Runloop Alternative for AI Coding Agents

Runloop is a serious platform for AI coding agents. Its docs describe Devboxes as sandboxed execution environments that use virtual machine technology, can pull and build code from Git repositories, run browsers, read and write files, and execute proprietary code or binaries. The product page goes further: Devboxes are full Linux virtual machines, with blueprints, disk snapshots, credential gateways, MCP tooling, network policies, tunnels, SSH, and SDK access.

That is exactly why "Runloop alternative" is an interesting search. Teams do not look for an alternative because Runloop is a toy. They look because their agent product has moved from "execute work for the agent" to "host the user's live development machine."

Those are related, but not identical, jobs.

An execution platform is optimized around agent runs: create an environment, run commands, snapshot disk, evaluate results, and tear down when the task is done. A user-facing agent workspace has a different center of gravity. It needs terminals that users can watch and reattach to, services that keep listening on stable ports, SSH for support and power users, branches and review for code, and enough machine state to come back later without reconstructing the session from logs.

For that product shape, the stronger Runloop alternative is a real VM primitive. Freestyle VMs are the most powerful VMs for AI agents: hardware-virtualized machines that run real Linux, can run forever when you disable idle timeout, and expose the operating-system surface agents actually use.

The Runloop alternative search

Most teams start by asking a narrow question: where should my AI agent run code?

Runloop has a good answer. The Devbox overview describes cloud-based virtual workstations that can be stateful or stateless. It lists common agent tasks like querying APIs, pulling code from Git repositories, running a headless browser, reading and writing files, and running proprietary binaries. Its lifecycle docs cover provisioning, running, shutdown, suspending, suspended, and resuming states. Its snapshot docs explain disk snapshots for build caches, rollback, and fan-out.

That is a strong model for agent execution.

The question is what happens when the devbox is no longer just a place the agent visits. In an AI coding product, the environment often becomes the product surface itself. The user expects a machine they can inspect. The agent expects a machine it can mutate. Support expects a machine it can debug. The product expects a machine it can route to, fork, suspend, resume, and eventually delete.

That is where a VM-first architecture matters.

Freestyle starts with the computer as the unit of abstraction. The VM is not a hidden implementation detail behind a command runner. It is the thing your product owns: a Linux machine with files, processes, users, SSH, PTYs, ports, lifecycle operations, and API control.

Disk state is not the whole workspace

The sharpest difference is state.

Runloop's lifecycle docs say suspended Devboxes preserve disk state, not in-memory state, and that daemons or other processes running at suspend time must be manually restarted after resume. Runloop's snapshot docs also state that only disk snapshots are currently supported.

Disk snapshots are useful. They are great for build caches, known-good checkpoints, and fan-out from a filesystem state. But a coding agent workspace is not only files on disk. It is also a running dev server, a package install mid-stream, a shell prompt in the right directory, a terminal screen full of recent errors, a debugger, a REPL, and a browser automation loop that has not finished.

Freestyle's VM lifecycle is designed around the machine as a durable runtime object. You can create a VM, run work, stop it, start it again later, resize it, fork it for parallel exploration, and delete it when the workspace is finished. Forking creates a new VM from the current running state, which is the behavior you want when an agent reaches a risky decision point and needs multiple attempts from the same live environment.

For coding agents, that difference changes product design. You do not have to teach the agent to constantly serialize intent into a provider-specific checkpoint. You can let it use the operating system normally, then fork or resume the machine at the boundary your product cares about.

Terminals should be product state

Runloop has named shells. Its named shell docs describe persistent shell sessions that preserve working directory and environment variables across commands. That is useful for sequential agent execution, especially when a workflow depends on cd, exported variables, or ordered command output.

For a user-facing coding agent, a terminal is more than command context. It is a live interface.

Freestyle PTY sessions are long-lived interactive shells inside the VM. They can be attached, detached, and reattached over WebSocket. The docs describe them as surviving client disconnects, VM suspends, and VM forks. They are real PTYs, so line editing, prompts, job control, REPLs, debuggers, package managers, and terminal UIs behave like they do on a normal Linux machine.

That matters when the terminal appears in your product UI. A user can close the tab and come back. A support engineer can attach later. An agent can start an interactive installer, receive output, send input, interrupt the process, or reattach to the same session after a frontend reconnect.

This is the difference between "remember the shell's working directory" and "the terminal is part of the workspace."

Previews should be infrastructure, not just access URLs

Coding agents eventually need previews. They generate web apps, run local servers, test callbacks, open notebooks, expose dashboards, or hand a running service to a user.

Runloop documents tunnels for services running inside a Devbox. The tunnels docs cover HTTP/HTTPS, WebSockets, and Server-Sent Events, and note that services need to bind to 0.0.0.0. That is a useful capability.

Freestyle's VM domains model treats the preview as ordinary infrastructure. A domain mapping routes public HTTPS traffic from a hostname you control to a port inside the VM. The VM runs the service. The product maps the hostname to the port. HTTPS is provisioned automatically. The result is not a special preview object the agent has to understand; it is a Linux service listening on a port.

This sounds mundane, but mundane infrastructure is exactly what serious app-builder and coding-agent products need. The agent can start a Vite server, a Rails app, a Jupyter notebook, a database admin UI, or a custom HTTP service. Your product can route traffic to it. Users can click it. The same machine still has SSH, PTYs, files, logs, and process state.

SSH is the escape hatch you will need

Runloop documents SSH access to Devboxes, and that is the right direction. Any platform for AI coding agents needs a human escape hatch because agents fail in ways that logs alone do not explain.

Freestyle also exposes SSH, but the important part is how it fits into the rest of the VM model. Freestyle VMs accept SSH through scoped identities and tokens. You can grant an identity access to a VM, scope it to specific Linux users, and connect from editors like VS Code or Cursor through the same SSH proxy.

That matters because production agent products eventually become collaborative systems. A user needs one level of access. A support engineer needs another. An agent process needs a third. The VM already has normal Linux users, files, permissions, and shells, so your product can map access into a familiar operating-system model instead of inventing every permission boundary from scratch.

When the agent writes code, the VM should not be the only durable record. Put source code in a repository, branch risky work, and review changes before shipping. Freestyle Git is the natural companion when agent state includes repos, branches, diffs, and review, while Freestyle VMs are where that code actually runs.

Runloop vs. Freestyle

Here is the practical comparison.

Choose Runloop when you want an agent execution platform with Devboxes, blueprints, disk snapshots, credential gateway features, MCP tooling, network policies, tunnels, SSH, SDKs, and benchmarking infrastructure around the same environment model. It is a good fit when the primary unit is an agent run, benchmark, or controlled execution environment.

Choose Freestyle when the runtime is becoming a product workspace. That includes AI coding agents, app builders, browser-based development environments, long-running assistants, preview environments, notebook products, and internal tools where users and agents share one live Linux machine.

The deciding questions are simple:

This is not a claim that every Runloop user should migrate. If your product is centered on agent jobs, evals, and execution infrastructure, Runloop is worth evaluating carefully.

The claim is narrower and more important: if your AI coding product is becoming a live machine that users depend on, choose the platform whose core primitive is that machine.

The bottom line

The best Runloop alternative for AI coding agents is not a thinner sandbox. It is a stronger computer.

Agents do not only need command execution. They need an environment they can inhabit: a real Linux machine with terminals, files, packages, ports, SSH, services, long-running processes, forks, and reviewable source code. The more your product exposes that environment to users, the more expensive it becomes to fake it with a narrower abstraction.

Freestyle is built for that version of the problem. Start with Freestyle VMs when the runtime has to behave like the user's computer, and pair it with Git when the agent's work needs to become reviewable software.