FlexNetOS Star Mono

A unified monorepo combining the best open-source AI/ML tools and frameworks.

🏗️ Architecture

star-mono/
├── apps/          # User-facing applications
├── services/      # Microservices
├── packages/      # Shared libraries
├── trainer/       # ML training pipelines
├── vendor/        # Git subtree imports
├── external/      # Git submodule imports
└── notebooks/     # Jupyter notebooks

🚀 Quick Start

# Clone the repository
git clone --recursive https://github.com/FlexNetOS/star-mono.git
cd star-mono

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

# Build all services
cargo build --workspace

# Run the API gateway
cargo run --bin api-gateway

🔧 Services

• API Gateway - Central API orchestration (Axum)
• Inference - ML model serving (Candle/llama.cpp)
• Agents - Autonomous agent coordination (Goose/Letta)
• Retrieval - RAG and vector search (Qdrant/Chroma)
• Data - Data processing pipelines (DataFusion/DuckDB)

📦 Included Projects

Core Infrastructure

• Pingora - Cloudflare's proxy framework
• Axum - Web framework
• Tokio - Async runtime

ML/AI

• Burn - Rust ML framework
• Candle - Rust tensor operations
• llama.cpp - LLM inference

Agents

• Letta - Memory-enhanced agents
• OpenHands - Code generation
• Browser-use - Web automation

🔄 Repository Management

Repositories are managed via:

• Git Subtrees - For vendored dependencies we modify
• Git Submodules - For large external projects

Update all dependencies:

# Update subtrees
./scripts/sync-subtrees.sh

# Update submodules
git submodule update --remote --recursive

📊 Observability

Built-in observability with:

• OpenTelemetry tracing
• Structured logging
• Prometheus metrics (coming soon)

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Run tests: cargo test --workspace
5. Submit a pull request

📄 License

This project is dual-licensed under MIT and Apache-2.0. Individual vendored/external projects retain their original licenses.