LangChain vs LlamaIndex: Which Framework Should You Use for RAG?

Use LlamaIndex when your primary goal is RAG (Retrieval Augmented Generation) with documents. Use LangChain when you need complex agent workflows with multiple tools and chains.

Quick Answer

Both are Python/TypeScript frameworks for building LLM applications, but they have different strengths:

• LlamaIndex was built specifically for connecting LLMs to your data—it’s the specialist for document indexing, retrieval, and RAG pipelines
• LangChain is a general-purpose framework for AI agents, chains, and workflows—RAG is one of many things it can do

In 2026, many production applications use both: LlamaIndex for the retrieval layer, LangChain for orchestration and agents.

Feature Comparison

Capability	LangChain	LlamaIndex
RAG/Retrieval	Good	Excellent
Agent Workflows	Excellent	Good
Document Loaders	Many	Many
Vector Store Support	All major	All major
Tool Integration	100+ tools	Growing
Learning Curve	Steeper	Gentler
Community Size	Larger	Growing fast

Key Differences

LlamaIndex Strengths

• Purpose-built for RAG: Indexing, retrieval, and response synthesis are first-class
• Data Connectors: 160+ data loaders (PDFs, databases, APIs, etc.)
• Query Engines: Multiple retrieval strategies out of the box
• Simpler Mental Model: Focused API, easier to learn
• LlamaParse: Best-in-class PDF parsing

LangChain Strengths

• Agent Ecosystem: LangGraph for complex stateful agents
• Tool Integration: Vast library of pre-built tools
• LangSmith: Production observability and tracing
• Flexibility: Can build almost any LLM workflow
• Community: Larger ecosystem, more examples

When to Choose Each

Choose LlamaIndex When:

• Your main goal is chatting with documents
• Building a knowledge base or Q&A system
• You need advanced retrieval (re-ranking, hybrid search)
• You want a simpler codebase for RAG

Choose LangChain When:

• Building AI agents with multiple tools
• Need complex multi-step workflows
• Want production observability (LangSmith)
• Building applications beyond just RAG

Use Both When:

• Production RAG + Agent hybrid systems
• LlamaIndex as retriever, LangChain as orchestrator

Related Questions

• Best RAG frameworks in 2026?
• How to use RAG with your documents?
• What is RAG (Retrieval Augmented Generation)?

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Last verified: 2026-03-02