agentOS

Product Introduction

1. Definition: agentOS is a virtual operating system library for AI agents, built on WebAssembly and V8 isolates. It provides a lightweight, in-process runtime that delivers Linux-compatible execution, a POSIX filesystem, orchestration, and application hosting capabilities as a software library.
2. Core Value Proposition: It exists to eliminate the need for heavyweight sandboxes, virtual machines (VMs), or external SaaS platforms for running AI agents. Its primary value is enabling developers to run secure, multi-tenant agent workloads directly within their existing backend infrastructure with dramatically lower latency, memory overhead, and cost.

Main Features

1. In-Process Execution Engine: agentOS runs agent code within V8 isolates, the same technology used in Chrome and Cloudflare Workers. This allows for native JavaScript/Node.js performance with a full JIT compiler, not JavaScript compiled to WebAssembly. It supports subprocesses for Bash, Python, and other tools, and provides built-in type checking for agent-generated code.
2. Unified Virtual Filesystem: Every agent gets its own persistent POSIX filesystem. Developers can mount remote storage like S3 buckets or Google Drive, as well as host directories, as normal filesystem paths. This allows agents to use standard shell tools and file operations across all storage backends seamlessly.
3. Durable Agent Orchestration: Integrated with Rivet Actors, agentOS provides built-in primitives for durable workflows, cron jobs, agent-to-agent communication, and multiplayer sessions. This allows agents to pause, resume, retry, and maintain state across executions without custom infrastructure.
4. Application Deployment (Apps): The library can deploy and serve entire AI-generated applications (REST APIs, frontends) for individual end-users. Each app runs in an isolated agentOS VM with features like SQLite databases, workflows, and real-time multiplayer state, all managed from the host application.

Problems Solved

1. Pain Point: High latency and cost of traditional agent sandboxes. Booting a microVM or container for each agent execution introduces significant cold-start delays (hundreds of milliseconds) and requires reserving large chunks of memory (often 1GB+), making concurrent agents prohibitively expensive.
2. Target Audience: Developers and engineering teams building production AI agent platforms, AI-powered developer tools, coding assistants (like those using Claude Code or Codex), and applications that require secure, scalable execution of untrusted code (e.g., AI-generated apps, plugin systems).
3. Use Cases: Running coding assistants that need to execute shell commands and manage files; hosting user-specific AI applications with isolated data; orchestrating complex, stateful multi-agent workflows; providing a secure runtime for AI-generated code within a larger SaaS platform.

Unique Advantages

1. Differentiation: Unlike SaaS sandbox providers (e.g., E2B, Daytona) or DIY container solutions, agentOS is a library that runs in-process. This eliminates network hops, VM boot overhead, and per-second billing. It shares a single runtime across thousands of isolated agent VMs, whereas competitors require a full environment per agent.
2. Key Innovation: The fusion of WebAssembly (for compiled tools and security boundaries) and V8 isolates (for native JavaScript performance) within a single, lightweight runtime. This "library-first" architecture, combined with the actor model for orchestration, allows developers to treat agent infrastructure as a software component rather than external platform-as-a-service.

Frequently Asked Questions (FAQ)

1. How does agentOS security compare to a full sandbox or VM? agentOS uses WebAssembly and V8 isolates, which are the same security primitives securing billions of browser tabs and Cloudflare Workers. It provides scoped access to host resources via bindings, preventing agents from accessing raw credentials. For workloads needing absolute isolation, it can mount a full Linux sandbox only when required.
2. Can I run any Linux binary inside agentOS? agentOS is Linux-compatible at the POSIX API level. It runs Node.js, Python, and Bash natively. For arbitrary Linux binaries that require specific kernel syscalls, you can use the sandbox mounting feature to attach a full microVM when necessary, combining the efficiency of isolates with the compatibility of VMs.
3. Is agentOS an open-source project? Yes, agentOS is open-source software released under the Apache 2.0 license. The core library is available on npm and GitHub, allowing for self-hosting and customization.
4. What AI agent frameworks does agentOS support? agentOS provides native integrations and support for major agent frameworks including Claude Code, Codex, OpenCode, Pi, Eve, and Flue. Its standard execution environment allows any framework that can generate code or commands to operate within it.
5. How does the pricing model work for agentOS? The agentOS library itself is free and open-source. Costs are based on your own infrastructure (e.g., AWS, GCP) where it runs. Rivet also offers a managed cloud version (Rivet Cloud) with a usage-based pricing model, but the core technology avoids the per-second, per-sandbox billing typical of standalone sandbox services.