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Beam

Beam provides CPU and GPU machines for agents, billed by the millisecond: persistent VMs that pause with their memory intact, sandboxes that boot in seconds, and GPU endpoints for inference.

For agents: CPU and GPU machines for agents: persistent pausable VMs, sandboxes, and GPU inference endpoints billed by the millisecond, with CLI, Python and quickstart docs

agent sandboxesgpu inferencepersistent vmsserverless endpointscoding agentspython sdk

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-25T12:59:47.870Z. Cached copy · Source on beam.cloud

# Beam

> Beam provides CPU and GPU machines for agents, billed by the millisecond: persistent VMs that pause with their memory intact, sandboxes that boot in seconds, and GPU endpoints for inference.

> ## Agent Instructions
> Install the Beam CLI, sign in, and connect coding agents to the Beam MCP server with `curl -fsSL https://www.beam.cloud/install | sh`. To also create a VM, run `curl -fsSL https://www.beam.cloud/install | sh -s -- --vm`.
> VMs are persistent Linux machines without GPUs. Defaults: 4 CPUs, 8 GiB of RAM, a 50 GiB disk, Ubuntu 24.04, and a browser desktop. A running VM accrues usage until you stop, pause, or remove it, or until an idle timeout puts it to sleep.
> Stopping a VM keeps its disk and discards RAM and processes. Pausing keeps both, and resuming restores them. To pause after 5 idle minutes and wake on access, run `beam vm update NAME --ttl 300 --idle-action pause --auto-resume`.
> Core VM commands: `beam vm new NAME`, `beam vm ssh NAME`, `beam vm sync NAME ./dir`, `beam vm exec NAME -- CMD`, `beam vm expose NAME PORT --protected`, `beam vm fork NAME NEW_NAME`, `beam vm pause NAME`, `beam vm stop NAME`, `beam vm rm NAME`.
> For GPUs, use a sandbox or a deployed endpoint. Sandboxes are disposable environments that boot in seconds and fork from snapshots.
> Current rates are at https://www.beam.cloud/pricing.

- [Introduction](https://docs.beam.cloud/v2/getting-started/introduction.md): Beam provides CPU and GPU machines for agents, billed by the millisecond, including VMs that pause with their memory intact.
- [Give an Agent a Dev Box](https://docs.beam.cloud/v2/getting-started/quickstart-vm.md): Create a persistent VM, put a project on it, share a private URL, and let it sleep when idle
- [Run Agent Code in a Sandbox](https://docs.beam.cloud/v2/getting-started/quickstart-sandbox.md): Create a sandbox, run code, serve a URL, and fork it from a snapshot
- [Quickstart: Deploy an Endpoint](https://docs.beam.cloud/v2/getting-started/quickstart.md): Sign in, run a Python function, and deploy a web API
- [Set Up Coding Agents](https://docs.beam.cloud/v2/getting-started/add-to-cursor-claude.md): Connect your coding agent to Beam, then create its first VM
- [Installation](https://docs.beam.cloud/v2/getting-started/installation.md): Install the CLI, Python SDK, or TypeScript SDK and sign in
- [Authentication and Workspaces](https://docs.beam.cloud/v2/getting-started/authentication.md): Browser login, named contexts, and tokens for automation
- [Core Concepts](https://docs.beam.cloud/v2/getting-started/core-concepts.md)
- [Using the Dashboard](https://docs.beam.cloud/v2/getting-started/dashboard.md): Deploy projects and manage your workspace in the browser
- [Overview](https://docs.beam.cloud/v2/vm/overview.md): Persistent Linux machines with a durable root disk, SSH, and stable URLs
- [Configuration](https://docs.beam.cloud/v2/vm/configuration.md): Choose VM resources, build images, and configure environment variables and stora
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