LangChain vs LlamaIndex: Choosing the Right Framework for Your AI Project

LangChain and LlamaIndex are leading frameworks for building LLM-powered applications in Python. LangChain focuses on action-centric orchestration for multi-tool agents and complex workflows, while LlamaIndex specializes in data-centric RAG with advanced document indexing and retrieval capabilities.

Core Architecture

LangChain: Action-Centric Framework

LangChain provides modular building blocks for creating complex agent systems. Its core philosophy centers on chains and agents that orchestrate tools, memory, and LLMs through graph-based control flow.

LangChain Python v0.3.x Architecture: LangChain Python v0.3.x focuses on production-ready agent development with stable APIs:

• LCEL (LangChain Expression Language): Declarative pipe-based composition (| operator)
• LangGraph: Stateful graph orchestration with nodes, edges, and conditional routing
• Pydantic Integration: Type-safe components with Pydantic v2 validation
• Standardized Interfaces: Consistent abstractions across LLMs, tools, and retrievers

Modular Package Structure (v0.3.x): The modular package structure in LangChain Python v0.3.x:

• langchain-core: Base interfaces and abstractions
• langchain: Core chains and agents
• langchain-openai, langchain-anthropic: Partner integrations
• langchain-community: Community-maintained integrations
• langgraph: Stateful graph orchestration (separate package)

Key Components:

• LangGraph: Stateful graph orchestration with nodes, edges, and conditional routing
• LCEL: Declarative pipe-based composition (| operator)
• Agents: ReAct and function-calling patterns for autonomous decision-making
• Tools: Extensive library of integrations (web search, databases, APIs)

LlamaIndex: Data-Centric Framework

LlamaIndex excels at ingesting, indexing, and querying structured and unstructured data. Its architecture prioritizes efficient data retrieval through sophisticated indexing strategies.

Key Components:

• Indices: VectorStoreIndex, KeywordTableIndex, SummaryIndex
• Query Engines: Retrieval-augmented generation with customizable retrieval
• Workflows: Event-driven, async orchestration for multi-step processes
• Data Connectors: 150+ loaders for PDFs, SQL, APIs, vector databases
• LlamaParse: Advanced parsing for complex PDFs, tables, and multi-column layouts

Use Case Comparison

Choose LlamaIndex For

• Complex RAG applications requiring advanced retrieval strategies
• Document parsing from heterogeneous sources (PDFs, Notion, Slack)
• Complex PDF/table extraction using LlamaParse for production-grade parsing
• High-volume data ingestion with efficient chunking and embedding
• Custom retrieval with hybrid search, reranking, and metadata filtering
• Production RAG pipelines with built-in evaluation metrics

Choose LangChain For

• Multi-tool agents that orchestrate external APIs and services
• Complex logic flows with branching, loops, and conditional execution
• Chat applications with sophisticated memory management
• Quick prototyping with prebuilt agent templates and chains
• Stateful workflows requiring persistent agent memory

Code Examples

LlamaIndex RAG Implementation

import os
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.settings import Settings
from llama_index.llms.openai import OpenAI

# Configure API key
os.environ["OPENAI_API_KEY"] = "your-api-key"

# Configure LLM
Settings.llm = OpenAI(model="gpt-4", temperature=0)

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

# Create index
index = VectorStoreIndex.from_documents(documents)

# Create query engine
query_engine = index.as_query_engine(
    similarity_top_k=5,
    response_mode="tree_summarize"
)

# Query
response = query_engine.query("What are the key findings?")
print(response)

This example demonstrates LlamaIndex's data-first approach: load documents, index them with embeddings, then query with built-in retrieval-augmented generation.

LangChain RAG Implementation (LCEL)

import os
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_chroma import Chroma
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser
from langchain_community.document_loaders import TextLoader
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain

# Configure API key
os.environ["OPENAI_API_KEY"] = "your-api-key"

# Initialize components
llm = ChatOpenAI(model="gpt-4", temperature=0)
embeddings = OpenAIEmbeddings()

# Load and index documents
loader = TextLoader("data/document.txt")
documents = loader.load()
vectorstore = Chroma.from_documents(documents, embeddings)
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

# Define prompt
prompt = ChatPromptTemplate.from_template("""
Answer the question based on the context:
{context}

Question: {input}
""")

# Create chain using LCEL
question_answer_chain = create_stuff_documents_chain(llm, prompt)
rag_chain = create_retrieval_chain(retriever, question_answer_chain)

# Query
result = rag_chain.invoke({"input": "What are the key findings?"})
print(result["answer"])

This example shows LangChain's modern LCEL approach: compose retrievers, prompts, and LLMs using the pipe operator for declarative chain construction.

Orchestration Models

LlamaIndex Workflows

LlamaIndex uses event-driven async workflows with typed events for flexible branching and parallel execution.

from llama_index.core.workflow import StartEvent, StopEvent, Workflow, step, Context, Event
from llama_index.core import VectorStoreIndex
from llama_index.core.response_synthesizers import get_response_synthesizer

class RetrievalEvent(Event):
    nodes: list
    query_str: str

class RAGWorkflow(Workflow):
    def __init__(self, index: VectorStoreIndex):
        super().__init__()
        self.index = index
        self.response_synthesizer = get_response_synthesizer()
    
    @step
    async def retrieve(self, ctx: Context, ev: StartEvent) -> RetrievalEvent:
        query = ev.query
        nodes = await self.index.aretrieve(query)
        return RetrievalEvent(nodes=nodes, query_str=query)
    
    @step
    async def synthesize(self, ctx: Context, ev: RetrievalEvent) -> StopEvent:
        response = await self.response_synthesizer.asynthesize(
            query_str=ev.query_str,
            nodes=ev.nodes
        )
        return StopEvent(result=response)

LangGraph Stateful Graphs

LangGraph uses stateful graphs with shared state and conditional edges for iterative agent behavior.

from langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated
from operator import add

class AgentState(TypedDict):
    messages: Annotated[list, add]
    tool_calls: list

def agent_node(state: AgentState):
    # Agent logic here
    return {"messages": ["Agent response"], "tool_calls": []}

def tool_node(state: AgentState):
    # Tool execution logic here
    return {"messages": ["Tool result"], "tool_calls": []}

def should_continue(state: AgentState):
    return "tools" if state["tool_calls"] else END

workflow = StateGraph(AgentState)
workflow.add_node("agent", agent_node)
workflow.add_node("tools", tool_node)
workflow.add_conditional_edges("agent", should_continue)
workflow.add_edge("tools", "agent")
workflow.set_entry_point("agent")

Integration Ecosystem

Vector Database Support

Both frameworks integrate with major vector stores:

LlamaIndex:

• Native support for Chroma, Pinecone, Weaviate, Milvus, Qdrant
• Built-in vector store abstractions with unified API
• Advanced retrieval: hybrid search, auto-merging, recursive retrieval

LangChain:

• 50+ vector store integrations via langchain-community
• Consistent retriever interface across all stores
• Self-querying retriever for metadata filtering

LLM Provider Compatibility

LlamaIndex:

• OpenAI, Anthropic, Cohere, HuggingFace, local models via Ollama
• Pydantic programs for structured outputs with validation
• Streaming responses and async support

LangChain:

• Broadest LLM support (100+ providers)
• Unified chat/completion interfaces
• Native function calling and tool binding

Performance Considerations

Retrieval Efficiency

LlamaIndex optimizes for retrieval quality:

• Advanced chunking strategies (semantic, recursive, parent-child)
• Reranking and query transformation
• Fusion retrieval combining multiple strategies
• LlamaParse for complex document parsing

LangChain optimizes for orchestration flexibility:

• Parallel tool execution
• Streaming intermediate steps
• Efficient state management in graphs

Evaluation

LlamaIndex provides:

• Built-in RAG evaluation (faithfulness, relevancy, context precision)
• Ragas integration for automated metrics
• Tracing via Langfuse, Arize Phoenix, Weights & Biases

LangChain offers:

• LangSmith for end-to-end tracing and evaluation
• Custom evaluators with LangSmith
• Integration with MLflow and Weights & Biases

Decision Matrix

Factor	LlamaIndex	LangChain
Primary Focus	Data ingestion and RAG	Agent orchestration and tools
Learning Curve	Moderate (RAG-focused)	Steeper (broader abstractions)
Best For	Document-heavy applications	Multi-tool autonomous agents
Retrieval Quality	Superior with advanced strategies	Standard, customizable
Agent Capabilities	Growing (Workflows, Agents)	Mature (LangGraph, Agents)
Code Complexity	Lower for RAG use cases	Higher for complex workflows

Learning Curve Justification: LangChain's steeper curve stems from its highly granular abstractions (chains, agents, tools, memory, retrievers) requiring understanding of multiple composition patterns. LlamaIndex provides higher-level defaults optimized for RAG, reducing initial complexity for document-focused applications.

Getting Started

For LlamaIndex

1. Install: pip install llama-index llama-parse
2. Choose your data loader from 150+ connectors
3. Use LlamaParse for complex PDFs and tables
4. Create an index: VectorStoreIndex.from_documents()
5. Configure retrieval strategy (similarity, hybrid, auto-merging)
6. Build query engine with response mode customization

For LangChain

1. Install: pip install langchain langchain-openai langchain-core langchain-community langgraph
2. Select components: LLM, embeddings, vector store, retriever
3. Compose chains using LCEL (| operator) or build LangGraph workflows
4. Add memory for conversational applications
5. Deploy with LangSmith for observability

Note: LangChain Python v0.3.x maintains backward compatibility while providing improved type safety and performance.

Hybrid Approach

Many production systems use both frameworks:

• Use LlamaIndex for data ingestion, LlamaParse, indexing, and retrieval
• Use LangChain for agent orchestration, tool calling, and complex workflows
• Connect via shared vector stores or custom retriever adapters

This combination leverages each framework's strengths: LlamaIndex's data expertise and LangChain's orchestration capabilities.