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An automation is a task you hand to a coding agent, plus the rules for when it runs and what it can use. Superlog runs each task in a fresh sandbox with your repositories checked out.

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The parts of an automation

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What happens during a run

1

A trigger fires

A Slack message arrives in a watched channel, Sentry reports a new issue, someone runs /automate in Discord, or the schedule comes due. You can also start a run with Run now or Test.
2

Superlog starts a sandbox

Each run gets its own sandbox. Superlog checks out your repositories into it. Provider credentials stay outside the sandbox.
3

The agent works

The agent reads the trigger event, follows your instructions, and uses the selected connectors. It can read and edit code, run commands, query Sentry and Datadog, and read Slack and Linear.
4

The agent acts

Depending on its connectors, the agent opens a pull request, replies in Slack, or creates a Linear issue. These write actions run without a human approval step, so choose connectors with that in mind.
5

You review the run

The run page shows the agent’s messages, the tools it used, and anything it created. Send a follow-up to continue the same run in the same sandbox.

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Where inference comes from

For each run, Superlog uses the first available source:
  1. Your ChatGPT subscription, when the automation uses the Codex harness.
  2. Your newest API key for the model’s provider.
  3. The usage included in your plan.
Runs on your own key or subscription are billed by your provider. See Models and harnesses and Billing.

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Tag mode

Tag mode is separate from automations. When someone mentions Superlog in Slack, it answers in the thread using your connected integrations and repositories. It can read from more integrations than automations can, including AWS, Google Cloud, Grafana, and PostHog. See Tag mode.

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Workspaces

Everything in Superlog belongs to a workspace: automations, integrations, secrets, runs, and members. Data and credentials are never shared across workspaces. See Workspace and members.