方塊

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

API=https://api.glac.ai/v1
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

   部署映像檔

   版本一準備好就接手流量，並有自己的網址。

   glac deploy ghcr.io/acme/api:1.4 \
     --name api --port 8080

   curl $API/apps -H "$AUTH" --json '{"name": "api",
     "image": "ghcr.io/acme/api:1.4", "port": 8080}'

   app = glac.cube.deploy("ghcr.io/acme/api:1.4",
                          name="api", port=8080)

   const app = await glac.cube.deploy("ghcr.io/acme/api:1.4",
     { name: "api", port: 8080 });

   cube_deploy {"app": "api", "image": "ghcr.io/acme/api:1.4",
                "port": 8080}

2. 02

   呼叫它

   第一個請求會喚醒一個執行個體。

   curl https://api-‹project›.glac.run/

   curl https://api-‹project›.glac.run/

   import urllib.request
   print(urllib.request.urlopen(app.url).status)

   console.log((await fetch(app.url)).status);

   # from a sandbox made for the one command
   sandbox_run {"command":
     "curl -s https://api-‹project›.glac.run/"}

3. 03

   觀察它

   每個請求一行日誌，註明它是否喚醒了執行個體，以及花了多久。

   glac cube logs api -f

   curl -N "$API/apps/api/logs?follow=true" -H "$AUTH"

   for line in app.logs(follow=True):
       print(line)

   for await (const line of app.logs({ follow: true }))
     console.log(line);

   cube_logs {"app": "api", "kinds": ["requests"]}

‹project› 是你專案 ID 的六個字元。API 把方塊稱為 app。