# markitai **Category:** πŸ’» Developer Tools **Repository:** https://github.com/Ynewtime/markitai **Views:** 0 **Installs:** 0 **Upvotes:** 0 **Directory Page:** https://allmcps.com/mcp/markitai ## Description Convert documents, images and web pages to clean Markdown, for agents: single, URL and batch tools. ## Claude Desktop Quick Installation Heuristic fallback β€” verify the package name and runner against the repository README before running it. Uses `npx` (confidence: low): ```json "mcpServers": { "markitai": { "command": "npx", "args": ["-y","markitai"] } } ``` ## Documentation & README # Markitai [![PyPI](https://img.shields.io/pypi/v/markitai)](https://pypi.org/project/markitai/) [![Python](https://img.shields.io/pypi/pyversions/markitai)](https://pypi.org/project/markitai/) [![CI](https://github.com/Ynewtime/markitai/actions/workflows/ci.yml/badge.svg)](https://github.com/Ynewtime/markitai/actions/workflows/ci.yml) [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://github.com/Ynewtime/markitai/blob/main/LICENSE) Opinionated Markdown converter with native LLM enhancement support. - **Multi-format**: DOCX, PPTX, XLSX, PDF, EPUB, EML, TXT, MD, images (JPG/PNG/WebP), and URLs β†’ clean Markdown; legacy `.doc`/`.ppt` via the `legacy` extra - **LLM enhancement**: format cleaning, frontmatter metadata, and vision analysis of embedded images, all through [litellm](https://github.com/BerriAI/litellm), so any provider works (OpenAI, Anthropic, Gemini, local CLIs, and more) - **Batch processing**: concurrent conversion with progress display and `--resume` for interrupted jobs - **OCR**: scanned PDFs and images via local RapidOCR (optional extra, see below), or `--ocr --llm` to have the vision model read the page images directly (VLM-OCR) - **Web fetching**: static HTTP with cache revalidation, or Playwright rendering for JS-heavy pages - **Local web workspace**: upload files or folders, submit URLs, configure LLM providers, compare results, retry failures, and revisit conversion history. CLI runs can opt in to that history too, with `--record-history` Docs: ## Install **Guided installer** (recommended). It installs Python and uv if they are missing, lets you pick extras and the Playwright browser, and offers a mirror when the default index is unreachable. Bilingual (EN/δΈ­ζ–‡). ```bash # Linux/macOS curl -fsSL https://markitai.dev/setup.sh | sh # Windows (PowerShell) powershell -ExecutionPolicy ByPass -c "irm https://markitai.dev/setup.ps1 | iex" ``` **Already have Python 3.11–3.13?** Install the package on its own and run the two setup steps yourself: ```bash uv tool install markitai # or: pipx install markitai markitai doctor # check core and optional capabilities markitai init # config and LLM provider ``` Browser rendering needs the `browser` extra, then Chromium: ```bash uv tool install "markitai[browser]" --force markitai doctor --fix ``` Both routes install `markitai` and the shorter `mkai` alias. ### Extras | Extra | Enables | | --- | --- | | `browser` | Playwright rendering for JS-heavy pages | | `claude-agent` | Claude Agent SDK as an LLM provider | | `copilot` | GitHub Copilot SDK as an LLM provider | | `extra-fetch` | curl-cffi HTTP client (better anti-bot compatibility) | | `heif` | HEIC/HEIF/AVIF image input | | `legacy` | Legacy Office conversion (`.doc`/`.ppt`) via the anydoc Rust backend | | `mcp` | Bundled `markitai-mcp` server for AI agents (Model Context Protocol) | | `ocr` | Local OCR for scanned PDFs and images (`--ocr`) | | `serve` | Local web workspace and REST API | | `svg` | SVG rasterization via cairosvg | | `all` | Everything above | `ocr` is opt-in because it pulls in ~160MB of models. The guided installer asks whether you want it, and `markitai doctor` prints the install command when it is missing: ```bash uv tool install "markitai[ocr]" --force ``` Launch the local web workspace with: ```bash uv tool install "markitai[serve]" --force markitai serve ``` ## Quick start ```bash markitai document.pdf -o out/ # convert a file markitai https://example.com -o out/ # convert a URL markitai ./docs -o out/ # batch convert a directory markitai ./docs -o out/ --json # machine-readable results for automation markitai https://example.com --no-remote-fetch -o out/ # local URL extraction only markitai doctor # check dependencies and configuration ``` For LLM enhancement, export any supported provider key. markitai picks the model up from the environment; you don't need a config file: ```bash export GEMINI_API_KEY=... # or OPENAI_/ANTHROPIC_/DEEPSEEK_/OPENROUTER_API_KEY markitai document.pdf -o out/ --llm # clean formatting + generated frontmatter markitai document.pdf --preset rich # LLM + alt text + descriptions + screenshots markitai init # or configure it interactively, once ``` ## MCP server `markitai-mcp` exposes conversion to AI agents over the Model Context Protocol with four tools: `convert_document`, `convert_url`, `batch_convert`, `job_status`. There is nothing to install, since `uvx` runs it on demand, and large outputs land on disk instead of in the model context. For Claude Code: ```bash claude mcp add markitai -- uvx --from "markitai[mcp]" markitai-mcp ``` The [MCP guide](https://markitai.dev/guide/mcp) covers other clients, LLM enhancement and batch jobs. `markitai mcp` starts the same server through the CLI itself, which is how the [MCP Registry](https://registry.modelcontextprotocol.io) lists it. ## Documentation - [Getting started](https://markitai.dev/guide/getting-started): install, first conversion, output layout, supported formats - [CLI reference](https://markitai.dev/guide/cli): every command and flag - [Configuration](https://markitai.dev/guide/configuration): config file, environment variables, LLM providers, every setting - [Fetch policy](https://markitai.dev/guide/fetch-policy): the URL strategy cascade, domain profiles, what stays local - [Output profiles](https://markitai.dev/guide/output-profiles): `rag`, `obsidian` and `okf` output shaping - [Web workspace](https://markitai.dev/guide/serve): `markitai serve`, its history and REST API - [Python API](https://markitai.dev/guide/python-api): `convert()` and `aconvert()` as a library - [Conversion performance](https://markitai.dev/guide/performance): measured local-conversion results and how to reproduce them - [Why Markitai](https://markitai.dev/guide/comparison): how it compares with markitdown, docling and anydoc Contributors start at [CONTRIBUTING.md](https://github.com/Ynewtime/markitai/blob/main/CONTRIBUTING.md). ## How markitai compares Two of the tools we compare markitai with are also its dependencies: markitdown converts the Office formats, and anydoc handles legacy `.doc`/`.ppt` behind `markitai[legacy]`. Against them and docling, markitai gives up ecosystem reach, ML document-structure models and dependency-free speed, and gets a built-in LLM pipeline, live web fetching and a local workspace in return. The feature-by-feature table lives in [Why Markitai](https://markitai.dev/guide/comparison). ## License markitai's own source code is [MIT](https://github.com/Ynewtime/markitai/blob/main/LICENSE). The default installation is not uniformly MIT, because the PDF engine is not. The PyMuPDF packages `pymupdf`, `pymupdf-layout` and `pymupdf4llm` come from Artifex Software and are dual-licensed under **AGPL-3.0 or a commercial licence from Artifex**. They are required dependencies: PDF conversion does not work without them. Running the CLI on your own machine, or a `markitai serve` instance only you talk to, carries no AGPL obligation. Redistributing the combined work, or offering it to other people over a network, does. [NOTICE](https://github.com/Ynewtime/markitai/blob/main/NOTICE) carries the full terms, the rest of the dependency licensing, and the attribution for the code markitai ports from [defuddle](https://github.com/kepano/defuddle) (MIT) and [marker](https://github.com/VikParuchuri/marker) (Apache-2.0).