LangExtract vs LlamaIndex

A side-by-side look at pricing, capabilities, pros, cons, and our editorial scores.

	LangExtract RAG	LlamaIndex RAG
Tagline	Google's open-source Python library for LLM-driven structured extraction from unstructured text, with source-grounded outputs.	Data framework for connecting LLMs to your data.
Category	RAG	RAG
Pricing	Free· Library is free (Apache-2.0); LLM API costs depend on chosen backend	Freemium· Free open-source; LlamaCloud paid
Model	Multi-model (Gemini, GPT-4/4o, Ollama-hosted local models)	BYO (Claude / GPT / open)
Editorial score	—	8.7 / 10
Use cases	structured-extractiondocument-parsingentity-extractionlong-document-qaclinical-textlegal-document-parsing	RAGdata ingestionindexing
Pros	Source grounding maps every extracted field back to its character span in the original text Handles long documents via chunking and multi-pass extraction Works with Gemini, OpenAI, and local Ollama models behind one API Built-in interactive HTML visualizer for reviewing extractions Apache-2.0 and pip-installable with no vendor lock-in	Focused on retrieval (not general agent stuff) Many ingestion connectors Strong production patterns LlamaCloud for managed ingestion
Cons	Python-only; no hosted UI or no-code interface Quality and cost still hinge entirely on the backing LLM you choose Not an officially supported Google product, so SLAs are community-grade	API surface is large Documentation can be hard to navigate
Website	pypi.org	www.llamaindex.ai

Pick LangExtract if

✅ Source grounding maps every extracted field back to its character span in the original text
✅ Handles long documents via chunking and multi-pass extraction
✅ Works with Gemini, OpenAI, and local Ollama models behind one API
✅ Built-in interactive HTML visualizer for reviewing extractions

Pick LlamaIndex if