Most modern writing apps force you to choose between privacy and power. Cloud-based AI editors train on your data, while local editors often lack intelligent features.
Markdown Reader bridges the gap. It is a native desktop application built on Tauri, ensuring your files stay locally on your machine. But it also seamlessly integrates with OpenAI, Anthropic, OpenRouter, and Local LLMs, bringing powerful AI-assisted writing directly to your offline workflow.
- 🔒 Local-First Architecture: Your files never leave your machine unless you explicitly call an external AI API.
- 🤖 Bring-Your-Own-AI: Configure OpenAI, Anthropic, OpenRouter, OpenAI-compatible providers, or local models.
- ✍️ Intelligent Editing: AI-assisted translation, summarization, formatting, and code-block cleanup.
- ⚡ Slash Commands: Type
/in the editor to trigger AI actions or insert a table inline, without opening the AI panel. - 🛡️ Audit & Rollback: Review all AI suggestions before applying them. Reject or rollback changes with a single click.
- 🖥️ Native Desktop Feel: Built with Next.js and packaged with Tauri for blazing-fast performance.
Get the app running locally for development in under a minute:
# Prerequisites: Python 3.11+, Node.js 18+, and uv
git clone https://github.com/petertzy/markdown-reader.git
cd markdown-reader
# Install dependencies
uv sync
cd frontend && npm install && cd ..
# Configure frontend environment variables
cp frontend/.env.local.example frontend/.env.local
# Launch the Tauri desktop app
./scripts/dev-tauri.shThe development helper starts the FastAPI backend on 127.0.0.1:8000 when that
port is available. If port 8000 is already occupied by another process, it
automatically chooses a free local port and passes it to the Tauri shell. To
request a specific backend port, run:
MARKDOWN_READER_BACKEND_PORT=8010 ./scripts/dev-tauri.shNote for Contributors: After cloning, run
uv sync --extra devanduv run pre-commit installto set up linting hooks.
We provide official desktop release builds through GitHub Actions.
| OS | Architecture | Download |
|---|---|---|
| macOS | Apple Silicon | Download .dmg |
| Windows | x64 | Download .exe |
| Linux | x64 | Download .AppImage |
(Note: macOS Intel builds are currently unsupported due to advanced PDF dependency constraints).
Markdown Reader is split into three main layers to ensure maximum performance and maintainability:
- Backend (
/backend): A blazing-fast FastAPI application handling rendering, export, and all AI API communications. - Frontend (
/frontend): A Next.js UI providing the rich editor experience, packaged within a Tauri desktop shell. When packaged for desktop use, the Python backend is bundled as a silent Tauri sidecar process, dynamically assigning ports at runtime to avoid conflicts. In development,./scripts/dev-tauri.shuses the same port-handoff path when it needs to avoid a local port conflict.
Type / at the start of a line (or after whitespace) in the editor to open an inline command menu:
| Command | Behavior |
|---|---|
/summarize |
Summarizes the document via AI |
/translate |
Opens the AI panel's Translate tab |
/format |
Applies Markdown formatting via AI |
/toc |
Generates a table of contents via AI |
/fix-code |
Fixes code block fences via AI |
/insert-table |
Inserts a Markdown table (no AI call) |
Use the arrow keys to navigate, Enter to run a command, or Escape to dismiss the menu.
Markdown Reader can call locally hosted OpenAI-compatible model servers. In the AI settings panel, choose Local Model, select LM Studio, Ollama, or Custom, fetch available models, and save the model you want to use. Local endpoints do not require an API key unless your local server is configured to require one.
Contributions are highly welcome! Whether you're fixing a bug, suggesting a feature, or helping us write documentation, we'd love to have you.
To get started:
git checkout -b your-feature-branch- Run linting:
uv run ruff check . - Run tests:
uv run python -m unittest discover -s tests - Open a Pull Request!
See our full Contribution Guidelines for more details.