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Documentation Index

Fetch the complete documentation index at: https://docs.runloop.ai/llms.txt

Use this file to discover all available pages before exploring further.

Runloop provides secure sandboxed execution environments called Devboxes. Runloop Devboxes provide a full-featured execution environment for your AI agents. We use virtual machine technology to provide isolation and safety for your API keys, code, secrets, sensitive data, and internal systems.

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Common AI Agent Tasks

The most powerful and useful AI agents do more than just chat. Sophisticated development teams need agents that can:
  • Query external APIs
  • Pull, build, and execute code from git repositories
  • Run a headless browser to scrape or interact with websites
  • Read and write files on a filesystem
  • Run proprietary code or binaries
While it is tempting at first for developers to do these on their local workstations, this doesn’t scale to many concurrent work streams and it introduces security and safety concerns. This is where Devboxes come in. Runloop Devboxes are the virtual, sandboxed workstations where your AI agents do their work. By running your agents in Runloop Devboxes, your developers can maintain multiple parallel workstreams, while preserving the safety of your internal data and systems.

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Key Devbox Features

  • Isolated, ephemeral virtual machines: Devboxes are cloud-based virtual workstations, created on demand, and deleted when they are no longer needed.
  • Super fast execute times: Startup to running your first command takes a few seconds.
  • Stateful or stateless: For short-running tasks, you can start a Devbox in seconds, perform arbitrary work and then throw away the box. For long-running tasks, take advantage of snapshot, suspend and resume operations to control the lifecycle of your devbox using simple API calls.
  • Customizable sizes and images: You can choose machine size and resources from a range of options, and you can create and customize team-shared images with blueprints.
  • Network security: Control egress network access with Network Policies to restrict which external services your Devboxes can reach.
  • Secure integrations: Connect agents to LLM APIs via Agent Gateways and to MCP tool servers via MCP Hub — without exposing your real credentials to the devbox.
Our starter image is what starts when you start a devbox. You get the following packages available to your devbox:Core tools: curl, ca-certificates, jq, sudo, git
Extras: dnsutils, iputils-ping, less, vim, wget, rsync, gh
Python stack: Python 3.12, pip, uv
Node stack: Node 22.15.0, npm, Yarn 1.22.22 via corepack

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Starting a Devbox

Here’s a minimal example showing how to create and use a Devbox: 
import asyncio
from runloop_api_client import AsyncRunloopSDK

# API Key is auto-loaded from "RUNLOOP_API_KEY" env var
runloop = AsyncRunloopSDK()

async def run_example():
    # Create the devbox and wait for it to be ready
    devbox = await runloop.devbox.create()
    print(f'Created Runloop Devbox: {devbox.id}')

    # Execute a command and wait for it to complete
    result = await devbox.cmd.exec(command="echo 'Hello from Runloop!'")
    print(await result.stdout())  # Hello from Runloop!

    # Clean up the Devbox
    await devbox.shutdown()

asyncio.run(run_example())

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Working with Devboxes

Your agent code will interact with Devboxes through the Runloop API. We provide client SDKs for Python and Typescript. You can also use the Runloop CLI and the Runloop Dashboard to view, manage, and monitor your Devboxes. Ready to get started? Read on for quick examples showcasing common Devbox uses.