沙箱

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

   建立一個

   一台專屬的機器，可用任何映像檔建立。

   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

   在裡面執行

   指令和檔案，要多久就多久。

   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

   完整保存，再分叉

   記憶體、行程和磁碟，複製成從那一刻繼續執行的副本。

   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}

凡是能用 ID 的地方，都能用它的名稱。沒有專門的喚醒呼叫：任何需要休眠中沙箱的操作都會喚醒它。