What programming languages does LlamaIndex support?

LlamaIndex is available as a framework for both Python and TypeScript.

What types of data sources can LlamaIndex ingest?

It supports ingestion from PDFs, Notion, Confluence, Slack, S3, and over 300 other sources.

How does LlamaIndex differ from general LLM frameworks?

Unlike general frameworks, LlamaIndex focuses specifically on the retrieval-and-grounding problem rather than the entire LLM application surface.

Is LlamaIndex free to use?

The core framework is free and open-source, while the LlamaCloud hosted platform is a paid service.

What is a potential downside of using LlamaIndex?

The API surface is large, and the documentation can be difficult to navigate due to multiple ways to achieve similar results.

LlamaIndex

Featured✓ Editorially verified

Data framework for connecting LLMs to your data.

Freemium· Free open-source; LlamaCloud paidRAGBYO (Claude / GPT / open)8.7 / 10

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In short

LlamaIndex is a specialized framework for building Retrieval-Augmented Generation (RAG) systems. It excels at connecting LLMs to diverse data sources through robust ingestion and indexing strategies. It is best for teams where retrieval quality is the primary bottleneck.

Best for

Pick LlamaIndex when retrieval quality is the bottleneck in your RAG system.

Skip if

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.

Editor's take

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

RAGdata ingestionindexing

Frequently asked

What programming languages does LlamaIndex support?: LlamaIndex is available as a framework for both Python and TypeScript.
What types of data sources can LlamaIndex ingest?: It supports ingestion from PDFs, Notion, Confluence, Slack, S3, and over 300 other sources.
How does LlamaIndex differ from general LLM frameworks?: Unlike general frameworks, LlamaIndex focuses specifically on the retrieval-and-grounding problem rather than the entire LLM application surface.
Is LlamaIndex free to use?: The core framework is free and open-source, while the LlamaCloud hosted platform is a paid service.
What is a potential downside of using LlamaIndex?: The API surface is large, and the documentation can be difficult to navigate due to multiple ways to achieve similar results.