangChain for orchestration
from langchain.tools import Tool
tool = Tool(
    name="Knowledge Base",
    func=index.as_query_engine().query,
    description="Query the knowledge base"
)

Performance Comparison

Benchmarks (Retrieval Quality)

LlamaIndex tends to have better out-of-box retrieval

Latency

Community and Ecosystem

GitHub Stats (Dec 2025)

Learning Resources

Recommendations

By Project Type

"The choice between LangChain and LlamaIndex isn't binary. Understanding each framework's strengths helps you use the right tool for each part of your application."