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A research assistant that runs on your own machine. Your data stays put.
Unlike general-purpose AI chat, Yanmo is purpose-built for researchers: deconstruct vague advisor directions, track papers across multiple sources, cross-validate formula derivations, evaluate project proposals objectively, and assist with thesis writing. Five purpose-built tools, each with its own interface — not crammed into a single chat box.
Three core principles: data stays local, formulas must not be wrong, plugin system for extensibility. Plus AI-powered dedup, research gap finder, and AI-text humanizer.
Desktop application, not a web app. Runs on Windows, Linux, and macOS.
| Yanmo | General AI Chat | Research Rabbit | Zotero | |
|---|---|---|---|---|
| Data stays local | Yes (Ollama) | No | No | Partial |
| Formula verification | Dual-channel (SymPy + basic) | No | No | No |
| Multi-source paper search | ArXiv + Semantic Scholar + DBLP | No | Yes | No |
| Triple dedup | Title + arxiv_id + DOI | N/A | Partial | Partial |
| AI-text humanizer | Built-in (Chinese + English) | No | No | No |
| Plugin extensibility | Yes | No | No | Yes |
| Advisor direction decoder | Yes | Prompt engineering | No | No |
- Graduate students drowning in advisor feedback, paper tracking, and thesis writing
- Research engineers who need formula safety guarantees and can't send proprietary work to cloud APIs
- Independent researchers who want a unified toolchain without switching between 5 different apps
Requires Python 3.11+, Node.js 18+, and Ollama.
One-shot install:
# Linux / macOS
curl -fsSL https://raw.githubusercontent.com/sixtdreanight/Yanmo/master/scripts/install.sh | bash
# Windows (PowerShell)
Invoke-WebRequest https://raw.githubusercontent.com/sixtdreanight/Yanmo/master/scripts/install.ps1 | Invoke-ExpressionManual install:
pip install -e ".[dev]"
python -m backend.mainBackend runs at 127.0.0.1:8000. Then start the frontend:
cd frontend
npm install
npm run devOpen http://localhost:5173 in your browser.
Docker:
docker compose upDownload the installer for your platform from Releases:
| Platform | Format | Size |
|---|---|---|
| Windows 10/11 | .msi |
~50MB |
| macOS 12+ | .dmg |
~50MB |
| Linux (x86_64) | .AppImage / .deb |
~50MB |
Note: The desktop app bundles the React frontend with a Tauri shell. Python 3.11+ and Ollama are required as runtime dependencies (not bundled).
The mobile app connects to your desktop backend over the same WiFi network:
| Platform | Format |
|---|---|
| Android 8+ | .apk |
| iOS 16+ | Developer-signed .ipa |
- On desktop: go to Settings → 移动端配对, generate a pairing code
- On mobile: enter the desktop IP and pairing code to connect
- Your phone becomes a remote control for all desktop features
Note: The Qt frontend (
frontend_qt/) is deprecated. Use the Tauri desktop app or the React Web app instead.
Five tabs across the top (+ chat panel on the right): Advisor Decoder, Paper Tracker, Project Reviewer, Formula Validator, Thesis Writer.
Paste your advisor's words. Auto-decomposes into executable tasks with ordering and time estimates. Each task expands to show sub-steps and suggested resources. Advisor data is classified as confidential, forced to local model only.
Set research keywords. Parallel fetches from ArXiv, Semantic Scholar, and DBLP. Papers displayed in a feed with one-click Chinese summaries. Auto-updates every hour in the background.
Triple dedup (title similarity + arxiv_id + DOI) ensures no preprint appears twice. Click "Find Ideas" to analyze the current paper list across 10+ dimensions (theory, efficiency, robustness, fairness, etc.), flagging research gaps sorted by confidence.
Submit a project name and description. Scores on three dimensions — novelty, soundness, methodology — with specific weaknesses and improvement suggestions.
Split-pane LaTeX source and rendered preview. Submit for dual-channel validation: basic checks (bracket matching, division by zero, domain) + SymPy symbolic computation. Both channels must pass for a green light. No LLM involved — pure local symbolic computation.
Generates structured outlines with estimated word counts. Supports BibTeX paste-and-parse. Bottom panel has an "AI-text humanizer" tool that detects AI writing traces and auto-cleans.
Plugins live in ~/.yanmo/plugins/. Load them via Settings → Plugin Manager. Minimum requirement: plugin.toml + plugin.py. See plugin_schema/API.md for the development guide. Supports lifecycle management, hot-load/unload, and event bus.
| Component | Description |
|---|---|
| Plugin Engine | Lifecycle management, hot-load/unload, event bus |
| Task Scheduler | Auto-fetch papers hourly |
| WebSocket | Real-time push of paper updates to frontend |
| Security Layer | 3-tier data classification + cloud operation interception + audit logs |
| Storage | SQLite (structured) + ChromaDB (vectors) + local files |
Default: all operations use local Ollama. Cloud API keys are optional — if you don't fill them in, nothing leaves your machine.
- Confidential data is forced local; cloud operations are auto-blocked
- Cloud outbound traffic has audit logs
- Data stored in
~/.yanmo/, backup anytime
| Layer | Stack |
|---|---|
| Backend | Python, FastAPI, SQLite, ChromaDB, SymPy, SciPy |
| Frontend | React 18, TypeScript, Vite, KaTeX |
| Desktop Shell | Tauri (Rust) |
| Fonts | Noto Sans SC, Noto Serif SC |
- myBlog — Author's blog, more research-related articles
MIT