LlamaIndex vs LangChain (2026)
LlamaIndex is strongest at getting data in and retrieved well: loaders, parsing, indexing and query engines for RAG. LangChain is a broader toolkit for LLM applications (models, prompts, retrievers, tools), with LangGraph for stateful agents. Many teams use LlamaIndex for ingestion and retrieval with LangGraph or plain code for orchestration, and some use neither for simple apps.
Comparison
| LlamaIndex | LangChain | |
|---|---|---|
| Focus | Data ingestion, indexing, retrieval | General LLM app building blocks |
| Agents | Agent and workflow support | LangGraph |
| Strength | RAG over many document types | Integrations and ecosystem |
| Watch | Abstractions can hide retrieval details | Abstractions can hide prompts and calls |
When to choose which
- RAG-heavy app with many sources: LlamaIndex for ingestion and retrieval.
- Agents with state and approvals: LangGraph.
- Small app, few calls: the provider SDK and your own code.
Don't let the framework hide what the model saw
Whatever you choose, log the rendered prompt and the retrieved chunks for every call. When an answer is wrong, the first question is what the model received, and a framework that hides that makes debugging much slower. See observability and tracing.
Learn the pieces first
Build one retrieval pipeline by hand (load, split, embed, store, retrieve, generate) before relying on a framework, so you know what each abstraction does and where to look when it fails.
From the floor
In the financial RAG session, I installed LangChain because it can create a retriever, a generator and other pieces with single lines of code, and I added LlamaIndex as well, for indexing data so it can be accessed easily, saying we'd come back to it later.
Related
- Retrieval and RAG
- Agent frameworks
- LangGraph vs CrewAI vs Microsoft Agent Framework
- Observability and tracing