Getting Started
Install, authenticate, and create your first autonomous DeepBox workflow.
1. Install the CLI
Run `npm install -g deepbox` or use the Docker image to get the latest tooling for orchestrating experiments.
2. Authenticate
Generate an API token from the DeepBox dashboard and run `deepbox login --token <TOKEN>` to authorize the CLI.
3. Launch your first agent
Use `deepbox init` to scaffold a project, point it at your dataset, and run `deepbox up` to deploy an autonomous research loop.
4. Explore project scaffolding
The generated project includes example configs, workflow templates, and optional GitHub Actions to automate retraining. Tweak the YAML files and rerun `deepbox up` to iterate quickly.
5. Visualize progress
The CLI streams structured logs and optionally uploads to DeepBox Studio, where you can view traces, compare checkpoints, and attach notes for future experiments.
6. Invite collaborators
Run `deepbox team add <email>` to share access to agents, datasets, and experiment history. Role-based permissions ensure production safeguards remain intact.
7. Roll back safely
Use `deepbox rollback <deployment>` to revert to a previous checkpoint while keeping the current workflow configuration intact for future tweaks.
8. Customize templates
DeepBox includes curated workflow templates. Clone the repo, tweak the YAML, and point your CLI to the local directory to start from best practices.