🕵️‍♀️ Cartai 🤖— The AI supervisor agent, for AI

Crafting intelligent E2E supervision & documentation for trustworthy AI

👩‍💼 Agent-powered project intelligence, from PRD to production

cartai is a library that enables end-to-end traceability and lineage of your AI projects. PRDs, data lineage, training experiments, deployments, monitoring, and third-party vibe-coding platforms.

🔌 MCP Integrations

Integration	Status	AI Governance Step	Key Features	Description
🏃 MLFlow	✅ Active v0	Model Training & Experimentation	• Experiment Tracking • Model Registry • Artifact Management	Comprehensive tracking of ML experiments, model versions, and metrics for reproducible AI development
🎲 dbt	✅ Active v0 (Official)	Data Lineage & Feature Engineering	• Data Transformation • Feature Pipeline Tracking • SQL Model Management	End-to-end visibility into data transformations and feature engineering processes
📝 Notion	✅ Active v0 (Official)	Project Documentation & Requirements	• PRD Management • Documentation Sync • Project Timeline Tracking	Seamless integration of project documentation, requirements, and AI governance documentation

📂 Codebase Structure

WIP

⚙️ Installation

To get started with the Cartai project, follow these instructions to set up your environment:

1. Clone the repository:

   git clone https://www.github.com/ContrastoAI/cartai
   cd cartai

2. Ensure you have uv and pre-commit installed. You can check their installation with:

   make .uv
   make .pre-commit

3. Install all dependencies and set up your environment:

   make install-all

💻 Usage

You can run the project using the provided Makefile commands. For example, to generate the README documentation, you can use:

make run_readme

This command will execute the documentation generation process with the description "Crafting intelligent E2E documentation for trustworthy AI." and output it to README_new.md.

Other Makefile Commands

• Format code:

  make format

• Lint code:

  make lint

• Run tests:

  make test

• Run pre-commit hooks:

  make pre-commit

🚀 Deployment

To deploy the project, follow the standard deployment procedures for your environment. Ensure all dependencies are installed, and run the necessary commands as needed.

🤝 Contributing

We welcome contributions! Here's how you can contribute:

1. Fork the repository 🍴
2. Create your feature branch:

   git checkout -b feature/YourFeature

3. Commit your changes:

   git commit -m 'Add YourFeature'

4. Push to the branch:

   git push origin feature/YourFeature

5. Open a pull request 📬

Please follow the coding guidelines and check the Makefile or contributing docs if available.