A CLI for ML experimentation.
Define pipelines as DAGs, track metrics step-by-step, and monitor runs from a single control plane — all from your terminal.
Install
Get the CLI in seconds.
curl -fsSL https://ml-code.com/install.sh | bashQuickstart
Up and running in five steps.
Set up AI tools
mlc uses Claude to build pipelines from natural language. Export your Anthropic API key before starting the CLI.
export ANTHROPIC_API_KEY=sk-ant-...Start the CLI
Run the mlc command to open the interactive REPL. It connects to the control plane and starts a local MCP server.
mlcLog in
Inside the REPL, run /login. A browser window opens to complete authentication. Your credentials are stored locally.
/loginCreate and run a pipeline
Describe your pipeline in plain English. mlc creates the DAG, adds blocks, and asks you to confirm before executing.
Create a pipeline with two blocks: one that generates 1000 random numbers and one that computes the mean and logs itmlc will show each block it creates and ask for confirmation before running.
Monitor in the dashboard
Open the web dashboard to track block-level run status, view metric charts step-by-step, and inspect logs in real time.
open https://app.ml-code.comDashboard
Monitor runs in real time.
The web dashboard gives you a live view of every pipeline run — block statuses, metrics, and logs, all in one place.
DAGs
Metrics
train_loss
0.083
Logs
Features
Everything you need.
DAG pipelines
Define blocks of Python code with explicit dependencies. mlc resolves execution order automatically.
Metric tracking
Emit scalar metrics per step with ctx.report(). Compare runs side-by-side in the control plane.
Structured logs
Use ctx.log() to emit structured log lines. Filter by block or level in the control plane.