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RAG and workflowOpen sourceUpdated 2026

LlamaIndex

Intermediate · Python/TS framework

Data framework for connecting LLMs to documents, databases, retrieval, and agents.

Best for

RAG applications where data ingestion, indexing, and retrieval are central.

Why use it

Strong fit for turning private data into retrieval and agent workflows.

Tradeoffs

You still need to evaluate retrieval quality, chunking, and source grounding.

Key features

  • Data connectors
  • Indexes
  • RAG workflows

Alternatives

LangChain, Haystack, Dify

Where it fits

LlamaIndex belongs in the rag and workflow layer of an open AI stack. Evaluate it against your model runtime, privacy needs, deployment target, and the amount of operational complexity your team can support.

CategoryRAG and workflowLicenseMITDeploymentPython/TS frameworkModeCode framework
LlamaIndex GitHub →

Recommendation

Use LlamaIndex when retrieval over data is the main problem.