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Runloop AI

Agenticness15/36·Adaptive Collaborator

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 →

See pricing ↓

API
Integrations
B2B
For Developers
CLI
Copilot (Human-in-Loop)
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Recent activity

What's happened with Runloop AI lately

  • 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.

    See the news that prompted this

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
Adaptation
State & Memory
Safety
How agenticness works →

Categories

Agent Infrastructure
Pricing

Pricing not publicly available

Details
runloop.ai
AddedApril 26, 2026
RefreshedApril 26, 2026
Agenticness
Adaptive Collaborator
Quick Facts
DeploymentCloud-hosted
AutonomySemi-autonomous
Model supportSingle model
Team supportSmall team
Pricing modelSubscription
Interfaceapi, cli
Sources
Last updated May 23, 2026
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