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LlamaIndex

A framework for connecting LLMs to your data — RAG pipelines, document indexing, query engines. LlamaIndex specializes in the 'retrieve the right data for the LLM' problem. More focused than LangChain, better at data ingestion and retrieval.

Created by

Jerry Liu · Created LlamaIndex in 2022

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When to use
  • RAG apps (chat over your documents).
  • Data ingestion + retrieval for LLMs.
  • When you want a focused RAG framework.
When not to
  • General agent apps (use LangChain/LangGraph).
  • When you need full control (write it yourself).
  • Simple one-shot LLM calls.

Definition

LlamaIndex is a data framework for LLM apps. It ingests documents (PDFs, APIs, databases), chunks and indexes them (vector stores, keyword indexes), and provides query engines that retrieve relevant data to augment LLM prompts. Simpler than for RAG-focused apps.

Why it exists

RAG is the most common LLM pattern — but chunking, indexing, and retrieval are fiddly. LlamaIndex handles the data pipeline so you focus on the app, not the plumbing.

How it works

Ingest documents → chunk and embed → store in a vector DB or → query engine retrieves relevant chunks → prepends to LLM prompt → LLM generates answer. Tools for chunking strategies, node parsing, and multiple index types.

Scaling limits

RAG-focused (not a general agent framework like ). Chunking strategy matters a lot. Abstraction can hide what's happening. Performance tuning is needed for large datasets.

Cost shape

Free and open source. You pay for /LLM APIs and vector DB storage.

Alternatives

  • LangChain — More general, more abstraction, more complex.
  • Raw RAG (vector DB + LLM) — Full control, more code.
  • Haystack — NLP pipeline focus, less LLM-specific.

Failure modes

  • Poor chunking.
  • Retrieval quality.
  • Abstraction overhead.
  • staleness.

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