Cloudflare Computer

Cloudflare Computer is an open-source workspace and execution library for developers building AI agents on Cloudflare. The @cloudflare/computer package gives an agent durable files and tools to act on them. It supplies infrastructure for your agent, rather than a ready-made personal assistant.

Key Highlights

• SQLite-backed files stored inside a Durable Object
• File tools for reading, writing, editing and searching
• JavaScript execution in Dynamic Workers
• Optional shell and Linux container backends
• Integrations for agent harnesses and AI SDK tools

What Makes It Different

A workspace keeps its authoritative filesystem in Durable Object storage, so files survive restarts. Developers can choose an execution backend against those same files. Simple file work can run without starting a full Linux container; tasks requiring native binaries can use a container backend.

Features & Capabilities

Install the package, create a Workspace inside a Durable Object and expose the tools your agent needs. The filesystem can hold notes, generated documents, source code and skills across turns. Git support and read-only mounts can bring repository or storage content into the workflow.

The runtime has a common execution entry point with a selectable backend. JavaScript can use workspace-backed file APIs and approved host modules. AI SDK, Think and other harness integrations connect these capabilities to an agent loop; you still configure the model, application logic and access boundaries.

User Ratings and Testimonials

The repository provides runnable examples, including agent, MCP and document-generation workflows. Assess the backend required for your own application: the core package is marked suitable for production, while the container and Worker shell backends remain experimental. An example is not a guarantee that every component is production-ready.

Pricing & Value

• Library: MIT-licensed source code, available without a separate software subscription.
• Deployment: Your Cloudflare resources and model usage determine operating costs.

This suits teams that want control of an agent's files and execution environment. Budget for the deployed infrastructure rather than comparing it with the subscription price of a finished assistant app.