LangChain vs LlamaIndex: Which AI Framework for RAG Should You Choose?
Compare LangChain and LlamaIndex on RAG, document processing, and developer experience. Discover which framework fits your LLM application.
LangChain
A broad AI framework for chains, agents, tools, and RAG. Large community, many integrations, and suited for complex workflows.
LlamaIndex
A framework specialized in data connectors and retrieval. Excellent for document indexing, RAG, and knowledge-driven LLM applications.
Comparison table
| Feature | LangChain | LlamaIndex |
|---|---|---|
| Focus | Chains, agents, tools — broad | Data indexing, retrieval — specialized |
| RAG quality | Good, many options | Excellent — purpose-built for retrieval |
| Document processing | Loaders available, less deep | Rich ecosystem of connectors and parsers |
| Learning curve | Steeper — many abstractions | Lower for RAG use cases |
| Community | Very large, many tutorials | Growing, RAG-focused |
Verdict
LangChain is broader and suited for agents and chains; LlamaIndex is the better choice for RAG and document-heavy applications. For pure RAG we recommend LlamaIndex; for agent-like flows LangChain is stronger.
Our recommendation
AVARC Solutions uses both: LangChain for agent and tool-based projects; LlamaIndex for RAG and document QA. We increasingly combine them — LlamaIndex for retrieval, LangChain for orchestration — or choose the lighter Vercel AI SDK for simple chat flows.
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