LlamaIndex
Featured✓ Editorially verifiedData framework for connecting LLMs to your data.
Pick LlamaIndex when retrieval quality is the bottleneck in your RAG system.
Skip it for general LLM app scaffolding — LangChain has the broader integration surface.
LlamaIndex is a Python and TypeScript framework purpose-built for RAG. It covers the full pipeline: ingestion connectors (PDFs, Notion, Confluence, Slack, S3, 300+ others), indexing strategies (vector, keyword, hybrid, hierarchical), query engines, and agentic retrieval flows.
The library's focus on retrieval — rather than general LLM application building — is its strength. Where LangChain spreads across the whole LLM-app surface, LlamaIndex stays deep on the retrieval-and-grounding problem, and it shows in the API quality. The LlamaCloud hosted platform layers managed ingestion + indexing on top of the open-source core.
The API surface is large and the documentation can be hard to navigate — there are many ways to do similar things, and choosing the right approach takes some research. For serious production RAG pipelines, that investment pays back.
LlamaIndex is the framework that takes retrieval seriously as its own discipline. For teams whose product success hinges on RAG quality (legal, medical, technical search), it's the obvious pick.
— The AI Tool Bible editorial team
Pros
- ✅ Focused on retrieval (not general agent stuff)
- ✅ Many ingestion connectors
- ✅ Strong production patterns
- ✅ LlamaCloud for managed ingestion
Cons
- ⚠️ API surface is large
- ⚠️ Documentation can be hard to navigate
Use cases
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