LangChain vs LlamaIndex
Both are popular Python/JS frameworks for building LLM apps, and they overlap. The simplest lens: LangChain is a broad orchestration toolkit for chains and agents; LlamaIndex is a focused data framework for indexing and retrieval.
| LangChain | LlamaIndex | |
|---|---|---|
| Primary focus | General LLM orchestration — chains, agents, tools | Data framework for indexing and retrieval (RAG) |
| Strength | Broad integrations, agent and workflow abstractions | Ingestion, indexing, and query pipelines for RAG |
| RAG ergonomics | Flexible but more assembly required | First-class, opinionated retrieval primitives |
| Agents & tools | Mature agent and tool abstractions | Supported, retrieval-centric |
| Learning curve | Larger surface area | Focused, quicker for RAG |
When to choose which
You're building tool-using agents, multi-step workflows, or need a wide range of integrations.
Your core problem is ingesting and retrieving over lots of documents with strong RAG defaults.
Note: They interoperate — many teams use LlamaIndex for retrieval inside a LangChain app.
Frequently asked questions
Can I use LangChain and LlamaIndex together?
Yes. A common setup uses LlamaIndex for document ingestion and retrieval, then LangChain for agent orchestration and tool use.
Which is better for RAG?
LlamaIndex is more opinionated and faster to a solid RAG baseline. LangChain is more flexible if RAG is one part of a larger agentic workflow.
Related reading
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