Most LLM extraction hands you clean JSON but no way to trust it: you can't tell which part of the source produced each field, so catching a hallucination means re-reading the whole document. LangExtract makes source grounding a first-class output — every extracted value carries the exact character offsets it came from, so review becomes clicking a highlight instead of re-reading.
What Sets It Apart
- Character-level grounding maps each extraction to its span in the original text and ships an interactive HTML view that highlights entities in context, so an auditor can confirm a result in seconds.
- Schema by example, not fine-tuning — you supply a prompt plus a few annotated examples, and controlled generation enforces a consistent output shape. No training run, no labeled dataset.
- Built for long inputs via chunking, parallel processing, and multi-pass extraction that recovers entities a single pass misses, instead of silently truncating at the context limit.
- Model-agnostic across Gemini, OpenAI, and local models through Ollama, so you can prototype on a cloud model and move sensitive data to on-prem inference without rewriting prompts.
Who It's For
Great fit if you pull entities from dense, domain-heavy text — clinical notes, radiology reports, contracts — and need every result traceable to its origin for audit or compliance. The example-driven setup pays off when accuracy matters more than zero configuration. Look elsewhere if you want turnkey document parsing with no prompt design, or if your task is simple key-value scraping where a regex or a single LLM call already suffices — the grounding and multi-pass machinery is overhead you won't use. Note it is a Google open-source project, not an officially supported Google product.
