Sandbox

curl -fsSL https://glac.ai/install.sh | sh
export GLAC_API_KEY=ak_...

API=https://api.glac.ai/v1/sandboxes
AUTH="Authorization: Bearer $GLAC_API_KEY"

from glac import Glac
glac = Glac()

import { Glac } from "glac";
const glac = new Glac();

# give your agent the tools (Claude Code here)
claude mcp add glac -e GLAC_API_KEY=ak_... -- npx glac-mcp

1. 01

   Buat sandbox

   Mesin sendiri, dari image apa pun.

   glac sandboxes create --name web \
     --image python:3.12

   curl $API -H "$AUTH" \
     --json '{"name": "web", "image": "python:3.12"}'

   sbx = glac.sandboxes.create(name="web", image="python:3.12")

   const sbx = await glac.sandboxes.create(
     { name: "web", image: "python:3.12" });

   # no call of its own: the first sandbox_run
   # that names "web" makes it, from the image

2. 02

   Jalankan di dalamnya

   Perintah dan file, berapa pun lamanya.

   glac sandboxes exec web -- \
     pip install pandas

   curl $API/web/run -H "$AUTH" \
     --json '{"command": "pip install pandas"}'

   print(sbx.run("pip install pandas").stdout)

   console.log((await sbx.run("pip install pandas")).stdout);

   sandbox_run {"sandbox": "web", "image": "python:3.12",
                "command": "pip install pandas"}

3. 03

   Simpan utuh, lalu buat fork

   Memori, proses, dan disk, menjadi salinan yang berlanjut dari titik itu.

   glac sandboxes snapshots create web \
     --name ready
   glac sandboxes fork web --count 16

   curl $API/web/snapshots -H "$AUTH" \
     --json '{"name": "ready"}'
   curl $API/web/fork -H "$AUTH" --json '{"count": 16}'

   snap = sbx.snapshot(name="ready")
   copies = sbx.fork(16)

   const snap = await sbx.snapshot({ name: "ready" });
   const copies = await sbx.fork(16);

   sandbox_snapshot {"sandbox": "web", "name": "ready"}
   sandbox_fork {"sandbox": "web", "count": 16}

Namanya bisa dipakai di mana pun ID-nya bisa dipakai. Tidak ada panggilan khusus untuk membangunkan: apa pun yang membutuhkan sandbox yang tidur akan membangunkannya.