LangExtract vs Pathway

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

	LangExtract RAG	Pathway RAG
Tagline	Google's open-source Python library for LLM-driven structured extraction from unstructured text, with source-grounded outputs.	Live data framework for production RAG and streaming ETL pipelines in Python.
Category	RAG	RAG
Pricing	Free· Library is free (Apache-2.0); LLM API costs depend on chosen backend	Freemium· Community free (BSL 1.1, 8GB/4 cores); Scale and Enterprise tiers with license key
Model	Multi-model (Gemini, GPT-4/4o, Ollama-hosted local models)	Multi-model
Editorial score	7.1 / 10	7.3 / 10
Use cases	structured-extractiondocument-parsingentity-extractionlong-document-qaclinical-textlegal-document-parsing	live-ragstreaming-etldocument-indexingmultimodal-raganomaly-detection
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	Genuinely live indexing - documents update without rebuild jobs Self-hosted under BSL 1.1, no data leaves your infra Rich connector library (Kafka, S3, SharePoint, Postgres, Delta Lake) Same pipeline handles batch and streaming 20+ production-ready templates including multimodal and adaptive RAG
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	Steeper learning curve than prompt-chain frameworks BSL is not OSI-approved - commercial restrictions apply at scale Smaller community than LangChain/LlamaIndex Pricing for Scale/Enterprise tiers not transparent
Website	pypi.org	pathway.com

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 Pathway if

✅ Genuinely live indexing - documents update without rebuild jobs
✅ Self-hosted under BSL 1.1, no data leaves your infra
✅ Rich connector library (Kafka, S3, SharePoint, Postgres, Delta Lake)
✅ Same pipeline handles batch and streaming

Compare a different pair →