LLM-based structured extraction from unstructured text/documents
LangExtract is a Python library that extracts structured information from unstructured text using LLMs, with precise source grounding into the source text and interactive visualization.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
Direct Python peer for grounded LLM extraction from documents; choose when you want active maintenance and do not need Google-specific visualization.
ContextGem: Effortless LLM extraction from documents
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Choose for vision-based document AI when your inputs include scanned images or complex layouts requiring OCR.
Structured data extraction, instruction calling and agentic workflows with ML, LLM and Vision LLM
Choose when you need graph/hypergraph output instead of flat JSON schemas from text.
Hypergraph is more powerful. Transform unstructured text into structured knowledge with LLMs. Graphs, hypergraphs, and spatio-temporal extractions — with one command.
Choose for an OCR-free, model-based document extraction and benchmarking toolkit if you prefer deterministic ML over LLM calls.
An on-premises, OCR-free unstructured data extraction, markdown conversion and benchmarking toolkit. (https://idp-leaderboard.org/)
Choose for OCR-backed document conversion and structured extraction when inputs are images, PDFs, or PowerPoints.
Extract and convert data from any document, images, pdfs, word doc, ppt or URL into multiple formats (Markdown, JSON, CSV, HTML) with intelligent structured data extraction and advanced OCR.
Lower-level structured-output library for LLMs; choose when you want model-agnostic Pydantic schemas and will build sourcing/grounding yourself.
structured outputs for llms
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
Earlier prompt-template library for getting structured responses; choose only for simple legacy schemas and limited model support.
Prompt Engineering | Prompt Versioning | Use GPT or other prompt based models to get structured output. Join our discord for Prompt-Engineering, LLMs and other latest research
TypeScript library for structuring web pages with LLMs; choose when source is a webpage and your stack is JavaScript/Node.
Turn any webpage into structured data using LLMs
Archived LangChain extraction prototype; choose only if staying in LangChain and accepting unmaintained code.
🦜⛏️ Did you say you like data?
Does the same job, but ships as an app. Useful to a person, not swappable into a codebase.
Runnable OCR/LLM document-extraction HTTP API with anonymization, not an embeddable library.
Document (PDF, Word, PPTX ...) extraction and parse API using state of the art modern OCRs + Ollama supported models. Anonymize documents. Remove PII. Convert any document or picture to structured JSON or Markdown
LLM extraction platform with UI and ETL pipelines; choose it for no-code deployments rather than an importable library.
LLM-Driven Extraction of Unstructured Data — Built for API Deployments & ETL Pipeline Workflows
These projects were analysed and named langextract among their alternatives. The relationship is not symmetric — how langextract rates them is a separate judgement, made when langextract is analysed in its own right.
calls langextract “Choose it for extraction with precise source grounding and visualization, but it's more specialized than Instructor's general validation loop.”
structured outputs for llms
calls langextract “choose when you need grounded, attributable LLM extractions from raw text with source visualization rather than a full document-API platform.”
LLM-Driven Extraction of Unstructured Data — Built for API Deployments & ETL Pipeline Workflows