Guides

Move GitHub Actions jobs to Promigence sandboxes with one workflow line, on runners we host or ones you start with one command, and run GitLab CI jobs the same way with a tagged runner.

A complete MCP server in TypeScript and Python whose execute tool runs model-written code in a Promigence sandbox of its own per session, with no network, a spend cap and a timeout.

Run each user's code in a sandbox of their own: a key for your backend, a project with a monthly cap, a sandbox found again by the user's id, paused when idle and capped in dollars, and the self-host question answered.

Build your application into a Promigence snapshot once, then give your security-testing agent a fresh copy of it for every run, behind an outbound allow-list that names only your model API, with the model key kept out and a spend cap on the run.

Keep the Claude Agent SDK loop in your process and run Claude Code itself inside a Promigence sandbox, with your Anthropic key stored as a secret.

Start a coding agent in its own Promigence sandbox with one command, keep the model key off the command line, and drive sandboxes from Claude Code.

Point a Google ADK agent's environment tools or code executor at a Promigence sandbox, so its commands and code run there.

Select Promigence in Harbor with one flag to run Terminal-Bench and other Harbor tasks in cloud sandboxes, each trial in its own copy.

Use sandbox="promigence" in Inspect to give every sample its own cloud sandbox, with Docker-style tasks running unchanged.

Use a Promigence sandbox as the backend for a LangChain Deep Agent, or as the sandbox provider for deepagents-code.

Give each user or session its own sandbox, decide what it may reach on the network, cap its spend and time, keep credentials out of it, and end it when done.

Give an OpenAI Agents SDK SandboxAgent a Promigence sandbox, so its shell and file tools run there instead of on your machine.

Give a Strands agent a Promigence sandbox, so its shell and file-editor tools run there with output streamed back as it is written.

Pass a Promigence sandbox to the Vercel AI SDK as experimental_sandbox, or to bash-tool, so an agent's commands and files live there.

Install dependencies once in a sandbox, take a snapshot of it while it runs, and start ten parallel attempts from that snapshot, in TypeScript, Python or the CLI.