import ComparisonTable from ’../../components/ComparisonTable.astro’;
LangChain and LlamaIndex are the dominant Python frameworks for building AI applications. They started with different focuses and have converged in some areas while maintaining distinct strengths.
Core Focus
LangChain: General-purpose framework for building LLM applications — chains, agents, tools, and memory. More flexible, more general.
LlamaIndex: Data framework focused on ingesting, structuring, and querying data for LLM applications. Better RAG, better data connectors, better document handling.
Feature Comparison
<ComparisonTable headers={[“Feature”, “LangChain”, “LlamaIndex”]} rows={[ [“RAG quality”, “Good”, “Excellent”], [“Data connectors”, “Good”, “Excellent (140+)”], [“Agent framework”, “LangGraph (advanced)”, “Agentic RAG”], [“Memory/persistence”, “ConversationBuffer”, “Built-in persistence”], [“Evaluation”, “LangSmith”, “Ragas integration”], [“Observability”, “LangSmith (native)”, “LlamaCloud (paid)”], [“Documentation”, “Extensive”, “Good”], [“Learning curve”, “High”, “Medium”], [“Community”, “Larger”, “Growing fast”], [“Best for”, “Agents, complex flows”, “RAG, data Q&A”], ]} />
LlamaIndex for RAG
For building applications that answer questions from your documents:
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.anthropic import Anthropic
from llama_index.core import Settings
# Configure
Settings.llm = Anthropic(model="claude-haiku-4-5-20251001")
# Load and index
documents = SimpleDirectoryReader("./docs").load_data()
index = VectorStoreIndex.from_documents(documents)
# Query
query_engine = index.as_query_engine()
response = query_engine.query("What are our return policies?")
print(response)
# Includes source nodes with citationsLlamaIndex’s design makes RAG applications cleaner and its default chunking strategies are better calibrated.
LangChain for Agents
For complex multi-step workflows with tool calling:
from langchain_anthropic import ChatAnthropic
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_core.prompts import ChatPromptTemplate
llm = ChatAnthropic(model="claude-haiku-4-5-20251001")
tools = [DuckDuckGoSearchRun()]
prompt = ChatPromptTemplate.from_messages([
("system", "You are a helpful research assistant."),
("human", "{input}"),
("placeholder", "{agent_scratchpad}"),
])
agent = create_tool_calling_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)
result = executor.invoke({"input": "Research the latest AI model releases"})LangChain’s agent ecosystem and LangGraph for complex state machines are more mature.
LangGraph vs. LlamaIndex Workflows
LangGraph (part of LangChain) is specifically for building stateful multi-actor workflows:
- Complex agent routing
- Human-in-the-loop workflows
- Multi-agent coordination
- State machines for complex processes
LlamaIndex’s agent framework is more focused on data retrieval workflows.
For complex orchestration: LangGraph. For data-centric agents: LlamaIndex.
Observability
LangSmith (LangChain): Built-in tracing, debugging, and evaluation. Essential for production LangChain deployments. Paid tiers for team features.
Arize Phoenix / Ragas (LlamaIndex): Third-party integrations for evaluation. Less native but functional.
For production visibility: LangChain + LangSmith has an edge.
Learning Curve
LangChain’s abstraction layers are deeper and sometimes confusing. Complex chains often require understanding multiple layers of abstractions that interact in non-obvious ways.
LlamaIndex has cleaner abstractions for its core use case (data indexing and retrieval). Getting to a working RAG application is faster.
When to Use Each
| Use Case | Framework |
|---|---|
| Document Q&A | LlamaIndex |
| Multi-step AI agents | LangChain/LangGraph |
| Chatbot with knowledge base | LlamaIndex |
| Complex workflows | LangChain |
| Just RAG, clean code | LlamaIndex |
| Production observability | LangChain + LangSmith |
| Getting started quickly | LlamaIndex |
The Convergence
Both frameworks have been expanding into each other’s territory. LlamaIndex now has agents; LangChain has better data ingestion. The gap is narrowing, but the strengths remain distinct.
Bottom Line
For most new AI applications involving document retrieval and Q&A: start with LlamaIndex. For complex multi-step agents and workflows: use LangChain with LangGraph. Neither choice is wrong — the frameworks are different tools for overlapping use cases.