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OpenComputer

Persistent cloud VMs for AI agents. Beyond sandboxes.

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llms.txt
https://opencomputer.dev/llms.txt
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AI and machine learning
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Interface: a documented API or declared MCP endpoint an agent can call
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2026-10-25T18:07:39.746Z
Last checked
2026-10-26T06:14:31.578Z
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Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-26T06:14:29.494Z. Cached copy · Source on opencomputer.dev

# OpenComputer

> Persistent cloud VMs for AI agents. Beyond sandboxes.

OpenComputer provides long-running cloud infrastructure for AI agents. Every OpenComputer is a real machine with a full filesystem, full OS access, and persistent state. It stays always on, and you can resize memory and CPU at runtime or hibernate and wake it up at any point.

## When to use OpenComputer

Reach for OpenComputer when an agent needs to run unattended, for minutes to days, on a computer of its own:

- Deploy an agent written in TypeScript as a managed service: durable sessions that survive crashes, schedules, webhooks, memory across sessions, and Slack, GitHub and Linear connections. Start with `npx @opencomputer/cli init my-agent`, then `opencomputer deploy` ([quickstart](https://docs.opencomputer.dev/agents/quickstart.md)).
- Run a background coding agent that clones a repository, runs the tests and opens a pull request ([example](https://docs.opencomputer.dev/agents/examples/slack-coder.md)).
- Start agent work from your own application: create a session and send turns over HTTP, then read results from the event log ([Management API](https://docs.opencomputer.dev/agents/api.md), [OpenAPI spec](https://opencomputer.dev/openapi.json)).
- Give each of your users a persistent, isolated Linux VM for untrusted or AI-generated code that keeps its files between sessions, can checkpoint and fork, and resizes CPU and memory while running (`@opencomputer/sdk` or `opencomputer-sdk`).
- Keep model and service credentials out of the agent's runtime: the platform holds them and injects them on outbound requests.

It is not the right tool for a single stateless function call or a short code snippet that finishes in milliseconds, or for a chat UI with no work behind it.

How to call it as an agent: read the docs through the [MCP server](https://docs.opencomputer.dev/mcp) or [docs llms.txt](https://docs.opencomputer.dev/llms.txt), use the CLI to create and deploy agents, and call the Management API with an organization API key in the `x-api-key` header from server code. Never put the key in a browser.

## Developer resources

- [OpenComputer API reference](https://docs.opencomputer.dev/agents/api.md): Management API for sessions, turns, events, projects, webhooks and memory; base URL `https://app.opencomputer.dev/api/managed-agents`
- [OpenComputer OpenAPI spec](https://opencomputer.dev/openapi.json): OpenAPI 3.1 description of the Management API
- [OpenComputer API catalog](https://opencomputer.dev/.well-known/api-catalog): RFC 9727 linkset for API discovery
- [OpenComputer authentication](https://docs.opencomputer.dev/agents/api.md#base-url-and-authentication): API key in the `x-api-key` header
- [OpenComputer webhooks](https://docs.opencomputer.dev/agents/webhooks.md): start agent sessions from external services
- [OpenComputer MCP server](https://docs.opencomputer.dev/mcp): Streamable HTTP MCP server for searching the docs; [server card](https://opencomputer.dev/.well-kno
…

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