LangChain vs LlamaIndex
LangChain is often chosen for broader application orchestration and agents; LlamaIndex is often chosen for data indexing and retrieval-heavy workflows.
| Criterion | LangChain | LlamaIndex |
|---|---|---|
| ease of use | Developer-oriented | Developer-oriented with data-focused abstractions |
| target user | LLM application developers | RAG and data pipeline developers |
| visual workflow support | Usually external tools | Usually external tools |
| developer flexibility | High | High |
| RAG features | Composable retrieval chains | Strong data indexing/retrieval focus |
| agentic workflow support | Strong through ecosystem | Available through workflows and agents |
| integrations | Very broad | Broad data and vector ecosystem |
| self-hosting | Runs in your application | Runs in your application |
| production readiness | Depends on engineering and observability | Depends on engineering and observability |
| learning curve | Moderate to high | Moderate |
| best use cases | Agents, chains, app orchestration | Knowledge-heavy RAG systems |