LangChain vs LlamaIndex: Which Framework for Building AI Agents?

When building AI agents in Python, two frameworks dominate the conversation: LangChain and LlamaIndex. Both enable you to build sophisticated applications powered by large language models, but they evolved from different starting points and excel in different scenarios. This comparison breaks down their architectures, strengths, and ideal use cases to help you choose the right tool for your project.

Origins and Philosophy

LangChain

LangChain launched in late 2022 as a framework for “chaining” together different components in LLM applications. Its original insight was that powerful applications require more than just prompting a model—they need structured workflows connecting prompts, models, tools, and memory.

Core philosophy: LangChain treats LLM applications as compositions of modular components. You build by connecting chains, agents, tools, and memory systems. The framework prioritizes flexibility and supports almost any architecture you can imagine.

LlamaIndex

LlamaIndex (originally GPT Index) emerged from the observation that connecting LLMs to external data was the most common and challenging task developers faced. It started as a data framework for LLMs, providing sophisticated indexing, retrieval, and data transformation capabilities.

Core philosophy: LlamaIndex treats data as the central challenge. It provides opinionated, optimized patterns for ingesting, structuring, and querying data with LLMs. The framework prioritizes getting data retrieval right.

Architecture Overview

LangChain’s Component Model

LangChain organizes functionality into several packages:

• langchain-core: Base abstractions and interfaces
• langchain: Chains, agents, and orchestration logic
• langchain-community: Third-party integrations
• langgraph: Graph-based agent orchestration

A typical LangChain agent setup looks like this:

from langchain_openai import ChatOpenAI
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_core.prompts import ChatPromptTemplate
from langchain_community.tools import TavilySearchResults

# Initialize components
llm = ChatOpenAI(model="gpt-4o")
search_tool = TavilySearchResults(max_results=3)

# Define agent prompt
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful research assistant."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}")
])

# Create and run agent
agent = create_tool_calling_agent(llm, [search_tool], prompt)
executor = AgentExecutor(agent=agent, tools=[search_tool], verbose=True)

result = executor.invoke({"input": "What are the latest developments in AI agents?"})

LangChain’s strength is the uniformity of its abstractions. Whether you’re building a simple chain or a complex multi-agent system, you work with consistent interfaces.

LlamaIndex’s Data-Centric Model

LlamaIndex organizes around data concepts:

• Documents and Nodes: Raw data and processed chunks
• Indexes: Structures for organizing and querying data
• Retrievers: Components that fetch relevant data
• Query Engines: End-to-end query processing pipelines

A typical LlamaIndex setup:

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
from llama_index.core.agent import ReActAgent
from llama_index.core.tools import QueryEngineTool

# Load and index documents
documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()

# Create tool from query engine
query_tool = QueryEngineTool.from_defaults(
    query_engine=query_engine,
    name="documentation",
    description="Search the product documentation for answers"
)

# Create agent
llm = OpenAI(model="gpt-4o")
agent = ReActAgent.from_tools([query_tool], llm=llm, verbose=True)

response = agent.chat("How do I configure authentication?")

LlamaIndex’s design shines when your primary challenge is making data accessible to an LLM.

Feature Comparison

Feature	LangChain	LlamaIndex
Primary focus	General LLM orchestration	Data retrieval and indexing
Learning curve	Moderate to steep	Moderate
RAG capabilities	Good, via chains	Excellent, core strength
Agent frameworks	Multiple options (AgentExecutor, LangGraph)	ReAct, OpenAI agents
Data connectors	Many via community	Extensive, first-class support
Index types	Basic vector store support	Many specialized index types
Memory systems	Flexible, multiple options	Built into chat engines
Streaming	Full support	Full support
Evaluation tools	LangSmith integration	Built-in evaluation module
Async support	Comprehensive	Available

Data Ingestion and Processing

LlamaIndex excels at data handling:

from llama_index.core import SimpleDirectoryReader
from llama_index.core.node_parser import SentenceSplitter

# Load from multiple sources
documents = SimpleDirectoryReader(
    input_dir="./data",
    recursive=True,
    required_exts=[".pdf", ".docx", ".txt"]
).load_data()

# Sophisticated chunking
parser = SentenceSplitter(chunk_size=512, chunk_overlap=50)
nodes = parser.get_nodes_from_documents(documents)

LlamaIndex provides 100+ data loaders for everything from databases to Notion to Slack. Its node parsing preserves document structure and metadata intelligently.

LangChain handles data loading but with less sophistication:

from langchain_community.document_loaders import DirectoryLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter

loader = DirectoryLoader("./data", glob="**/*.txt")
documents = loader.load()

splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
chunks = splitter.split_documents(documents)

LangChain’s loaders work well, but LlamaIndex’s data processing is more refined out of the box.

Agent Capabilities

LangChain offers more agent architecture options:

• AgentExecutor: Classic tool-calling agent loop
• LangGraph: State machine approach for complex workflows
• OpenAI Functions: Native function calling
• ReAct: Reasoning and acting pattern

LangGraph in particular enables sophisticated multi-agent systems:

from langgraph.graph import StateGraph
from langgraph.prebuilt import create_react_agent

# Define a stateful agent workflow with branching logic
# Supports cycles, conditional edges, and human-in-the-loop

LlamaIndex provides focused agent options:

• ReActAgent: Standard reasoning agent
• OpenAIAgent: Optimized for OpenAI function calling
• StructuredPlannerAgent: For multi-step planning

LlamaIndex agents are typically simpler but integrate seamlessly with its query engines.

Tool and Integration Ecosystem

Both frameworks support custom tools, but their ecosystems differ:

LangChain has broader integration coverage for non-data tools:

• API integrations (REST, GraphQL)
• Code execution environments
• Browser automation
• Third-party services

LlamaIndex has deeper data integration:

• Database connectors (SQL, graph, document stores)
• Knowledge graph construction
• Multi-modal data handling
• Structured data extraction

Performance Considerations

Retrieval Quality

LlamaIndex’s specialized focus on retrieval often translates to better out-of-the-box performance for RAG applications. Features like:

• Hierarchical indices that organize documents at multiple levels
• Recursive retrieval that follows references between chunks
• Fusion retrieval combining multiple strategies

LangChain can achieve similar results but requires more manual configuration.

Token Efficiency

LlamaIndex’s response synthesizers are optimized for token efficiency:

# LlamaIndex offers different synthesis strategies
query_engine = index.as_query_engine(
    response_mode="compact",  # Minimize token usage
    # Other options: "tree_summarize", "refine", "simple"
)

LangChain’s chains are flexible but may consume more tokens without careful prompt engineering.

Development Speed

LlamaIndex gets you to a working RAG prototype faster. LangChain takes longer initially but offers more customization for complex requirements.

When to Choose LangChain

LangChain is the better choice when:

• Building general-purpose agents: Agents that combine multiple capabilities beyond just data retrieval
• Complex orchestration needs: Multi-step workflows with branching, loops, or human-in-the-loop
• Integration-heavy applications: Connecting many external services and APIs
• Experimental architectures: Trying novel agent designs or research implementations
• You need LangGraph: Stateful, graph-based agent workflows

Example use cases: Customer support agents with CRM integration, multi-agent research systems, automated workflow orchestration, chatbots with diverse tool access.

When to Choose LlamaIndex

LlamaIndex is the better choice when:

• Data retrieval is primary: Your application mainly answers questions from documents
• Complex document collections: Multiple document types, structured and unstructured data
• RAG optimization matters: You need the best possible retrieval accuracy
• Knowledge base applications: Building searchable documentation or knowledge systems
• Rapid RAG prototyping: Getting a working retrieval system quickly

Example use cases: Enterprise knowledge bases, document Q&A systems, research assistants, code documentation search, legal document analysis.

Using Both Together

These frameworks aren’t mutually exclusive. A common pattern uses LlamaIndex for data handling within a LangChain orchestration:

from langchain.tools import Tool
from llama_index.core import VectorStoreIndex

# Create LlamaIndex query engine
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()

# Wrap as LangChain tool
doc_tool = Tool(
    name="documentation_search",
    description="Search product documentation",
    func=lambda q: str(query_engine.query(q))
)

# Use in LangChain agent with other tools
agent = create_tool_calling_agent(llm, [doc_tool, web_search, calculator], prompt)

This hybrid approach gives you LlamaIndex’s retrieval quality with LangChain’s orchestration flexibility.

Making Your Decision

Quick decision guide:

1. What’s your primary challenge?

   • Getting data into the LLM effectively → LlamaIndex
   • Orchestrating complex agent behavior → LangChain

2. How important is retrieval quality?

   • Critical, and documents are complex → LlamaIndex
   • Important, but tools matter more → LangChain

3. What’s your timeline?

   • Need RAG working today → LlamaIndex
   • Building a full agent platform → LangChain

4. How much customization do you need?

   • Standard patterns with optimized defaults → LlamaIndex
   • Unique architectures and workflows → LangChain

Both frameworks are mature, well-documented, and actively maintained. LlamaIndex recently raised significant funding and is expanding beyond pure retrieval. LangChain continues to evolve with LangGraph becoming increasingly powerful. Your choice should be guided by your specific requirements rather than general preference.

Getting Started

LangChain: Begin with the official tutorials and explore the LangGraph documentation for agent patterns.

LlamaIndex: Start with the starter tutorial and the RAG examples.

Both communities are active and helpful. Whichever you choose, you’re building on solid foundations for AI application development.

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For a hands-on tutorial, check out our guide on building a RAG agent with LangChain. Coming tomorrow: a deep dive into agent memory systems.