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OpenComputer Review

OpenComputer is an AI tool that allows users to deploy managed agents that are always on, steerable mid-run, and accessible via a permanent URL without requiring infrastructure.

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OpenComputer — product screenshot

Why it matters

1Offers a freemium pricing model with a Basic free tier.
2Provides persistent, KVM-based Linux VMs for AI agents, maintaining state across sessions.
3Supports API access with configurable rate limits, such as 5,000 tokens per hour for agentgateway.
4Features include checkpoints, preview URLs, and per-tenant package control for agent environments.

About OpenComputer

Business Model
Subscription SaaS
Usage Pricing
null per session
Free Credits
null
Headquarters
null
Team Size
null
Funding
null
Total Raised
null
Platforms
Web, API
Target Audience
Developers and teams looking to deploy code agents easily.

Pricing Plans

Basic
Free
  • • Always on agents
  • • Deployment CLI
  • • Durable sessions

Cost Examples

  • • Deploy 1 agent: Free

Leadership

nullnullLinkedIn

Investors

null

API DocsGitHubOpen Source

Specs

API Docs

View Documentation →

API Available

Yes, public API

Screenshots

overview

What is OpenComputer?

OpenComputer is an AI agent deployment tool that enables developers and teams to deploy and manage persistent, steerable AI agents. It provides durable infrastructure, specifically KVM-based Linux Virtual Machines (VMs), for LLM agents, ensuring state survival across sessions and eliminating the need for users to manage their own infrastructure. The platform allows AI agents to interact with desktop environments across macOS, Linux, and Windows, performing actions such as inspecting applications, clicking, typing, scrolling, dragging, and capturing screenshots.

features

Key Features of OpenComputer

OpenComputer provides a robust set of features designed for the deployment and management of AI agents, emphasizing persistence and control. These capabilities facilitate the creation of complex, long-running agent tasks.

  • Always-on agents, ensuring continuous operation without manual restarts.
  • Steerable mid-run, allowing real-time intervention and guidance of agent behavior.
  • Permanent URL for accessing agents, simplifying integration and access without infrastructure setup.
  • Durable sessions with persistent state across reboots and hibernation, crucial for complex tasks.
  • Integration with multiple coding platforms, including Codex and Cursor, for streamlined development.
  • Checkpoints for saving agent state at specific intervals, enabling rollback and recovery.
  • Preview URLs for monitoring agent execution and output in real-time.
  • Per-tenant package control for agent environments, allowing customized dependencies and configurations.
  • Full Linux Virtual Machines (VMs) with complete filesystem and operating system access.
  • Machine Control Protocol (MCP) service for standardized desktop automation across macOS, Linux, and Windows.

use cases

Who Should Use OpenComputer?

OpenComputer is primarily designed for developers and teams requiring robust, persistent infrastructure for AI agents. Its capabilities are particularly suited for scenarios involving complex automation and long-running tasks.

  • Developers building AI agents who need a managed environment for deployment and execution.
  • B2B agent platforms requiring durable, always-on infrastructure for their AI services.
  • Teams building production agent infrastructure that demands state persistence and mid-run steerability.
  • Researchers and developers automating repetitive desktop tasks or integrating desktop control into AI-powered testing frameworks.
  • Organizations creating AI-driven user interaction simulations or custom AI assistants for cross-platform environments.

how to use

How to Use OpenComputer

To begin using OpenComputer, users can deploy managed agents via its platform, which provides a permanent URL for access. The tool is npm-installable and offers an API for programmatic control.

  • 1Access the OpenComputer platform via opencomputer.dev/agentdeploy.
  • 2Utilize the API (available at opencomputer.dev/runtimes) to build and deploy AI agents.
  • 3Configure agent environments, including per-tenant package control.
  • 4Run agent loops, incorporating provider SDKs, model calls, and tool use.
  • 5Monitor agent execution using preview URLs and manage state with checkpoints.
  • 6Integrate agents with external systems like Slack, webhooks, or cron jobs.

pricing

OpenComputer Pricing & Plans

OpenComputer operates on a freemium model with a pay-as-you-go structure for compute and disk resources beyond the free tier. Billing is active only while the VM is running, with hibernation preserving state.

  • Basic: Free tier available.
  • Compute: Elastic memory and CPU, resizable at runtime. For example, 4 GB memory and 1 vCPU costs $0.004 per minute ($0.24 per hour, $168.72 per month).
  • Disk: 20 GB of disk space included per VM. Additional disk usage is metered at $0.0000001 per GB-second (approximately $0.26 per GB-month), billed for the lifetime of the sandbox (running or hibernated).
  • API Rate Limits: Configurable per-provider and per-tier. Examples include 5,000 tokens per hour and 60 requests per second for agentgateway. Cooldown periods are implemented for rate limit breaches, escalating from 1 minute for the first 429 error to 60 minutes.
  • Token Pricing (per 1k tokens): Varies by model. Claude Opus 4.6: $0.005 input, $0.025 output. GPT-5.4 Mini: $0.00075 input, $0.0045 output. Claude Sonnet 4.6: $0.003 input, $0.015 output.

Pros

  • +Provides persistent, KVM-based Linux VMs, ensuring state survival across sessions for AI agents.
  • +Allows agents to be steered mid-run, offering real-time control and intervention.
  • +Offers a permanent URL for agent access, simplifying deployment and integration without infrastructure management.
  • +Includes a free tier and a pay-as-you-go model for compute and disk, billing only when VMs are active.
  • +Supports desktop automation across macOS, Linux, and Windows via a Machine Control Protocol (MCP) service.
  • +Features like checkpoints and preview URLs enhance agent management and monitoring.

Cons

  • −Currently managed-cloud only, lacking support for Bring Your Own Cloud (BYOC) deployments.
  • −Does not offer GPU acceleration, which may limit performance for certain AI workloads.
  • −May require technical expertise for effective implementation and customization due to its open-source nature.
  • −Community support and documentation may be less extensive compared to more established commercial tools.
  • −Rate limits are per-provider and per-tier, requiring careful configuration and management to avoid cooldown periods.
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