Spinup vs OpenComputer
Both OpenComputer and Spinup keep agents running in persistent environments. OpenComputer gives agents the machine: a long-running Linux VM with checkpoints and elastic compute. Spinup gives agents the layer above it: their identity, which AI tool is running, their secrets, and the org-level controls that follow them across every run.
Common Ground
Where they agree
Both products reject the ephemeral-sandbox model for serious agent work. That shared conviction shapes everything else.
Persistence over ephemeral execution
OpenComputer runs environments for hours or days, not minutes. Spinup keeps agent state alive between runs by default. Neither treats the environment as something to throw away after each request.
Real environments for real workloads
Both give agents a real Linux environment with filesystem access, package management, and process isolation. Neither is optimized for single-function serverless workloads.
Key Differences
Same premise, different layer
OpenComputer owns the machine. Spinup owns the runtime above it. That one-layer difference determines what you build yourself and what the platform handles.
VM vs agent object
OpenComputer's primary abstraction is the sandbox: a full Linux VM with checkpoints, forks, and elastic scaling. Spinup's primary abstraction is the agent: a runtime object that bundles the AI tool, skills, secrets, and environment into a single managed unit. The same underlying question (how do I run a persistent agent?) gets answered at different levels of the stack.
Harness portability
Spinup treats harnesses as swappable first-class components. Switch from Claude Code to OpenClaw and the agent's skills, secrets, and environment stay intact. OpenComputer provides the VM that harnesses run inside, but does not track which harness is running or manage transitions between them. Harness selection stays in your application code.
Skills and secrets
Spinup manages skills and secrets at the agent level. They follow the agent across harness changes and environment rebuilds. OpenComputer's environment gives you the machine to configure however you want, but secrets and skills live wherever your application puts them.
Organization controls
Spinup provides organization-level controls: membership, audit logging, and secrets scoped across a team's agent fleet. OpenComputer exposes TypeScript and Python SDKs plus a CLI for individual environment control. Governance beyond the per-environment layer stays in your stack.
Side-by-Side
Spinup vs OpenComputer at a glance
| Spinup | OpenComputer | |
|---|---|---|
| Primary abstraction | The agent: harness, skills, secrets, and environment as a managed unit | The sandbox: a persistent Linux VM with checkpoints and forks |
| Persistence model | Agent-scoped state, intact across harness changes and environment rebuilds | Full VM persistence: hours to days, with named checkpoints to fork from |
| Harness management | Harnesses are swappable runtime components, managed by the platform | Run any harness inside the VM; harness selection stays in your code |
| Skills and secrets | Bound to the agent, portable across harnesses and environments | Configured in the VM however your application manages them |
| Snapshot semantics | Agent-scoped snapshot policy at the config layer | Named checkpoints with fork-from-checkpoint branching |
| Elastic scaling | Environment resources managed at the runtime level | Adjust CPU and memory at runtime; release after intensive tasks |
| Organization controls | Membership, audit logging, and org-scoped secrets management | SDK and CLI access per environment |
| Best fit | Teams that want the runtime to manage agent lifecycle, AI tool selection, and org policy | Teams that want powerful VM primitives and prefer building agent semantics themselves |
The Layer Distinction
Infrastructure vs the runtime above it
OpenComputer's headline, "long-running cloud infrastructure for AI agents," is the closest in the market to Spinup's own category language. That overlap is not accidental. Both products are betting on the same thesis: agents doing real work need persistent environments, not one-shot execution.
The fork is in which layer each product calls home. OpenComputer owns the VM: full Linux environments with checkpoints you can fork from, elastic resource scaling, and a clean SDK for TypeScript and Python. It is a well-executed infrastructure layer for teams comfortable building agent semantics in their own application code.
Spinup owns the managed layer above the VM: the canonical agent object that bundles the AI tool (Spinup calls these harnesses), skills, secrets, and environment into a single unit. When a team switches from Claude Code to OpenClaw, the agent's identity and configuration travel with it. Organization-level secrets, audit logging, and membership controls live at this layer rather than scattered across individual environments.
The practical question is how much agent infrastructure you want to assemble yourself. OpenComputer gives you the machine and leaves agent behavior, harness management, and governance to your code. Spinup gives you those pieces managed so your code focuses on what the agent does.
When to Choose
Different abstractions, different workloads
Both products are for teams that take agent infrastructure seriously. The question is where you want to draw the platform boundary.
Choose Spinup when
You want the runtime to manage agent lifecycle, AI tool selection, and secrets so your application code does not have to. You may switch or compare AI tools (harnesses), and you need that change to be transparent to the agent's identity and configuration.
Choose Spinup when
Your team spans multiple agents, and organization-level controls (secrets scoping, audit logging, membership policy) matter as much as per-environment configuration.
Choose OpenComputer when
You want powerful VM primitives close to the metal. OpenComputer's checkpoint-and-fork model is useful for workflows that need to branch from a known state and run multiple approaches in parallel.
Choose OpenComputer when
You prefer building agent semantics in your own code and want the infrastructure layer to stay thin. OpenComputer's SDK gives you the machine. What runs on it stays yours to define.
FAQ
Spinup vs OpenComputer questions
What is the main difference between Spinup and OpenComputer?+
OpenComputer gives agents a persistent Linux VM with checkpoints, forks, and elastic scaling. Spinup gives agents the managed layer above that infrastructure: a canonical agent object with its own identity, AI tool (Spinup calls these harnesses), skills, secrets, and lifecycle. OpenComputer is the environment layer. Spinup is the runtime that decides what runs inside it, how it persists, and what it can access.
Is Spinup an OpenComputer alternative?+
They occupy different floors of the same stack. OpenComputer is persistent VM infrastructure for agents. Spinup is the agent runtime layer above that infrastructure. OpenComputer gives you the machine. Spinup gives you the managed agent object that runs on top of it, with swappable AI tools, projected secrets, and organization-level controls included.
Can I use OpenComputer and Spinup together?+
In principle, yes. Spinup separates the agent model from the underlying compute primitive. A future path where Spinup delegates environment execution to OpenComputer VMs, Firecracker microVMs, or other backends depending on workload is architecturally coherent. No integration exists today, but the two products are more complementary in the stack than they are substitutes.
When should I choose OpenComputer over Spinup?+
When you want powerful VM primitives and are comfortable building agent semantics in your own code. OpenComputer's checkpoint-and-fork model is genuinely useful for workflows that need branching from a known state. If you do not need to swap the AI tool without rebuilding your setup, agent-level secrets, or organization-level governance, and you prefer assembling agent behavior yourself, OpenComputer's infrastructure layer keeps things closer to the metal.
Related
Understand the runtime layer
These pages explain the concepts behind Spinup's approach and how the runtime layer differs from infrastructure primitives.
Early access
See what the runtime layer manages before you build it yourself.
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