Cargo Orchestrator Workspace

A uv workspace containing tools for Rust development and model evaluation.

Packages

📦 packages/cargo-orchestrator

A Python library for running cargo build and cargo clippy commands with structured output parsing.

Features:

• Run cargo build/clippy with various options
• Parse JSON and human-readable output
• Extract structured error and warning messages
• Support for nightly, custom targets, features

Install: pip install cargo-orchestrator

🖥️ apps/cargo-orchestrator-cli

Command-line interface for the cargo-orchestrator library.

Features:

• Colored, formatted output summaries
• JSON export for CI/CD integration
• Quiet mode for scripts
• Clippy integration

Install: pip install cargo-orchestrator-cli

Usage: cargo-orchestrator --root-dir /path/to/project --clippy

🔬 apps/openzt-eval

Model evaluation tool for testing language models using braintrust and autoevals.

Features:

• Local model support via lmstudio
• Remote model support via braintrust proxy
• Automated evaluation scoring
• Support for multiple model providers
• Rich reporting and metrics

Install: See package README

Usage: openzt-eval --models gpt4:openai claude:anthropic --test-file tests.json

Workspace Structure

.
├── packages/           # Library packages
│   └── cargo-orchestrator/
├── apps/              # Application packages
│   ├── cargo-orchestrator-cli/
│   └── openzt-eval/
├── examples/          # Example scripts
├── tests/             # Workspace tests
└── test_projects/     # Test Rust projects

Development

Prerequisites

• Python 3.10+
• uv for package management
• Rust/Cargo (for cargo-orchestrator)

Setup

# Clone and setup workspace
git clone <repo>
cd cargo-orchestrator
uv sync

# Run examples
uv run python examples/example.py

# Run CLI tools
uv run cargo-orchestrator --help
uv run -m openzt_eval --help

Testing

# Run cargo-orchestrator tests
uv run python tests/run_tests.py

# Test CLI tools
uv run cargo-orchestrator --root-dir test_projects/success_project
uv run -m openzt_eval --models test:openai --no-braintrust --quiet

Adding New Packages

1. Library package: Add to packages/
2. Application package: Add to apps/
3. Update workspace: Add to pyproject.toml workspace members
4. Dependencies: Use tool.uv.sources for workspace dependencies

License

MIT

Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for your changes
4. Ensure all tests pass: uv run python tests/run_tests.py
5. Submit a pull request