Runloop AI
Run agents in sandboxes and keep them in the loop on GitHub PRs
Runloop AI provides sandboxed devboxes for agent workflows, including turn-based interaction through GitHub pull requests. It’s aimed at developers building coding agents that need to execute commands, keep state across turns, and respond to reviewer comments.
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What's happened with Runloop AI lately
- Score changeRubric upgrade v3_0 → v3.1: score 14/32 → 15/3614 → 15/36(+1)
Rubric upgrade: agenticness v3.0 (8 dims, /32) → v3.1 (9 dims, /36). Adds Dim 9 (Operator Sovereignty), splits Dim 6 into 6a/6b lenses, tightens Dim 4 autonomous-retry distinction. Not a product change — score shift reflects new dimension + recalibrated rubric, not a change in the tool. Fanout suppressed.
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News mentions sourced from our news feed; score changes from periodic re-evaluations.
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About
Runloop AI is an agent infrastructure and developer tooling platform for running AI agents inside sandboxes/devboxes. The documentation shown here focuses on a turn-based workflow where an agent monitors a GitHub pull request, posts progress updates, and acts on PR comments as new tasks.
This is a practical pattern for human-in-the-loop coding agents: reviewers can guide the agent through PR comments, and the agent can keep context across multiple comments. The docs also note a production-friendly approach using GitHub webhooks instead of polling.
Proposes and executes multi-step plans with your approval.
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