Monastery

Build in silence. Deploy with purpose.

A calm, focused, self-hosted sanctuary for AI-assisted coding. Monastery is a fully self-hosted, browser-based AI coding environment where you prompt local or frontier LLMs to generate, edit, run, debug, and deploy full applications. The harness runs as a standalone service (Docker-first) and connects to LLM backends over the network.

This is an in progress project.

Key Features

• Decoupled Architecture: Harness runs independently of LLM servers. Auto-discovery or manual config for local endpoints.
• 100% Self-Hosted: Everything containerized, network-aware, and privacy-focused.
• Lightweight: Harness container <1GB RAM idle; works on low-power nodes.
• Homelab Native: Deep integrations with Proxmox, Coolify, PocketBase, MQTT, etc.
• OpenAI-Compatible: Works with Ollama, vLLM, llama.cpp, OpenAI, Groq, and more.

Quick Start

Prerequisites

• Docker and Docker Compose
• An LLM endpoint (e.g., Ollama, vLLM, or OpenAI API key)

1. Clone and Configure

git clone https://github.com/jherforth/Monastery.git
cd Monastery
cp .env.example .env

Edit .env to configure your LLM endpoint:

# For Ollama on same host (Linux/Mac):
LLM_BASE_URL=http://host.docker.internal:11434

# For Ollama in separate container:
LLM_BASE_URL=http://ollama:11434

# For OpenAI:
LLM_BASE_URL=https://api.openai.com/v1
OPENAI_API_KEY=sk-...

2. Run with Docker Compose

docker compose up -d

The harness will be available at http://localhost:3000.

3. Connect to Your LLM

1. Open the web UI at http://localhost:3000
2. Navigate to Settings → LLM Endpoints
3. Add your LLM endpoint or use auto-discovery to find Ollama on your LAN
4. Test the connection and start prompting!

Architecture

┌─────────────┐     HTTP      ┌──────────────┐
│   Browser   │ ◄──────────► │   Harness    │
│   (Web UI)  │   WebSocket   │   (Rust)     │
└─────────────┘               └──────┬───────┘
                                     │
                          ┌──────────┼──────────┐
                          │          │          │
                          ▼          ▼          ▼
                   ┌──────────┐ ┌────────┐ ┌─────────┐
                   │  Ollama  │ │ vLLM   │ │ OpenAI  │
                   │ (local)  │ │(local) │ │(cloud)  │
                   └──────────┘ └────────┘ └─────────┘

Tech Stack

• Backend: Rust (Axum) - lightweight, safe, performant
• Frontend: React + TypeScript with Vite - modern, responsive UI with Monaco editor
• Database: SQLite - embedded, easy backup
• Sandbox: Docker-based isolated execution
• LLM Client: OpenAI-compatible protocol
• Styling: Tailwind CSS with custom Monastery theme

Configuration

Environment Variable	Description	Default
PORT	Server port	3000
DATA_DIR	Data directory path	./data
LOG_LEVEL	Logging level	info
LLM_BASE_URL	Default LLM endpoint	-
DISABLE_DISCOVERY	Disable mDNS discovery	false

API Endpoints

• GET /api/health - Health check
• GET /api/models - List available models
• POST /api/models/:id/chat - Stream chat completion
• GET /api/endpoints - List configured endpoints
• POST /api/endpoints - Add new endpoint
• DELETE /api/endpoints/:id - Remove endpoint
• POST /api/endpoints/:id/test - Test connectivity
• GET /api/discovery - Discover local services

Development

Build from Source

# Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Build
cargo build --release

# Run
cargo run

Run Tests

cargo test

Self-Hosting Wizard

The built-in wizard helps you:

1. Generate docker-compose.yml for your generated apps
2. Configure network connections between services
3. Set up reverse proxies (Traefik, Nginx)
4. Deploy to Coolify, Proxmox, or Kubernetes

Security

• Minimal outbound connectivity by default
• Sandboxed code execution
• No unnecessary permissions
• Encrypted credential storage (optional)

License

MIT License - see LICENSE for details.

Contributing

Contributions welcome! Please read our contributing guidelines before submitting PRs.

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Built with calm intention for the homelab community

"Build in silence. Deploy with purpose."