LlamaParse

LlamaIndex is an open-source data framework and developer platform (originally GPT-Index) that provides connectors, indexing structures, and retrieval utilities to connect documents and external data to large language models. It is maintained by the LlamaIndex team and is used to build LLM-powered pipelines, agents, and document workflows.

LiteParse is an open-source, standalone document parser from LlamaIndex focused on fast, local parsing of PDFs and other documents without proprietary LLM features or cloud dependencies. Its Rust-based pipeline converts Office files and images to PDF when needed, extracts native text with PDFium, optionally applies Tesseract or an HTTP OCR service, merges the results, and reconstructs spatial layout using text positions and bounding boxes. It can output Markdown, JSON, or layout-preserved plain text, generate page screenshots, detect documents that need OCR or heavier processing, and expose classified layout blocks such as headings, paragraphs, lists, tables, code, and figures; the JSON representation can include per-cell table coordinates and other opt-in PDF metadata. LiteParse is distributed through CLI, Rust, Python, Node.js/TypeScript, and browser WebAssembly packages, and is licensed under Apache 2.0.

Docling is an open-source document-processing toolkit from the Docling project for preparing files for generative-AI applications. It parses formats including PDF, DOCX, PPTX, XLSX, HTML, EPUB, images, plain text, audio, and video, and represents the results in a unified DoclingDocument structure. For PDFs, it analyzes page layout, reading order, headings, tables, code, formulas, images, and scanned content through OCR. Audio and video inputs can be processed with automatic speech-recognition models; video parsing produces an ASR transcript and representative keyframes. Parsed content can be exported as Markdown, HTML, WebVTT, DocLang, DocTags, or lossless JSON. Docling supports local and air-gapped execution, a command-line interface, Python usage, an API server named docling-serve, and an MCP server for connecting agents. The project also provides integrations for LangChain, LlamaIndex, CrewAI, and Haystack. It is installable with pip and runs on macOS, Linux, and Windows on x86_64 and arm64 systems.

DocJev is an open-source Python library, CLI, and local app for classifying and splitting PDF, DOCX, and PPTX files with user-written natural-language rules. LiteParse extracts page text locally, then the hosted Jev decision engine assigns document categories or identifies boundaries between component documents; optional LlamaParse tiers provide cloud OCR for difficult inputs. Classification returns a category, probabilities, and review flags. Splitting returns ordered categories and contiguous page ranges, with optional export of each segment as a PDF and review reasons for uncertain boundaries. DOCX and PPTX processing requires LibreOffice, and inference is not offline because Jev is a hosted service. The project is distributed under the Apache-2.0 license.

doc7 is an open-source document-to-Markdown tool that converts PDFs, Office files, scans, screenshots, charts, formulas, and diagrams into AI-ready Markdown using an OpenAI-compatible multimodal vision model. It renders and understands complete pages rather than relying on character extraction, preserving text, tables, formulas, chart and diagram relationships, image meaning, and visible UI state; the video also describes page-level retry and resume. The model can run locally through LM Studio or Ollama or through a remote endpoint, with no required OCR stack or document-processing service. The same binary provides an interactive CLI, batch processing, model checks, MCP support, a Go SDK, and an asynchronous HTTP service, with installers for macOS, Linux, and Windows.