Quickstart

Rebase Toolkit lets you define Python functions and Rebase Workflows locally, run them on your machine, and submit ephemeral cloud runs from the terminal.

This quickstart creates two minimal examples:

1. Install

During early development, install Rebase Toolkit directly from GitHub into a clean uv environment:

uv venv .venv
source .venv/bin/activate
uv pip install "rebase-toolkit @ git+ssh://git@github.com/rebase-energy/rebase-toolkit.git"

Once the package is published to PyPI, install from PyPI:

pip install rebase-toolkit

2. Set Up

Authenticate this computer and select or create a workspace:

rebase setup

The setup wizard signs you in with Google or GitHub, creates or selects a workspace, and stores a local auth session and workspace profile. The hosted Rebase API URL is built into rebase-toolkit, so there is no API URL to configure for normal usage.

GitHub source backing is optional. If you connect GitHub, the wizard first asks whether to use an existing repository or create a new one, then asks you to install the Rebase GitHub App on the selected repository.

3. Run a Function

Create hello_function.py:

import rebase

@rebase.function(name="hello-function")
def hello(name: str = "World") -> dict:
    return {"message": f"Hello, {name}!"}


if __name__ == "__main__":
    print(hello(name="Rebase"))

Run it locally:

python hello_function.py

Expected output:

{"message": "Hello, Rebase!"}

Run the same file in the cloud without deploying it:

rebase run hello_function.py --param name=Rebase

Functions are private Python callables. By default, Rebase uses run_type="quick", which runs each call synchronously on an isolated Cloud Run service that stays warm after the first call. Use run_type="long" for long-running jobs. Introduce deploys later, when you want a reusable, versioned function target.

4. Run a Workflow

Create hello_workflow.py:

import rebase

@rebase.step()
def load_name(name: str = "World") -> dict:
    return {"name": name}


@rebase.step()
def build_message(payload: dict) -> str:
    return f"Hello, {payload['name']}!"


@rebase.step()
def package_result(message: str) -> dict:
    return {"message": message}


@rebase.workflow(name="hello-workflow")
def hello_workflow(name: str = "World") -> dict:
    payload = load_name(name)
    message = build_message(payload)
    return package_result(message)


if __name__ == "__main__":
    print(hello_workflow(name="Rebase"))

Run it locally:

python hello_workflow.py

Expected output:

{"message": "Hello, Rebase!"}

Run the same workflow in the cloud without deploying it:

rebase run hello_workflow.py --param name=Rebase

Workflows are Python entrypoints that Rebase can submit through Prefect orchestration. Step calls inside a workflow are compiled into a Prefect-backed DAG for cloud runs, giving you multiple observable steps, retries, and per-step status. Introduce deploys later, when you want a reusable, versioned workflow target or a scheduled workflow.

5. Next