docling

Docling turns messy documents into clean, structured data for AI. Feed it PDFs, Word docs, PowerPoints, spreadsheets, images, even HTML or email, and it produces Markdown or JSON with the layout, reading order, tables, formulas, and code preserved. It runs OCR on scanned pages too. Started by IBM Research and now under the LF AI and Data Foundation, it's MIT licensed and free.

It's a Python library (3.10+) that runs locally, which is the point: your documents never have to leave your machine, so it works in air-gapped or sensitive environments. It plugs straight into RAG and LLM pipelines with ready integrations for LangChain, LlamaIndex, Crew AI, and Haystack, and there's a CLI and an API server if you'd rather not code against it directly.

If your goal is feeding documents to an LLM, Docling is aimed squarely at you. It's less a raw OCR engine and more a document-to-structured-data framework, which is exactly what Textract's Analyze Document does, minus the per-page bill. Solo to large teams: free, and genuinely strong at the RAG-prep job. For plain text off a photo, a lighter tool like Tesseract is enough.

The catch is that quality depends on the document. Docling's layout and table understanding is good, but complex or badly scanned PDFs still trip it up, and heavy parsing with vision models wants real compute. It's a framework, so you own the pipeline and the tuning. Powerful, but not a fire-and-forget API.