Docling

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.

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.

anydoc is an open-source Rust library from Firecrawl that converts Word, PowerPoint, Excel, OpenDocument, RTF, EPUB, CSV, and PDF files into GitHub-Flavored Markdown. It uses a shared document model and provides Node.js, Python, browser WebAssembly, and Rust interfaces, along with a command-line interface that reads files or standard input and writes Markdown to standard output or a file. The library can return either Markdown or its intermediate document model, which carries embedded assets. A browser demo runs the WebAssembly build locally, while hosted OCR through Firecrawl Parse can process scanned PDF pages that anydoc cannot read by itself. It is also distributed as an agent skill compatible with agent tools such as Claude Code, Codex, Cursor, and OpenCode.

LLM Wiki is a cross-platform desktop application maintained by nash_su that turns imported documents into a persistent, organized, interlinked knowledge base. It uses a three-layer structure of immutable raw sources, LLM-generated wiki pages, and configuration rules, with ingest, query, and lint operations; generated pages use YAML frontmatter, source traceability, an index, an operation log, and [[wikilink]] cross-references. Its ingest pipeline first analyzes each source for entities, concepts, connections, contradictions, and recommended structure, then generates or updates wiki pages, summaries, indexes, research queries, and human-review items. SHA256 caching skips unchanged files, while a persistent queue supports serialized processing, retries, cancellation, and crash recovery. It accepts PDFs, Office documents, EPUB/MOBI, Org mode, images, media, web clips, and URL batches, with optional vision-based image captions and MinerU processing for complex PDFs. Retrieval combines tokenized keyword search, optional LanceDB vector search through OpenAI-compatible embedding endpoints, and graph expansion based on direct links, shared sources, Adamic-Adar similarity, and page-type affinity. The application also provides Louvain community detection, graph insights, deep research through Tavily, SerpApi, or SearXNG, a Rust tool-using chat agent, review workflows, Mermaid and KaTeX rendering, a Chrome web clipper, and project export/import. It includes a token-protected local HTTP API and MCP server for searching, reading files, traversing the graph, rescanning sources, and calling the backend agent. The application is built with Tauri v2, React, TypeScript, and Rust, has macOS, Windows, and Linux builds, and is licensed under GPL-3.0.

OfficeCLI is an open-source command-line Office suite developed by iOfficeAI for AI agents to create, read, edit, render, and automate Word (.docx), Excel (.xlsx), and PowerPoint (.pptx) files without a Microsoft Office installation or external dependencies. It is distributed as a single binary and provides commands for creating documents, adding, modifying, moving, copying, and removing elements, reading text and structure as plain text or JSON, and saving changes. Its built-in HTML rendering engine converts DOCX, XLSX, and PPTX files to HTML or PNG for a render-and-review workflow. The `watch` command provides a live browser preview that refreshes after document changes, while the tool can also analyze formatting and structural issues, evaluate Excel formulas, and install an agent skill for supported AI coding agents.