LlamaIndex
The focused RAG framework — data in, grounded answers out.
90
sovereigntyLlamaIndex (MIT, ~49k stars) is the framework to pick when your problem is specifically retrieval — getting your documents ingested, indexed, and answered over. Its primitives are built around the data side (loaders, indexes, retrievers, query engines) rather than trying to abstract everything, which keeps it noticeably leaner to reason about than LangChain for RAG builds. The company monetizes hosted parsing/extraction (LlamaCloud); the framework itself stays open and self-sufficient.
OPEN SOURCEMITSELF-HOSTLOCAL-FIRST
LicenseMIT
PricingFree (MIT); optional hosted LlamaCloud for managed parsing/extraction
Open sourceYes
Self-hostableYes
Local-first dataYes
What it does well
- +Purpose-built for RAG — the data primitives are the product
- +Leaner mental model than LangChain for retrieval apps
- +Huge loader/integration ecosystem for document types
Where it falls short
- −Less suited to general agent orchestration than dedicated tools
- −Fast-moving API surface — pin versions
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LlamaIndex head-to-head
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