LLM-driven document extraction / structured data extraction from unstructured files
Unstract is an LLM-powered platform that turns PDFs, images, scans, and other unstructured documents into structured JSON defined by natural-language prompts, deployable as an API or ETL pipeline.
Same problem, same approach. Swapping one for another is a config change, not a rewrite.
choose when you want a full self-hosted IDP platform with ML/LLM/Vision and agent workflows under GPL instead of AGPL.
Structured data extraction, instruction calling and agentic workflows with ML, LLM and Vision LLM
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
choose when you want an embeddable Apache-licensed Python library for schema-driven LLM extraction instead of a full AGPL platform/API.
ContextGem: Effortless LLM extraction from documents
choose when you need a permissive, deterministic parsing/ETL layer for document pipelines rather than an LLM prompt/API system.
Convert documents to structured data effortlessly. Unstructured is open-source ETL solution for transforming complex documents into clean, structured formats for language models. Visit our website to learn more about our enterprise grade Platform product for production grade workflows, partitioning, enrichments, chunking and embedding.
choose when you want an Apache-licensed, OCR-free Python extraction/benchmark toolkit that fits your own pipeline better than an AGPL platform.
An on-premises, OCR-free unstructured data extraction, markdown conversion and benchmarking toolkit. (https://idp-leaderboard.org/)
choose when you want a self-hosted MIT REST API combining OCR/Ollama and PII removal to produce structured JSON.
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
choose when you need grounded, attributable LLM extractions from raw text with source visualization rather than a full document-API platform.
A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.
choose when your desired structured output is knowledge graphs/hypergraphs from text rather than document JSON.
Hypergraph is more powerful. Transform unstructured text into structured knowledge with LLMs. Graphs, hypergraphs, and spatio-temporal extractions — with one command.
An earlier or more limited way to do the job. Still the right call when you need something small, proven, or CPU-only.
choose when you want a lightweight MIT converter/OCR wrapper for many file formats rather than a prompt-based JSON platform.
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.
choose when you need deterministic, auditable PDF layout JSON without running LLM inference.
Transforms PDF, Documents and Images into Enriched Structured Data
These projects were analysed and named unstract among their alternatives. The relationship is not symmetric — how unstract rates them is a separate judgement, made when unstract is analysed in its own right.
calls unstract “LLM extraction platform with UI and ETL pipelines; choose it for no-code deployments rather than an importable library.”
A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.
calls unstract “Choose if you want a platform to build LLM extraction pipelines via UI/API; it's a heavier deployment with AGPL licensing.”
PDF Parser for AI-ready data. Automate PDF accessibility. Open-source.