LangChain vs LlamaIndex: Which AI Framework Should You Use in 2025?

If you're building AI applications in 2025, you've likely encountered two dominant frameworks: LangChain and LlamaIndex. Both help you build applications on top of Large Language Models, but they take fundamentally different approaches. Choosing the wrong one can cost you weeks of development time.

The Core Philosophy

LangChain is a general-purpose framework for building LLM-powered applications. It provides abstractions for chains, agents, memory, and tools. Think of it as the Swiss Army knife of AI development.

LlamaIndex (formerly GPT Index) is a data framework specifically optimized for connecting LLMs with your data. It excels at indexing, retrieving, and querying structured and unstructured data.

Architecture Comparison

LangChain Architecture

User Query → Chain/Agent → LLM → Tools → Memory → Response
                              ↕
                        Vector Store
                        Web Search
                        Database
                        APIs

LlamaIndex Architecture

Documents → Indexing Pipeline → Vector Index → Query Engine → Response
                                    ↕
                              Embedding Model
                              Re-ranking
                              Post-processing

When to Use LangChain

Choose LangChain when you need:

LangChain Example: Research Agent

python
from langchain.agents import create_react_agent
from langchain.tools import WikipediaQueryRun, DuckDuckGoSearchRun

tools = [WikipediaQueryRun(), DuckDuckGoSearchRun()]
agent = create_react_agent(llm, tools, prompt)
result = agent.invoke({"input": "Compare React and Vue.js frameworks"})

When to Use LlamaIndex

Choose LlamaIndex when you need:

LlamaIndex Example: Document Q&A

python
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What are the key findings in the report?")

Performance Benchmarks

The Verdict

Use LlamaIndex if your primary goal is building a RAG system or document Q&A application. It's purpose-built for this and will give you better retrieval quality with less code.

Use LangChain if you're building complex AI agents that need to use multiple tools, maintain conversation history, and perform multi-step reasoning.

Use both together for the best of both worlds — LlamaIndex for retrieval and LangChain for orchestration.