LangChain vs llamaindex: Key Differences and When to Use Each
LangChain is a comprehensive framework for building AI applications with modular components like chains and agents, while llamaindex focuses on creating and querying custom indexes over documents for retrieval-augmented generation. Both integrate with language models but serve different roles: LangChain for orchestration and llamaindex for data indexing and retrieval.Quick Comparison
This table summarizes the main differences between LangChain and llamaindex across key factors.
| Factor | LangChain | llamaindex |
|---|---|---|
| Primary Purpose | Build AI workflows with chains, agents, and tools | Create and query custom indexes over documents |
| Core Feature | Modular chains and agent orchestration | Flexible document indexing and retrieval |
| Integration | Supports many LLMs and external APIs | Focuses on document ingestion and retrieval with LLMs |
| Use Case | Complex AI apps with multiple steps and tools | Efficient retrieval-augmented generation from data |
| Community & Ecosystem | Large, active with many integrations | Growing, specialized on indexing and retrieval |
| Learning Curve | Moderate, due to many components | Simpler for indexing-focused tasks |
Key Differences
LangChain is designed as a full framework to build AI applications by connecting language models with various components like chains, agents, and tools. It helps developers orchestrate complex workflows where multiple steps and external APIs are involved. This makes it ideal for building chatbots, question answering systems, and multi-step reasoning applications.
On the other hand, llamaindex (formerly GPT Index) specializes in creating custom indexes over your documents or data sources. It focuses on efficient retrieval-augmented generation by enabling fast and relevant document search before querying a language model. This makes it perfect when your main goal is to build a system that answers questions based on your own data.
While both integrate with language models, LangChain is broader and more modular, supporting many AI workflows, whereas llamaindex is more focused on the indexing and retrieval part of the pipeline. You can even use them together: llamaindex for indexing your data, and LangChain to build the overall application logic.
Code Comparison
Here is a simple example showing how LangChain loads documents and queries a language model with a retrieval chain.
from langchain.document_loaders import TextLoader from langchain.vectorstores import FAISS from langchain.embeddings import OpenAIEmbeddings from langchain.chains import RetrievalQA from langchain.llms import OpenAI # Load documents loader = TextLoader('example.txt') docs = loader.load() # Create embeddings and vector store embeddings = OpenAIEmbeddings() vectorstore = FAISS.from_documents(docs, embeddings) # Create retrieval QA chain llm = OpenAI() qa = RetrievalQA(llm=llm, retriever=vectorstore.as_retriever()) # Query query = 'What is the main topic of the document?' answer = qa.run(query) print(answer)
llamaindex Equivalent
This example shows how llamaindex loads documents, builds an index, and queries it with a language model.
from llama_index import SimpleDirectoryReader, GPTVectorStoreIndex, LLMPredictor, ServiceContext from langchain.chat_models import ChatOpenAI # Load documents from directory documents = SimpleDirectoryReader('data').load_data() # Setup LLM predictor llm_predictor = LLMPredictor(llm=ChatOpenAI(temperature=0, model_name='gpt-4')) service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor) # Build vector index index = GPTVectorStoreIndex.from_documents(documents, service_context=service_context) # Query index query_engine = index.as_query_engine() response = query_engine.query('What is the main topic of the documents?') print(response.response)
When to Use Which
Choose LangChain when you want to build complex AI applications that require chaining multiple steps, integrating various tools, or orchestrating agents with language models. It is best for workflows that go beyond simple document retrieval and involve multi-turn interactions or external API calls.
Choose llamaindex when your main goal is to create efficient, customizable indexes over your own documents or data sources for retrieval-augmented generation. It is ideal for building question answering or search systems tightly focused on your data.
For advanced projects, you can combine both: use llamaindex to handle document indexing and retrieval, and LangChain to manage the overall application logic and multi-step workflows.