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.

Agenticness = how independently a tool can take action, scored across 9 dimensions. Scored independently by David Kooi, Skylark Creations — see full rubric →

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API

Integrations

B2B

For Developers

CLI

Copilot (Human-in-Loop)

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Recent activity

What's happened with Runloop AI lately

May 23, 2026
Score change
Rubric 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

What It Is

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.

What to Know

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.

Key Features

Runs agents inside sandboxed devboxes

Maintains shell state with named shells

Posts status updates to GitHub pull requests

Reads PR comments as agent tasks

Resumes the latest Claude Code conversation with the `-r` flag

Use Cases

Build a coding agent that updates a GitHub PR as it works

Let reviewers assign follow-up tasks by commenting on a pull request

Run code-changing workflows in an isolated sandbox

Agenticness: Adaptive Collaborator

Proposes and executes multi-step plans with your approval.

High evidence

Last evaluated: May 23, 2026

Dimension Breakdown

Action Capability

Autonomy

Planning

Adaptation

State & Memory

Reliability

Interoperability

Safety

Operator Sovereignty

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